Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

Srinivas, Kadivendi; Vundavilli, Pandu R.; Hussain, M. Manzoor; Saiteja, M

doi:10.1088/1757-899x/149/1/012033

Cited by 6 publications

(2 citation statements)

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“…The holes at the interface will close rapidly and disappear with the effect of creep as the temperature increases. With the disappearance of the pinning effect because of the holes, grain boundary migration occurs at the interface, and the original bonding interface disappears [17]. The effect of pressure on the bonding performance is different from that of temperature.…”

Section: Double-factors Analysis Based On Prediction Data Of Dnn Modelmentioning

confidence: 99%

“…The ability to accurately characterize the complex constraint relationship between input and output give it an obvious advantage in characterizing nonlinear relations [13,14]. The trained neural network model can also be used to accurately predict the response of bonding strength (output) to the setting (input) of process parameters [15][16][17]. On the basic of a series of experiments of AA5083 and AA7075 aluminum alloy and training neural network, Sagai et al predicted the tensile strength and shear strength of joints under different bonding parameters [18].…”

Section: Introductionmentioning

confidence: 99%

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

Mei

Lang

et al. 2020

Metals

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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.

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Section: Double-factors Analysis Based On Prediction Data Of Dnn Modelmentioning

confidence: 99%