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Data-Driven Engineering Design

Ang Liu, Yuchen Wang & Xingzhi Wang

Data-Driven Engineering Design
Data-Driven Engineering Design

Data-Driven Engineering Design

Ang Liu, Yuchen Wang & Xingzhi Wang

Hardback / bound | English
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Description

Dr. Ang Liu is an Associate Professor of Engineering Design at the School of Mechanical and Manufacturing Engineering, University of New South Wales, Australia. He received his M.S. and Ph.D. degrees from the University of Southern California, in 2008 and 2012, respectively. He is an Associate Member of the International Academy for Production Engineering (CIRP), Fellow of the PLuS Alliance, and Senior Fellow of the Higher Education Academy (SFHEA). He chaired multiple international design conferences such as the 13th International Conference on Axiomatic Design (ICAD2019). He serves in the editorial boards of multiple journals such as the Chinese Journal of Mechanical Engineering, Digital Twin, Scientific Reports, etc. He has published over 100 book chapters, journal articles, and conference papers. His research interests include innovative design thinking, design theory and methodology, smart manufacturing, digital twin, and engineering education.

Mr. Yuchen Wang is a Ph.D.candidate in Mechanical Engineering. He completed his undergraduate degree in Aerospace Engineering at the University of New South Wales (UNSW). His research lies at the intersections of design methodology, data science, and digital twin. As a head tutor, he had been teaching engineering design to a large cohort of college student at UNSW. He has published more than 10 journal articles, conference papers, and book chapters.

Mr. Xingzhi Wang is a Ph.D. candidate in Mechanical Engineering at the University of New South Wales (UNSW). He obtained his undergraduate degree and master’s degree at the Sichuan University and UNSW, respectively. His research focuses on leveraging machine learning to enhance design customization. 




Dr. Ang Liu is an Associate Professor of Engineering Design at the School of Mechanical and Manufacturing Engineering, University of New South Wales, Australia. He received his M.S. and Ph.D. degrees from the University of Southern California, in 2008 and 2012, respectively. He is an Associate Member of the International Academy for Production Engineering (CIRP), Fellow of the PLuS Alliance, and Senior Fellow of the Higher Education Academy (SFHEA). He chaired multiple international design conferences such as the 13th International Conference on Axiomatic Design (ICAD2019). He serves in the editorial boards of multiple journals such as the Chinese Journal of Mechanical Engineering, Digital Twin, Scientific Reports, etc. He has published over 100 book chapters, journal articles, and conference papers. His research interests include innovative design thinking, design theory and methodology, smart manufacturing, digital twin, and engineering education.

Mr. Yuchen Wang is a Ph.D.candidate in Mechanical Engineering. He completed his undergraduate degree in Aerospace Engineering at the University of New South Wales (UNSW). His research lies at the intersections of design methodology, data science, and digital twin. As a head tutor, he had been teaching engineering design to a large cohort of college student at UNSW. He has published more than 10 journal articles, conference papers, and book chapters.

Mr. Xingzhi Wang is a Ph.D. candidate in Mechanical Engineering at the University of New South Wales (UNSW). He obtained his undergraduate degree and master’s degree at the Sichuan University and UNSW, respectively. His research focuses on leveraging machine learning to enhance design customization. 


Specifications

  • Publisher
    Springer Nature Switzerland AG
  • Pub date
    Oct 2021
  • Theme
    Technical design
  • Dimensions
    235 x 155 mm
  • EAN
    9783030881801
  • Hardback / bound
    Hardback / bound
  • Language
    English

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