34th INTERNATIONAL CONFERENCE ON PRODUCTION ENGINEERING29. - 30. September 2011, Niš, Serbia

University of Niš, Faculty of Mechanical Engineering

TOWARDS A CONCEPTUAL DESIGN OF AN INTELLIGENT MATERIAL TRANSPORT BASED ON MACHINE LEARNING AND AXIOMATIC DESIGN THEORY

Milica PETROVIĆ1, Zoran MILJKOVIĆ1, Bojan BABIĆ1, Najdan VUKOVIĆ2, Nebojša ČOVIĆ3

1 University of Belgrade – Faculty of Mechanical Engineering, Production Engineering Department, Kraljice Marije 16 11120 Belgrade 35, Republic of Serbia:

mmpetrovic@mas.bg.ac.rs, zmiljkovic@bg.ac.rs, bbabic@mas.bg.ac.rs 2 University of Belgrade-Faculty of Mechanical Engineering, Innovation Center, Kraljice Marije 16 11120

Belgrade 35, Republic of Serbia:nvukovic@mas.bg.ac.rs

3 Company FMP d.o.o. - Belgrade, Lazarevački drum 6, 11030 Belgrade, Republic of Serbia:nebojsa.covic@fmp.co.rs

Abstract: Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems.

Keywords: intelligent manufacturing systems, conceptual design, axiomatic design theory, neural networks, mobile robot

1. INTRODUCTION

For the last thirty years manufacture concepts have had several redefinitions. In the eighties and nineties, the concept of flexible manufacturing systems (FMS) was introduced to develop a new family of products with similar dimensions and constraints [1]. The manufacturing enterprises of the 21st century are in an environment where markets are frequently shifting, new technologies are continuously emerging, and competition is globally increasing. Rapid changes in product demand, product design, introduction of new products and increasing global competition require manufacturing systems to be highly flexible, adaptable and responsive [1].A methodology that includes the technological migration [1] from established flexible manufacturing systems (FMS) to intelligent manufacturing system (IMS) is presented in this paper. For needs to be addressed at the design stage of new manufacturing system with all intelligent characteristics, this paper would like to present a methodology for conceptual design of manufacturing systems using axiomatic design approach.Beside axiomatic design methodology, the mentioned requirements cannot be fulfilled without artificial intelligence. According to the literature published by CIRP and other manufacturing periodicals during the past decade, nearly 34 modern manufacturing systems and

production modes have been proposed and 35 mathematical methods have been used for building intelligent systems [2]. The wide application of these intelligent mathematical methods or their combinations in manufacturing will definitely enhance the development of manufacturing system modelling and provide the new solutions. Some of the methods are: machine learning, artificial neural networks, heuristic search, and graph theory, etc. Evolutionary computation (i.e. genetic algorithms, genetic programming, evolutionary programming, and evolutionary strategies) and artificial neural network are the most widespread [3]. Intelligent material transport implies solving path generation problem and control movement of an intelligent agent - a mobile robot. The graph algorithms are used to generate path and artificial neural networks for prediction of duration of manufacturing operations. In [4] different graph search algorithms are presented.

2. AXIOMATIC DESIGN THEORY

Axiomatic design theory is an attempt at synthesis of the basic principles of design in various engineering fields and in all phases of design. This design methodology is based on identifying customer needs and their transformation into correspondent functional

requirements in the physical domain. According to [5], going from one domain to another is called mapping and it happens in the each design phase: conceptual, product and process design phase, respectively. Furthermore, the

design process is done through the iterative mapping between the functional requirements (FRs) in the functional domain, and the design parameters (DPs) in the physical domain, for each hierarchical level (Fig.1).

Fig.1. Concept of domain, mapping and axiomatic decomposition

In mathematical terms, the relationship between the FRs and DPs is expressed as [5]:

{FR} = |A| ⋅ {DP} (1

where {FR} denotes the functional requirement vector, {DP} denotes the design parameter vector, and |A| denotes the design matrix that characterizes the design process. The structure of the matrix |A| defines the type of design being considered and for the three hierarchical

levels particular design matrices |A| are presented in the Table 1. It can be concluded that |A| matrix in the second hierarchical level is triangular and for that reason we can change some DPs to set some other FRs without affecting the rest of FRs [5]. Such a design is called a decoupled design. In the third hierarchical level |A| matrix is diagonal and each of the FRs can be satisfied independently by means of one DP. Such a design is called an uncoupled design.

