minimizing the energy consumption of holes machining
TRANSCRIPT
Minimizing the Energy Consumption of HolesMachining Integrating the Optimization of Tool Pathand Cutting Parameters on CNC MachinesChunhua Feng ( [email protected] )
University of Shanghai for Science and TechnologyXiang Chen
University of Shanghai for Science and TechnologyJingyang Zhang
University of Shanghai for Science and TechnologyYugui Huang
University of Shanghai for Science and TechnologyZibing Qu
University of Shanghai for Science and Technology
Research Article
Keywords: energy-e�cient machining, holes machining, cutting parameters, tool path optimization, multi-objective optimization
Posted Date: September 3rd, 2021
DOI: https://doi.org/10.21203/rs.3.rs-859774/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
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Minimizing the energy consumption of holes machining integrating the optimization of tool path and cutting parameters on CNC machines
Chunhua Feng1, Xiang Chen1, Jingyang Zhang1, Yugui Huang1, Zibing Qu1
1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093
The corresponding author: Chunhua Feng, E-mail: [email protected]
Abstract The application of sustainable manufacturing technologies is the new challenge faced by enterprises, industries, and researchers under the background of supporting carbon peak and carbon neutral. This paper studies how to reduce the energy consumption of holes machining through optimizing tool path and cutting parameters simultaneously. The integrated optimization methodology can further reduce the energy consumption comparing with optimizing the tool path or cutting parameters separately. Firstly, the energy model of holes machining is established based on machine tools’ energy composition, tool path planning, and process parameters. Due to tool path planning as air cutting process has big relationship with reducing energy, especially for holes group with a big proportion in the whole process. The tool path of holes processing is optimized by the improved ant colony algorithm to solve the issue considering the distance from one hole to the next hole. Based on this optimized path, a multi-objective optimization model for hole cutting parameters is established, considering the spindle speed and feed rate as the optimization variables and machining time, energy consumption, and surface roughness as the objective function. The non-dominated sorting genetic algorithm (NSGA-Ⅱ) is employed to solve the multi-objective optimization problem of holes machining. The case study with 50 holes is used to testify the application of the proposed method to provide the practical energy efficiency strategy for holes group or multi-hole parts on CNC machines assisting in achieving sustainable production in manufacturing sectors. Keywords: energy-efficient machining; holes machining; cutting parameters; tool path optimization; multi-objective optimization
1. Introduction
With the continuous improvement of global environmental laws and regulations, green manufacturing has become the theme of the manufacturing industry in the new era. From the China Energy Research Office data of Lawrence Berkeley National Laboratory of the U.S. Department of Energy, the energy consumed and carbon emissions account for about 70% of the country's total energy consumption and total carbon emissions produced by the manufacturing industry [1]. The manufacturing industry consumes much energy and consequently employed many energy-saving techniques like optimizing of structure of heavy mass components, schedule, machining process, and process path. Additionally, the sharp increase in energy prices prompts companies and designers to devote more attempt to energy conservation and emission reduction when design products and processes [2]. Therefore, it is necessary to realize energy saving in the process of product manufacturing, which contributes to not only benefit economically, but also help protect the environment. Meanwhile, the optimization of the machining process becomes a multi-objective problem when tradeoff energy, cost, and productivity for different machined features.
Among the many processing features, holes processing occupies a large proportion in CNC machining. Any kind of machine cannot be made without holes. For example, the connection of two or more components need screw holes, pinholes, or rivet holes of various sizes to fix the transmission components. On the other hands, the machine parts themselves also have various holes (such as oil holes, process holes, weight-reducing holes, etc.). Hole-group machining consists of multiple drilling with tool movement from one point to the next point. Multi-hole parts in a plane and regular surfaces are common in machined features, and thereby it is necessary to optimize process planning for energy and cost-efficiency. The current research on optimization of cutting parameters mostly focuses on specific machining processes such as turning, milling, grinding, and so on. They mainly consider the power of the material
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removal process. However, air cutting power also has a big effect on the energy consumption of the whole machining process, especially holes machining with more time in air cutting than cutting process. Therefore, it is necessary to optimize the tool path with air cutting and cutting parameters simultaneously for improving the energy efficiency of holes processing for reducing processing costs and energy consumption.
