Applications of machine learning to friction stir welding process optimization

• Main Authors: Nasir, Tauqir, Asmaela, Mohammed, Zeeshan, Qasim, Solyali, Davut
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2020
• Online Access: http://journalarticle.ukm.my/15322/1/01.pdf; http://journalarticle.ukm.my/15322/; http://www.ukm.my/jkukm/volume-322-2020/
• Institution: Universiti Kebangsaan Malaysia

Machine learning (ML) is a branch of artificial intelligent which involve the study and development of algorithm for computer to learn from data. A computational method used in machine learning to learn or get directly information from data without relying on a prearranged model equation. The applications of ML applied in the domains of all industries. In the field of manufacturing the ability of ML approach is utilized to predict the failure before occurrence. FSW and FSSW is an advanced form of friction welding and it is a solid state joining technique which is mostly used to weld the dissimilar alloys. FSW, FSSW has become a dominant joining method in aero-space, railway and ship building industries. It observed that the number of applications of machine learning increased in FSW, FSSW process which sheared the Machine-learning approaches like, artificial Neural Network (ANN), Regression model (RSM), Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main purpose of this study is to review and summarize the emerging research work of machine learning techniques in FSW and FSSW. Previous researchers demonstrate that the Machine Learning applications applied to predict the response of FSW and FSSW process. The prediction in error percentage in result of ANN and RSM model in overall is less than 5%. In comparison between ANN/RSM the obtain result shows that ANN is provide better and accurate than RSM. In application of SVM algorithm the prediction accuracy found 100% for training and testing process.