Table 1. List of the functional requirements, correspondent design parameters and correspondent |A| matrices

X

DP1

: Mob

ile ro

bot

DP1

1: O

dom

etry

mot

ion

mod

el

DP1

2: P

ath

plan

ning

mod

ule

DP1

3: M

anuf

actu

ring

proc

ess

s

imul

atio

n

DP1

4: N

eura

l net

wor

ks

DP1

11: S

enso

ry in

form

atio

n fr

om

enc

oder

s

DP1

12: S

enso

ry in

form

atio

n fr

om th

e

cam

era

DP1

21: P

ath

plan

ning

alg

orith

ms

DP1

22: C

ontro

l alg

orith

ms

DP1

41: P

aram

eter

s for

neu

ral

n

etw

orks

tra

inin

g

FR1: Intelligent material transport X FR11: Determining mobile robot position and orientation X X X X FR12: Path planning 0 X X X FR13: Prediction of manufacturing process parameters 0 0 X X FR14: Machine learning of material transport flows 0 0 0 X FR111: Determining parameters in motion model X 0 0 0 0 FR112: Determining position and orientation of the characteristic objects in the environment 0 X 0 0 0 FR121: Generating path nodes 0 0 X 0 0 FR122: Path following 0 0 0 X 0 FR141: Getting expected performance of IMS 0 0 0 0 X

FR1

FR11 FR12 FR13 FR14

FR111

FR112

FR121

FR122

FR141

DP112

DP121

DP111

DP141

DP122

DP11 DP12 DP14DP13

DP1

Functional Requirements {FRs}

Customer Attributes {CAs}

Design

Parameters {DPs}

Customer

Domain

Functional

Domain

Physical Domain

Needs specification

Impact0 No impact

3. MOBILE ROBOT IN A MANUFACTURING ENVIROMENT

To explain mobile robot motion and actions in manufacturing environment, five modules are developed.

3.1. Motion modelThe position of the mobile robot is determined by the system state vector xt = (x, y, θ), where x and y are the components that define the position vector, and θ is the angle orientation. Mathematical formulation for mobile robot odometry is given by (2):

(2

where x', y' and θ'are the components of the state vector at time t', x, y and θ components at time t; Δs the incremental path lengths [6].

3.2. Material flow analysisMaterial transport analysis in manufacturing environment was recognized as the first task in a path planning module. First of all, flow line layout design is adopted. After that, the data about machines, parts and time duration of operations should be gathered and analyzed. Table 2 presents a list of machines, and Table 3 presents a list of parts.

Table 2. List of machines in manufacturing plant

Machine Machine typeM#1 Shearing machine

M#2 CNC punch press for punching and blanking

M#3 Hydraulic punch press

M#4 Punch press for punching and blanking

M#5 and M#6 Pillar drill (bench drill)M#7 Circular sawM#8 Whetting machine

M#9 Line for machining parts made of cooper

Table 3. List of representative parts in manufacturing plant

Part DescriptionP#1 Transport fuseP#2 Mainbusbar supportP#3 Support d800P#4 Busbar 2 L1

After defining number of parts and machines, we need to define quantitative relations between them. In general, this dependence can be presented with matrix MDM, which

is written using matrix M (matrix of machines) and D (matrix of parts) [7].

(3

If we need time dependence between machines and parts, we put the time duration of machine operation to a correspondent machine instead of parameter pij. At the end, using graph theory, we define matrix of distances between machines (R) [6]. 3.3. Path planning algorithms Three algorithms are developed and implemented for the mobile robot path planning task. The first one is A* search algorithm, that is used for finding the shortest path between start and goal points. It combines Dijkstra algorithm and bread-first search algorithms. Using MDM

matrix, the second algorithm determinates sequence of machines for each representative part and chooses machine the robot should visit, according to a minimal distance criteria. Finally, the third algorithm is used for determining the order of machines in accordance to manufacturing process. This algorithm generates characteristic time parameters of the manufacturing process (the duration of the operation on the machine) and time parameters related to part transport to the machine (time needed for mobile robot to travel between machines).

3.4. Prediction of manufacturing process parameters

It is known that engineering processes generally do not have deterministic nature. The processes that are important for the material transport task in terms of duration are the machining process and the process of robot movement between the defined nodes (machines). Considering the fact that these processes have stochastic nature, we can conclude that nominal time duration of operations, as well as time of transport from one node to another, are different for each part. For that reason, uniform distribution is chosen to model stochasticity of the nominal time duration.

