Adaptive Neural Network for Autonomous Quality Assurance in Large-Scale Additive Manufacturing: A Comprehensive Approach to Flow Dynamics, Geometric Precision, and Thermal Process
Author(s): Sai Kothapalli
Publication #: 2506025
Date of Publication: 11.02.2025
Country: USA
Pages: 1-10
Published In: Volume 11 Issue 1 February-2025
DOI: https://doi.org/10.5281/zenodo.15699231
Abstract
Construction 3D printing faces significant quality control challenges including incorrect flow rates, layer misalignments, and temperature fluctuations that compromise structural integrity and geometric accuracy. This paper presents a comprehensive machine learning framework integrating computer vision, predictive modeling, and real-time control systems to address these critical issues. This research approach combines convolutional neural networks (CNNs) for visual defect detection, long short-term memory (LSTM) networks for temperature prediction, and reinforcement learning for flow rate optimization. Experimental validation on a large-scale concrete 3D printer demonstrates 87% reduction in flow rate deviations, 92% improvement in layer alignment accuracy, and 78% decrease in temperature fluctuation-induced defects. The proposed system achieves real-time performance with 15 ms response time, enabling immediate corrective actions during the printing process.
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