Stress–strain evaluation of structural parts using artificial neural networks

• Published in: Proceedings of the Institution of Mechanical Engineers. Part L, Journal of materials, design and applications Journal of materials, design and applications, 2021-06, Vol.235 (6), p.1271-1286
• Main Authors: Ribeiro, João PA, Tavares, Sérgio MO, Parente, Marco
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Complexity; Computing costs; Design optimization; Internal pressure; Neural networks; Reduced order models; Response time (computers); Stress concentration
• ISSN: 1464-4207; 2041-3076
• DOI: 10.1177/1464420721992445

The last decades have been driven by significant progress in the computational capacity, which have been supporting the development of increasingly realistic and detailed simulations. However, despite these improvements, several problems still do not have an effective solution, due to their numerical complexity. As a result, the answer to these problems can be improved by using techniques that enable the description of phenomena with less resolution, but with lower computational costs, which is the case of the reduced order models. The main objective of this article is the presentation of a new approach for reduced order model development and application in the design and optimization of structural parts. The selected method is the artificial neural networks. Artificial neural networks allow the prediction of certain variables based on a given dataset. Two typical case studies are addressed: the first is a fixed plate subjected to uniformly distributed pressure and the second is a reinforced panel also subjected to internal pressure, with regular reinforcements to improve the specific strength. With this method, a substantial reduction in the simulation time is observed, being, approximately, 40 times faster than the solution obtained with Ansys. The developed neural network has a relative average difference of about 20 %, which is considered satisfactory given the complexity of the problem and considering it is a first application of these networks in this domain. In conclusion, this research made it possible to highlight the potential of reduced order model: including the shorter response time, the less computational resources, and the simplification of problems in detriment of less resolution in the description of structural behaviour. Given these advantages, it is expected that these models will play a key role in future applications, as in digital twins.