energy consumption analysis in discrete manufacturing …

The Pennsylvania State University

The Graduate School

The Harold and Inge Marcus

Department of Industrial and Manufacturing Engineering

ENERGY CONSUMPTION ANALYSIS IN DISCRETE MANUFACTURING BASED ON

SIMULATION APPROACH

A Thesis in

Industrial Engineering

by

Hyun Woo Jeon

2013 Hyun Woo Jeon

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

May 2013

ii

The thesis of Hyun Woo Jeon was reviewed and approved* by the following:

Vittal Prabhu

Professor of Industrial and Manufacturing Engineering

Thesis Advisor

Chia-Jung Chang

Assistant Professor of Industrial and Manufacturing Engineering

Paul Griffin

Professor of Industrial and Manufacturing Engineering

Peter and Angela Dal Pezzo Department Head Chair

*Signatures are on file in the Graduate School

iii

ABSTRACT

The amount of consumed energy to manufacture products is important managerial

information for decision making as well as environmental considerations. It is, however, difficult

to predict the amount or filter out energy-critical factors from which the amount can be

approximated. The reason is twofold: many factors play their roles for machine cutting, and they

are interwoven; the current approximation method to calculate energy consumption in machining

considers only a brief span of time. Hence, to address two difficulties together, this thesis

proposes a new methodology. In detail, the simulation software HySPEED (Hybrid Simulator for

Production, Energy, and Emission Dynamics) has been developed and is used to measure a spent

energy amount with each set of various parameters as well as to take yearlong continuous

production into consideration. The collected data from designed simulation experiments is then

analyzed with ANOVA to find more energy-influential factors, and identified factors are

compared with other factors of a regression model built on industrial energy surveys. The

comparison suggests the result of simulation experiments agrees with that of general

investigations, and this conformity gives a clue about how this research can be expanded to

further mathematical models.

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TABLE OF CONTENTS

LIST OF FIGURES ................................................................................................................. vi

LIST OF TABLES ................................................................................................................... viii

ACKNOWLEDGEMENTS ..................................................................................................... ix

Chapter 1 Fundamental Foundation for Energy Consumption Analysis in Discrete

Manufacturing System ..................................................................................................... 1

1.1 Introduction ................................................................................................................ 1

1.2 Definition of Machine States and Time Threshold .................................................... 2

1.3 Energy Control Policy ................................................................................................ 4

1.4 Power Consumption Levels ....................................................................................... 4

Chapter 2 Simulation Model for Energy Consumption Analysis in Discrete

Manufacturing System ..................................................................................................... 7

2.1 HySPEED................................................................................................................... 7

2.2 HySPEED User Interface ........................................................................................... 8 2.3 HySPEED Worksheets ............................................................................................... 12

2.3.1 HySPEED Setup Worksheet ........................................................................... 12

2.3.2 Workstations Worksheet ................................................................................. 12 2.3.3 HySPEED KPI Result ..................................................................................... 12 2.3.4 Parts ................................................................................................................. 13 2.3.5 HySPEED PartPower Result ........................................................................... 13 2.3.6 WS Power ........................................................................................................ 13

2.3.7 Departure ......................................................................................................... 14 2.4 HySPEED Discrete Event Simulation Algorithm ...................................................... 14

2.4.1 Outer Most Loop ............................................................................................. 14

2.4.2 Middle Loop .................................................................................................... 14 2.4.3 Inner Most Loop .............................................................................................. 14

2.5 Simulation Result and Analysis ................................................................................. 15 2.6 Validation and Verification of HySPEED ................................................................. 17

Chapter 3 Energy Consumption Analysis in Discrete Manufacturing System Based on

Design of Experiment ...................................................................................................... 24

3.1 Introduction ................................................................................................................ 24

3.2 Methodology .............................................................................................................. 24 3.3 Work Piece ................................................................................................................. 26

3.3.1 Turn Decorative Groove .................................................................................. 27

3.3.2 RH Rough Turn OD ........................................................................................ 27 3.3.3 Mill Sloped Sides of Obelisk .......................................................................... 27 3.3.4 Mill Sloped Sides of Obelisk .......................................................................... 27

v

3.4 Modified Process Plan for a Rook Piece .................................................................... 29

3.5 Factors of Interest for Experimental Design .............................................................. 29

3.5.1 Machine Type .................................................................................................. 29

3.5.2 Demand ........................................................................................................... 29 3.5.3 Material Type .................................................................................................. 30

3.5.4 Production Level ............................................................................................. 30

3.5.5 Volume to Be Removed .................................................................................. 30 3.6 Experimental Design .................................................................................................. 31

3.7 Model Assumptions ................................................................................................... 33 3.8 HySPEED................................................................................................................... 33 3.9 Analysis ...................................................................................................................... 34

3.9.1 Response = Energy Saving (EC OFF – EC ON) ............................................. 34

3.9.2 Response = (Energy Spent with EC ON / Number of Products) ..................... 36

Chapter 4 Regression Analysis on Data of Industrial Assessments Centers Data ................. 40

4.1 Introduction ................................................................................................................ 40

4.2 IAC Dataset ................................................................................................................ 40 4.3 Response Variable ...................................................................................................... 41

4.4 Regression Analysis ................................................................................................... 42

4.4.1 Basic Analysis ................................................................................................. 42

4.4.2 Multiple Regression Model ............................................................................. 42

4.5 Discussion .................................................................................................................. 43

Chapter 5 Conclusions and Future Work ............................................................................... 45

5.1 Conclusions ................................................................................................................ 45

5.2 Future Research .......................................................................................................... 46

References ................................................................................................................................ 47

Appendix A: Minitab Result for 3.9.1 ..................................................................................... 49

Appendix B: Minitab Result for 3.9.2 ..................................................................................... 50

Appendix C: Minitab Result for 4.4.2 ..................................................................................... 51

vi

LIST OF FIGURES

Figure 1-1: Mapping of Discrete Production States to Energy States. .................................... 2

Figure 1-2: Power Signature of a Machine on in Ramp-Up State. .......................................... 5

Figure 1-3: Power Signature of Milling in a Processing State. ................................................ 6

Figure 2-1: Architecture of HySPEED. ................................................................................... 7

Figure 2-2: HySPEED Setup Worksheet. ................................................................................ 8

Figure 2-3: Structure of HySPEED Discrete Event Algorithm. .............................................. 15

Figure 2-4: Random IAT (Inter-arrival Time) and Processing Time. ...................................... 18

Figure 2-5: Exponential Random IAT Histogram (Mean = 15). ............................................. 19

Figure 2-6: Exponential Random Processing Time Histogram (Mean = 15). ......................... 19

Figure 2-7: Exponential Random IAT (Mean = 15). ............................................................... 20

Figure 2-8: Exponential Random IAT Histogram (Mean = 40). ............................................. 20

Figure 2-9: Squared Frequency Deviation ............................................................................... 21

Figure 2-10: HySPEED Discrete Event Algorithm Engine. .................................................... 22

Figure 3-1: Pennsylvania State University Chess Set Pieces ................................................... 25

Figure 3-2: Penn State Chess Set CAD Data (Rook) ............................................................... 26

Figure 3-3: Process Plan for a Rook Piece (1 of 2) .................................................................. 28

Figure 3-4: Process Plan for a Rook Piece (2 of 2) .................................................................. 28

Figure 3-5: Modified Process Plan for a Rook Piece ............................................................... 29

Figure 3-6: Parameters for Factorial Design ....................................................................... 30

Figure 3-7(a): Residuals VS. Fitted Values before Transformation. ....................................... 34

Figure 3-7(b): Residuals VS. Fitted Values after Square Root Transformation ...................... 35

Figure 3-7(c): Normal Probability Plot for Residuals after Square Root Transformation ....... 35

vii

Figure 3-7(d): Normal Probability Plot of Effects ................................................................... 36

Figure 3-8(a): Residuals VS. Fitted Values after Log Transformation ................................... 37

Figure 3-8(b): Normal Probability Plot for Residuals after Log Transformation .................... 38

Figure 3-8(c): Normal Probability Plot of Effects ................................................................... 38

viii

LIST OF TABLES

Table 2-1: HySPEED Simulation Parameters. ......................................................................... 15

Table 2-2: HySPEED Simulation Result. ................................................................................ 17

Table 2-3: K-S Test Result. ..................................................................................................... 23

Table 4-1: Simple Linear Regression Result ........................................................................... 42

ix

ACKNOWLEDGEMENTS

First and foremost, I like to mention my gratitude to my thesis advisor, Dr. Vittal Prabhu.

