Laser-Assisted Machining of Nickel-Based Super Alloys and Optimization of Cutting Force Using ANN

In recent years, laser-assisted machining (LAM) technology has proven to be a cost-effective way to cut high-temperature materials like nickel-based super alloys, which are used a lot in the aircraft and car industries. The main goal of LAM is to keep the structural and mechanical properties of the material while getting the best cutting force. This is done by controlling the factors of the machining process, such as feed rate, depth of cut, cutting velocity, and laser power. So, the main goal of this study is to make an AI model that can predict the cutting force in laser-assisted machining. The artificial neural network (ANN) model was made after the structure, method, and number of neurons in the network was carefully thought. The Levenberg–Marquardt (LM) technique is used to make an ANN model, and the root-mean-square method is used to reduce error. Both the trial model and the ANN model are more in line with each other. With a confidence level of 93.63%, the suggested model can correctly predict the amount of cutting force needed for LAM of nickel-based super alloys.