Case-Based Reasoning with Fuzzy Logic Knowledge Acquisition for Adaptive Fastener Die Design on Bi-material Forming

Purpose: This paper proposes the novel study by using Case-based reasoning with fuzzy logic knowledge-based acquisition for adaptable die design on cold forging fastener for bi-material. New knowledge and parameters are validated by DEFORM simulation which is widely used for practitioners. Design/methodology/approach: Case-based reasoning is applied to collect data, classify and store in the case library before retrieving them to be a pattern, modify and adapt the most similar case to fit a new case. In addition, the cases are also created by Fuzzy logic which assists to create new knowledge and parameters for new bi-material that never applied to industry. DEFORM simulation is used to verify the feasible new process. Findings: It was found that CBR is effective to combine previous experience and modify to solve current problem that have not occur. The solution is used, tested, modified and store in the library can retrieve for the future use. Light-weight with bi-material data for forging is developed for academic and practitioner references. Research limitations/implications: The limitation is that the new case of knowledge should be verify by experience before using effectively. Practical implications: The CBR concept has implemented by the case study to verify the library knowledge workable in practical routine process design with new material that have not design and production before. Originality/value: The new concept for new material for cold forging design is developed to deal with design problem without data and knowledge availability. Analogy solution by CBR approach is the best alternative and adopted to this work. In addition, knowledge was created and generated by fuzzy logic and verification by DEFORM.