Data-Driven Optimization of Manufacturing Processes

Data-Driven Optimization of Manufacturing Processes

Kanak Kalita, Ranjan Kumar Ghadai, Xiao-Zhi Gao
Indexed In: SCOPUS
Release Date: December, 2020|Copyright: © 2021 |Pages: 298
DOI: 10.4018/978-1-7998-7206-1
ISBN13: 9781799872061|ISBN10: 1799872068|EISBN13: 9781799872085
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Description & Coverage
Description:

All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing.

Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Computational Intelligence
  • Conventional Machining Processes
  • Genetic Algorithm
  • Manufacturing
  • MOORA
  • Non-Traditional Machine Processes
  • Optimization
  • Particle Swarm Optimization
  • Predictive Modeling
  • Soft Computing
  • TOPSIS
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Editor/Author Biographies

Kanak Kalita received his B.E in mechanical engineering from RGTU, Bhopal, India; M.E and Ph.D. in aerospace engineering and applied mechanics from Indian Institute of Engineering, Science & Technology, Shibpur, India. He has over 6 years of teaching, research and industrial experience. Currently, he is with Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India as assistant professor in mechanical engineering department. He is on the editorial board of 2 international journals and has reviewed 170+ manuscripts for 30+ journals and conferences. He has been awarded thrice by Publons for his reviewing efforts. He has published 20 SCI and 42 SCOPUS research articles and edited 1 book volume for IOP publishing. His areas of interests include metamodeling, process optimization, finite element method and composites.

Ranjan Kumar Ghadai received his B. Tech in Mechanical Engineering from Biju Patnaik University of Technology, Odisha, India, M.E and PhD from Indian Institute of Engineering, Science & Technology, Shibpur, India. He has over 6 years of teaching and research experience. Currently, he is working as an assistant professor in the mechanical engineering department of Sikkim Manipal Institute of Technology, Sikkim. He has published more than 35 SCI/Scopus indexed research articles. His areas of interests include thin-film coatings and its characterization, heat treatment, optimization of coatings and machining process parameters. He is on the editorial board of several peer-reviewed journals. He also serves as reviewer of many peer-reviewed journals. He has given several expert talks in many conference and workshop as a resource person.

Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China in 1993 and 1996, respectively. He obtained his D.Sc. (Tech.) degree from the Helsinki University of Technology (now Aalto University), Finland in 1999. He has been working as a professor at the University of Eastern Finland, Finland since 2018. Prof. Gao has published more than 400 technical papers in refereed journals and international conferences. His current Google Scholar H-index is 31. His research interests are nature-inspired computing methods with their applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.

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