Comparison of RSM with ANN in predicting tensile strength offriction stir welded AA7039 aluminium alloy joints

来源期刊：中国有色金属学报(英文版)2009年第1期

论文作者：A. K. LAKSHMINARAYANAN V. BALASUBRAMANIAN

文章页码：9 - 18

Key words：friction stir welding; aluminium alloy; tensile strength; response surface methodology; artificial neural network

Abstract: Friction stir welding(FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminium, magnesium, zinc and copper alloys. The FSW process parameters such as tool rotational speed, welding speed, axial force, play a major role in deciding the weld quality. Two methods, response surface methodology and artificial neural network were used to predict the tensile strength of friction stir welded AA7039 aluminium alloy. The experiments were conducted based on three factors, three-level, and central composite face centered design with full replications technique, and mathematical model was developed. Sensitivity analysis was carried out to identify critical parameters. The results obtained through response surface methodology were compared with those through artificial neural networks.