使用进化算法对数控制造系统中基于生态制造标准的多属性优化

Rdv D.V. Prasad, Dr. Arun Vikram Arun Kothapalli, Srinivasa Rao Mss, Lakshmi VK Vennela, Vijaya Krishna Kanth Tammi
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引用次数: 0

摘要

随着数控制造系统、柔性制造、快速成型、智能制造等技术的发展,制造技术也在不断进步。与此同时,为满足特定要求而开发的新型奇特材料也给制造带来了新的问题。迄今为止开发的材料需要使用特殊工具、润滑剂,并在不影响质量的前提下在加工过程中格外小心。此外,鉴于制造业中的碳排放对生态平衡造成的破坏性影响,对碳排放的研究也变得异常重要。因此,必须对制造系统进行相应的设计和开发,使其在不牺牲质量和工具形态等首要目标的前提下,产生最少的排放量。本研究的主要目的是分析切削参数对温室气体排放率、刀具磨损和工件温度的影响。这些研究是在计算机数控加工系统的干湿两种条件下完成的。加工过程涉及淬硬材料 Ti-6Al-4V 的光面加工。实验研究使用单点和多点切削工具,并辅以多目标遗传算法(MOGA)的应用。针对四种加工条件,MOGA 生成了一组帕累托前沿,并通过 VIKOR、TOPSIS 和 LINMAP 决策方法得出了决策变量的最佳值。在干燥条件下,单点切削刀具加工的最佳切削参数是转速(873.2 rpm)、进给量(0.199 mm/rev)和切削深度(0.25 mm),而相应的响应值是刀具磨损(67.19 μm)、工件温度(39.36 oC)和碳排放(0.138 Kg-CO2)。多点切削的等效值确定为 899.8 rpm、0.195 mm/rev 和 0.25 mm,而这些最佳条件的响应值依次为 69.92μm、39.48oC 和 0.137 Kg-CO2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-attribute optimization of eco-manufacturing based criteria in CNC manufacturing systems using an evolutionary algorithm
Manufacturing technology has evolved over the years with the development of CNC manufacturing systems, flexible manufacturing, rapid prototyping, smart manufacturing etc. Simultaneously, the development of new and exotic materials to match specific requirements has shaped new problems in manufacturing. The materials developed thus far require special tools, lubricating agents, and extra-care in machining without compromising the quality. Further the study of carbon emissions in manufacturing sector has also gained unusual spotlight in view of their deleterious effect on the ecological balance. The manufacturing systems have to be consequently designed and developed, such that they generate minimal quantity of emissions without forfeiting the prime objectives of quality and tool morphology. The present work is principally intended to analyse the effect of cutting parameters on the emission rate of greenhouse gases, tool wear and work-piece temperature concurrently. These studies are accomplished in both dry and wet conditions on computer numerical control machining system. The machining process involved plain facing of a Ti-6Al-4V hardened material. The experimental studies are realized using both single point and multi-point cutting tools and are supplemented with the application of Multi-Objective Genetic Algorithm (MOGA). The MOGA generated set of pareto-fronts for the four machining conditions were subjected to VIKOR, TOPSIS and LINMAP decision making approaches to arrive at the optimum values of decision variables. The optimum cutting parameters obtained in single point cutting tool machining in dry conditions are speed (873.2 rpm), feed (0.199 mm/rev) and depth of cut (0.25 mm), while the corresponding values of responses are tool wear (67.19 μm), work-piece temperature (39.36 oC) and carbon emission (0.138 Kg-CO2). The equivalent values for multi-point cutting were determined as 899.8 rpm, 0.195 mm/rev and 0.25 mm, while the responses for these optimal conditions are 69.92μm, 39.48oC and 0.137 Kg-CO2 in that order.
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