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Full Description
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.
Contents
1. Artificial Intelligence in Thermal Spray Industry: Introduction and Benefits. 2. Unsupervised and Supervised Machine Learning Techniques in Wear Prediction. 3. Artificial Intelligence-Based Image Processing Techniques for Assessment of Patterns and Mechanisms in Thermal Spray. 4. Artificial Intelligence and Automation in Sustainable Development. 5. Role of Machine Learning Techniques in Coating Process Monitoring, Controlling, and Optimization. 6. Challenges of Using Artificial Intelligence in Thermal Spray Industry: Implementation, Optimization, and Control. 7. Neural Network Model for Wear Prediction of Coatings: Case Study. 8. Implementation of Regression Modes for Wear Analysis of Coating: Case Study.
