Energy-Efficient Machining Process Analysis and Optimisation Based on BS EN24T Alloy Steel as Case Studies

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1 Citation (Scopus)

Abstract

Computer Numerical Controlled (CNC) machining, which is one of the most widely-deployed manufacturing techniques, is an energy-intensive process. It is important to develop energy-efficient CNC machining strategies to achieve the overall goal of sustainable manufacturing. Due to the complexity of machining parameters, it is challenging to develop effective modelling and optimisation approaches to implement energy-efficient CNC machining. To address the challenge, in this paper, BS EN24T alloy (AISI 4340) has been used as a case study to conduct energy-efficient analysis and optimisation. Using a combination of experimentation and Taguchi analysis, the impact of the key machining parameters of CNC machining processes on energy consumption has been investigated in detail. A multi-objective optimisation model has been formulated, and a novel improved multi-swarm Fruit Fly optimisation algorithm (iMFOA) has been developed to identify optimal solutions. Case studies and algorithm benchmarking have been conducted to validate the effectiveness of the optimisation approach. The relationships between energy consumption and key machining parameters (e.g., cutting speed, feed per tooth, engagement depth) have been analysed to support process planners in implementing energy-saving measures efficiently. The optimisation approach developed is effective in fine-tuning key parameters for enhancing energy efficiency while meeting other technical requirements of production.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalRobotics and Computer-Integrated Manufacturing
Volume58
Early online date6 Feb 2019
DOIs
Publication statusPublished - Aug 2019

Fingerprint

Alloy steel
Energy Efficient
Machining
Steel
Optimization
Energy Consumption
Energy utilization
Manufacturing
Fruit
Benchmarking
Energy Saving
Fruits
Multiobjective optimization
Swarm
Energy Efficiency
Optimization Model
Multi-objective Optimization
Experimentation
Energy efficiency
Tuning

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Robotics and Computer-Integrated Manufacturing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Robotics and Computer-Integrated Manufacturing, [58], (2019) DOI: 10.1016/j.rcim.2019.01.011

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

European Commission

Keywords

  • Bs en24t (aisi4340)
  • Cnc machining
  • Optimisation
  • Sustainable manufacturing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

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title = "Energy-Efficient Machining Process Analysis and Optimisation Based on BS EN24T Alloy Steel as Case Studies",
abstract = "Computer Numerical Controlled (CNC) machining, which is one of the most widely-deployed manufacturing techniques, is an energy-intensive process. It is important to develop energy-efficient CNC machining strategies to achieve the overall goal of sustainable manufacturing. Due to the complexity of machining parameters, it is challenging to develop effective modelling and optimisation approaches to implement energy-efficient CNC machining. To address the challenge, in this paper, BS EN24T alloy (AISI 4340) has been used as a case study to conduct energy-efficient analysis and optimisation. Using a combination of experimentation and Taguchi analysis, the impact of the key machining parameters of CNC machining processes on energy consumption has been investigated in detail. A multi-objective optimisation model has been formulated, and a novel improved multi-swarm Fruit Fly optimisation algorithm (iMFOA) has been developed to identify optimal solutions. Case studies and algorithm benchmarking have been conducted to validate the effectiveness of the optimisation approach. The relationships between energy consumption and key machining parameters (e.g., cutting speed, feed per tooth, engagement depth) have been analysed to support process planners in implementing energy-saving measures efficiently. The optimisation approach developed is effective in fine-tuning key parameters for enhancing energy efficiency while meeting other technical requirements of production.",
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