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Multi-objective Evolutionary Optimisation for Product Design and Manufacturing pp 161–187Cite as

Product Portfolio Selection of Designs Through an Analysis of Lower-Dimensional Manifolds and Identification of Common Properties

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Abstract

Functional commonalities across product families have been considered by a large body of product family design community but this concept is not widely used in design. For a designer, a functional family refers to a set of designs evaluated based on the same set of qualities; the embodiments and the design spaces may differ, but the semantics of what is being measured (e.g., strength of a spring) remain the same. Based on this functional behaviour we introduce a product family hierarchy, where the designs can be classified into phenomenological design family, functional part family and embodiment part family. And then, we consider the set of possible performances of interest to the user at the embodiment level, and use multi-objective optimisation to identify the non-dominated solutions or the Pareto-front. The designs lying along this front are mapped to the design space, which is usually far higher in dimensionality, and then clustered in an unsupervised manner to obtain candidate product groupings which the designer may inspect to arrive at portfolio decisions. We highlight and discuss two recently suggested techniques for this purpose. First, with help of dimensionality reduction techniques, we show how these clusters in low-dimensional manifolds embedded in the high-dimensional design space. We demonstrate this process on three different designs (water faucets, compression springs and electric motors), involving both continuous and discrete design variables. Second, with the help of a data analysis of Pareto-optimal solutions, we decipher common design principles that constitute the product portfolio solutions. We demonstrate this so-called ‘innovization’ principles on a spring design problem. The use of multi-objective optimisation (evolutionary and otherwise) is the key feature of both approaches. The approaches are promising and further research should pave their ways to better design and manufacturing activities.

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Authors and Affiliations

  1. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur, 208016, Uttar Pradesh, India

    Madan Mohan Dabbeeru & Kalyanmoy Deb

  2. Computer Science and Engineering Department, Indian Institute of Technology Kanpur, Kanpur, 208016, Uttar Pradesh, India

    Amitabha Mukerjee

Authors
  1. Madan Mohan Dabbeeru
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  2. Kalyanmoy Deb
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  3. Amitabha Mukerjee
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Corresponding author

Correspondence to Madan Mohan Dabbeeru .

Editor information

Editors and Affiliations

  1. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Lihui Wang

  2. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Amos H. C. Ng

  3. , Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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Dabbeeru, M.M., Deb, K., Mukerjee, A. (2011). Product Portfolio Selection of Designs Through an Analysis of Lower-Dimensional Manifolds and Identification of Common Properties. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_5

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  • DOI: https://doi.org/10.1007/978-0-85729-652-8_5

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  • Print ISBN: 978-0-85729-617-7

  • Online ISBN: 978-0-85729-652-8

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