Some research was conducted on the energy consumption model and processing technology of machine tool processing. Gutowski et al. [3] established the relationship model between processing and the material removal rate, which shows that the variable energy consumption of the machine tools in the cutting process accounts for 85.8% of the total energy consumption of the machine tools and the rest is the fixed energy consumption of the machine tool. Frigerio al. [4] analyzed the energy consumption status of the main energy-consuming components during the machining process through cutting experiments. Yoon et al. [5] divided the energy consumption model into basic energy consumption, spindle energy consumption, phase energy consumption, and material removal energy consumption. The energy consumption model is well established in existing literature from different viewpoints [6-12], and hence, they could be used directly based on the characteristic of a specific machining process.
The machining parameters have a direct effect on cutting energy consumption, and hence, most research focus on how to optimize these parameters to reduce energy. Reasonable selection of the cutting parameters in machining can not only improve the efficiency of cutting but also effectively reduce the energy consumption of the machine tool and further reduce the negative impact of the machine tool process on the environment. The current research on optimizing cutting parameters could be classified into single goal (energy or power) optimization and multi-objective optimization (energy, cost, processing time, and so on). For example, Camposeco-Negrete [13] optimized cutting parameters of the turning process to obtain minimum cutting energy consumption through analyzing the contribution of different factors. Campatelli et al. [14] conducted the optimization of process parameters in the milling of carbon steel to achieve the minimization of power consumption. Albertelli et al. [15] conducted multi-cutting parameters (cutting speed, the feed and the radial depth of cut) using energy as an optimization goal in face milling, which refined energy consumption composition. Deng et al. [16] optimized the process parameters using cutting specific energy consumption.
Except for the energy efficiency, it is also necessary to focus on processing quality for obtaining an excellent processing surface, improve processing efficiency and reduce processing time, which are also optimization goals that need to be paid attention. Therefore, it is more reasonable to optimize cutting parameters based on cutting energy consumption, processing time, and processing quality. Bhattacharya et al. [17] studied the effect of cutting parameters on surface finish and power consumption under high-speed machining. Bagaber et al, [18] proposed the integration optimization of energy and cost for turning process to select the optimal cutting speed, feed rate, and cutting depth. Chen et al. [19] optimized not only the cutting parameters but also the cutting tool considering the goal of energy footprint and production time as objectives in face milling. In order to increase the tool life and reduce power consumption for cutting AI alloy SiC material, Bhushan [20] optimized the cutting speed, feed rate, depth of cut and nose radius in turning process. Bagaber and Yusoff [21] proposed the multi-objective optimization of cutting parameters to reduce the energy consumption of dry turning. Balaji et al. [22] employed an orthogonal method to conduct drilling experiments and used surface roughness and amplitude as targets to evaluate the significance of tool life and cutting parameters and obtain the optimal level of cutting parameters. Rajesh [23] used high-speed steel tools to perform turning experiments on Al6061 workpiece materials under different cutting parameter values. With cutting temperature and surface roughness as the target, a regression model related to cutting temperature and surface roughness was established, and then a genetic algorithm was used. Liu et al. [24] proposed a multi-objective optimization model of cutting parameters for high-efficiency and low-carbon manufacturing with cutting speed and feed rate as optimization variables, carbon emissions and processing time as optimization targets, and a non-dominated sequencing genetic algorithm was used to obtain the best turning cutting parameters. Meanwhile,
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intelligent algorithms are used to solve the optimization solution and multi-objective issues, such as the ant colony algorithm [25-27], Taguchi-Grey relational analysis [28], Pareto front [29], multi-pass turning optimization [30], Taguchi based grey relational analysis [31], NSGA-Ⅱ[32], and so on.
