| Affiliation: | 1. School of Mechanical Engineering, University of Shanghai for Science and Technology, China;2. School of Mechanical Engineering, Jiangsu University, China;3. College of Engineering, Coventry University, Coventry, UK;4. School of Physics, Engineering & Computer Science, University of Hertfordshire, UK;1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China;2. College of Mechanical Engineering, Donghua University, Shanghai 201620, China;3. Shanghai Waigaoqiao Shipbuilding Company, Shanghai 200137, China;1. School of Management, Huazhong University of Science and Technology, Wuhan 430074, PR China;2. Sino-European Institute for Intellectual Property, Huazhong University of Science and Technology, Wuhan 430074, PR China;3. Law School, Huazhong University of Science and Technology, Wuhan 430074, PR China;1. Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China;2. Department of Industrial Engineering, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China |
| Abstract: | Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%. |
