Foorginejad, Abolfazl, Azargoman, majid, babaiyan, vahide, Mollayi, Nader, Taheri, Morteza. (1398). Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process. نشریه مهندسی عمران امیرکبیر, 51(2), 121-130. doi: 10.22060/miscj.2019.15189.5127
Abolfazl Foorginejad; majid Azargoman; vahide babaiyan; Nader Mollayi; Morteza Taheri. "Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process". نشریه مهندسی عمران امیرکبیر, 51, 2, 1398, 121-130. doi: 10.22060/miscj.2019.15189.5127
Foorginejad, Abolfazl, Azargoman, majid, babaiyan, vahide, Mollayi, Nader, Taheri, Morteza. (1398). 'Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process', نشریه مهندسی عمران امیرکبیر, 51(2), pp. 121-130. doi: 10.22060/miscj.2019.15189.5127
Foorginejad, Abolfazl, Azargoman, majid, babaiyan, vahide, Mollayi, Nader, Taheri, Morteza. Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process. نشریه مهندسی عمران امیرکبیر, 1398; 51(2): 121-130. doi: 10.22060/miscj.2019.15189.5127

Application of the Extreme Learning Machine for Modeling the Bead Geometry in Gas Metal Arc Welding Process

AUT Journal of Modeling and Simulation
مقاله 5، دوره 51، شماره 2، اسفند 2019، صفحه 121-130    اصل مقاله (957.53 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2019.15189.5127
نویسندگان
Abolfazl Foorginejad* 1؛ majid Azargoman2؛ vahide babaiyan3؛ Nader Mollayi3؛ Morteza Taheri4
1Department of Mechanical Engineering, Birjand University of Technology
2Departmentof Mechanical Engineering, Birjand University of Technology, Birjand, Iran
3Department of Computer Engineering, Birjand University of Technology, Birjand, Iran
4Department of Mechanical Engineering, University of Birjand, Birjand, Iran
چکیده
Gas metal arc welding (GMAW) is a widespread process used for rapid prototyping of metallic parts. In this process, in order to obtain a desired welding geometry, it is very important to predict the weld bead geometry based on the input process parameters, which are voltage, wire feed rate, welding speed and welding nozzle angle. For this purpose, a global model of the welding geometry must be defined based on these parameters. Due to the non-linear and coupled multivariable relationship between the process parameters and the weld bead geometry, it is not possible to define this model in form of an explicit mathematical expression, and therefore application of supervised learning algorithms can be investigated as an efficient alternative in this problem. In this paper, application of the extreme learning machine (ELM) and support vector machine (SVM), as two efficient and powerful machine learning algorithms for predictive modelling of this process has been investigated and error analysis of the proposed models suggest that the output parameters of this process can be predicted by the ELM algorithm with higher precision and generalization capability.
کلیدواژه‌ها
Rapid Prototyping؛ Gas Metal Arc Welding؛ Bead Geometry؛ Support Vector Machine؛ Extreme Learning Machine
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