Integration of hybrid grey based ANFIS tool for enhanced laser beam welding of nickel alloy using computational modelling

The past decade has witnessed a significant increase in the use of laser sources, leading to decreased costs and improved productivity in manufacturing. This trend is largely attributed to the development of advanced technologies, such as laser beam welding systems, which facilitate the production of both micro and macro components. Nickel alloy, particularly Inconel 625, is widely employed in industries including chemical, nuclear, maritime, aeronautics, and automotive because of its exceptional mechanical characteristics and corrosive resistance. This paper focuses on enhancing the laser beam welding (LBW) process for nickel alloys through the integration of computational modelling and simulation techniques. Specifically, we develop a grey-based Adaptive Neuro Fuzzy Inference System (ANFIS) to predict LBW variables accurately. Our model’s predictions are validated against experimental data, revealing a strong correlation and demonstrating the model’s effectiveness. The effectiveness of the evolved model has been analysed and findings of the analysis indicates that the MAPE is 0.0456, RMSE is 0.000310, MAE is 0.000456 and the correlation coefficient is 0.999. The attained error values show the capability of the evolved predictive model and it is evidenced that the model is proficient of prophesying the preferred output metrics with least error. The findings underscore the potential of computational tools for optimising design and manufacturing processes, enabling data-driven decision-making and improving production efficiency.