Modeling and parameter optimization for cutting energy reduction in MQL milling process

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• 作者：Duk-yong Jang ; Jeehyun Jung ; Jongwon Seok
• 关键词：Environmentally conscious manufacturing ; Minimum quantity lubrication ; Neural network ; Particle swarm optimization ; Specific cutting energy
• 刊名：International Journal of Precision Engineering and Manufacturing-Green Technology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：3
• 期：1
• 页码：5-12
• 全文大小：636 KB
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• 作者单位：Duk-yong Jang (1)
  Jeehyun Jung (1)
  Jongwon Seok (1)
  
  1. School of Mechanical Engineering, College of Engineering, Chung-Ang University, 47, Heukseok-ro, Dongjak-gu, Seoul, 06911, South Korea
  
• 刊物类别：Industrial and Production Engineering; Energy Efficiency (incl. Buildings); Sustainable Development;
• 刊物主题：Industrial and Production Engineering; Energy Efficiency (incl. Buildings); Sustainable Development;
• 出版者：Korean Society for Precision Engineering
• ISSN：2198-0810

文摘

Environmentally conscious manufacturing (ECM), a key concept for modern manufacturing, emphasizes the efficient and optimal use of raw materials and natural resources and minimization of the negative effects on nature and society. This study focused on achieving ECM for milling processes. Toward this end, a model predicting the specific cutting energy was developed and optimized to determine the cutting conditions that minimize the specific cutting energy. A minimum quantity lubrication scheme was employed to minimize the amount of cutting oil used, thereby minimizing the associated process cost. Four process variables or cutting conditions (cutting speed, depth of cut, feed rate, and flow rate) were selected for the specific cutting energy model, and their appropriate ranges were determined through a preliminary experiment. The specific cutting energy model was developed based on an artificial neural network, where the Levenberg-Marquardt back propagation algorithm was implemented and the number of hidden layers was determined through comparison with controlled experimental data. The cutting conditions to minimize the specific cutting energy were determined using a global optimization process-the particle swarm optimization algorithm. In this algorithm, all computations were confined within the experimental range via constraint conditions, and the resulting optimized process variables were experimentally verified. Keywords Environmentally conscious manufacturing Minimum quantity lubrication Neural network Particle swarm optimization Specific cutting energy