Milan Zdravković, Nikola Korunović

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Mass-customization is related to optimizing the balance between flexibility, strongly required by the customer-focused industries and manufacturing efficiency, which is critical for market competitiveness. In the conventional industries, the process of designing, validating and manufacturing a product is long and expensive. Some of the common approaches for addressing those issues are parametric product modeling and Finite Element Analysis (FEA). However, the costs involved are still relatively high because of the very special expertise needed and the cost of the specialized software. Also, the specific design of the product cannot be validated in a real-time, which often leads to making hard compromises between the specific customer requirements and the structural properties of the product in its exploitation. In this paper, we propose the novel methodology for real-time structural analysis assistance for custom product design. We introduce the concept of so-called compiled FEA model, a Machine Learning (ML) model, consisting of dataset of characteristic product parameters and associated physical quantities and properties, selected ML algorithms and the sets of associated hyperparameters. A case study of creating a compiled FEA model for the case of internal orthopedic fixator is provided.


Machine Learning, Gradient Boosting, Finite Element Analysis, Parametric Modeling

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ISSN: 2335-0164 (Online)

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