Machine Learning Applications in Mechanical Engineering

Machine Learning Applications in Mechanical Engineering is a comprehensive guide exploring the transformative role of machine learning (ML) across key domains in mechanical engineering. It combines theoretical insights and practical applications to address design optimization, predictive maintenance, robotics, material discovery, and energy systems, making it invaluable for students, researchers, and professionals.The book begins with an introduction to ML, highlighting its relevance and challenges in mechanical engineering. It explores learning models like supervised, unsupervised, and semi-supervised learning, alongside neural networks, Bayesian techniques, and support vector machines. Chapters delve into ML-driven innovations in material design, predictive maintenance, and meta surface optimization, showcasing tools like deep learning and generative models.This book equips readers to leverage ML in tackling engineering challenges, paving the way for intelligent, data-driven solutions in mechanical engineering.

Autorentext
Dr. Jiyaul Mustafa, Assistant Professor at Bennett University, excels in machine design, vibration control, and ML in mechanical systems. Dr. Shahnawaz Ahmad, also at Bennett, specializes in cloud computing and ML security, with 30+ publications. Dr. Shahadat Hussain focuses on ML/Deep Learning for healthcare, notably cardiovascular data analysis.