Under the premise of the cutting parameters are determined, tool path of air cutting process has also effect on energy consumption. He et al. [33] proved the necessity of optimization of tool path through a sample workpiece. At present, the research on the optimization of machine tool cutting parameters mostly focuses on the research of machine tool processing efficiency, and there are relatively few researches considering the energy consumption of machine tools. In engineering practice, CNC machining personnel usually determine the cutting amount of the machine tool in the machining process based on actual machining experience. These machining experiences are often determined by machine tool manufacturers and tool manufacturers or based on their actual machining. Such processing methods often lack theoretical guidance, leading to insufficient efficiency of the machine tool and excessive energy consumption in the processing of the machine tool. In addition, traditional machining that considers the energy consumption of machine tool cutting often ignores the efficiency of machine tool processing. The current research on holes machining focuses on single-objective optimization of productivity or cost, while neglecting energy and social benefits. In the actual mold manufacturing process, most of the NC codes processed are obtained through post-processing of CAM software, which is prone to the phenomenon of porous position jump and unreasonable hole group path planning, resulting in low processing efficiency and low energy utilization. Meanwhile, the process planning of machined parts has a great effect on energy efficiency, cost, productivity, surface quality, and so on. Therefore, optimization of processes for achieving multi-objectives is crucial in the field of reducing consumption and cost, as well as creating benefits for society.
This paper studies the problem of machine tool path optimization, and further optimizes the holes processing parameters through heuristic algorithms. Firstly, in Section 2, systematically analyze the energy consumption characteristics of CNC drilling and the energy consumption period characteristics of the machining process, and establish the energy consumption function of CNC drilling. Secondly, the holes processing path optimization model is established with the minimum processing path as the optimization goal and used the ant colony algorithm to optimize the model. The different optimization algorithms are compared for achieving the most suitable algorithm in the field of tool path planning of holes machining using the minimum global distance in Section 3. Finally, holes machining multi-objective integrated optimization model with minimum processing energy consumption and processing time as the optimization objective is established. A fast non-dominant multi-objective optimization algorithm (NSGA-Ⅱ) with an elite retention strategy is used to perform optimized solution in Section 4. Finally, the conclusion is shown in Section 5.
2. Energy consumption model for holes machining
The cutting process requires the cooperation of various components of machine tools, and each energy-consuming part of the machine tool is a complex system. In this paper, a vertical machining center is used to carry out aluminum plane drilling experiments, and the power curve of the drilling process is obtained as shown in Fig. 1. Taking the drilling and processing of a part on a machine tool as an example, the real-time power of the whole process from the machine opening to the part processing and the final processing is obtained through the energy efficiency monitoring platform.
According to the power curve of the machining process, the total power of the cutting process is divided into standby power, spindle idling power, cutting fluid power, feed power and cutting power. The calculation formula is as follows:
)()()()()()( tanw tPtPtPtPtPtP cutfeedfluidspindledbys (1)
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Where )(tan tP dbys ——The standby power of machine tools;
)(tPspindle ——Spindle idling power of machine tools;
)(tPfluid ——The power of cutting fluids;
)(tPfeed ——The feed power of machine tools;
)(tPcut ——Material removal power.
Fig. 1. The power curve of processing
The standby power of the machine tools and the power of cutting fluids are the fixed value, which is not changed with the cutting state of the machine tools. Mativenga and Rajemi [34-35] found that there is an approximately linear relationship between the spindle idling power Pspindle and the spindle motor speed through experimental research. Similarly, the relationship between the feed power Pfeed and the feed rate f is approximately linear. The calculation formula of spindle idling power and feed power is as follows:
aNKtPspindle 1)( (2)
bfKtPfeed 2)( (3)
Where N——Spindle speed; K1——Spindle motor power coefficient related to the characteristics of the machine tools; a——Spindle motor power loss coefficient; f——Feed rate; K2——Feed motor power coefficient related to the characteristics of the machine tool; b——Feed motor power loss coefficient.
Material removal power )(tPcut is the power of the drill bit to remove material from the workpiece during the drilling process, according to concise manual of cutting [36]. The torque when drilling is：
M
yMzM
Mc kfdCM 0 (4)
The formula for calculating cutting power during drilling is:
o
ccc
d
vMP
30 (5)
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Where MC , Mz , My , Mk is the torque coefficient of the drilling process. d0, f, vc are the tool diameter, feed speed and cutting speed respectively.
According to Concise manual of cutting [36], the available cutting speed formula is:
1000
nv o
c
d (6)
Where n——Spindle speed, d0——Tool diameter.
