An even-load-distribution design for composite bolted joints using a novel circuit model and neural network. (1st January 2022)

• Record Type: Journal Article
• Authors: Qiu, Cheng; Han, Yuzi; Shanmugam, Logesh; Jiang, Fengyang; Guan, Zhidong; Du, Shanyi; Yang, Jinglei
• Is Part Of: Composite structures. Volume 279(2022)
• Publication Date: 2022-01-01
• Subjects: Mechanical joint -- Bolt load distribution -- Machine learning -- Optimization -- Finite element modeling; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2021.114709
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: Due to the brittle feature of carbon fiber reinforced plastic laminates, mechanical multi-joint within these composite components shows uneven load distribution for each bolt, which weakens the strength advantage of composite laminates. In order to reduce this defect and achieve the goal of even load distribution in mechanical joints, we propose a machine learning-based framework as an optimization method. Since that the friction effect has been proven to be a significant factor in determining bolt load distribution, our framework aims at providing optimal parameters including bolt-hole clearances and tightening torques for a minimum unevenness of bolt load. A novel circuit model is established to generate data samples for the training of artificial networks at a relatively low computational cost. A database for all the possible inputs in the design space is built through the machine learning model. The optimal dataset of clearances and torques provided by the database is validated by both the finite element method, circuit model, and an experimental measurement based on the linear superposition principle, which shows the effectiveness of this general framework for the optimization problem. Then, our machine learning model is further compared and worked in collaboration with commonly used optimization algorithms, which shows a potential of greatly increasing computational efficiency for the inverse design problem.