A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning

Qinge Xiao, Congbo Li, Ying Tang, Lingling Li, Li Li

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Selection of optimum process parameters is often regarded as an effective strategy for improving energy efficiency during computer numerical control (CNC) turning. Previous optimization methods are typically developed for specific machining configurations. To generalize the energy-aware parametric optimization for multiple machining configurations, we propose a two-stage knowledge-driven method by integrating data mining (DM) techniques and fuzzy logic theory. In the first stage, a modified association rule mining algorithm is developed to discover empirical knowledge, based on which a fuzzy inference engine is established to achieve preliminary optimization. In the second stage, with the knowledge obtained by investigating the effects of parameters on specific energy consumption covering a variety of configurations, an iterative fine-tuning is carried out to realize Pareto-optimization of turning parameters for minimizing specific energy consumption and processing time. The simulation results show that the method has a high potential for enhancing energy efficiency and time efficiency in turning system. Furthermore, compared with three heuristic optimization techniques, i.e. Genetic Algorithm, Ant Colony Algorithm and Particle Swarm Algorithm, the proposed method demonstrates certain superiority.

Original languageEnglish (US)
Pages (from-to)142-156
Number of pages15
JournalEnergy
Volume166
DOIs
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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