Mathematical modeling and optimization of vacuum diffusion bonding parameters for predicting and enhancing the strength of dissimilar IN-718/MSS-410 joints using RSM for power generation applications

The dissimilar welding of Inconel 718 (IN-718) alloy and AISI 410 martensitic stainless steel (MSS-410) is crucial in advanced gas turbines, and ultra-supercritical power plants to meet the demands of different operating conditions and lower the cost. However, the dissimilar fusion welding of IN-718/MSS-410 is challenging due to the differences in thermal expansion coefficient, physical and mechanical properties of base metals. In this study, the solid-state vacuum diffusion bonding (VDB) technology is employed to develop the dissimilar IN-718/MSS-410 joints. The aim of this study is to find the optimal combination of VDB parameters such as diffusion bonding pressure-DBP (MPa), diffusion bonding temperature-DBT (°C) and diffusion bonding time-DBt (min) for enhancing the strength of IN-718/MSS-410 joints. The response surface methodology (RSM) was integrated for designing the experimental matrix. The strength performance of VDB joints was evaluated by conducting the lap shear strength (LSS) and bonding strength (BS) tests. The mathematical LSS and BS predicting models were established using regression analysis and verified employing the variance analysis. The microstructural features were analyzed using optical and scanning electron microscopy (SEM). The X-ray diffractometer (XRD) was employed to identify the phases evolution in the joint interface. The experimental results revealed that the IN-718/MSS-410 joints diffusion bonded using the DBP of 14 MPa, DBT of 960 °C and DBt of 90 min exhibited the greater LSS of 280 MPa and BS of 373 MPa. The prediction models accurately predicted the LSS and BS of IN-718/MSS-410 joints within 2 % error at 95 % confidence. It is primarily concerned with developing the optimal bonding width with the fewest possible embrittlement implications and better joining interface coalescence. According to variance analysis, the DBt was the most significant parameter influencing the LSS and BS of joints followed by the DBP and DBT.