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Machine Learning Based Deflection Prediction And Inverse Design For Discrete Variable Stiffness Units
Conference proceeding   Peer reviewed

Machine Learning Based Deflection Prediction And Inverse Design For Discrete Variable Stiffness Units

Jiaming Fu, Qianyu Guo and Dongming Gan
Proceedings of ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 8: 47th Mechanisms and Robotics Conference (MR), Vol.8, v008t08a043
Mechanisms and Robotics Conference (MR): Mechanisms Synthesis and Analysis, 47th (Boston, Massachusetts, USA, 08/20/2023–08/23/2023)
11/2023
DOI: https://doi.org/10.1115/DETC2023-117322
Web of Science ID: WOS:001221657300043

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Abstract

Large deflection modeling is a crucial field of study in the analysis and design of compliant mechanisms (CM). This paper proposes a machine learning (ML) approach for predicting the deflection of discrete variable stiffness units (DSUs) that cover a range from small to large deflections. The primary structure of a DSU consists of a parallel guide beam with a hollow cavity that can change stiffness discretely by inserting or extracting a solid block. The principle is based on changing the cross-sectional area properties of the hollow section. Prior to model training, a large volume of data was collected using finite element analysis (FEA) under different loads and various dimensional parameters. Additionally, we present three widely used machine learning-based models for predicting beam deflection, taking into account prediction accuracy and speed. Several experiments are conducted to evaluate the performance of the ML models that were compared with the FEA and analytical model results. The optimal ML model, multilayer perceptron (MLP), can achieve a 7.9% maximum error compared to FEA. Furthermore, the model was employed in a practical application for inverse design, with various cases presented depending on the number of solved variables. This method provides a innovative perspective for studying the modeling of compliant mechanisms and may be extended to other mechanical mechanisms.

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