Fault analysis in additive manufacturing: Identifying causes of three-dimensional printer faults using machine learning and large language models

Abstract

Fault detection in additive manufacturing, particularly within 3D printing systems, is a critical issue that impacts the quality and reliability of the final products. Solving these challenges is essential for ensuring high standards and consistent performance in manufacturing processes. We have developed a sophisticated system that combines traditional machine learning classifiers with advanced convolutional neural networks (CNNs) and large language models (LLMs) to enhance fault detection and diagnostic capabilities. This system employs diverse machine learning models to achieve robust image-based fault classification, supported by CNNs and cutting-edge prompt engineering techniques. