Machine learning based predictive approaches for designing hot crack free weldments

Abstract

Hot cracking, also known as solidification cracking, poses a significant challenge in welding AA7075, stainless steel 310, and Inconel 625 alloys, limiting their applications in industries like automotive and aerospace due to high cracking sensitivity. This study aims to address these issues by employing ultrasonic vibration during welding to eliminate hot cracking and enhance joint strength. The focus is on conventional TIG welding, incorporating ultrasonic vibration, and filler materials, and optimizing welding parameters for Houldcroft weldability test specimens. The goal is to identify optimal parameters to mitigate hot cracking and improve joint strength. Additionally, the study aims to develop predictive models using machine learning algorithms for hot cracking sensitivity and microhardness. The literature review underscores the significance of ultrasonic-assisted TIG welding in various manufacturing sectors, especially for hot cracking susceptible materials like AA7075, Inconel 625, and stainless steel 310 alloys. While some reports suggest that certain solid-state welding processes can mitigate fusion welding challenges, concerns such as joint softening, mechanical property deterioration, and process complexities hinder their widespread adoption. The use of appropriate filler material is explored to locally alter weld chemistry and reduce hot cracking sensitivity, although this may come at the expense of joint strength. Recent literature suggests that external vibratory fields, such as mechanical and ultrasonic vibrations, can significantly alleviate hot cracking sensitivity by promoting the formation of fine-grained structures in the weld metal. While various studies have explored traditional TIG welding aspects, there is a gap in utilizing statistical analysis for ultrasonic-assisted TIG welding. This research aims to fill this gap by modifying TIG welding for hot cracking susceptible materials and investigating both conventional and ultrasonic-assisted methods, with and without fillers newline