Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (12:31:08 CEST)
How to cite:
Sun, G.; Xu, L.; Wang, Q. Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars. Preprints2024, 2024071503. https://doi.org/10.20944/preprints202407.1503.v1
Sun, G.; Xu, L.; Wang, Q. Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars. Preprints 2024, 2024071503. https://doi.org/10.20944/preprints202407.1503.v1
Sun, G.; Xu, L.; Wang, Q. Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars. Preprints2024, 2024071503. https://doi.org/10.20944/preprints202407.1503.v1
APA Style
Sun, G., Xu, L., & Wang, Q. (2024). Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars. Preprints. https://doi.org/10.20944/preprints202407.1503.v1
Chicago/Turabian Style
Sun, G., Linqian Xu and Qi Wang. 2024 "Application of Bayesian Statistics in Analyzing and Predicting Carburizing-Induced Dimensional Changes in Torsion Bars" Preprints. https://doi.org/10.20944/preprints202407.1503.v1
Abstract
This paper explores the feasibility of applying Bayesian statistical methods to study the distortion patterns induced by carburizing heat treatment. By establishing posterior and predictive distribution models for torsion bar dimensions, we aim to accurately understand and predict the expansion behavior, thus enhancing control over carburizing-induced dimensional changes. Bayesian methods allow for the integration of prior knowledge and real-time data, providing a more comprehensive understanding of the distortion phenomena. This approach not only improves the precision of dimensional predictions but also contributes to optimizing the overall manufacturing process, ensuring that the torsion bars meet the rigorous standards required for high-performance applications in demanding industrial environments.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