This thesis would not be finalized in time without his academic expertise and infinite patience.

Advices of Dr. Chia-Jung Chang have been also very helpful in addressing problems of this thesis,

and her thoughtful comments have to be appreciated. Finally I thank my family and friends for

their love and support. I wish all of them would know they are always more important than any

academic achievement of me.

1

Chapter 1

Fundamental Foundation for Energy Consumption Analysis in Discrete

Manufacturing System

1.1 Introduction

The U.S. industrial sector deserves much of attention from energy consumption analysis

as the sector accounted for 30.8% of the total U.S. energy demand in 2010, a figure suggesting

that the sector is most energy demanding [1]. Among sub-sectors of the industrial sector,

manufacturing took the largest proportion, and the fraction is greater than total sum of what other

sub-sectors were taking up in 2010 [2]. Thus it seems to be a logical next step to look into energy

consumption of manufacturing to identify driving force of energy spending in the industrial sector.

In the light of the energy consumption of manufacturing, it is noteworthy that only 20 -

70% of the total energy consumption by machining processes is spent on actually cutting

materials from raw stocks in various manufacturing machines [3]. In other words 30 - 80% of the

total energy spending of machining processes is wasted for being idle or other non-cutting related

processes. Since this 30 - 80% fraction of the total energy consumption could have been saved,

some opportunities to save energy wasted are likely to be in manufacturing, and appropriate

methodologies will be able to actualize the idea into a well-defined theory.

Among methodologies in energy consumption analysis, one of the most well-known is

LCA (Life Cycle Assessment). While LCA is able to provide detailed energy requirements for

each manufacturing process given a product, some disadvantages have been pointed out that the

method requires the large amount of data and time to be implemented. Another drawback of LCA

is that results from different LCA analyses sometimes show significantly different energy

2

estimates for the same product [4]. Thus there is a need to introduce a better tool to scrutinize the

energy consumption of a manufacturing sector, and this research considers how to meet the need

in the view of the energy consumption analysis of manufacturing sectors.

An approach of this thesis is as follows; after the introduction of machine states, energy

control policies, and energy consumption levels in Chapter 1, a simulation model for energy

analysis is built and its validity is checked in comparison with the existing simulation software

throughout Chapter 2. In the next chapter, experiments are designed and performed to see which

factors have more influence on energy consumption in an exemplary manufacturing process.

Results of experiments are analyzed with ANOVA (analysis of variance), and in the light of DOE

(design of experiment), those factors are discussed in terms of their statistical significance and

effect. Finally the comparison is made between experiments and regression results to see whether

two analyses show the consistent result in the last chapter.

1.2 Definition of Machine States and Time Threshold

Figure 1-1: Mapping of Discrete Production States to Energy States. Source: [8].

Idle Busy

Off Standby

Ramp Up

Ramp Down

Wait Process

EnergyStates

ProductionStates

3

In general a manufacturing machine is composed of a large number of parts, and

therefore it is not simple to define the state of machines. However, in the perspective of power

consumption, machine states can be roughly divided into two states: busy and idle. The busy state

is defined to be a generic state for any working or cutting state of machines, and an idle state is

for any non-working or non-cutting related state of machines. In fact, this definition could be

unclear as we can see from Figure 1-1. While the figure illustrates how real states of machines

could be mapped into power consumption states, it also shows vagueness in defining the mapping.

For example, a decision could be different on whether the standby state is supposed to be mapped

into an idle or busy state. To keep the consistency of definitions in energy consumption states

throughout this thesis, machine states are assumed to be mapped to energy states as follows:

Busy state: This state represents all working or cutting state of machines. It may be

regarded as a generic working state of a machine.

Nominal power idling state: In this thesis under the energy control policy , an idle

state is divided into two states, and the nominal power idling state is one of them.

More specifically it is assumed that an idle machine enters a nominal power idling

state if the current idle duration is less than a time threshold . When the machine

enters a busy state again, it immediately quits the nominal power idling state.

Low power idling state: This state is the other state of two idle states under the

energy control policy . Contrary to the nominal power idling state, an idle

machine enters a low power idling state if the idle duration is greater than a time

threshold . When any machine enters a busy state again, it immediately quits the

nominal power idling state.

A time threshold is defined as time duration which is used for determining whether a machine

enters a nominal power idling or a low power idling state. More specifically if the length of an

4

idle time window is less than , a machine is assumed to enter a nominal power idling state, and

if it is greater than or equal to , a machine is assumed to enter a low power idling state. However

this definition of machine states and other related parameters is different for each energy control

policy to be adopted. Thus the discussion about energy control policies is given in the next

section.

1.3 Energy Control Policy

It is assumed that a manufacturing system for which any energy control policy is not

considered is under the energy control policy . With this energy control policy, a

manufacturing system operates without any energy saving scheme. On the other hand implies

the energy control policy which governs time threshold in each machine of the manufacturing

system. Thus can be defined as a vector ( ) where n is the total number of

machines in the system as different values of time threshold can be applied to machines. Another

distinctive characteristic of compared with is that machines can enter a low power idling

state when under . Since does not consider any power saving plans, machines under

enter only a nominal power idling state when idle regardless of the length of each idleness. Thus

machines under can save energy in a low power idling state, spending less energy than in a

nominal power idling state.

1.4 Power Consumption Levels

As machines are supposed to use less or more energy in different states, it is necessary to

discuss how to define power consumption levels of machines in each state. One way of doing so

is to take average of power consumption over time of each state. Thus this power consumption

level or signature can be used to represent the average power consumption of a given state. In

detail, power consumption of a working machine varies over time, and it is assumed that power

5

consumption of a machine can be captured as in Figure 1-2. This figure illustrates power

signature of a machine in a ramp-up state, and its power changes over time. As is

averaged power consumption over time, the total sum of consumed energy is supposed to be

calculated before the sum is divided by time duration. More specifically, the total sum of energy

consumed is the sum of duration of peak time multiplied by peak power consumption level ( )

and duration of non-peak time multiplied by non-peak time power consumption level

( ) . Dividing the sum by the total time in the state (40 units) in order to have time

average provides the following equation:

* ( )+

Figure 1-2: Power Signature of a Machine on in Ramp-Up State. Source: [8].

Whereas the above approach can be a good alternative in computing average power

consumption of each state, other methods can be also used. Thus this thesis assumes that average

power consumption of a machine in each state can be computed somehow and that the power

consumption levels for three states are assumed as follows:

: This power signature is used to represent the average power consumption of a

busy state. Thus is the amount of power a machine spends in a generic working

state for a unit time. As discussed previously, there could be multiple ways of

0

0.2

0.4

0.6

0.8

1

1.2

Tim

e 1 3 5 7 9

20 40Pmax

Plow

Ts

6

calculating , and each method will be described in detail whenever it is used

throughout this thesis. For example, Figure 1-3 shows how a busy machine typically

consumes power over time. In this example, can be defined as a function of the

following parameters: , , , and . Since a typical milling work is the

combination of material cutting and air cutting, the total spent energy for this process

is the sum of power consumption of material cutting multiplied by the time

duration and power consumption of air cutting multiplied by the time

duration .


nominal power idling state. Thus is the amount of power a machine spends in a

nominal power idling state for a unit time.


low power idling state. Thus is the amount of power a machine spends in a low

power idling state for a unit time. The state is defined only under , and

therefore when under .

Again values of and depend on an adopted energy control policy between and

. Under there does not exist as does not a low power idling state. On the other hand

is defined as the above description since a machine in the state is well defined under as

well as .

