Text mining of resilient objects absorbing change and uncertainty
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nadège Troussier
Author: Panarotto, Massimo (1); Giordano, Vito (2); Chiarello, Filippo (2); Brahma, Arindam (1); Alonso Fernández, Inigo (1); Fantoni, Gualtiero (2)
Series: ICED
Institution: 1: Chalmers University of Technology;2: University of Pisa
Section: Design Methods
Page(s): 3325-3334
DOI number: https://doi.org/10.1017/pds.2023.333
ISBN: -
ISSN: -
Abstract
The current ways of coping with uncertainty such as changes during product design or use have been through methods such as easy restructuring (e.g., modularity with buffer in interface definition), by overdesign and so on. The present investments on maintaining products in the economy for “as long as possible” is challenging these strategies from a cost and environmental perspective. Moreover, these strategies often lead to highly overdesigned products. An alternative strategy is to introduce features in a design, called “resilient objects”, which are able to absorb such uncertainties without wasteful overdesign of other parts. By applying a ‘text-mining’ approach on patents, this paper has identified 5,552 candidates for such resilient objects that can be recombined and inserted in regions of the product that are likely to be most affected by current and future uncertainties. The application of resilient objects is demonstrated on a case study (a cooling system for battery electric vehicles). The case study highlights the ability of these objects to 1) significantly increase protection against uncertainties without the need for restructuring, 2 ) reduce the risk for overdesign and 3) dampen effects of change propagation.
Keywords: Resilient objects, Semantic data processing, Tech mining, Product architecture, Uncertainty