Experimental investigation of process parameters for pack boronizing of SS410 using anova and machine learning approaches

Abstract

In recent years, applications of Machine Learning and Artificial Intelligence are gaining momentum to the production researchers to analyze the complex interdependencies present in the production dataset. The manufacturers have started to incorporate machine learning approaches to the production process & predictive algorithms to fine-tune the quality of the product. The objective of the proposed work is to apply classification and regression algorithms to analyze the input process parameters for the pack boronizing process of SS410. To prepare the dataset, 9 experiments were carried out and the test specimens having ø 55 mm & thickness of 10 mm are pack boronized using the boronizing agent 325 mesh size. This process is carried out in 4.5 kW ‘INDFURR’ electric furnace with varying input parameters of temperature, time and gas pressure. The output parameters are boronizing thickness and microvickers hardness. The SEM and optical microscopic images of the specimen confirm the formation of the boronizing layer. To find the influence parameters, it is analyzed using ANOVA and Decision Tree algorithm. Both the techniques confirmed that time as the most significant parameter for boronizing thickness. For surface hardness, time & temperature are the major influencing parameters. Various regression models from machine learning were formulated to find the relationship between variables. Among these models, multilayer perception produced maximum correlation co-efficient & minimum root mean square error.