Figure 1-3: Power Signature of Milling in a Processing State. Source: [8]

Pcut

Pair

Time

Tair

Tcut

7

Chapter 2

Simulation Model for Energy Consumption Analysis in Discrete

Manufacturing System1

2.1 HySPEED

In order to deal with energy consumption problems in manufacturing systems, the

simulation software HySPEED (Hybrid Simulator for Production, Energy, and Emission

Dynamics) was developed so that the software can simulate the total energy consumption of a

single or multiple machines with various parameters on a given specific time horizon. In the

consideration of familiar user interface and easy accessibility for students and researchers, Excel

2007/2010 VBA was used to develop HySPEED. This choice of a development tool is for

providing broad opportunities to use this tool throughout academia and research institutions.

Since one of objectives of HySPEED development is to provide a tool for discrete event

simulation for energy consumption analysis of manufacturing systems, HySPEED considers

discrete events, and its main architecture is shown in Figure 2-1.

Figure 2-1: Architecture of HySPEED

1 Much of the material in this chapter is based on:

Prabhu, V. V., Jeon, H. W., and Taisch, M.: Simulation Modeling of Energy Dynamics in Discrete

Manufacturing Systems, In Service Orientation in Holonic and Multi Agent Manufacturing and Robotics,

Eds. T. Borangiu, A. Thomas, and D. Trentesaux, Springer-Verlag Berlin Heidelberg, pp. 293-311,

(2013)

8

As seen in Figure 2-2 before the beginning of HySPEED simulation running, users can

setup simulation parameters, and based on these initial parameters important workstation

(machine) data are generated. Simulation results are calculated from generated parameters, and

they are shown after each run. In the following subsections, detailed description about how

HySPEED runs is suggested.

2.2 HySPEED User Interface

Figure 2-2: HySPEED Setup Worksheet. Source: [8].

After opening HySPEED, users are supposed to see ‗HySPEED Setup‘ worksheet as

shown in Figure 2-2. The worksheet is the main worksheet among seven worksheets, and there

users can setup most of simulation parameters. In relationships among parameters while the most

of parameters are independent, some of them depend on others. Thus the dependent parameters

9

are painted with grey color and inactivated so that users cannot change the value of dependent

parameters. Values of these dependent parameters are supposed to be refreshed after each run of

simulation is finished. Major parameters in the worksheet are introduced as follows:

Number of Simulations: The total number of replications of simulation experiments with

the same set of parameters. This parameter is an integer type variable.

Total Simulation Time horizon (sec): This parameter defines the maximum time for

which the simulation runs, and its unit is of seconds in default. A variable type of this

parameter is double for storing a decimal number (time).

Inter-Arrival Time (mean): Average inter-arrival time for arrival distributions. This

parameter is of a double type for storing a decimal number (time).

[Dependent Variable] Inter-Arrival Time (stdev): This parameter defines standard

deviation for arrival distribution and is only active for the normal distribution. In default

this is automatically calculated as the mean multiplied by CV (coefficient of variation),

and therefore this is a dependent variable. This is also set as 1 for exponential

distributions even when different values are shown. This parameter is a double type

variable.

Inter-Arrival time CV (Coefficient of variation): CV is defined as standard deviation

divided by mean. This parameter is a double type variable.

Processing Time (mean): This parameter defines average processing time for each

workstation. This parameter is a double type variable.

[Dependent Variable] Processing Time (stdev): This parameter defines standard deviation

for processing time distribution and is only active for normal distributions. In default this

is automatically calculated as the mean multiplied by CV. This is set as 1 for exponential

distributions even when different values are shown. This parameter is a double type

variable.

10

Processing time CV: This defines a coefficient of the variation of processing time

distribution. It is calculated as standard deviation divided by the mean. This parameter is

a double type variable.

Number of workstations: This defines the total number of machines in a serial line.

Minimum is 1. This parameter is an integer type variable.

[Inactive/Dependent Variable] Number of jobs: This shows the total number of parts

which have been processed within the total simulation run time. After each simulation

run is made, a new value will be displayed. This number will be approximately (total

simulation time) divided by (inter-arrival time). This parameter is an integer type variable.

Queueing model: This parameter defines the probability distribution for inter-arrival and

processing time between normal and exponential distributions. If 1 is entered, G/G/1

model will be selected, and inter-arrival and processing times will be normally distributed

with given average and standard deviation values. If 2 is entered, M/M/1 model will be

selected, and inter-arrival/processing times will be exponentially distributed with given

average values above. This parameter is an integer type and between 1 and 2.

Simulation random number seed: This defines the random number seed for newly

generated random numbers following the uniform distribution (0, 1). The objective of this

parameter is to guarantee having the same random number stream in each simulation

replication. This parameter is an integer type variable.

EC setting: ‗ON‘ selects energy control on ( ) and ‗OFF‘ selects energy control off

( ). This parameter is of a string type between ‗ON‘ and ‗OFF‘.

Workstation Power Trace: ‗ON‘ will collect power trace data in ‗WS Power‘ worksheet.

This parameter is a string type variable, and its value is between ‗ON‘ and ‗OFF‘.

11

Part Power Trace: ‗ON‘ will collect power trace data in ‗HySPEED PartPower Result‘

worksheet. This parameter is a string type variable, and its value is between ‗ON‘ and

‗OFF‘.

[Inactive] Generate Workstation Data: ‗ON‘ will newly create workstation data as the

simulation run starts. This is a fixed parameter and can‘t be changed in order to guarantee

that workstation data always be generated for each simulation run.

Departure: ‗ON‘ will collect departure time of each part from each workstation in

‗Departure‘ worksheet. This parameter is a string type variable, and its value is between

‗ON‘ and ‗OFF‘.

: This defines the power consumption level of a nominal idling state of each machine.

Generally it is greater than and less than . This parameter is defined to be a double

type variable.

: This defines the power consumption level of a low power idling state of each

machine. Generally it is less than . This parameter is defined to be a double type

variable.

[Dependent Variable] (mean cutting power watts): This defines the power

consumption level of a busy state of each machine. Since this value is dependent on other

variables such as MRR (material removal rate), it is a dependent variable. This parameter

is defined to be a double type variable and calculated as follows:

( ) ( )

: This parameter is about a time threshold for workstations. If a machine idle time is

longer than , the workstation enters a low power idling state, and its power

consumption will be dropped into . Otherwise the consumed power would be or

12

. Typically the half of the mean processing time is used for simulation runs. This

parameter is a double type variable.

2.3 HySPEED Worksheets

HySPEED consists of seven worksheets, and each of them is introduced below.

2.3.1 HySPEED Setup Worksheet

Users can set all variables and parameters of HySPEED application in this worksheet.

This is also the default screen users see in opening the HySPEED file. After setting all parameters

and variables, users can run HySPEED by clicking the button on the right-upper side of the

worksheet as seen in Figure 2-2. After each simulation running, a screen uses are seeing is

automatically re-directed to the KPI worksheet for showing users the simulation result.

2.3.2 Workstations Worksheet

In this worksheet, data for workstations is stored for HySPEED running. Generally users

do not need to consider this worksheet except for reference purposes about how each workstation

data is generated.

2.3.3 HySPEED KPI Result

After each simulation run is finished, the result is shown in this worksheet. Important

results are shown as follows:

Sim Num: Replication number for this running.

Throughput

Average Flow Time

13

Energy productive: the total amount of energy spent during a busy state.

Energy waste: the total amount of energy spent during nominal/low power idling states.

Mean IAT Used: Average of all inter-arrival times randomly generated.

Mean p Used: Average of all processing time randomly generated.

Experimental Factors: This is the automatically assigned character string about the

simulation run. As each simulation result accumulates in the KPI worksheet, this

information allows users to have easy identification of the simulation parameters used for a

specific run. Typical example could be: ―EC=ON; DD Type=DIS; IAT=40; Num Sim=10;

Num Iter=1; Queueing model =Model 2: M/M/1‖ suggesting ; mean inter-arrival time =

40; total number of simulation = 10; exponential random number used. Other information is

about DATC [9] and irrelevant to this thesis.

2.3.4 Parts

This worksheet shows time-series data of each part. In default, this data is not collected

unless it is ON in the Setup worksheet.

2.3.5 HySPEED PartPower Result

This worksheet shows time-series data of power consumed for each part. In default, this

data is not collected unless it is ON in the Setup worksheet.

