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.
Highlights
Optimization of manufacturing processes has been an attractive topic for research
We developed a framework for performing an automated multi-objective optimization of machining processes by using finite element (FE) simulations
flexible pattern mining (FPM) treats the solutions of multi-objective optimization as “customers” and the variables as “transactions” and uses the apriori algorithm [36] to find clusters of solutions that take the same values for a subset of variables
Summary
Optimization of manufacturing processes has been an attractive topic for research. A considerable number of attempts have been made to optimize different manufacturing processes [1,2,3,4]. To gain a deeper understanding of the turning process and help identify or create high-performing solutions for similar applications in the future, it is essential to analyze the trade-off solutions using data mining and machine learning techniques. Such a process has been referred to as innovization in the past [17], which is a portmanteau of “innovation through optimization,” implying that analysis of solutions from optimization can lead to innovations in constructing high-performing initial solutions for similar problems in the future. We developed a framework for performing an automated multi-objective optimization of machining processes by using FE simulations This framework eliminates the time spent in the manual tasks during FE model setup and MOO and enables parallel computing. The modules and the automated system are described in detail
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