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符号熵驱动的零件机加工艺过程的多维度相似性度量方法

李春磊1,2), 王肖烨1,2), 李亮1,2)
1) (宝鸡文理学院机械工程学院 宝鸡 721016)2) (陕西省机器人关键零部件先进制造与评估省市共建重点实验室 宝鸡 721016)
分类号: TP391.41 DOI: 10.3724/SP.J.1089.2021.18735
出版年,卷(期):页码: 2021 , 33 ( 10 ): 1604-1616 李春磊
摘要: 机加工艺过程从表象看是一道道工序加工方法组成的序列, 但从内里看其实是工件从初始毛坯模型到最终成品模型的几何结构演变过程, 单纯从工序加工序列或三维几何结构的角度出发进行相似性检索会降低检索到工艺的有效性. 针对以上问题, 提出一种符号熵驱动的零件机加工艺过程的多维度相似性度量方法. 首先建立基于符号熵的序列相似性度量方法, 直接对加工方法序列进行相似性计算; 然后建立与加工方法序列对应的几何演变序列, 并在符号熵方法的支持下实现对几何演变过程的相似性度量; 最后将加工方法序列的相似性计算结果及对应几何演变过程的相似性计算结果进行融合, 实现对机加工艺过程相似性的多维度和精准评价. 实例结果表明, 所提方法在度量不同类型机械零件的工艺相似性时, 评价结果更贴近实际制造工艺过程和工艺人员的经验认知, 证明方法是可行的.
关键词: 符号熵; 机加工艺过程; 几何演变序列; 多维相似性度量
Multidimensional Similarity Measurement Method of Machining Processes Driven by Sign Entropy
Li Chunlei1,2), Wang Xiaoye1,2), and Li Liang1,2)
1) (School of Mechanical Engineering, Baoji University of Arts and Sciences, Baoji 721016) 2) (Shaanxi Key Laboratory of Advanced Manufacturing and Evaluation of Robot Key Components, Baoji 721016)
abstract: From the outside, the machining process seems a process sequence formed by processing operations one by one. But from the inside, it is a geometry variation process from the initial blank model to final CAD model. It is clear that individually measuring the similarity of processing operation sequences or geometry variation sequences will reduce the effectiveness of machining process similarity retrieval. To solve this problem, a multidimensional similarity measurement method of machining processes driven by sign entropy is presented. First, a similarity measurement way of sequence based on sign entropy is proposed, which can directly realize the similarity measurement of processing operation sequences. Then, the geometry variation sequence corresponding to processing operation sequence is established, and further achieved the goal of measuring the similarity of geometry variation sequences in the support of the sign entropy way. Finally, make the above two similarity measurement results are fused together, and thus evaluating the similarity of machining processes is evaluated multidimensionally and accurately. Case studies show that the evaluation result by using proposed method to measure the similarity of different kinds of parts is closer to actual manufacturing process and cognitive experience, which proves that the proposed method is feasible.
keyword: sign entropy; machining process; geometry variation sequence; multidimensional similarity measurement
 
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