Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars

This study investigates the application of Bayesian statistical methods to analyze and predict the dimensional changes in torsion bars made from 20CrMnTi alloy steel during carburizing heat treatment. The process parameters, including a treatment temperature of 920 °C followed by oil quenching, were selected to optimize surface hardness while maintaining core toughness. The dimensional changes were measured pre- and post-treatment using precise caliper measurements. Bayesian statistics, particularly conjugate normal distributions, were utilized to model the dimensional variations, providing both posterior and predictive distributions. These models revealed a marked concentration of the posterior distributions, indicating enhanced accuracy in predicting dimensional changes. The findings offer valuable insights for improving the control of carburizing-induced deformations, thereby ensuring the dimensional integrity and performance reliability of torsion bars used in high-stress applications such as pneumatic clutch systems in mining ball mills. This study underscores the potential of Bayesian approaches in advancing precision engineering and contributes to the broader field of statistical modeling in manufacturing processes.