2.3.6 WS Power

This worksheet shows time-series data of power consumed for each workstation. In

default, this data is not collected unless it is ON in the Setup worksheet.

14

2.3.7 Departure

This worksheet shows time-series data of departure process of each part leaving

workstations. In default, this data is not collected unless it is ON in the Setup worksheet.

2.4 HySPEED Discrete Event Simulation Algorithm

The discrete event simulation algorithm of HySPEED consists of three different loops to

repeat what‘s given for each step. How these loops work is shown in Figure 2-3 and below.

2.4.1 Outer Most Loop

This loop repeats simulation as many as the number of replications given in the Setup

worksheet. In each repetition a stream of random numbers is generated for inter-arrival times, and

simulation is repeated with the same set of parameters.

2.4.2 Middle Loop

This loop repeats simulation for the number of DATC (Distributed Arrival Time Control)

iterations [9]. Since this analysis does not consider DATC, this loop is repeated once.

2.4.3 Inner Most Loop

This loop is repeated based on discrete events. The algorithm makes events sorted in an

ascending order of occurring time on simulation time horizon and executes the event on the top of

the list one by one. After executed, each event is removed from the list, and simulation is

terminated when there are no events to be executed in the list. In executing events, the algorithm

generates various time series data. These time series data include power consumption, parts, inter-

arrival time, processing time, departure, etc.

15

Figure 2-3: Structure of HySPEED Discrete Event Algorithm

2.5 Simulation Result and Analysis

Two different scenarios are tested to validate and verify HySPEED. As a control group,

Simio is used to make comparison between results of HySPEED and Simio. Thus the parameters

used for simulation remain the same for both Simio and HySPEED. Simulation parameters can be

found in Table 2-1, and description for each parameter is as follows:

Scenario 1 2

Distribution Exponential Normal Exponential Normal

0.025 0.025 0.025 0.025

0.033 0.033 0.067 0.067

0.750 0.750 0.375 0.375

15.000 15.000 7.500 7.500

2140 2140

1000 1000

100 100

Replication 30 30

Table 2-1: HySPEED Simulation Parameters

16

Distribution: probability distributions for inter-arrival process and average processing time in

workstations. Normal distributions and exponential distributions are considered respectively.

: arrival rate to workstations. The arrival rate is defined as the number of part arrivals

divided by a unit time.

: processing rate of each workstation. The processing rate is defined as the number of parts

processed divided by a unit time.

: workstation utilization. System utilization is defined as the arrival rate divided by the

processing rate .

: time threshold. This defines when an idle workstation can enter a low power idling state. If

the idle duration of a workstation is greater than , a workstation is assumed to enter a low

power idling state. On the other hand, if the idle duration of a machine is less than or equal to

, a machine is supposed to enter a nominal power idling state.

: power consumption level of a busy state. A busy machine is assumed to spend per a

unit time.

: power consumption level of a nominal power idling state. A machine in the state is

assumed to spend per a unit time.

: power consumption level of a low power idling state. A machine in the state is assumed

to spend per a unit time.

Replication: this parameter defines the total number of simulation runs in the same set of

simulation parameters.

17

Table 2-2: HySPEED Simulation Result

First of all, let us discuss what appears from HySPEED and Simio in common. Since

utilization is greater in scenario 1 (0.75) than scenario 2 (0.375), consumed energy for scenario

1 is larger than 2. The difference is approximately 30% for normal distributions of with

HySPEED and is 98% for with the same parameters. This large difference can be explained

in that low utilization is supposed to cause more frequent occurrences of low power idling

states consuming less energy. Also the difference between two distributions is worthy of note.

Since CV (coefficient of variation) is set as 0.1 for normal distributions in experiments, the most

significant difference between normal and exponential distributions seems to be CV. As CV is 1

for all exponential distributions, any spent energy difference between two distributions could be

regarded as caused by larger (exponential) or less (normal) variation of the mean values. In this

sense, the result does not seem definitive because either case shows the similar results. As the

analysis to look for influential parameters on energy consumption is performed in Chapter 2, it

seems better to move on to issues of HySPEED validation and verification in what follows.

2.6 Validation and Verification of HySPEED

In checking whether a simulation model is properly built, validation and verification are

considered [10], [11], [12]. In order to check validation and verification together, this thesis

Scenario Distribution HySPEED Simio Delta = (H-S)/S

1 Normal 55976083 51330910 55582700 52875900 0.7% -2.9%

Exponential 55963844 49841974 55566900 48680700 0.7% 2.4%

2 Normal 43026926 25970286 42780100 25899100 0.6% 0.3%

Exponential 43376421 25934264 42773000 26167300 1.4% -0.9%

18

makes comparison between results of HySPEED and the discrete event simulation software Simio.

In result comparison between HySPEED and Simio, the difference is less than 3% even in the

worst case, and therefore it is not well supported to assert that HySPEED and Simio show

significantly different results. However this conjecture is not a statistical result, and we need to

have more rigorous evidence that the two simulation tools show the same result in statistical or

more reliable sense. Even though there are many points to look into for comparison of HySPEED

and Simio, it seems to be better to focus on random numbers generated by two tools if we can

agree that fundamentals of HySPEED algorithm such as basic arithmetic operations are correct.

Figure 2-4: Random IAT (Inter-arrival Time) and Processing Time

Since a random number generator adopted by the Microsoft Excel failed some empirical

tests for good random numbers [13], let us make comparison of random numbers created by

HySPEED and Simio at the beginning. In Figure 2-4, sequences of all random IAT (inter-arrival

time) and processing times are plotted. In both cases of HySPEED and Simio, the average of IAT

is 15, and that of processing time is 40. Between distributions, an exponential distribution is

chosen since it has larger CV in expectation to see greater variation or difference for either case.

19

Figure 2-5: Exponential Random IAT Histogram (Mean = 15)

The Figure 2-5 and 2-6 are plots of probability density-like functions. Figure 2-5 shows

exponential random numbers with mean 15, and Figure 2-6 with mean 40 for HySPEED, Simio,

and theoretical values respectively. In both figures HySPEED and Simio are somewhat following

the trajectory of a theoretical case. Even though trajectories of Simio and HySPEED show slight

difference in some cases (e.g., x=25-40 of Figure 2-6), it is premature to think that random

numbers by Simio and HySPEED are from different distributions.

Figure 2-6: Exponential Random Processing Time Histogram (Mean = 15)

20

In Figure 2-7 and 2-8, plots of proportional cumulative frequency are illustrated for

HySPEED, Simio, and theoretical cases. As in Figure 2-5 and 2-6, it is thought that two sets of

random numbers out of HySPEED and Simio seem to be following the same distribution.

However we can‘t draw the definitive conclusion from this observation since it is based on an

empirical observation. Thus it is necessary to more rigorously check the difference between

random numbers of HySPEED and Simio.

Figure 2-7: Exponential Random IAT (Mean = 15)

Figure 2-8: Exponential Random IAT Histogram (Mean = 40)

21

To see the difference between streams of random numbers from HySPEED and Simio,

SFD (squared frequency deviation) between the theoretical value and each simulation tool is

calculated for each random number interval as follows:

* +

Since there are 20 intervals in Figure 2-9 and 41 intervals in the Figure 2-10, x axis of

each plot is defined so as to have the number of intervals respectively.

Figure 2-9: Squared Frequency Deviation

Generally the SFD values are greater in lower x values than in higher x values. This

observation can be explained in that the lower x values with higher frequency can cause larger

difference between the theoretical distribution and numbers from samples of each distribution.

This difference can be observed both in Figure 2-9 and 2-10, and therefore SFD decreases as x

increases.

22

Figure 2-10: HySPEED Discrete Event Algorithm Engine

Even though the above SFD analysis could show that the deviation of each random

number stream of two simulation tools from theoretical values is quite small, it is also an

empirical analysis. To draw a statistical conclusion about difference between two random number

streams, Kolmogorov-Smirnov test is performed to see whether or not each random number

stream is following an exponential distribution. The null hypothesis that each random number

stream from HySPEED and Simio is following exponential distribution is rejected when

√

| ( ) ( )|

( )

∑ * +

( )

( ) √

∑ ( )

( )

23

Since the level of confidence is 95%, , and correspondingly . This K-

S test is preformed 4 times, and the results are given below:

Table 2-3: K-S Test Result

As shown in Table 2-3, HySPEED and Simio both passed K-S test, suggesting that two

random number streams are following the theoretical exponential distribution with the

corresponding mean. In the next chapter, we will analyze which factors have greater impact on

energy consumption with HySPEED.

