A study about dimensional change of industrial parts using fuzzy rules
This work presents the development of a fuzzy model aimed to predict the dimensional changes of actual industrial parts made in a new emerging material called austempered ductile iron (ADI). The dimensional change is an important factor that must be taken into account to improve the fabrication process of any actual part of ADI. This change is produced during a heat treatment cycle and in the factory practice, its prediction is difficult due to the high number of variables affecting it. In the present case, it was considered that the dimensional change is strongly influenced by 11 operative variables of the fabrication process, associated to the chemical composition and heat treatment temperatures and holding times. The data employed to build the model corresponds to measurements done by experts on different actual industrial parts. The knowledge acquisition about the origin of the data was necessary to understand the uncertainty factors and to adjust the parameters for obtaining a proper model. The model was developed with a Takagi-Sugeno-type structure, and its parameters adjusted with the ANFIS learning algorithm and introduced into an Expert System previously developed. It allows the accurate prediction of dimensional changes and helps in the efficient design of new industrial ADI parts. (C) 2002 Elsevier B.V. All rights reserved.
industrial parts fuzzy model austempered ductile iron rule base system fuzzy clustering