Data Science in Metal Forming
- 1st Edition - July 28, 2025
- Latest edition
- Authors: Li-Liang Wang, Heli Liu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 4 5 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 4 6 - 3
Data Science in Metal Forming provides readers with a framework to collect, visualize, analyze, and characterize metal forming metadata, thus enabling improved design, more effici… Read more
Data Science in Metal Forming provides readers with a framework to collect, visualize, analyze, and characterize metal forming metadata, thus enabling improved design, more efficient production, and more effective application of a range of metals. Chapters introduce concepts and discuss industry 4.0, digital manufacturing, and more. Other sections feature case studies of metal forming data collection spanning several essential procedures and outline methods for data processing when lacking essential information.
The book also includes data visualization techniques, insights into how to analyze data from various metal forming processes (stamping, hydroforming, incremental, extrusion, and more) and details on how readers can setup, manage, and most effectively apply their own data repositories.
The book also includes data visualization techniques, insights into how to analyze data from various metal forming processes (stamping, hydroforming, incremental, extrusion, and more) and details on how readers can setup, manage, and most effectively apply their own data repositories.
- Demonstrates effective data collection processes for metal forming
- Outlines how to visualize, process, analyze, and characterize this data, with a goal of better design, production, and application of various metals
- Discusses how to process and characterize information where there are missing data elements
- Provides guidance on how to setup and effectively manage metal forming data repositories
Researchers and graduate students in mechanical engineering, manufacturing, information design, and materials science
1. Introduction to Data Science in Metal Forming
2. Review of Advanced Metal Forming Technologies
3. Metal Forming Metadata
4. Information Absent Metal Forming Data Processing
5. Digital Characteristics of Data Science in Metal Forming
6. Developing and analyzing Digital Characteristics of forming processes
7. Digital Characteristics Space of manufacturing processes
8. Applications of Data Science in Metal Forming
2. Review of Advanced Metal Forming Technologies
3. Metal Forming Metadata
4. Information Absent Metal Forming Data Processing
5. Digital Characteristics of Data Science in Metal Forming
6. Developing and analyzing Digital Characteristics of forming processes
7. Digital Characteristics Space of manufacturing processes
8. Applications of Data Science in Metal Forming
- Edition: 1
- Latest edition
- Published: July 28, 2025
- Language: English
LW
Li-Liang Wang
Li-Liang Wang is Head of the Metal Forming and Modeling Group in the Department of Mechanical Engineering, Imperial College. He received his PhD degree from Delft University of Technology. He joined Imperial College in 2009. Dr Wang’s major research interests include the design and development of advanced metal forming technologies and manufacturing system. His work has made fundamental contributions to characterization and modelling of materials and interfacial behaviors of engineering materials. Particularly, Dr Wang’s research has direct impacts on sustainable manufacturing, e.g., Hot stamping of Aluminum alloy (International Journal of Machine Tools and Manufacture 87, 39-48); Data sciences in metal forming (Nat. Commun., 2022, 13: 5748,); novel lightweight forming technology: FAST (Int. J. Plast., 2019, 119: 230-248); Tribology in metal forming (Friction 10 (6), 911-926) and innovative material characterization techniques (Addit. Manuf. 2021, 37: 101720).
Affiliations and expertise
Department of Mechanical Engineering, Imperial College, South Kensington, London, UKHL
Heli Liu
Heli Liu currently works as a research assistant in the Department of Mechanical Engineering, Imperial College London. His areas of focus are data science in manufacturing, information design, metal forming, anti-fatigue analysis, and tribology.
Affiliations and expertise
Department of Mechanical Engineering, Imperial College, South Kensington, London, UKRead Data Science in Metal Forming on ScienceDirect