Design of bonding process parameters for experimentation and ANN-GA model development to maximise diffusion bond strength. (8th October 2020)

• Record Type: Journal Article
• Authors: Britto, A. Sagai Francis; Raja, R. Edwin; Mabel, M. Carolin
• Is Part Of: International journal of computational materials science and surface engineering. Volume 9:Number 3(2020)
• Page Start: 177
• Page End: 197
• Publication Date: 2020-10-08
• Subjects: aluminium alloys -- diffusion bonding -- soft computing techniques -- response surface methodology -- RSM; Surfaces (Technology) -- Periodicals; Materials -- Computer simulation -- Periodicals; Materials -- Mathematical models -- Periodicals; 620.110151
• Languages: English
• ISSNs: 1753-3465

Abstract

Challenges in joining dissimilar aluminium alloys like AA1100 and AA7075 by conventional methods find an alternate methodology in diffusion bonding. The major process parameters of diffusion bonding, the temperature, pressure and holding time were appropriated to maximise the joint strength. Experimental parameters were designed at strategical points to cover the domain of its influence with design expert software and the empirical results were analysed using response surface methodology (RSM). Input-output mapping of results was also done by stochastic modelling tool, the artificial neural network (ANN) and later the process parameter was optimised with genetic algorithm (GA). It is found that the prediction accuracy of ANN model was twice accurate than that of RSM. The optimised temperature, pressure and holding time for sound bonding is 380°C, 10 MPa and 46 min respectively, which were confirmed by experimental results.