HySPEED Simio

√ Comparison

IAT 0.670715 0.72232 < 1.36

Processing Time 1.122953 0.632068 < 1.36

24

Chapter 3

Energy Consumption Analysis in Discrete Manufacturing System Based on

Design of Experiment

3.1 Introduction

Many factors are supposed to get involved in energy consumption for manufacturing

products. Among those factors having influence on energy consumption of machining processes,

some could have greater impact on electricity usage of machines than others. In this chapter

therefore related factors are categorized, and best independent factors are drawn from them so

that experiments can be conducted to see which independent factors have greater/less impact on

energy consumption of manufacturing products by a machine. These factors with significant

effects are compared with those by the regression analysis later.

3.2 Methodology

Since the new simulation tool HySPEED was developed for the energy consumption

analysis of machining, this chapter considers a methodology which could be fully using the

simulation tool. This point is what makes a methodology of this thesis different from existing

energy or power consumption methods of machining. Methods currently broadly used for

approximating power or energy consumption of machining are based on calculations with

textbook table values [14]. Whereas these methods are handy to estimate required time, power,

and energy for each machining process, they provide crude values rather than accurate values

since required time, power, and energy of manufacturing processes are dependent on a specific

machine and its MRR (material removal rate) [3], [15]. Thus to better estimate them, a method

25

specifying a machine and MRR with a detailed process plan needs to be in consideration since

once a specific machine type and MRR are determined from a predefined process plan,

processing times can be estimated as volume to be removed divided by MRR. The processing

time calculated above is then used for getting HySPEED parameters such as mean processing

time and machine utilization. After all parameters are available, experiments are designed to see

most influential parameters of interest. Based on various combinations of parameters with

different levels, an experimental design is determined, and experiments are performed with 2

replications for each HySPEED run. Setting the total energy spent or the total energy saved as a

response provides an opportunity to see influential parameters with ANOVA, and consequently

this will have a main focus of this chapter. In fact, experiment approach with simulation is not

new in analyzing energy consumption. For example some research [16] also used experiment to

see how much each set of parameters has influence on energy consumption of a machine.

However while two methods rely on experiments, they show difference in that a method of

previous research did not design experiments and used experimental results in a form of average.

Contrary to that, in this thesis a factorial design is adopted to fully use results from experiments

with help of ANOVA. In the next, a piece for which analysis will be conducted is introduced.

Figure 3-1: Pennsylvania State University Chess Set Pieces

26

3.3 Work Piece (Penn State Chess Set Pieces)

As a product to be manufactured, a piece of Penn State Chess Set is considered, and a

rook piece (the most left piece in Figure 3-1) of Figure 3-2 is selected since it has a relatively

simple shape and a short process plan to manufacture. The original CAD design and process plans

for a rook have been added in Figure 3-2, 3-3, and 3-4. Whereas the CAD design is quite useful,

the process plan provides sketchy data for the processing time for each step in manufacturing.

Since the processing time is considered as important in this model, a better way of estimating

time for processing in each step is in need, and an alternative way is adopted, a method to

calculate required time for each process by dividing the removed volume by the material removal

rate. To have the accurate volume to be removed from each process, the following calculations

are performed:

Figure 3-2: Penn State Chess Set CAD Data (Rook). Source: [17].

27

3.3.1 Turn Decorative Groove (No. 10 Process of Figure 3-3)

Description about the volume to be removed: The volume of the inner half of the torus with the

smaller radius 0.1 and the larger radius 0.625

∫ *( ) + √

( )

3.3.2 RH Rough Turn OD (No. 20 Process of Figure 3-3)

Description about the volume to be removed: the volume of rough cutting around the obelisk =

the volume of the cylinder of the diameter 1.25 – the volume of the cylinder of the diameter

√ :

( ) (

√ )

( ) ( )

3.3.3 Mill Sloped Sides of Obelisk (No. 10 Process of Figure 3-4)

Description about the volume to be removed for shaping the hexahedron (Main part of obelisk)

The volume of the hexahedron (Height = 1.35 inches)

∫ {

( )}

The volume of the cylinder (Height = 1.35 inches) left behind from (3.3.2)

(

√ )

( )

( )

3.3.4 Mill Sloped Sides of Obelisk (No. 20 Process of Figure 3-4)

Description: the volume to be removed for shaping the pyramid (Top of obelisk)

The volume of the pyramid (Height = 0.1 inches)

28

∫ ( )

The volume of the cylinder (Height = 0.1 inches) left behind from (3.3.2)

(

√ )

( )

Figure 3-3: Process Plan for a Rook Piece (1 of 2). Source: [17].

Figure 3-4: Process Plan for a Rook Piece (2 of 2). Source: [17].

29

3.4 Modified Process Plan for a Rook Piece

For each processing step, the volume to be removed is calculated and shown in Figure 3-

5. Then as mentioned, the estimated time for each step is calculated by dividing each volume by

the material removal rate, and this is more specifically discussed in the following section.

Figure 3-5: Modified Process Plan for a Rook Piece

3.5 Factors of Interest for Experimental Design

After the consideration about the independency of many factors, the following five factors are

chosen for experiments:

3.5.1 Machine Type [3]

(+1) Production Machining Center 2000

(-1) Automated Milling Machine 1988

3.5.2 Demand (this factor is about adjusting arrival rates since processing rate is dependent on the

material removal rate and the volume to be removed.)

(+1) Machine utilization 90%

(-1) Machine utilization 50%

30

3.5.3 Material Type (adjusting the max material removal rate recommended by a reference for

each machine in 3.5.1) [3]

(-1) Aluminum: Max MRR for each machine

(+1) Steel: Max MRR for each machine

3.5.4 Production Level (adjusting the processing time)

(-1) High: 100% of the max material removal rate

(-1) Low: 50% of the max material removal rate

3.5.5 Volume to Be Removed

(+1) High: 120% of the current values (assuming a larger piece would be manufactured.)

(-1) Low: The current volume

Figure 3-6: Parameters for Factorial Design

31

3.6 Experimental Design

Each parameter or factor in Figure 3-6 is:

Machine Type: This factor is the same one as in 3.5.1.

Demand: This factor is the same one as in 3.5.2.

Utilization (1): This parameter is the machine utilization of the first process (turning/lathe

station) and depends on the demand above.

Utilization (2): This parameter is the machine utilization of the second process (milling

station) and depends on the demand above.

Inter-arrival Time: Since a queueing model is used for machines, the inter-arrival time is

defined as the average interval length between arrivals of raw stocks to the first process.

As two machines are connected in a serial line, and departures of the first machine are

supposed be arrivals to the second machine, inter-arrival time is defined only for the first

machine.

Material Type: Total two different types of materials are considered between steel and

aluminum. This factor is also the same on as in 3.5.3.

Production Level: This factor is the same as in 3.5.4.

Max MRR: Maximum material removal rate of a machine for each material type.

MRR: Material removal rate adjusted by production level of 3.5.4.

Product Size: This factor is the same as in 3.5.5.

VTR (1): This parameter is about the volume to be removed by the first machine (turning)

and depends on other factors such as the product size and the machine type.

VTR (2): This parameter is about the volume to be removed by the second machine

(milling) and depends on other factors such as the product size and the machine type.

32

Mean Processing Time (1): This parameter is the average processing time of the first

machine (turning) and depends on other factors such as material removal rates, machine

types, and demands.

Mean Processing Time (2): This parameter is the average processing time of the second

machine (milling) and depends on other factors such as material removal rates, machine

types, and demands.

Mu (1): This is a reciprocal of the mean processing time of the first machine.

Mu (2): This is a reciprocal of the mean processing time of the second machine.

Constant Startup Operation: Power consumption level of an idle machine in watts for

each machine type. This number is from the reference [3].

