A Hybrid Machine Learning Framework for Predicting Mechanical and Metallurgical Properties of Bimetallic TIG Welds

Sahil Dangi; Hari Parshad

A Hybrid Machine Learning Framework for Predicting Mechanical and Metallurgical Properties of Bimetallic TIG Welds

Author(s)	Mr. Sahil Dangi, Dr. Hari Parshad
Country	India
Abstract	Bimetallic weld joints are “frequently applied in aerospace, automotive, petrochemical, marine and nuclear industries for their advantageous combination of two distinct metals in a single structure.But welding different metals is a tough process because to variances in thermal conductivity, melting temperature, chemical composition and thermal expansion characteristics. In recent years, the integration of Artificial Intelligence (AI), Machine Learning (ML) and intelligent optimization methodologies with welding engineering has opened new paths for improving the welding efficiency, defect prediction and process optimization.The purpose of this study is to evaluate the influence of different filler metals on the microstructural and mechanical behaviour of TIG welded bimetallic joints, using hybrid AI techniques for predictive analysis and optimization. Tensile strength, hardness profile, weld bead geometry, microstructural alterations and thermal characteristics of the welded specimens are assessed through experiments. The proposed system integrates the experimental data with computational intelligence to correlate the filler rod composition, welding parameters, and the weld performance. The use of AI-assisted prediction methods is expected to improve the quality of welds, reduce the experimental cost, minimize defects and boost the process efficiency.”
Keywords	Bimetallic weld, Machine Learning, welding parameters, Artificial Intelligence (AI), petrochemical
Field	Engineering
Published In	Volume 7, Issue 6, June 2026
Published On	2026-06-18

View / Download PDF File

About IJLRP Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics	Join as a Reviewer Editors & Reviewers Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijlrp.com

International Journal of Leading Research Publication

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

A Hybrid Machine Learning Framework for Predicting Mechanical and Metallurgical Properties of Bimetallic TIG Welds

Share this