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Towards an automated decision support system for the identification of additive manufacturing part candidates

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Abstract

As additive manufacturing (AM) continues to mature, an efficient and effective method to identify parts which are eligible for AM as well as gaining insight on what values it may add to a product is needed. Prior methods are naturally developed and highly experience-dependent, which falls short for its objectiveness and transferability. In this paper, a decision support system (DSS) framework for automatically determining the candidacy of a part or assembly for AM applications is proposed based on machine learning (ML) and carefully selected candidacy criteria. With the goal of supporting efficient candidate screening in the early conceptual design stage, these criteria are further individually decoded to decisive parameters which can be extracted from digital models or resource planning databases. Over 200 existing industrial examples are manually collected and labelled as training data; meanwhile, multiple regression algorithms are tested against each AM potential to find better predictive performance. The proposed DSS framework is implemented as a web application with integrated cloud-based database and ML service, which allows advantages of easy maintenance, upgrade, and retraining of ML models. Two case studies of a hip implant and a throttle pedal are used as demonstrating examples. This preliminary work provides a promising solution for lowering the requirements of non-AM experts to find suitable AM candidates.

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Acknowledgements

Financial support from the National Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN 436055-2013); and McGill Engineering Doctoral Award (MEDA) is acknowledged with gratitude.

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Correspondence to Yaoyao Fiona Zhao.

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Yang, S., Page, T., Zhang, Y. et al. Towards an automated decision support system for the identification of additive manufacturing part candidates. J Intell Manuf (2020). https://doi.org/10.1007/s10845-020-01545-6

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Keywords

  • Additive manufacturing
  • Machine learning
  • Candidate identification
  • Conceptual design