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Fault Identification in Investment Casting Process Using Naive Bayes Method

Optimization in the investment casting industry is a difficult task as it contains multiple-stage operations for pro- duction. Although taking care of all the parameters investment casting industry is facing issues due to defects in the final product. This leads to a decrease in production, loss of production resources, and efficiency by analyzing the parameters and defects from the previous batches. We can predict the occurrence of defects for the given batch using a suitable machine learning technique. The Naive Bayes method, a supervised machine learning technique, is introduced to predict defects for the given batch from the model developed for it. Naive Bayes method includes four different models as, Gaussian NB, Multinomial NB, Complement NB, Bernoulli NB. This paper compared results for each model and analyzes which Naive Bayes model is best suitable for the Investment Casting industry and can predict the defect in the final product before it occurs.