{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:40:43Z","timestamp":1775022043126,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) of AA-5083 and AA-6061 friction stir welding. The machine learning model integrates two machine learning methods, Gaussian process regression (GPR) and a support vector machine (SVM), into a single model, and then uses the AMIS as the decision fusion strategy to merge SVM and GPR. The generated model was utilized to anticipate three objectives based on seven controlled\/input parameters. These parameters were: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism. The effectiveness of the model was evaluated using a two-experiment framework. In the first experiment, we used two newly produced datasets, (1) the 7PI-V1 dataset and (2) the 7PI-V2 dataset, and compared the results with state-of-the-art approaches. The second experiment used existing datasets from the literature with varying base materials and parameters. The computational results revealed that the proposed method produced more accurate prediction results than the previous methods. For all datasets, the proposed strategy outperformed existing methods and state-of-the-art processes by an average of 1.35% to 6.78%.<\/jats:p>","DOI":"10.3390\/computation11050100","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T02:20:13Z","timestamp":1684203613000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Multiple Response Prediction Model for Dissimilar AA-5083 and AA-6061 Friction Stir Welding Using a Combination of AMIS and Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2524-1869","authenticated-orcid":false,"given":"Rungwasun","family":"Kraiklang","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand"}]},{"given":"Chakat","family":"Chueadee","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand"}]},{"given":"Ganokgarn","family":"Jirasirilerd","sequence":"additional","affiliation":[{"name":"Department of Industrial and Environmental Management Engineering, Faculty of Liberal Arts and Sciences, Sisaket Rajabhat University, Sisaket 33000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9646-7876","authenticated-orcid":false,"given":"Worapot","family":"Sirirak","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Lanna Chiang Rai, Chiang Rai 57120, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-7510","authenticated-orcid":false,"given":"Sarayut","family":"Gonwirat","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Automation, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Laska, A., Szkodo, M., Cavaliere, P., and Perrone, A. 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