Development of ANFIS Predictive Model for Additive Manufacturing (Fusion Deposition Modeling) of PETG Material for Automotive Components

<div class="section abstract"><div class="htmlview paragraph">Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings demonstrated that the GRA approach is an effective tool for determining optimal parameter settings to enhance printing productivity and ensure the production of high-quality components. This study provides deeper insights into the Fused Deposition Modeling (FDM) process for Polyethylene terephthalate glycol (PETG) material, offering valuable strategies for improving manufacturing processes. By leveraging the GRA approach, this work highlights a reliable method for enhancing printing efficiency and quality, thereby promoting the wider adoption of FDM technology across various industries such as prototyping, manufacturing, and healthcare.</div></div>