Substituting the torque and cutting speed formula into the cutting power calculation formula Pc can be obtained：
30
0 M
yMzM
Mc
kfdnCP
(7)
Combining the above formulas, it is can be get as
ckfdnC
fkNk
PbfkaNkPPP
M
yMzM
M
cfluiddbysm
30
021
21tan
(8)
where baPPc fluiddbys tan , is constant coefficient.
For the holes group, the energy consumption optimization of the empty pass process has a great influence on the optimization of the overall energy consumption. The empty pass during the drilling process has relatively little influence and can be ignored in the shallow holes machining. Therefore, this article mainly optimizes the empty path when the tool moves between holes. After establishing the energy consumption model for holes machining, the tool path and cutting parameters will be optimized using the flowchart shown in Fig. 2.
Start
Input the component with holes
feature
Tool path optimization using
improved ant colony algorithm
Initialization parameter with
orthogonal design method
Establishing solution space
Reaching the
maximum
iterations?
Updating pheromone
Iteration
Output the optimal solution for
tool path
No
Yes
Optimization of cutting
parameters with NSGA-Ⅱ
Determination of variables as
cutting speed and feed rate
Determination of optimization
goals as machining time, energy
consumption and surface
roughness
Multi-objective optimization
using NSGA-Ⅱ
Output the optimal cutting
parameters
Obtaining the optimal holes machining strategy
Fig. 2 Flowchart of optimization of holes machining
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3. Optimization of tool path using improved ant colony algorithm
3.1 The ant colony algorithm based on orthogonal design
The initialization parameters of ant colony algorithm include ant colony size, pheromone importance factor, heuristic function importance factor, pheromone volatilization factor, total pheromone release, maximum number of iterations and initial value of iteration number. The tool path optimization using ant colony algorithm aims at finding the best path, and the parameters of the ant colony algorithm need to be set reasonably. Tool path construction and pheromone update are the core of ant colony algorithm. Each ant is placed randomly at different hole location, and the random probability of each ant's visit is calculated to the next hole.
Each ant randomly selects a hole position as its starting point, and maintains a path memory vector to store the hole position path that the ant passes through in turn. In each step of constructing the path, the ant selects the next hole position to be reached according to a random ratio rule. The length of the path passed by each ant is calculated, and the shortest path of the current iteration times is recorded. At the same time, the pheromone concentration on the connecting path of each hole is updated. After the algorithm initializes the parameters, it retains a fixed concentration value. After each iteration is completed, all the ants have gone out and come back. It calculates the pheromone concentration of the corresponding edge and update the pheromone concentration of the corresponding edge. The length that the ant walks is related. After iterations, the concentration of the short-distance line will be high, so that the approximate optimal solution can be obtained.
3.2 The tool path optimization of holes machining
Taking the tool path of the center drill (50 holes) as an example shown in Fig. 3. Five parameters including ant population size (A), pheromone importance factor (B), heuristic function importance factor (C), pheromone volatilization factor (D), and total pheromone release (E) are selected as factors. The five levels are selected to establish the orthogonal test factor level shown in Table 1. The position of each hole in the holes group is used as the path optimization object to optimize the processing path as shown in Fig.3.
Fig. 3. The processed location of holes group
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Table 1 The level of orthogonal test factor
Factor Population
size(A)
Pheromone importance
parameter(B)
Heuristic factor
importance parameter(C)
Pheromone
volatilization factor(D)
Pheromone increase
intensity factor(E)
Level 1 100 0.5 1 0.1 0.5
Level 2 200 0.75 3 0.2 1
Level 3 300 1 5 0.3 10
Level 4 400 1.25 7 0.4 50
Level 5 500 1.5 9 0.5 100
The 25 sets of parameter combinations is used to run each set 20 times for finding the average value of the shortest path through the optimal parameter combination in orthogonal test. The results of the shortest average path are shown in Table 2.
