Modeling of Wire Electro-Spark Machining of Inconel 690 Superalloy Using Support Vector Machine and Random Forest Regression Approaches
Abstract
Machine learning approaches are disseminating very rapidly in manufacturing industry. They emerge out as latent modeling approaches showing a great potential for solving manufacturing-related problems. This study aims to analyze and predict the micro-irregularities on the surface of Inconel 690 superalloy processed by wire electro-spark machining using machine learning approaches. Experimental runs are completed by using the box-Behnken design of response surface methodology. Spark-on time, spark-off time, current and voltage with varying constraints are used as the input process variables, whereas surface integrity is used as the output variable. Support vector machines with two different functions and random forest regression are exercised to predict the machining outcomes with the help of existing models and validate the experimental outcomes. Support vector machine with radial basis function emerged out as the best approach in predicting the process outcomes. It is also perceived that spark-on time is the most dominating input process variable.