Run-time Operation: Power consumption level of a busy (e.g. positioning) machine in

watts for each machine type. This number is from the reference [3].

Material Removal: Power consumption level of a busy machine in watts for each

machine type. This number is from the reference [3].

: This parameter is assumed to 10% of .

: This parameter is the same one as in the constant startup operation.

: This parameter is sum of power consumption levels with the constant startup

operation and the material removal.

Tau (1): This parameter is a time threshold to define a low power idling state of the first

machine. Any idling period greater than this parameter is regarded as in low power idling.

Tau (2): This parameter is a time threshold to define a low power idling state of the

second machine. Any idling period greater than this parameter is regarded as in low

power idling.

Simulation Run Time: This is defined as 5,000 time units in HySPEED.

33

Number of Replication: To reduce the impact of outliers somewhat, two replications are

made for each treatment.

Number of Machines: As mentioned above, a turning machine and a milling machine are

considered in this experiment.

3.7 Model Assumptions

Some assumptions to be notified are as follows:

The probability distributions for inter-arrival times and processing times are normal

distributions with CV (coefficient of variation) 0.3. Exponential distributions are not

adopted since the case can be relatively easily solved analytically [21].

For scarcity of power consumption data of various machines, , , and are

assumed to be same between turning and milling machines, and milling machine data are

used [3].

Utilization of machines is based on a machine which has a larger mean processing time to

keep utilization of the other machine less than 1.0.

Since this is not a physical experiment, randomization of the experiment order of 32

treatments is ignored and experiments are conducted in a standard order.

3.8 HySPEED

For all experiments, HySPEED is used, and the description about all parameters in

conducting experiments is given above. To have energy consumption with/without an energy

control policy, twice of the total treatments of experiments are run, and the total number of

experiments is 64.

34

3.9 Analysis

After all experiments, the total spent energy for each treatment is given from HySPEED.

Using this result, we analyze spent energy for EC on and EC off with the experimental design

technique and ANOVA (analysis of variance). To see different aspects of the result by

HySPEED, we consider two different response variables.

3.9.1 Response = Energy Saving (EC OFF – EC ON)

Since there are problems with normality and equality of variance as seen in Figure 3-7(a),

a square root is taken from the response. After the square root transformation on a response, the

residuals versus fitted values plot looks better as shown in Figure 3-7(b).

Fitted Value

Re

sid

ua

l

10000008000006000004000002000000

15000

10000

5000

0

-5000

-10000

-15000

Residuals Versus the Fitted Values(response is Saving)

Figure 3-7(a): Residuals VS. Fitted Values before Transformation

35

Fitted Value

Re

sid

ua

l

10008006004002000

10

5

0

-5

-10

Residuals Versus the Fitted Values(response is SR_C13)

Figure 3-7(b): Residuals VS. Fitted Values after Square Root Transformation

Residual

Pe

rce

nt

151050-5-10

99.9

99

95

90

80

7060504030

20

10

5

1

0.1

Normal Probability Plot of the Residuals(response is SR_C13)

Figure 3-7(c): Normal Probability Plot for Residuals after Square Root Transformation

36

Standardized Effect

Pe

rce

nt

6005004003002001000-100

99

95

90

80

70

60

50

40

30

20

10

5

1

Factor

Production Lev el

E Product S ize

Name

A Machine Ty pe

B Market Demand

C Material Ty pe

D

Effect Type

Not Significant

SignificantADE

AC

AB

B

A

Normal Probability Plot of the Standardized Effects(response is SR_C13, Alpha = .05)

Figure 3-7(d): Normal Probability Plot of Effects

Although there is still the normality issue as seen Figure 3-7(c), generally a little

departure from the normal line is not serious concern [18]. Thus it seems reasonable to draw a

conclusion that machine type, market demand, and the interaction of the two factors are

significant as seen in Figure 3-7(d). In other words, other factors are not significant, and therefore

machine type, market demand, and the interaction are sufficient to explain the change of the

variation in the response (energy saving). Since there is a normality issue, and we in fact already

know that the change of level of some factors is not in a linear relationship, the regression

analysis might not be meaningful, and it is not shown here. Regardless of the normality issue, R

square of this model is almost 99%, and it tells us that the three driving factors are able to explain

99% of the variation of the response. More details of Minitab result are added to Appendix A.

3.9.2 Response = (Energy Spent with EC ON / Number of Products)

37

As the case with a response of energy saving could be trivial, one more case is analyzed

with a response variable, energy consumption (EC1) for each product. Since the average number

of arrivals can be known for each treatment, EC1 (energy spent with the energy control policy) is

divided by the number of arrivals, and it is a response in this analysis. As there are similar

normality and equal variance problems to the previous case, a natural log is taken from a response

variable. The figures below are all plotted after the natural log transformation.

Fitted Value

Re

sid

ua

l

87654

0.04

0.03

0.02

0.01

0.00

-0.01

-0.02

-0.03

-0.04

Residuals Versus the Fitted Values(response is Ln_C15)

Figure 3-8(a): Residuals VS. Fitted Values after Log Transformation

38

Residual

Pe

rce

nt

0.040.030.020.010.00-0.01-0.02-0.03-0.04

99.9

99

95

90

80

7060504030

20

10

5

1

0.1

Normal Probability Plot of the Residuals(response is Ln_C15)

Figure 3-8(b): Normal Probability Plot for Residuals after Log Transformation

Standardized Effect

Pe

rce

nt

5004003002001000-100-200

99

95

90

80

70

60

50

40

30

20

10

5

1

Factor

Production Lev el

E Product S ize

Name

A Machine Ty pe

B Market Demand

C Material Ty pe

D

Effect Type

Not Significant

Significant

ACDE

CDE

ADE

DE

AB

E

D

C

B

A

Normal Probability Plot of the Standardized Effects(response is Ln_C15, Alpha = .05)

Figure 3-8(c): Normal Probability Plot of Effects

39

Figure 3-8(a) does not show any obvious pattern, and 3-8(b) shows that residuals are a

little apart from the normal distribution line. Since the existence of even very obvious pattern has

only slight impact on F test result, and the moderate departure from the normal line is not a

serious problem [18], it seems safe to draw a conclusion from Figure 3-8(c) that machine type,

material type, and production level are far from the normal probability line. This observation

suggests that the three factors are significant compared to other factors. Thus we can conclude

that machine type, material type, and production level have statistically significant influence on

energy required to manufacture a unit product. Even though many factors and interactions are

marked as significant (red squares in Figure 3-8(c)), their effects on a response can be added to

effects of the constant since their effects are very close to the normal probability line. In other

words, those effects are statistically significant but very small. Thus their influence on a response

can be negligible. As R square value is quite high (99%), this model seems able to explain

variation of a response very much. Even though R square will get lower after adding effects

nearby the normal probability line into a constant term, the change is expected to be very limited

as effects of factors to be added into a constant are low. More details of Minitab result are added

to Appendix B.

40

Chapter 4

Regression Analysis on Data of Industrial Assessments Centers Data

4.1 Introduction

In a previous chapter, more influential factors on energy spending have been identified.

However since the experiments were conducted within simulation, it is difficult to generalize the

result from the experiments for general cases. Thus there is a need to see if the analysis based on

a large amount of general industry data is suggesting that the similar factors have more impact on

energy consumption.

For this purpose, data of the IAC (Industrial Assessments Centers) is noteworthy. It is a

program the U.S. Department of Energy has financially supported since 1981 and has provided

more than 14,000 assessments about how each company spends energy for manufacturing final

products in various forms such as pieces, bushels, and tons of liquid [19]. Even though company

names are unidentifiable from the IAC dataset, it provides enough information for connecting

energy consumption with characteristics of each company in numeric values. In what follows let

us see the detail of the IAC dataset.

4.2 IAC Dataset

The IAC dataset consists of five different worksheets ASSESS, RECC1, RECC2, RECC3,

and RECC4 [19]. However since four of them are just recommendation data for companies, any

clue could not be found there to connect energy consumption of each company with various

parameters of the company. Thus only data of the assessment worksheet is supposed to deserve

attention, and data in other worksheets is excluded from this analysis.