Table 2 Orthogonal test simulation results
Population
size
Pheromone
importance parameter
Heuristic factor
importance parameter
Pheromone
volatilization factor
Pheromone increase
intensity factor
Path
length(mm)
100 0.5 1 0.1 0.5 9832.65
100 0.75 3 0.2 1 6064.73
100 1 5 0.3 10 5997.56
100 1.25 7 0.4 50 6073.24
100 1.5 9 0.5 100 6052.79
200 0.5 3 0.3 50 6025.79
200 0.75 5 0.4 100 5997.56
200 1 7 0.5 0.5 6052.79
200 1.25 9 0.1 1 6038.78
200 1.5 1 0.2 10 6063.89
300 0.5 5 0.5 1 6053.67
300 0.75 7 0.1 10 5993.44
300 1 9 0.2 50 6054.27
300 1.25 1 0.3 100 6034.06
300 1.5 3 0.4 0.5 6006.98
400 0.5 7 0.2 100 5956.49
400 0.75 9 0.3 0.5 5997.56
400 1 1 0.4 1 6048.43
400 1.25 3 0.5 10 6002.86
400 1.5 5 0.1 50 6002.86
500 0.5 9 0.4 10 5983.04
500 0.75 1 0.5 50 7390.58
500 1 3 0.1 100 5984.12
500 1.25 5 0.2 0.5 6002.86
500 1.5 7 0.3 1 6012.17
Comparison of the main effects of various factors is shown in Fig. 4. It can be obtained through analysis that the order affecting the optimal value is C>A>E>D>B, which is Heuristic function importance factor＞Population size＞Pheromone increase intensity factor＞Pheromone volatilization factor＞Pheromone importance parameter. The optimal parameter obtained by orthogonal test optimization is the ant population size A=400, pheromone importance factor B=1, heuristic function importance factor C=5, Pheromone volatilization factor D=0.3, total pheromone release E=100.
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Fig. 4. Comparison of the main effects of various factors
The ant colony algorithm initializes the parameter values of A, B, C, D, and E. Then, we input the coordinate data of the holes group position, and calculate the mutual distance between the holes and obtain the 50-dimensional distance square matrix data. The maximum number of iterations is 200 to find the best path. In order to verify the tool path optimization effect of the improved ant colony algorithm optimized by the orthogonal experiment, the traditional ant colony algorithm is selected to compare the path optimization, as well as randomly generated and genetic algorithm. Taking the center drill (50 holes) as an example, each test is performed 10 times to calculate the maximum, minimum, and average value of the tool path. The comparison results are shown in Table 3.
Table 3 Tool path optimization results of different algorithms
Test number Randomly generated (mm)
Genetic algorithm (mm)
Ant Colony algorithm (mm)
Improved ant colony algorithm (mm)
1 25924.91 8349.95 7133.29 5996.45
2 23723.40 8465.04 7620.02 6002.86
3 25747.86 7639.01 7532.11 5935.42
4 26167.50 8962.34 7349.31 5935.31
5 29190.93 8215.69 7662.01 5993.44
6 27162.09 8471.05 7033.47 5993.44
7 26245.94 8867.96 7176.21 5888.35
8 26024.25 7887.15 7735.79 5888.35
9 24217.77 8375.89 8074.74 5993.44
10 27029.23 8768.31 7653.64 5996.45
Maximum 29190.93 8962.34 8074.74 6002.86
Minimum 23723.40 7639.01 7033.47 5888.35
Average value 26143.39 8400.24 7497.06 5962.35
Genetic algorithm and ant colony algorithm can reasonably plan the tool path of large-scale holes, and can quickly converge and obtain the optimal path. Through the comparison of 10 tool path optimization experiments, it can be seen that the improved ant colony algorithm reduces the minimum path by 17835 mm compared with the randomly generated path, which is a reduction of 75%. Comparing with the standard genetic algorithm, the minimum path is reduced by 1751mm, which is a reduction of 22.9%. Meanwhile, comparing with the standard ant colony algorithm, the minimum path is reduced by 1145mm, which is a decrease of 19.4%. The results show that the
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improved ant colony algorithm could obtain the optimization tool path of holes group shown in Fig. 5.
