Towards Eco-Friendly Additive Manufacturing: An AI-Powered Model for Filament Waste Reduction through Failure Prediction
Keywords:
Eco-Friendly Manufacturing, Additive Manufacturing, 3D Printing, Filament Waste ReductionAbstract
The rapid growth of 3D printing (additive manufacturing) has revolutionized various industries, offering unprecedented capabilities for rapid prototyping, customized production, and complex geometries. However, this transformative technology is not without its environmental footprint, particularly concerning material waste generated from failed prints and support structures. Traditional quality control methods are often reactive, leading to significant filament waste and increased production costs. This article presents the development of an Artificial Intelligence (AI) based failure predictor model designed to proactively identify and mitigate print defects, thereby reducing filament waste and enhancing the sustainability of the 3D printing process. The methodology involves leveraging diverse sensor data, including computer vision and vibrational signals, to train advanced machine learning algorithms for real-time defect detection and prediction. Hypothetical results demonstrate the model's high accuracy in identifying common print failures such as warping, stringing, and spaghetti defects, enabling automated print intervention. The findings underscore the critical role of AI in improving print quality, optimizing material utilization, and fostering more sustainable additive manufacturing practices, paving the way for a greener future in industrial production.
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