Joint Optimization for Knowledge Mining: Evaluating Parameters of Manufacturing Processes

Abstract

In various kinds of manufacturing production, predicting the influence of process parameters in terms of machine performance is a necessity as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this paper presents a novel knowledge-based algorithm embedded with Artificial Intelligence for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts. The originality of this research is that the proposed knowledge-based system is equipped with fuzzy-guided genetic algorithm, enabling the identification of the best set of process parameters. Simulation using the RIE machine is provided to validate the practicability of the proposed approach.