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Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process

机译:基于模糊逻辑的专家系统预测磨料射流加工切削深度

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摘要

The development of an expert system for abrasive water jet machining (AWJM) process is considered in the present work. The expert system has been developed by using fuzzy logic (FL). It is to be noted that the performance of AWJM in terms of depth of cut depends on various process parameters, such as diam eter of focusing nozzle, water pressure, abrasive mass flow rate and jet traverse speed. Three approaches have been developed to predict the depth of cut in AWJM using FL system. The first Approach deals with the construction of Mamdani-based fuzzy logic system. It is important to note that the performance of the FL depends on its knowledge base. In Approach 2, the data base and rule base of the FL-system are optimized, whereas in the third Approach, the total FL-system is evolved automatically. A binary-coded genetic algorithm has been used for the said purpose. The developed expert system eliminates the need of extensive experimental work, to select the most influential AWJM parameters on the depth of cut. The performances of the developed FL-systems have been tested to predict the depth of cut in AWJM process with the help of test cases. The prediction accuracy of the automatic FL-system (i.e. Approach 3) is found to be better than the other two approaches.
机译:在目前的工作中,已经考虑开发用于磨料水射流加工(AWJM)工艺的专家系统。通过使用模糊逻辑(FL)开发了专家系统。应当指出,就切割深度而言,AWJM的性能取决于各种工艺参数,例如聚焦喷嘴的直径,水压,磨料质量流速和射流移动速度。已经开发出三种方法来预测使用FL系统在AWJM中的切割深度。第一种方法涉及基于Mamdani的模糊逻辑系统的构建。重要的是要注意,FL的性能取决于其知识库。在方法2中,优化了FL系统的数据库和规则库,而在第三种方法中,将自动开发整个FL系统。二进制编码的遗传算法已用于所述目的。先进的专家系统消除了进行大量实验工作的需要,从而无需在切削深度上选择最有影响力的AWJM参数。已对开发的FL系统的性能进行了测试,以借助测试案例来预测AWJM过程中的切削深度。发现自动FL系统(即方法3)的预测精度优于其他两种方法。

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