Data Science in Metal Forming

1 Edición - 19 de julio de 2025
Última edición
Autores: Li-Liang Wang, Heli Liu
Idioma: Inglés

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… Leer más

Descripción

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.

Puntos claves

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

De interès para

Researchers and graduate students in mechanical engineering, manufacturing, information design, and materials science

Índice

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

Detalles del producto

Edición: 1
Última edición
Publicado: 19 de julio de 2025
Idioma: Inglés

Sobre los autores

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).

Afiliaciones y experiencia

Department of Mechanical Engineering, Imperial College, South Kensington, London, UK

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.