41

The assessment data has 56 kinds of characteristics of a company (columns)

corresponding to 15,760 companies (rows) [19]. Every detail of the entire large amount of data is,

however, not useful in this analysis because the objective of this analysis is looking for energy

consumption characteristics of manufacturing companies which would have produced the

example chess set pieces in previous chapter. Hence among 56 characteristics of each company

the following columns are chosen:

SALES: Annual sales in the U.S. dollar.

EMPLOYEES: Total number of employees.

PLANT_AREA: Total amount of area for production and office in square feet.

PRODLEVEL: Total number of units annually produced.

PRODHOURS: Annual production hours.

The above variables are regarded as numerical variables. Even though many

characteristics are included to the dataset, most of them could not be considered since they are

irrelevant (e.g. usage of natural gas and coal). From a large number of rows (companies), only

assessments without any usage record in resources which are unlikely to be consumed in the

chess piece manufacturing process are included. For example, any observation (company) with

usage in natural gas, paper, or woods has been discarded for this analysis. Eventually 517

observations are left, and these assessments are used for building regression models. The

following subsection will give more details about this regression analysis.

4.3 Response Variable

For this analysis, a response variable electricity usage is considered. Although a

regression analysis can be performed with a different response variable electricity cost, the test is

not conducted here since the electricity usage seems to be a more appropriate response.

42

4.4 Regression Analysis

In this analysis, we expect to build a regression model and to see whether each factor is

significant or not. Thus a possible, resulting regression model is as follows:

Since there are five independent variables, let us build a multiple linear regression model

after performing a basic analysis about each independent variable.

4.4.1 Basic Analysis

Sales Employees Plant Area Prod. Level Prod. Hours

Regression

Equation

13.8% 12.1% 1.8% 5.9% 14.8%

Test for

Test for

(significant)

(significant)

(significant)

(significant)

(significant)

Residual

Normality

(assumption

violated)

(assumption

violated)

(assumption

violated)

(assumption

violated)

(assumption

violated)

Lack of fit

(assumption

violated)

(assumption

violated)

(assumption

violated)

(assumption

violated)

(assumption

violated)

Table 4-1: Simple Linear Regression Result

When checked with the response, every independent variable implies possible problems

such as issues of normality or curvature, and Table 4-1 shows these problems. Even though all

five independent variables are significant, a transformation is obviously in need to fix problems

described, and a natural log is taken from the response variable.

4.4.2 Multiple Regression Model

43

After the natural log transformation is performed from the response variable, a stepwise

(forward and backward) regression analysis is conducted to have only appropriate predictors in

the final model. As parameters, 0.15 is used for alpha to enter and remove, and the result from

Minitab is as follows:

Step 1 2 3 4 5

Constant 13.39 13.10 13.05 13.04 13.07

PRODHOURS 0.00025 0.00025 0.00024 0.00024 0.00024

T-Value 11.60 12.37 12.33 12.27 11.57

P-Value 0.000 0.000 0.000 0.000 0.000

SALES 0.00000 0.00000 0.00000 0.00000

T-Value 9.98 6.33 6.24 6.14

P-Value 0.000 0.000 0.000 0.000

EMPLOYEES 0.00080 0.00078 0.00079

T-Value 4.01 3.87 3.95

P-Value 0.000 0.000 0.000

PLANT_AREA 0.00000 0.00000

T-Value 1.55 1.52

P-Value 0.122 0.129

PRODLEVEL 0.00000

T-Value 1.47

P-Value 0.143

S 1.08 0.987 0.973 0.972 0.971

R-Sq 20.73 33.60 35.62 35.92 36.19

R-Sq(adj) 20.57 33.35 35.24 35.42 35.56

Mallows C-p 121.8 20.7 6.6 6.2 6.0

As shown in the above result, only production hours, sales, and employees are found

significant by the stepwise regression method, and therefore plant area and production level

should be removed from the final regression model. Since the objective of this regression analysis

is seeing whether each independent variable is significant or not rather than building an accurate

regression model, the final multiple regression model is not given here. More details of the

stepwise regression result and the final regression model with three variables are found in

Appendix C.

4.5 Discussion

44

This regression model clearly shows that production hours, the number of employees, and

sales are significant factors in determining electricity usage for American manufacturing

companies which mainly consume electricity for energy source. In the sense of important factors

on manufacturing electricity consumption, this regression result can be compared with that of the

previous experimental design, in which machine type, market demand, material type, and

production level are significant factors. After machine type and material type are ruled out

because the IAC data does not consider these factors, market demands can be directly related to

sales as well as production levels (processing time) can be connected to production hours and the

number of employees in the regression analysis. Thus results of the experimental design and the

regression analysis are similar to each other, and this observation suggests the experimental

design result of Chapter 3 can be generalized into broader industrial/manufacturing cases.

45

Chapter 5

Conclusion and Future Work

5.1 Conclusion

This thesis suggests the energy consumption analysis approach based on the simulation

software HySPEED. To give fundamental grounds of the simulation modeling, machine states

and corresponding power consumption levels are defined and illustrated in detail. Considering

different machine states and power consumption levels, HySPEED (simulation software) is able

to simulate multiple machines in a serial line with different parameters such as average

processing rates. Since accuracy and reliability of the new software is required to proceed with

this analysis, the validity of the software is checked, and verification is performed in comparison

with the simulation software Simio.

After HySPEED is validated and verified, experiments are designed to see which factors

are more influential on energy consumption of manufacturing an example work piece. When the

amount of potential energy saving is a response variable, machine type, demand, and the

interaction of the two factors are significant. For a response with the total energy spent for

producing one product, machine type, material type, and production level are significant and

influential in the written order of descending. In the regression analysis to see if the result of the

experimental design corresponds to general industry data, it turns out to be that product hours, the

number of employees and annual sales are significant factors in determining the total electricity

spent. Since these factors from two analyses can be related to each other as described in Chapter 4,

we can conclude that the experimental analysis by simulation shows results consistent with the

general industry data analysis.

46

5.2 Future Research

Since this thesis suggests the empirical approach for capturing dynamics of energy

consumption of machining processes, analytical approaches with mathematical models would be

good addition to this research. Especially as queueing models with M/M/1 could give a closed

form solution for the total amount of spent energy for manufacturing systems [21], the

consideration for more generalized arrival/processing processes provides an auspicious direction

toward further research.

47

References

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http://iac.rutgers.edu/database

49

Appendix A

Minitab Result for 3.9.1

Estimated Effects and Coefficients for SR_C13 (coded units)

Term Effect Coef SE Coef T P

Constant 489.65 0.6997 699.82 0.000

Machine Type 733.44 366.72 0.6997 524.13 0.000

Market Demand -141.69 -70.85 0.6997 -101.26 0.000

Material Type 2.36 1.18 0.6997 1.69 0.101

Production Level 1.10 0.55 0.6997 0.79 0.436

Product Size 2.18 1.09 0.6997 1.56 0.129

Machine Type*Market Demand -107.83 -53.92 0.6997 -77.06 0.000

Machine Type*Material Type 4.05 2.02 0.6997 2.89 0.007

Machine Type*Production Level 2.40 1.20 0.6997 1.72 0.096

Machine Type*Product Size 0.14 0.07 0.6997 0.10 0.921

Market Demand*Material Type 0.27 0.14 0.6997 0.20 0.846

Market Demand*Production Level 0.50 0.25 0.6997 0.36 0.722

Market Demand*Product Size 1.24 0.62 0.6997 0.89 0.381

Material Type*Production Level -1.65 -0.83 0.6997 -1.18 0.246

Material Type*Product Size 1.93 0.97 0.6997 1.38 0.177

Production Level*Product Size -2.73 -1.36 0.6997 -1.95 0.060

Machine Type*Market Demand* 1.80 0.90 0.6997 1.28 0.209

Material Type


Production Level


Product Size

Machine Type*Material Type* 0.42 0.21 0.6997 0.30 0.767

Production Level

Machine Type*Material Type* -1.05 -0.52 0.6997 -0.75 0.460

Product Size

Machine Type*Production Level* 4.58 2.29 0.6997 3.28 0.003

Product Size

Market Demand*Material Type* 1.87 0.93 0.6997 1.34 0.191

Production Level


Product Size

Market Demand*Production Level* -1.92 -0.96 0.6997 -1.38 0.179

Product Size

Material Type*Production Level* -2.45 -1.23 0.6997 -1.75 0.089

Product Size


Material Type*Production Level


Material Type*Product Size


Production Level*Product Size



Market Demand*Material Type* -2.21 -1.11 0.6997 -1.58 0.123


Machine Type*Market Demand* -0.87 -0.43 0.6997 -0.62 0.539

Material Type*Production Level*

Product Size

S = 5.59741 R-Sq = 99.99% R-Sq(adj) = 99.98%

50

Appendix B


Estimated Effects and Coefficients for Ln_C15 (coded units)

