Application Of Data Mining And Machine Learning Methods To Industrial Heat Treatment Processes For Hardness Prediction
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Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
Author: Lingelbach, Yannick
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-07-24
This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. - This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework.
Understanding Degradation Phenomena in Solid-Oxide Fuel-Cell Anodes by Phase-Field Modeling and Analytics
Author: Hoffrogge, Paul Wilhelm
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-10-09
The current work analyzes degradation effects in solid-oxide fuel cell anodes with the phase-field method. A model extension for interface diffusion is formulated and calibrated. Large-scale 3D-simulations provide interesting insights into phenomena at the microscale which are responsible for the degradation
Artificial Intelligence and Machine Learning in the Thermal Spray Industry
This book details the emerging area of the induction of expert systems in thermal spray technology, replacing traditional parametric optimization methods like numerical modeling and simulation. It promotes, enlightens, and hastens the digital transformation of the surface engineering industry by discussing the contribution of expert systems like Machine Learning (ML) and Artificial Intelligence (AI) toward achieving durable Thermal Spray (TS) coatings. Artificial Intelligence and Machine Learning in the Thermal Spray Industry: Practices, Implementation, and Challenges highlights how AI and ML techniques are used in the TS industry. It sheds light on AI’s versatility, revealing its applicability in solving problems related to conventional simulation and numeric modeling techniques. This book combines automated technologies with expert machines to show several advantages, including decreased error and greater accuracy in judgment, and prediction, enhanced efficiency, reduced time consumption, and lower costs. Specific barriers preventing AI’s successful implementation in the TS industry are also discussed. This book also looks at how training and validating more models with microstructural features of deposited coating will be the center point to grooming this technology in the future. Lastly, this book thoroughly analyzes the digital technologies available for modeling and achieving high-performance coatings, including giving AI-related models like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) more attention. This reference book is directed toward professors, students, practitioners, and researchers of higher education institutions working in the fields that deal with the application of AI and ML technology.