Experimental assessment of quality in injection parts using a fuzzy system with adaptive membership functions

被引：3
作者：
Chaves M.L. [1 ]
Sánchez-González L. [2 ]
Díez E. [3 ]
Pérez H. [2 ]
Vizán A. [4 ]
机构：
[1] Department of Computer and Systems Engineering, Universidad Católica de Colombia, Caracas 46-72, Bogotá
[2] Departmento de Ingenierías Mecánica, Informática y Aeroespacial, Universidad de León, León
[3] Departamento Ingeniería Mecánica, Universidad de La Frontera, Temuco
[4] Departamento de Ingeniería Mecánica, Universidad Politécnica de Madrid, José Gutiérrez Abascal, 2, Madrid
来源：
Neurocomputing | 2022年 / 391卷
关键词：
Defect behavior tendency curves; Fuzzy logic; Injection molding process; Intelligent system;
D O I：
10.1016/j.neucom.2019.06.108
中图分类号：
学科分类号：
摘要：
Expert systems provide valuable help for making decisions in those fields where the number of parameters involved is reasonably high. That is the case of plastic injection molding processes, where many parameters need to be determined in order to achieve parts with high quality. An intelligent system based on fuzzy logic is proposed and the membership functions are defined from defect behavior tendency curves. The proposed hybrid fuzzy logic system is tuned considering the expertise of an operator by means of a set of adaptive regression membership functions. So, the defined rules make possible for the expert system to correlate qualitative inspection of manufactured parts made by an operator with a quantitative inspection, determining the set of appropriate process parameters that produce high quality parts. Experimental results show that the effectiveness is improved and the process time is also reduced in 40%. © 2019
引用
收藏
页码：334 / 344
页数：10
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