Abstract
Modularization has played a significant role in product design and system configuration for both manufacturers and customers. Modularization enables mass customization, collaborative product design, concurrent engineering, and short product development cycle from the view point of manufacturers, while it enables high reusability, easy system configuration, and quick installation of parts from the viewpoint of customers. One of the key enablers of modularization is standardized interfaces that connect parts. The standardization has facilitated computational product design by enabling the automation of product design processes. As technology evolves, challenges from the variants of standardized interfaces, such as different versions of an interface, have emerged. A version mismatch causes incompatibility. In order to increase compatibility, interfaces are designed to support backward compatibility. This paper proposes an artificial intelligence planning–based mathematical framework for computational system configuration to support backward compatibility. The case study shows the significance of the design with the consideration of backward compatibility by demonstrating the capability of the proposed framework that automatically discovers a better design solution that cannot be identified when backward compatibility is not considered. Finally, experiments are conducted to prove the optimality of the solutions from the mathematical framework and to showcase the advantages of the framework. The proposed mathematical framework is expected to serve as a benchmarking tool, in terms of solution quality and time, for heuristic methods to be developed in the future.
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This research was partially supported by the Caterpillar Research Fellowship.
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Yoo, J.JW. Computational modular system configuration with backward compatibility. Int J Adv Manuf Technol 125, 3349–3362 (2023). https://doi.org/10.1007/s00170-023-10987-0
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DOI: https://doi.org/10.1007/s00170-023-10987-0