Application of Machine Learning Techniques in the Process Modeling of WEDM
نویسندگان
چکیده
Wire-EDM is a highly complex process, which is characterized by non-linear behavior. Owing to unique capabilities of machining complex shapes and hard materials with high accuracy and fine surface finish, WEDM is used in various manufacturing industries. There has been on-going research to develop automated capabilities for WEDM by understanding the interaction mechanism of various input parameters to get the requisite output measures like MRR, Surface Finish etc. To get t he desired output measures, WEDM depends mainly on the operator’s experience and trial and error methods. To overcome this, a standard method for predicting output measures based on the input parameters in WEDM is yet to be established, which is a key requirement for developing Automated Process Control System and Expert System for WEDM. As a part of research, data mining technique applied to model the WEDM process, to select the input parameters for the desired output measures. The model was built trained, tested and validated with the Experimental data and with additional data. It is observed that the model built using data mining approach results-in with desired accuracy.
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