Development of Hybrid Grey Based ANFIS Model for Laser Beam Welding of Inconel 718 Alloy for Automotive Industries

Laser Beam Welding (LBW) is one of the advanced methods of joining metals by fusion. The LBW process exhibits comparatively better welding performance than conventional processes and this method of welding approach is exclusively employed in higher volume applications such as automotive industries. One of the most common nickel alloys used in various engineering fields is Inconel 718. This material has high strength and corrosion resistance properties, and is commonly used in high-temperature applications, such as gas turbines and rocket engines. In this study, we aim to develop an artificial intelligence tool that can analyze the influence of various process variables on the design and performance of a metal. The experiments were planned using the design approach of Taguchi. An L27 orthogonal array was used for the experiments. The three performance measures are the top width, bottom width, and penetration. The influence of the process parameters on the selected performance was studied through a single response analysis. The use of artificial intelligence in the manufacturing industry has the potential to improve the efficiency of the process and reduce the cost of doing business. The goal of this study is to develop an artificial intelligence model that can make informed decisions regarding the welding process of Inconel 718 alloy joint. The ANFIS model is a decision-making tool that combines the capabilities of fuzzy models and neural networks. It was used to predict the performance of the various measures. A comparison was performed between the predicted and the actual values. The results of the study revealed that the ANFIS model was able to predict the desired performance measures with accuracy of about 90%.