Simulation-based machining condition optimization for machine tool energy consumption reduction

Wonkyun Lee, Seong Hyeon Kim, Jaesang Park, Byung Kwon Min

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Optimizing the machining condition is one of the effective ways for reducing the energy consumption of machine tools at a unit process level. Based on statistical approaches with design of experiments, various methods have been developed to reduce the energy consumption by optimizing the machining condition. However, the methods cannot be easily utilized when the optimization target or machine tool design is modified because the optimal solution is determined based on the experimentally measured data. In this study, a simulation-based method that utilizes a virtual machine tool (VMT) to optimize the machining condition is proposed. The VMT model is designed to focus on estimating the energy consumption during machining and is developed by replicating real machine tools. Based on the VMT model, a genetic algorithm is used to optimize the machining condition to reduce the energy consumption. The changes in the optimization target or machine tool design are easily considered by modifying the cost function or component model, respectively. The proposed method is applied to reduce the energy consumption of a three-axis milling machine. The optimal feed rate and spindle speed are obtained for each line of the part program when the thrust force is limited. An experimental setup of the machine tool with an energy consumption monitoring system is constructed to demonstrate the effectiveness of the proposed method. The results show that the total energy consumption of the machine tool reduces by 13% owing to the optimization.

Original languageEnglish
Pages (from-to)352-360
Number of pages9
JournalJournal of Cleaner Production
Volume150
DOIs
Publication statusPublished - 2017 May 1

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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