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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]
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Database: Engineering Source
Description
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]
ISSN:10599495
DOI:10.1007/s11665-025-12597-1