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Data-Driven Design Optimization for Industrial Products

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Optimization in Industry

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

Prescriptive data analytics along with optimization techniques in complex industrial processes lead to extraction of useful knowledge from the data, mapping of the relation between the inputs and outputs of the products and/or processes, product quality improvement, cost reduction, process simplification and designing new product with improved performance. There are several statistical methods and soft computing techniques, which are useful to generate the objective functions for optimization from industrial data. In this chapter, a case study is discussed where ANN model with desirability function forms the objective function. GA-based search is employed to get the optimized solutions, and to find the behaviour of the alloying elements in steel with desired performance.

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Author information

Authors and Affiliations

  1. Indian Institute of Engineering Science and Technology, Shibpur, 711103, India

    Swati Dey

  2. Birla Institute of Technology and Science, Pilani, 33303, India

    N. Gupta

  3. Texmaco Rail and Engineering Ltd., Belgharia, 700056, India

    S. Pathak & D. H. Kela

  4. SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Shubhabrata Datta

Authors
  1. Swati Dey
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  2. N. Gupta
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  3. S. Pathak
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  4. D. H. Kela
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  5. Shubhabrata Datta
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Corresponding author

Correspondence to Swati Dey .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India

    Shubhabrata Datta

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

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Dey, S., Gupta, N., Pathak, S., Kela, D.H., Datta, S. (2019). Data-Driven Design Optimization for Industrial Products. In: Datta, S., Davim, J. (eds) Optimization in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-01641-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-01641-8_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01640-1

  • Online ISBN: 978-3-030-01641-8

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