Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry
This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9186598/ |
| _version_ | 1819172997432344576 |
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| author | Csongor Mark Horvath Janos Botzheim Trygve Thomessen Peter Korondi |
| author_facet | Csongor Mark Horvath Janos Botzheim Trygve Thomessen Peter Korondi |
| author_sort | Csongor Mark Horvath |
| collection | DOAJ |
| description | This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0.40 - 1.56% for the BGP model and 4.49 - 7.52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications. |
| first_indexed | 2024-12-22T20:16:04Z |
| format | Article |
| id | doaj.art-2ac7b9e4a2a745f8b07a7c15efdebcb1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:16:04Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2ac7b9e4a2a745f8b07a7c15efdebcb12022-12-21T18:13:57ZengIEEEIEEE Access2169-35362020-01-01816486416488110.1109/ACCESS.2020.30219509186598Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead GeometryCsongor Mark Horvath0https://orcid.org/0000-0002-4518-9615Janos Botzheim1https://orcid.org/0000-0002-7838-6148Trygve Thomessen2Peter Korondi3https://orcid.org/0000-0002-0016-0384Department of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, HungaryPPM Robotics AS, Trondheim, NorwayDepartment of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, HungaryThis article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0.40 - 1.56% for the BGP model and 4.49 - 7.52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications.https://ieeexplore.ieee.org/document/9186598/Bacterial memetic algorithmfuzzy systemmachine learningTIG weldingweld bead geometry |
| spellingShingle | Csongor Mark Horvath Janos Botzheim Trygve Thomessen Peter Korondi Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry IEEE Access Bacterial memetic algorithm fuzzy system machine learning TIG welding weld bead geometry |
| title | Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry |
| title_full | Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry |
| title_fullStr | Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry |
| title_full_unstemmed | Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry |
| title_short | Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry |
| title_sort | bacterial memetic algorithm trained fuzzy system based model of single weld bead geometry |
| topic | Bacterial memetic algorithm fuzzy system machine learning TIG welding weld bead geometry |
| url | https://ieeexplore.ieee.org/document/9186598/ |
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