Data Science in Metal Forming

Data Science in Metal Forming by Li-Liang Wang; Heli Liu

Transform the way you approach manufacturing: this authoritative guide bridges cutting-edge data science with the practical challenges of metal forming. Ideal for engineers, researchers, and production managers, the book reveals how machine learning, statistical modeling, and process analytics deliver measurable improvements in forging, stamping, rolling, and extrusion.

Inside you’ll find clear, application-focused explanations of predictive modeling, process optimization, quality control, and real-time monitoring—grounded in materials engineering and industrial practice. Case studies demonstrate how data-driven techniques reduce scrap, shorten setup times, and improve part consistency across plants in Asia, Europe, and North America. Practical chapters explain sensor integration, feature engineering, anomaly detection, and building robust models for noisy manufacturing data.

This resource helps you move from experimentation to deployment: learn to design experiments, validate models, implement digital twins, and translate analytics into production-ready solutions that boost throughput and lower cost. Whether you’re modernizing a legacy press line or designing Industry 4.0 workflows for a high-volume plant, the strategies and examples are directly applicable.

Concise, technically rich, and easy to apply, Data Science in Metal Forming equips professionals and students with the tools to lead digital transformation in metalworking. Enhance process reliability, accelerate innovation, and gain a competitive edge—order your copy today and start turning data into consistent, high-quality metal parts.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.