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

10 Citations (Scopus)

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
DOIs
StatePublished - Jan 1 2019

Fingerprint

Energy efficiency
Machining
Energy utilization
Inference engines
Association rules
Fuzzy inference
Fuzzy logic
Data mining
Tuning
Genetic algorithms
Processing

All Science Journal Classification (ASJC) codes

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

Cite this

Xiao, Qinge ; Li, Congbo ; Tang, Ying ; Li, Lingling ; Li, Li. / A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning. In: Energy. 2019 ; pp. 142-156.
@article{97c726c51d8a4d21a67c421d3fee3417,
title = "A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning",
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.",
author = "Qinge Xiao and Congbo Li and Ying Tang and Lingling Li and Li Li",
year = "2019",
month = "1",
day = "1",
doi = "https://doi.org/10.1016/j.energy.2018.09.191",
language = "English (US)",
pages = "142--156",
journal = "Energy",
issn = "0360-5442",
publisher = "Elsevier Limited",

}

A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning. / Xiao, Qinge; Li, Congbo; Tang, Ying; Li, Lingling; Li, Li.

In: Energy, 01.01.2019, p. 142-156.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning

AU - Xiao, Qinge

AU - Li, Congbo

AU - Tang, Ying

AU - Li, Lingling

AU - Li, Li

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85055979413&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055979413&partnerID=8YFLogxK

U2 - https://doi.org/10.1016/j.energy.2018.09.191

DO - https://doi.org/10.1016/j.energy.2018.09.191

M3 - Article

SP - 142

EP - 156

JO - Energy

JF - Energy

SN - 0360-5442

ER -