Development of AI Prediction Tool for Fused Deposition Modeling of ABS Material for Automobile Applications

<div class="section abstract"><div class="htmlview paragraph">Fused Deposition Modeling (FDM), a form of Additive Manufacturing (AM), has emerged as a groundbreaking technology for the production of complex shapes from a variety of materials. Acrylonitrile Butadiene Styrene (ABS) is an opaque thermoplastic that is frequently employed in additive manufacturing (AM) due to its affordability and user-friendliness. The purpose of this investigation is to enhance the FDM parameters for ABS material and develop predictive models that anticipate printing performance by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS). Through experimental trials, an investigation was conducted to evaluate the influence of critical FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including mechanical properties, surface polish, and dimensional accuracy. The utilization of design of experiments (DOE) methodology facilitated a systematic examination of parameters. A predictive model was developed to forecast printing performance by utilizing input parameters and ANFIS. The ANFIS predictive models' ability to accurately predict the printing performance of ABS material was demonstrated by the results. Moreover, the models provide vital insights into the most effective parameter configurations for ensuring high-quality parts and maximizing printing efficiency. This investigation improves the understanding of Fused Deposition Modeling (FDM) for Acrylonitrile Butadiene Styrene (ABS) material and offers a practical instrument for manufacturing process optimization. By employing ANFIS predictive models, manufacturers can enhance the quality and productivity of printing. This will facilitate the expansion of the application of FDM technology in various sectors, including healthcare, manufacturing, and prototyping.</div></div>