Term Effect Coef SE Coef T P

Constant 6.0805 0.001900 3200.05 0.000

Machine Type 1.6900 0.8450 0.001900 444.72 0.000

Market Demand -0.0804 -0.0402 0.001900 -21.15 0.000

Material Type 1.4356 0.7178 0.001900 377.76 0.000

Production Level -0.6974 -0.3487 0.001900 -183.52 0.000

Product Size 0.1798 0.0899 0.001900 47.30 0.000

Machine Type*Market Demand -0.0191 -0.0096 0.001900 -5.04 0.000

Machine Type*Material Type 0.0059 0.0029 0.001900 1.54 0.132

Machine Type*Production Level -0.0028 -0.0014 0.001900 -0.73 0.471

Machine Type*Product Size 0.0003 0.0001 0.001900 0.07 0.942

Market Demand*Material Type -0.0010 -0.0005 0.001900 -0.26 0.797

Market Demand*Production Level 0.0030 0.0015 0.001900 0.80 0.432

Market Demand*Product Size -0.0015 -0.0007 0.001900 -0.39 0.697

Material Type*Production Level 0.0053 0.0026 0.001900 1.38 0.176

Material Type*Product Size -0.0035 -0.0018 0.001900 -0.93 0.359

Production Level*Product Size 0.0080 0.0040 0.001900 2.10 0.044


Material Type


Production Level


Product Size


Production Level


Product Size

Machine Type*Production Level* -0.0129 -0.0065 0.001900 -3.39 0.002

Product Size


Production Level


Product Size

Market Demand*Production Level* 0.0043 0.0021 0.001900 1.12 0.269

Product Size

Material Type*Production Level* 0.0078 0.0039 0.001900 2.06 0.047

Product Size


Material Type*Production Level


Material Type*Product Size








Material Type*Production Level*

Product Size

S = 0.0152010 R-Sq = 99.99% R-Sq(adj) = 99.98%

51

Appendix C


Stepwise Regression: LN_UE versus SALES, EMPLOYEES, ... Alpha-to-Enter: 0.15 Alpha-to-Remove: 0.15

Response is LN_UE on 5 predictors, with N = 517

Step 1 2 3 4 5

Constant 13.39 13.10 13.05 13.04 13.07

PRODHOURS 0.00025 0.00025 0.00024 0.00024 0.00024

T-Value 11.60 12.37 12.33 12.27 11.57

P-Value 0.000 0.000 0.000 0.000 0.000

SALES 0.00000 0.00000 0.00000 0.00000

T-Value 9.98 6.33 6.24 6.14

P-Value 0.000 0.000 0.000 0.000

EMPLOYEES 0.00080 0.00078 0.00079

T-Value 4.01 3.87 3.95

P-Value 0.000 0.000 0.000

PLANT_AREA 0.00000 0.00000

T-Value 1.55 1.52

P-Value 0.122 0.129

PRODLEVEL 0.00000

T-Value 1.47

P-Value 0.143

S 1.08 0.987 0.973 0.972 0.971

R-Sq 20.73 33.60 35.62 35.92 36.19

R-Sq(adj) 20.57 33.35 35.24 35.42 35.56

Mallows C-p 121.8 20.7 6.6 6.2 6.0

Best alternatives:

Variable SALES EMPLOYEES PLANT_AREA PRODLEVEL

T-Value 9.09 8.55 1.85 1.50

P-Value 0.000 0.000 0.065 0.135

Variable EMPLOYEES PLANT_AREA PRODLEVEL

T-Value 8.21 2.81 1.27

P-Value 0.000 0.005 0.204

Variable PRODLEVEL PRODLEVEL

T-Value 3.85 1.53

P-Value 0.000 0.127

Variable PLANT_AREA

T-Value 3.04

P-Value 0.002

52

Regression Analysis: LN_UE versus SALES, EMPLOYEES, PRODHOURS The regression equation is

LN_UE = 13.0 + 0.000000 SALES + 0.000803 EMPLOYEES + 0.000244 PRODHOURS

Predictor Coef SE Coef T P VIF

Constant 13.0460 0.1100 118.55 0.000

SALES 0.00000001 0.00000000 6.33 0.000 1.4

EMPLOYEES 0.0008026 0.0002004 4.01 0.000 1.4

PRODHOURS 0.00024369 0.00001976 12.33 0.000 1.0

S = 0.973056 R-Sq = 35.6% R-Sq(adj) = 35.2%

Analysis of Variance

Source DF SS MS F P

Regression 3 268.711 89.570 94.60 0.000

Residual Error 513 485.728 0.947

Lack of Fit 511 485.143 0.949 3.25 0.265

Pure Error 2 0.585 0.292

Total 516 754.439

513 rows with no replicates

Source DF Seq SS

SALES 1 104.277

EMPLOYEES 1 20.440

PRODHOURS 1 143.995

Unusual Observations

Obs SALES LN_UE Fit SE Fit Residual St Resid

1 250000000 17.4770 15.7683 0.2661 1.7087 1.83 X

7 300000000 16.9307 19.1774 0.5607 -2.2467 -2.83RX

8 45567855 16.8952 14.6001 0.0487 2.2951 2.36R

9 20000000 16.8338 14.6763 0.0885 2.1575 2.23R

10 310000000 16.7593 16.5988 0.3292 0.1604 0.18 X

17 13000000 16.5985 13.5473 0.0819 3.0512 3.15R

24 63000000 16.4651 15.3894 0.1720 1.0757 1.12 X

35 400000000 16.2824 17.8569 0.4272 -1.5745 -1.80 X

52 300000000 16.0834 19.1774 0.5607 -3.0940 -3.89RX

210 49400000 14.9625 15.5908 0.2565 -0.6284 -0.67 X

227 250000000 14.8101 16.2531 0.2529 -1.4430 -1.54 X

378 160000000 13.9822 14.8633 0.1771 -0.8810 -0.92 X

487 500000 12.8689 15.1049 0.0899 -2.2360 -2.31R

502 1000000 12.3216 14.2995 0.0555 -1.9779 -2.04R

505 5000000 12.1937 14.4817 0.0543 -2.2880 -2.36R

508 1500000 11.8625 14.0667 0.0555 -2.2042 -2.27R

509 7500000 11.8353 14.2567 0.0526 -2.4214 -2.49R

510 3000000 11.8268 14.1235 0.0541 -2.2967 -2.36R

512 1500000 11.6619 14.0993 0.0549 -2.4374 -2.51R

513 400000 11.6411 13.6145 0.0742 -1.9733 -2.03R

514 2000000 11.5256 13.5687 0.0772 -2.0431 -2.11R

515 800000 11.4916 13.6030 0.0750 -2.1114 -2.18R

53

516 2450000 11.1537 13.6791 0.0711 -2.5254 -2.60R

517 20000000 3.5835 14.7735 0.0496 -11.1900 -11.51R

R denotes an observation with a large standardized residual.

X denotes an observation whose X value gives it large influence.

Lack of fit test

Possible curvature in variable SALES (P-Value = 0.000 )

Possible interaction in variable SALES (P-Value = 0.000 )

Possible curvature in variable EMPLOYEE (P-Value = 0.000 )

Possible interaction in variable PRODHOUR (P-Value = 0.001 )

Possible lack of fit at outer X-values (P-Value = 0.001)

Overall lack of fit test is significant at P = 0.000

energy consumption analysis in discrete manufacturing …

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