{ "item_title" : "AI for Metal Forming", "item_author" : [" Rakesh Kumar "], "item_description" : "Metal forming has long been a cornerstone of modern manufacturing, enabling the production of critical components used across industries such as automotive, aerospace, construction, and energy. Processes like forging, rolling, extrusion, and sheet metal forming have traditionally relied on deep domain expertise, empirical knowledge, and iterative experimentation. While these methods have delivered remarkable results over decades, they are increasingly challenged by the demands of precision, efficiency, sustainability, and rapid innovation in today's competitive landscape.The emergence of Artificial Intelligence (AI) marks a transformative shift in how metal forming processes are designed, controlled, and optimized. AI technologies-including machine learning, deep learning, computer vision, and reinforcement learning-are enabling manufacturers to move from reactive and experience-based decision-making to predictive, data-driven, and autonomous systems. This transition is at the heart of what is often referred to as smart manufacturing or Industry 4.0.In metal forming, the complexity arises from highly non-linear material behavior, varying process parameters, and sensitivity to environmental conditions. Traditional modeling techniques such as finite element analysis (FEA) provide valuable insights but are often computationally intensive and time-consuming. AI complements and, in some cases, accelerates these approaches by learning patterns directly from data, enabling real-time predictions and optimization.This book, AI for Metal Forming, is designed to bridge the gap between manufacturing engineering and artificial intelligence. It provides a comprehensive exploration of how AI can be applied across the entire metal forming lifecycle-from design and simulation to production, quality control, and maintenance.Throughout this book, readers will discover how AI is revolutionizing key areas such as: Process Optimization: Using machine learning models to determine optimal parameters for temperature, pressure, and speed.Defect Detection and Prevention: Leveraging computer vision and deep learning to identify cracks, wrinkles, and surface imperfections.Predictive Maintenance: Anticipating equipment failures using sensor data and AI models.Digital Twins: Creating virtual replicas of forming processes for simulation and continuous improvement.Autonomous Control Systems: Applying reinforcement learning for real-time decision-making and adaptive process control.In addition to theoretical concepts, this book emphasizes practical implementation. Each chapter includes real-world case studies, sample datasets, and hands-on code examples to help readers understand how to build and deploy AI solutions in industrial environments. Whether you are an engineer, data scientist, researcher, or industry professional, this book will equip you with the knowledge and tools needed to harness AI effectively in metal forming applications.The journey toward AI-driven manufacturing is not without challenges. Issues such as data availability, model interpretability, integration with legacy systems, and workforce readiness must be addressed thoughtfully. This book also explores these challenges and provides guidance on best practices, implementation strategies, and future trends.As industries continue to evolve toward automation and intelligence, the integration of AI into metal forming is no longer optional-it is essential. Organizations that embrace this transformation will gain significant advantages in terms of quality, cost efficiency, flexibility, and innovation.This book invites you to explore this exciting intersection of materials science, mechanical engineering, and artificial intelligence, and to be part of the next generation of manufacturing excellence.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/825/701/9798257010965_b.jpg", "price_data" : { "retail_price" : "47.00", "online_price" : "47.00", "our_price" : "47.00", "club_price" : "47.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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