Fig. 5. The optimization tool path using the improved ant colony algorithm
4. Multi-objective optimization of holes cutting parameters
After determining the tool path of holes machining, the cutting parameters of each hole machining should be also optimized, which considers three aspects: processing time, processing energy consumption and surface quality. 4.1 The optimization process of cutting parameters
4.1.1 Description of the optimization problem
This paper takes VMC850E vertical machining center as the used machine to optimize the parameters of the holes machining process. The tool used is a three-tooth alloy drill (Φ8mm), and the workpiece material is aluminum. The size specification is 1000mm×1000mm×50mm. The optimization process adopts the processing technology of plane drilling, the drilling depth is 25mm, and the material removal amount is 50×25×42×π=62833mm³. The drilling route is shown in Fig. 5 obtained in Section 3. 4.1.2 Determination of optimization variables
In CNC machining, the cutting speed v, the feed rate f and the depth of cut ap are used the three cutting parameters, which are the main optimization variables. Since the depth of cut has a smaller impact on the wear resistance of the tool than the cutting speed and feed rate, the cutting width can be determined during drilling according to the workpiece allowance and specific processing requirements. So the optimized variables are mainly cutting speed vc
and feed f. 4.1.3 Establishment of a data acquisition system
The energy consumption collection experiment of the energy consumption model of holes group processing uses the Shenyang Machine Tool Vertical Machining Center (VMC850E) for plane drilling of aluminum. The cutting tool is made of alloy twist drills. The energy consumption data collection system collects through external hardware including vertical machining center (VMC850E), power sensor (WB-9128), data acquisition card (cDAQ-9201) and computer processing and display (labview), as shown in Fig. 6. The power signal is collected by installing a power sensor in the electrical control box of the machine tool, and the power signal collected by the sensor is processed by a data acquisition card. The machine tool power signal is converted into useful data that can be identified by the computer, and then transmitted to the computer for display, storage and analysis.
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Fig. 6. Energy consumption data acquisition system
According to the power requirements of the machine tools, the sensor selects the three-phase power sensor WB9128-1, and the input terminal wiring configuration selects the three-phase three-wire connection. The power measurement equipment uses NI cDAQ-9174 data acquisition box and 9201 data acquisition card to collect and convert power signals. The signal display part uses LabVIEW software to display the energy consumption data acquisition system collected by the sensor in real time, and the collected data is stored detailed information in literature [37]. 4.2 Determination of optimization goals
The processing time, surface roughness and energy consumption are used as the optimization objective function. The model of each goal is constructed as follows: 4.2.1 The processing time model
In the process of holes machining, the machining time Ttotal mainly includes the machine tools empty pass time tu and cutting time tc. This paper ignores the machine tool automatic tool change time and tool blunt tool change time, as shown:
cutotal ttT (9)
According to the processing trajectory diagram, the processing path is optimized by the ant colony algorithm, and the horizontal idling stroke is 5888mm. The distance between the tool positioning point and the workpiece surface is set to be 25mm. Because the drilling depth is also 25mm, so the vertical cutting stroke is 1250mm, and the vertical idling stroke is 3750mm. The processing time model is as follows:
ffTtotal n/1250/9638 (10)
4.2.2 Energy consumption model Using the Eq. (8)
ckfdnC
fkNk
PbfkaNkPPP
M
yMzM
M
cfluiddbysm
30
021
21tan
where baPPc fluiddbys tan is a constant coefficient. According to the cutting experiment, the standby power is 342.4w. The experimental data was fitted to obtain K2=0.22,
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b=327, K1=0.2704, and a=453.6. According to concise manual of cutting [36], available drilling/reaming cutting
force and cutting power coefficient are shown in Table 4. Seeking Table 4, the coefficient is determined as CM=0.305，
ZM=2.0，YM=0.8，KM=1.0.
