| Abstract Scope |
Weld Bonding (WB) is a hybrid joining technique that integrates adhesive bonding, driven by chemical adhesion, with resistance spot welding, which is based on physical forces. Properly executed, WB demonstrates significant advantages over conventional joining techniques, notably in fatigue performance, tensile strength, energy absorption capacity, stiffness, and resistance to corrosion. Achieving optimal outcomes in WB processes requires precise computational modeling of adhesive flow behavior during the squeezing stage. In this research, thermal-mechanical simulation methods are applied to study the weld bonding process of a stack consisting of two sheets of 1.5 mm-thick 22MnB5 steel. The simulations successfully predict critical phenomena such as weld nugget formation, surface indentation, and microstructural changes occurring during the welding process. Furthermore, this computational model accurately reproduces the resultant hardness distribution, which strongly correlates with mechanical properties evaluated through tensile shear and cross-tension tests. The described simulation strategy offers significant predictive benefits, facilitating the optimization of weld-bonded joints by identifying potential defects in advance and confirming desired joint properties. This contributes to greater reliability and efficiency in manufacturing practices involving ultra-high-strength steels. Future research directions could include broader experimental validations to further enhance the model’s accuracy and applicability. |