Scholarlink

<p>Accurate energy consumption modeling is crucial for sustainable industrial energy optimization, helping reduce energy use effectively. This study introduces a green decision-making approach specifically tailored for orthogonal turn-milling machining by integrating a detailed energy consumption model with a robust multi-objective optimization algorithm, the nondominated sorting genetic algorithm II (NSGA-II). Energy consumption was determined based on material removal rate and specific energy, derived from key machining parameters such as workpiece speed, depth of cut, and axial feed rate. Surface roughness was also calculated from these parameters to ensure a comprehensive assessment. A central composite experimental design generated coefficients for both the energy consumption model and an <i>Ra</i> response surface model, achieving an impressive accuracy rate of 99.1%. Using NSGA-II, optimal parameters were identified for minimizing energy usage while maintaining a minimum acceptable <i>Ra</i> of 1.5 µm: a workpiece speed of 20 rpm, a depth of cut of 2.93 mm, and a feed rate of 33.69 mm/min. This configuration yielded an energy use of 328.9 kJ and a machining time of 62 s. Experimental validation confirmed a <i>Ra</i> of 1.477 µm, energy consumption of 328.4 kJ, and machining time of 62 s, with accuracy surpassing 98%.</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

Sustainable orthogonal turn-mill process parameter decision-making based on specific consumption energy model and NSGA-II multi-objective optimization algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月12日

查看全文
  • Ke-Er Tang,
  • Guan-Yun Lin,
  • Chun-Wei Liu

摘要

Accurate energy consumption modeling is crucial for sustainable industrial energy optimization, helping reduce energy use effectively. This study introduces a green decision-making approach specifically tailored for orthogonal turn-milling machining by integrating a detailed energy consumption model with a robust multi-objective optimization algorithm, the nondominated sorting genetic algorithm II (NSGA-II). Energy consumption was determined based on material removal rate and specific energy, derived from key machining parameters such as workpiece speed, depth of cut, and axial feed rate. Surface roughness was also calculated from these parameters to ensure a comprehensive assessment. A central composite experimental design generated coefficients for both the energy consumption model and an Ra response surface model, achieving an impressive accuracy rate of 99.1%. Using NSGA-II, optimal parameters were identified for minimizing energy usage while maintaining a minimum acceptable Ra of 1.5 µm: a workpiece speed of 20 rpm, a depth of cut of 2.93 mm, and a feed rate of 33.69 mm/min. This configuration yielded an energy use of 328.9 kJ and a machining time of 62 s. Experimental validation confirmed a Ra of 1.477 µm, energy consumption of 328.4 kJ, and machining time of 62 s, with accuracy surpassing 98%.