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基于G1-CRITIC的不同距离TOPSIS法的机床工艺参数综合决策方法研究
引用本文:刘光辉,殷鸣,谢罗峰,殷国富. 基于G1-CRITIC的不同距离TOPSIS法的机床工艺参数综合决策方法研究[J]. 组合机床与自动化加工技术, 2021, 0(1): 146-151
作者姓名:刘光辉  殷鸣  谢罗峰  殷国富
作者单位:四川大学机械工程学院
基金项目:四川省科技计划项目(2019ZDZX0021)。
摘    要:针对机床加工工艺方案评选中存在多种影响因素问题,以基本时间、表面质量、表面粗糙度、磨削力和噪声因素为决策指标,建立了机械加工工艺方案的组合评价模型.考虑决策指标间的相关性和差异度,采用基于G1法和CRITIC法的组合赋权法对其指标进行综合赋权,根据加权矩阵求出正负理想解,然后分别采用欧式距离、马氏距离、垂直距离三种算法...

关 键 词:组合赋权法  相关性  差异度  马氏距离  垂直距离

Research about Comprehensive Decision-making Method of Machine Tool Process Parameters by Different Distance TOPSIS Based on G1-CRITIC
LIU Guang-hui,YIN Ming,XIE Luo-feng,YIN Guo-fu. Research about Comprehensive Decision-making Method of Machine Tool Process Parameters by Different Distance TOPSIS Based on G1-CRITIC[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021, 0(1): 146-151
Authors:LIU Guang-hui  YIN Ming  XIE Luo-feng  YIN Guo-fu
Affiliation:(School of Machnical Engineering,Sichuan University,Chengdu 610065,China)
Abstract:This paper puts forwards a comprehensive model for evaluating machining process plans,whose decision-making indexes include basic time,surface quality,surface roughness,grinding force,and noise,in order to tackle the problem that there are varied influence factors in machining process plans.Considering the relevance and variance degree among those indexes,we employ G1 method and combination weighting approach of CRITIC method to weight them comprehensively.Obtain the positive and negative ideal solutions based on weighting matrix,and then solve the distances among the indexes using Euclidean distance,Markova distance and vertical distance respectively,and finally sort these distances.After abandoning the plans with large variance and selecting the optimal compromised plan.The results show that the three methods are different in the degree of scheme differentiation,and the markov distance and vertical distance are better to some extent and we validate its feasibility using grinding process plan.
Keywords:combination weighting approach  relevance  variance degree  markova distance  vertical distance
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