Abstract
The current study presents an effective framework for automated multi-objective optimization (MOO) of machining processes by using finite element (FE) simulations. The framework is demonstrated by optimizing a metal cutting process in turning AISI-1045, using an uncoated K10 tungsten carbide tool. The aim of the MOO is to minimize tool-chip interface temperature and tool wear depth, that are extracted from FE simulations, while maximizing the material removal rate. The effect of tool geometry parameters, i.e., clearance angle, rake angle, and cutting edge radius, and process parameters, i.e., cutting speed and feed rate on the objective functions are explored. Strength Pareto Evolutionary Algorithm (SPEA2) is adopted for the study. The framework integrates and connects several modules to completely automate the entire MOO process. The capability of performing the MOO in parallel is also enabled by adopting the framework. Basically, automation and parallel computing, accounts for the practicality of MOO by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a recently proposed data mining technique to gain a deeper understanding of the turning process.
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Acknowledgements
This work was partially financed by the Knowledge Foundation (KKS), Sweden, through the Synergy project, Knowledge-Driven Decision Support via Optimization (KDDS). The authors gratefully acknowledge their provision of research funding, and thank the industrial partners, Volvo Car Corporation and AB Volvo, for their collaborative supports during the project. We also we would like to thank Dr. Mirza Cenanovic for developing the initial architecture of the automation system.
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Appendix: Implementation of the framework and description of the appended files
Appendix: Implementation of the framework and description of the appended files
The text-based modules of DEFORM which can be used to set up and run simulations in automatic mode without going through the graphic user interface (GUI) is used. The text-based pre-processor DEF_PRE.EXE is employed to generate the database files.
The jobs (database files or FE simulation files) are submitted by calling the simulation control script
DEF_ARM_CTL.COM.
There are two different types of keywords that can be read by the pre-processor: input keywords and action keywords. Input keywords contain data that is directly used as data for a simulation. This can be a geometry definition, convection coefficient values, or other such data. Action keywords perform certain operations when the pre-processor is reading the data.
The pre-processor can be controlled by redirecting a text input control file (.inp). The .inp file contains the user inputs if the text-based system were run in interactive mode. The .key files contains a series of Action Keywords which trigger the pre-processor to perform a series of options.
First, the turning problem is modeled in GUI of DEFORM software with required material properties, thermal and friction properties, boundary conditions, FE controls, and geometries. Then, a template KEY file is exported with all the properties (def.KEY). The nodes of the workpiece, which the speed should be imposed on, are also extracted as a separate key file (defSpeed.Key). The procedure and description of different files used to generate the FE file, a file that is ready to be run by DEFORM, is shown in Table 8.
In a setting file (setting.ini), all the initial settings of the automation system including the number of jobs in each generation of optimization, available work stations and their names, number of cores, and clock speed are set.
In the cloud project folder, two folders are created: CurrentRun and NextRun. Each set of variables (from the DoE) generated by optimization algorithm is saved in NextRun folder as a text file named by gene. The number of gene files is equal to the number of DoE in each generation of the optimization study.
The automation system, denoted by UAS2 (Ultimate Automation System 2), is a cloud-based Windows script that controls and distributes the modules to computational devices. The overall algorithm of the USA2 is as follows:
MainPC:
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1.
Wait while stop.txt exists
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2.
Clear all files and folders in ∖CurrentRun∖PCname∖ (for all PCnames)
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3.
Assign jobs to PC folders
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4.
Create go file, to signal the other PCs to run
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5.
Clear all Temp folders on local PC
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6.
RunWait AutoRun.exe on local PC
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7.
Run W4ALJ.exe (Wait for all local jobs) on local PC
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8.
Check if all PCs have started correctly
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9.
RunWait WaitForAllPCs.exe (Wait for all PCs)
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10.
Copy results to result folders
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11.
Delete genes in NextRun
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12.
Start MATLAB script
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13.
Wait for new genes in NextRun
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14.
Send Email about generation completion
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15.
Delete genes in CurrentRun
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16.
Copy genes from NextRun to CurrentRun
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17.
Delete genes in NextRun
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18.
Increment generation and goto 1
Other PCs:
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1.
Clear all Temp folders.
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2.
Wait for stop.txt
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3.
Wait for go.txt in CurrentRun∖PCname
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4.
Delete go.txt
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5.
RunWait AutoRun
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6.
Run W4ALJ (Wait for all local jobs)
-
7.
Go to 1
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Amouzgar, K., Bandaru, S., Andersson, T. et al. A framework for simulation-based multi-objective optimization and knowledge discovery of machining process. Int J Adv Manuf Technol 98, 2469–2486 (2018). https://doi.org/10.1007/s00170-018-2360-8
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DOI: https://doi.org/10.1007/s00170-018-2360-8