Scholarlink

<p>Under the pressures of climate change and energy crises, the manufacturing industry faces significant challenges, especially with stringent carbon emission regulations. This paper addresses optimizing energy consumption in high-precision machine tools, specifically during the grinding phase of wafer grinders. Traditional theoretical models fail to accurately predict energy consumption due to the varied energy types and complex flows in wafer grinders. To tackle this, we propose a combined theoretical and experimental mathematical modeling approach. We constructed a model with unknown coefficients, which were calibrated using experimental grinding data, allowing accurate power consumption predictions during fine grinding. We optimized grinding power consumption, material removal rate, and surface roughness using wheel speed, wafer speed, and feed rate as decision variables through a multi-objective genetic algorithm. Experimentally validated, our model enhances energy efficiency, offering theoretical support for sustainable manufacturing.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Energy consumption model establishment and multi-objective parameter optimization based on wafer thinning machine

  • 期刊论文

  • 发表日期：

    2025年11月5日

查看全文
  • Dongju Chen,
  • Anqing Wang,
  • Zhoujie Zhao,
  • Jinwei Fan,
  • Ri Pan,
  • Kun Sun

摘要

Under the pressures of climate change and energy crises, the manufacturing industry faces significant challenges, especially with stringent carbon emission regulations. This paper addresses optimizing energy consumption in high-precision machine tools, specifically during the grinding phase of wafer grinders. Traditional theoretical models fail to accurately predict energy consumption due to the varied energy types and complex flows in wafer grinders. To tackle this, we propose a combined theoretical and experimental mathematical modeling approach. We constructed a model with unknown coefficients, which were calibrated using experimental grinding data, allowing accurate power consumption predictions during fine grinding. We optimized grinding power consumption, material removal rate, and surface roughness using wheel speed, wafer speed, and feed rate as decision variables through a multi-objective genetic algorithm. Experimentally validated, our model enhances energy efficiency, offering theoretical support for sustainable manufacturing.