3.5. Neural Networks for prediction of duration of manufacturing operations

Implementation of the neural networks (NN) to model various problems in production engineering goes back to the 20th century. According to [8], there are three basic categories of their use: classification, prediction and functional approximation. Prediction of the next node (machine) in the path, where robot needs to go and deliver the part, is based on past values of the system state (in this case the time parameters of the process and the time of

1 Name of the author, title, company, address and e-mail.2 Name of the author, title, company, address and e-mail.

robot movement between the machines) and the current values of the system state (the node where the robot is currently located). For NN training the Matlab Neural Network Toolbox is used, with supervised learning algorithm (Levenberg-Marquardt) [8] and the sigmoid activation function.

4. EXPERIMENTAL RESULTS

The experimental model of manufacturing environment is static and positions of machines are a priori known. Experimental model and the Khepera II mobile robot are shown in Fig. 2. The first goal is test accuracy of path following. During tracking the trajectory, the robot has to deliver part to machines, according to manufacturing process, defined by matrix MDM. Coordinates of start and goal point is known. While executing the transport task, the robot optimizes the path between the machines using A* algorithm [6]. The mean position errors during the first experiment in x and y directions are Δx=0.5598 [cm] and Δy=1.4624 [cm].

Fig.2. Mobile robot motion in laboratory model of manufacturing environment

The next experiment is conducted in same conditions, but the coordinates of the goal point are not known at the beginning. This parameter depends on the time robot needes to travel from one machine to another. When the robot finishes transport of the last representative part to machine for the first operation, its current pose is passed to NN. Based on this information, NN predict the nearest machine where manufacturing operations are completed and generate information about the future robot actions.

5. CONCLUSION

This paper presents a method for conceptual design of mobile robot material transport in intelligent manufacturing system. Intelligent mobile robot, with a priori known static obstacles in the environment, has the

ability to generate an optimal motion path in accordance with the requirements of the manufacturing process and priority servicing of machine tools. Mobile robot learns the optimal transport routes and sequence of manipulation by using neural network [6]. Neural network was developed to predict the parameters of manufacturing process and to learn characteristic time parameters of the process. For the purposes of the simulation we used the nominal time parameters (estimated using empirical data) of the manufacturing process, and its stochastic nature is modeled according to uniform distribution [6]. Search algorithms and neural network models are developed in Matlab environment and implemented on a Khepera II mobile robot. Achieved positioning error of mobile robot indicates that conceptual design approach based on axiomatic design theory and neural networks can be used for material transport and handling tasks in intelligent manufacturing systems.

ACKNOWLEDGMENTS

This paper is part of the project: An innovative, ecologically based approach to implementation of intelligent manufacturing systems for production of sheet metal parts, financed by the Ministry of Education and Science of the Serbian Government, Grant TR-35004.

REFERENCES

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[3] BREZOCNIK, M., BALIC, J., BREZOCNIK, Z. (2003) Emergence of Intelligence in Next-generation Manufacturing Systems, Journal Robotics & Computer-Integrated Manufacturing, Vol. 19, pp. 55–63.

[4] SIEGWART, R., NOURBAKHSH, I. R., (2004) Introduction to Autonomous Mobile Robots, MIT Press, Cambridge, Massachusetts.

[5] SUH, N. P. (1990) The Principles of Design, Oxford University Press, New York.

[6] PETROVIĆ, М., MILJKOVIĆ, Z., BABIĆ, B., ČOVIĆ, N. (2011) Artificial neural networks and axiomatic design theory in conceptual design of intelligent material transport, Proceedings of the 37th

JUPITER CONFERENCE with foreign participants, Belgrade, pp. 3.72-3.79, (in Serbian).

[7] MILJKOVIĆ, Z., BABIĆ, B. (2005) Machine-Part Family Formation by Using ART-1 Simulator and FLEXY, FME Transactions, New Series, Vol.33, No.3, pp. 157-162.

[8] MILJKOVIĆ, Z., ALEKSENDRIĆ, D. (2009) Artificial Neural Networks – Solved Examples With Short Theory Background, Faculty of Mechanical

x

y

Start

Goal

Engineering, University of Belgrade, Belgrade, (in Serbian).

1 Name of the author, title, company, address and e-mail.2 Name of the author, title, company, address and e-mail.