Prediction of Tensile Strength and Deformation of Diffusion Bonding Joint for Inconel 718 Using Deep Neural Network

Abstract

Due to the acceptable high-temperature deformation resistance of Inconel 718, its welding parameters such as bonding temperature and pressure are inevitably higher than those of general metals. As a result of the existing punitive processing environment, it is essential to control the deformation of parts while ensuring the bonding performance. In this research, diffusion bonding experiments based on the Taguchi method (TM) are conducted, and the uniaxial tensile strength and deformation ratio of the experimental joints are measured. According to experimental data, a deep neural network (DNN) was trained to characterize the nonlinear relationship between the diffusion bonding process parameters and the diffusion bonding strength and deformation ratio, where the overall correlation coefficient came out to be 0.99913. The double-factors analysis of bonding temperature–bonding pressure based on the prediction results of the DNN shows that the temperature increment of the diffusion bonding of Inconel 718 significantly increases the deformation ratio of the diffusion bonding joints. Therefore, during the multi-objective optimization of the bonding performance and deformation of components, priority should be given to optimizing the bonding pressure and duration only.

Nonlinear Ultrasonic C-Scan Imaging for Contact-Type Defects in Diffusion-Bonded Joints—A Case Study

“…However, it requires extracting both amplitude and phasing characteristics during detection. For most nonlinear ultrasonic methods, including that in Ref [ 15 , 16 , 17 , 18 , 19 ], the excitation signal is a tone burst wave, which makes it difficult to extract the echo on the bond interface. Compared with the previous methods, the nonlinear ultrasonic C-scan imaging proposed in this work can easily obtain the echo on the bond interface and only needs simple signal processing to extract the nonlinear parameter and to form the C-can image.…”

“…This method is theoretically not limited by the detection frequency and has higher detection sensitivity to small defects, especially when contact-type defects exist, and significant nonlinear components will appear in the received signal [ 15 , 16 ]. Studies have shown that linear ultrasound is more sensitive to macro-sized defects, and for contact-type defects, the linear ultrasound echo is weak, but the nonlinear response is strong [ 17 , 18 , 19 ]. However, there are currently few studies on the use of nonlinear ultrasound methods in pulse-echo mode to detect contact-type defects in diffusion-bonded joints.…”

Numerical Investigations on the Shape Optimization of Stainless-Steel Ring Joint with Machine Learning

“…Olazagoitia et al [15] showed that artificial neural networks can be used to directly identify the parameters of a tire model for a real test without iterative fittings or initial iteration point definitions without using complex cost functions. Mei et al [16] used the deep neural network model to accurately characterize the nonlinear relationship between the parameters of the Inconel 718 bonding process and bonding performance. Hijazi et al [17] used artificial neural networks modeling to predict the residual stress of aluminum panels using multiple site damage, showing that it can predict with high accuracy the residual strength for all materials and constructions considered in the study.…”