基于FAHP及GRA的刀具优选方法研究  被引量:3

Tool Optimization Selection Method Research Based on Fuzzy Analytic Hierarchy Process and Grey Relational Analysis Method

在线阅读下载全文

作  者:王明海[1,2] 胡付红 郑耀辉[1] 王晓燕[1] 王奔[1] 李晓鹏[1] 

机构地区:[1]沈阳航空航天大学航空制造工艺数字化国防重点学科实验室,辽宁沈阳110136 [2]北京航空航天大学能源与动力工程学院,北京100191

出  处:《机床与液压》2016年第7期46-52,共7页Machine Tool & Hydraulics

基  金:中航航空科学基金资助项目(2013ZE54002)

摘  要:针对传统层次分析法(AHP)在构建刀具优选判断矩阵时没有考虑评价工程师主观判断的模糊性和指标属性的模糊性,导致刀具优选可信度和准确性降低的问题。分析了影响刀具选择的约束因素,建立了一种两级结构的多目标刀具优选模型,包括刀具的加工时间T、加工质量Q、加工成本C、资源消耗R、环境影响E五个优化目标;提出并设计了基于三角模糊数的模糊层次分析法(FAHP)及灰色关联分析(GRA)法进行求解刀具优选层次模型的算法,在三角模糊数互补判断矩阵传统计算权重方法的基础上,进行了算法优化。结合某航空制造企业叶片榫头铣削刀具优化选择的案例,证明了该方法用于刀具优选是可行且有效的。Vagueness of assessment engineers subjective judgment and fuzziness of index property aren' t considered when building the judgment matrix of tool optimization selection by analytic hierarchy process( AHP). This leads to reduce the credibility and accuracy of tool optimization selection. The constraint factors of tools optimization selection was analyzed and multi-objects tool optimization selection model of a two-stage structure was built. The five optimization objects include processing time T、processing quality Q、processing cost C、resources consumption R and environment impact E. The algorithm of solving tool optimization selection hierarchical model was put forward and designed based on fuzzy analytic hierarchy process( FAHP) of triangular fuzzy numbers and grey relational analysis( GRA) method. The traditional algorithm of calculating the weight of triangular fuzzy numbers complementary judgment matrix was optimized. Combined with a case of blade tenon milling tool optimization selection in aviation manufacturing enterprise,the method that is used for tool optimization selection is feasible and effective.

关 键 词:三角模糊数 模糊层次分析法 灰色关联分析法 刀具优选 判断矩阵 

分 类 号:TH186[机械工程—机械制造及自动化] TP391[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象