Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression
In this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/7/1087 |
| _version_ | 1797526564391354368 |
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| author | Jin Young Kim Dae Young Lee Jaeyoung Lee Seung Hwan Lee |
| author_facet | Jin Young Kim Dae Young Lee Jaeyoung Lee Seung Hwan Lee |
| author_sort | Jin Young Kim |
| collection | DOAJ |
| description | In this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relationship between the parameters causing strong nonlinearity should be considered. The analysis of variance-based Gaussian process regression (ANOVA-GPR) method was implemented to construct surrogate modeling, which can express nonlinearity including uncertainty of weld quality. Major parameters among several process parameters in this welding process can be extracted by use of this novel method. The weld quality used as a cost function in the optimization of process parameters is defined by characteristics related to penetration and bead shape, and the sequential quadratic programming (SQP) method was used to determine the optimal welding condition. This approach enabled sound weld quality at a high travel speed of 1.9 m/min, which is difficult to achieve in the hybrid-tandem GMAW process. |
| first_indexed | 2024-03-10T09:32:05Z |
| format | Article |
| id | doaj.art-06f700d9e13041c8a58c9a589243e9e8 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T09:32:05Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-06f700d9e13041c8a58c9a589243e9e82023-11-22T04:23:35ZengMDPI AGMetals2075-47012021-07-01117108710.3390/met11071087Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process RegressionJin Young Kim0Dae Young Lee1Jaeyoung Lee2Seung Hwan Lee3Department of Mechanical Engineering, Hanyang University, Wangsimniro, Seongdonggu 222, Seoul 04763, KoreaDepartment of Aerospace Engineering, Iowa State University, 537 Bissell Road, Ames, IA 50011, USADepartment of Mechanical Engineering, Hanyang University, Wangsimniro, Seongdonggu 222, Seoul 04763, KoreaDepartment of Mechanical Engineering, Hanyang University, Wangsimniro, Seongdonggu 222, Seoul 04763, KoreaIn this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relationship between the parameters causing strong nonlinearity should be considered. The analysis of variance-based Gaussian process regression (ANOVA-GPR) method was implemented to construct surrogate modeling, which can express nonlinearity including uncertainty of weld quality. Major parameters among several process parameters in this welding process can be extracted by use of this novel method. The weld quality used as a cost function in the optimization of process parameters is defined by characteristics related to penetration and bead shape, and the sequential quadratic programming (SQP) method was used to determine the optimal welding condition. This approach enabled sound weld quality at a high travel speed of 1.9 m/min, which is difficult to achieve in the hybrid-tandem GMAW process.https://www.mdpi.com/2075-4701/11/7/1087tandem gas metal arc weldingfiller-wireanalysis of varianceGaussian process regressionparameter optimizationfillet welding |
| spellingShingle | Jin Young Kim Dae Young Lee Jaeyoung Lee Seung Hwan Lee Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression Metals tandem gas metal arc welding filler-wire analysis of variance Gaussian process regression parameter optimization fillet welding |
| title | Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression |
| title_full | Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression |
| title_fullStr | Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression |
| title_full_unstemmed | Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression |
| title_short | Parameter Optimization of Hybrid-Tandem Gas Metal Arc Welding Using Analysis of Variance-Based Gaussian Process Regression |
| title_sort | parameter optimization of hybrid tandem gas metal arc welding using analysis of variance based gaussian process regression |
| topic | tandem gas metal arc welding filler-wire analysis of variance Gaussian process regression parameter optimization fillet welding |
| url | https://www.mdpi.com/2075-4701/11/7/1087 |
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