| Summary: | The cost and efficiency of experiment and test are still a major issue in manufacturing especially in welding. In this study, a machine learning technique i.e. support vector machine (SVM) was applied to develop a load level prediction system of friction stir spot welded joint aluminium alloy AA5052-H112. This load level prediction system model was proposed based on three levels of load group of the welded joint. Experimental works in friction stir spot welding was conducted on samples specimens of AA5052-H112 2mm thick overlap joint based on 27 combinations of governed parameters i.e. spindle speed, tool depth, and dwell time. Mechanical testing of tensile shear load was then carried out to those specimens to obtain 27 loads data to form the SVM classifier. These data were required for pattern classification and model development via training and testing the proposed prediction system. The result obtained via training and testing showed the classification of load data produced by the proposed system, matched to the required load level with the 100%. This study proved that the proposed load level prediction system offered a useful tool to predict the load level of friction stir welded joint aluminium alloy AA5052-H112 by using respected parameters without required experiments and tests. © 2018 Institute of Physics Publishing. All rights reserved.
|
|---|