Table 4 Drilling/reaming cutting force and cutting power coefficient CF1/CFK ZF1/ZFK yF1/yFK KF1/KFK E
412/231 2/1.2 0.8/0.8 1.0/1.0 550103
CM1/CMK ZM1/ZMK yM1/yMK KM1/KMK XMK
0.305/0.247 2.0/1.2 0.8/0.8 1.0/1.0 1.2
Combining the above coefficients and bringing it into the cutting power model, the cutting process power can be
obtained as:
127330
52.1922.02704.0
8.0
nf
fNPm
(11)
The power of the empty feed process is:
127322.02704.0u fNP (12)
Combining with the time consumption model data, the idle time of the drilling process is 9638/f, and the cutting
process time is 1250/nf. When the time is incorporated into the energy consumption model, we can get:
)/1250(*]127330
52.1922.02704.0[
)/9638(*)127322.0n2704.0(
8.0
nfnf
fN
ffEm
(13)
4.2.3 Surface roughness model An important criterion for the quality of drilling processing is the internal roughness of the hole wall. The
roughness of the hole wall also represents many properties of the part such as the machining accuracy, matching accuracy, wear resistance of the parts and the service life of the parts. Therefore, this paper designed a total of 16 sets of orthogonal experiments for surface roughness with two factors and four levels. The experimental design is shown in Table 5.
Table 5 Orthogonal experimental design of surface roughness Number Spindle speed Feed rate Surface roughness
1 300 0.15 0.92
2 300 0.2 1.12
3 300 0.25 1.64
4 300 0.3 2.08
5 400 0.1 0.53
6 400 0.2 0.76
7 400 0.25 1.58
8 400 0.3 1.98
9 500 0.15 0.39
10 500 0.2 0.53
11 500 0.25 0.98
12 500 0.3 1.78
13 600 0.15 0.42
14 600 0.2 0.60
15 600 0.25 1.02
16 600 0.3 1.94
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According to the designed 16 sets of surface roughness experiments, 16 sets of different cutting parameters are used for drilling, and three holes are processed under each set of cutting parameters. Fig. 7 shows the finished workpiece surface.
Fig. 7. Drilling processing diagram
The surface roughness of the processed holes obtained from 16 sets of experiments is measured, and the surface roughness data of each processed hole is obtained. The regression model of the surface roughness equation of the drilling process is fitted as:
0.1577f+0.001721n--0.267=Ra (14) 4.3 Restrictions
Referring to the technical parameter range of VMC850E vertical machining center and the recommended cutting parameter range of the drill bit, the constraint conditions is shown in Table 6.
Table 6 Constraints of the drilling process
Constraint Constraint range
Rated spindle speed range of machine tool 100r/min≦n≦6000r/min
Machine tool rated feed speed range
10mm/min≦vfx≦2000mm/min
10mm/min≦vfy≦2000mm/min
8mm/min≦vfz≦2000mm/min
Rapid movement speed of machine tool Vrf=600mm/min
Tool cutting parameter range
0≦n≦6000r/min
0≦vf≦1000mm/min
0≦ap≦8mm
0≦ae≦8mm
The main purpose of the research is to optimize the cutting parameters within the constraints to improve productivity and surface processing quality at the same time, and reduce cutting energy consumption. The optimization of cutting parameters in the drilling process is expressed by the following multi-objective optimization formula.
min ( , )
min ( , )
min ( , )
T n f
E n f
SR n f
(15)
4.4 Solve and optimization results analyze
The fast non-dominated multi-objective optimization algorithm (NSGA-Ⅱ) with elite retention strategy is used
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to solve the multi-objective optimization issue. The initial population size is set to 100, and the number of iterations is set to 200. In the integrated optimization model of tool path and process parameters, the detailed results of optimizing Etotal, Ttotal and SR separately and simultaneously optimizing these three objectives are shown in Table 7.
Table 7 Summary of optimization results
Optimized target Process parameters Etotal
(KW∙h) Ttotal
(min)
SR
(μm) n (r/min) f (mm/min) ap (mm) The tool path, empirical processing parameters 2000 300 8 3.12 59.5 43.6
Optimization Etotal only 1355 500 8 0.57 18.8 76
Optimization Ttotal only 6000 500 8 0.98 18.6 68
Optimization SR only 6000 10 8 4.71 19.29 0.45
Three-objective optimization 1078 468 8 1.32 20 0.89
From the optimization results in Table 7, the following points can be seen: (1) When optimizing the processing energy consumption separately, the energy consumption value is 0.57KW∙h,
while the total processing time is 18.8min, and the characteristic surface roughness is 76μm. When optimizing the
processing time separately, the energy consumption value is 0.98KW∙h, while the total processing time is 18.6min,
and the characteristic surface roughness is 68μm. Similarly, when optimizing the surface roughness separately, the
energy consumption value is 4.71KW∙h, the total processing time is 19.29min, and the characteristic surface
roughness is 0.45μm. The results show that single objective optimization could obtain the corresponding optimal
goal. However, another goal could not get a better result. Therefore, three goals are optimized for getting better
processing energy consumption, time and surface roughness simultaneously. In this situation, the energy consumption
value is 1.32KW∙h, the total processing time is 20min, and the characteristic surface roughness is 0.89μm.
