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Optimization of CMT Welding for 18/8 Stainless Steel: A Teaching Learning Based Algorithm-Driven Approach.

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Title: Optimization of CMT Welding for 18/8 Stainless Steel: A Teaching Learning Based Algorithm-Driven Approach.
Authors: Vinothkumar, K.1 (AUTHOR) vinometvm@gmail.com, Mathivanan, A.1 (AUTHOR)
Source: Journal of Materials Engineering & Performance. Apr2026, Vol. 35 Issue 14, p13439-13453. 15p.
Subjects: Welding, Optimization algorithms, Electron backscattering, Electric welding, Austenitic stainless steel, Microhardness, Tensile strength
Abstract: The present investigation is a computational optimization of cold metal transfer (CMT) welding parameters such as boost correction, current, voltage and traverse speed for welding a vessel grade 18/8 stainless steel sheet metal of thickness 3 mm utilized for food processing industries. The optimization was executed using a teaching learning-based optimization (TLBO) algorithm using three levels for each input parameters. The investigation intends to determine the optimal parameters that enhance bead geometry, weld penetration and microhardness of the weld. Among the chosen parameters, the optimized input parameters say, 2.5 boost correction, 160 A of current, 16 V of voltage and 300 mm/min of traverse speed resulted the best responses like deeper weld penetration (3.06 mm), wider bead geometry (8.6 mm) and highest microhardness of 203 HV. Moreover, the weld produced using the optimized parameters showcased a tensile strength of 640 MPa with a total elongation of 45.7 %. The microscopy studies confirmed the presence of directional dendrites and minimal segregation in the weld. Besides, the electron backscattered diffraction studies represented epitaxial growth and formation of strong orientation of grains during solidification. Moreover, the x-ray diffraction reported the presence of favorable M23C6 precipitates that supports additional strength to the weld. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Materials Engineering & Performance is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Optimization of CMT Welding for 18/8 Stainless Steel: A Teaching Learning Based Algorithm-Driven Approach.
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  Data: <searchLink fieldCode="DE" term="%22Welding%22">Welding</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Electron+backscattering%22">Electron backscattering</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+welding%22">Electric welding</searchLink><br /><searchLink fieldCode="DE" term="%22Austenitic+stainless+steel%22">Austenitic stainless steel</searchLink><br /><searchLink fieldCode="DE" term="%22Microhardness%22">Microhardness</searchLink><br /><searchLink fieldCode="DE" term="%22Tensile+strength%22">Tensile strength</searchLink>
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  Data: The present investigation is a computational optimization of cold metal transfer (CMT) welding parameters such as boost correction, current, voltage and traverse speed for welding a vessel grade 18/8 stainless steel sheet metal of thickness 3 mm utilized for food processing industries. The optimization was executed using a teaching learning-based optimization (TLBO) algorithm using three levels for each input parameters. The investigation intends to determine the optimal parameters that enhance bead geometry, weld penetration and microhardness of the weld. Among the chosen parameters, the optimized input parameters say, 2.5 boost correction, 160 A of current, 16 V of voltage and 300 mm/min of traverse speed resulted the best responses like deeper weld penetration (3.06 mm), wider bead geometry (8.6 mm) and highest microhardness of 203 HV. Moreover, the weld produced using the optimized parameters showcased a tensile strength of 640 MPa with a total elongation of 45.7 %. The microscopy studies confirmed the presence of directional dendrites and minimal segregation in the weld. Besides, the electron backscattered diffraction studies represented epitaxial growth and formation of strong orientation of grains during solidification. Moreover, the x-ray diffraction reported the presence of favorable M23C6 precipitates that supports additional strength to the weld. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Materials Engineering & Performance is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11665-025-12597-1
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        Text: English
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        Type: general
      – SubjectFull: Optimization algorithms
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      – SubjectFull: Electron backscattering
        Type: general
      – SubjectFull: Electric welding
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      – SubjectFull: Austenitic stainless steel
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      – SubjectFull: Microhardness
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      – SubjectFull: Tensile strength
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      – TitleFull: Optimization of CMT Welding for 18/8 Stainless Steel: A Teaching Learning Based Algorithm-Driven Approach.
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              Text: Apr2026
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