Neuro-fuzzy estimation of the most influential factors of laser melt pool depth during direct deposition process

Abstract

In this article the main aim was to identify the most influential attributes for optimal conditions for directed energy deposition through the melt pool optimization and monitoring. The main goal is to track the melt pool geometries such as width and depth. The goal of this study is to use an adaptive neural fuzzy inference system (ANFIS) to categorize the various melt pool depth input values. The procedure was optimized using ANFIS based on seven processing factors. Laser power (P), scanning speed (V), melt pool width (W), melt pool length (L), build height (BH), melt pool height (H), and melt pool tilt are the input parameters (I). Skillful prediction might be critical in achieving optimal circumstances throughout the deposition process. According to the findings, laser power has the greatest influence on melt pool depth. The combination of laser power and melt pool width produces the least training error and hence has the greatest impact on melt pool depth. The study, which takes into account many input parameters at the same time, is thought to be the first on a modest scale and will pique everyone's curiosity.