(2) From the comparing results, we can see that the minimum processing energy consumption is 0.57KW∙h, and the maximum is 4.71 KW∙h. The three-objective optimized processing energy consumption value is 1.32KW∙h, which is 71% lower than the maximum energy consumption. The minimum total drilling time is 18.8min, and the maximum
is the three-target optimization time, which is 20min. The minimum surface roughness of the processed features is
0.45μm and the maximum value is 76μm. After the three-objective optimization, the surface roughness value is the
only 0.89μm, and the result is ideal. (3) Comparing the results of the unoptimized path and empirical processing parameters with optimized path and
three-objective optimization parameters, the processing energy consumption can be saved by 1.8KW∙h, which
reduces 57.7%. The processing time is saved by 39.5 minutes, and the range is 66.4%. On the other hand, the surface
roughness is improved from 43.6μm to 0.89μm, and the surface accuracy is greatly improved. (4) At the same time, optimizing the process parameters obtained by Etotal, Ttotal and SR can achieve the optimal
coordination of processing energy consumption, processing time and workpiece quality, and finally achieve an ideal
processing result.
5. Conclusions
This paper studies how to reduce the energy consumption of holes machining through optimizing tool path and cutting parameters. According to the power curve of the machining process obtained from the experiment, the energy consumption of the machine tool is decomposed, and the total power of the cutting process is divided into standby power, spindle idling power, cutting fluid power, feed rate power and cutting power. Meanwhile, the material removal power model of the drilling process is established. Based on the proposed energy model, the tool path of the holes processing is optimized using the improved ant colony algorithm. For analyzing the necessity of path optimization for holes processing, we carried out the comparison of the optimization results with other algorithms and empirical schemes. The result shows that the path of the improved ant colony algorithm is 29.1% shorter than that of the
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standard genetic algorithm, and the path of the improved ant colony algorithm is 20.5% shorter than that of the standard genetic algorithm. Additionally, we establish a multi-objective optimization model for holes machining that takes the spindle speed and feed rate of the machine tool as the optimization variables, and takes the machining energy consumption, machining time and surface roughness as the optimization goals. Based on the fast non-dominated multi-objective optimization algorithm (NSGA-Ⅱ) with elite retention strategy, the optimization solution is carried out. Using a case study, the results of the unoptimized path, empirical processing parameters, optimized path, and three-objective optimized parameters were compared. The result of saving processing energy consumption by 57.7%, processing time by 66.4%, and greatly improving surface accuracy were obtained. Realize the optimal coordination of processing energy consumption, processing time and workpiece quality, and finally achieve the common goal of energy saving and consumption reduction and precision processing.
Funding
This research is funded by the National Natural Science Foundation of China Grant No. 51605294. Guozhen Bai,
Yilong Wu and Haohao Guo are thanked for providing technical support during the experiments.
Conflicts of interest/Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could
have appeared to influence the work reported in this paper.
Availability of data and material: All the data have been presented in the manuscript.
Code availability: Not applicable
Authors’ contributions Chunhua Feng: Conceptualization, Methodology, Software, Validation, Writing-Original Draft, Funding acquisition.
Xiang Chen: Investigation, Data Curation, Software. Jingyang Zhang: Investigation, Data Curation, Resources.
Yugui Huang: Investigation, Data Curation, Resources. Zibing Qu: Investigation.
Ethical approval: Not applicable.
Consent to participate: The authors declare that they all consent to participate this research.
Consent for publication: The authors declare that they all consent to publish the manuscript.
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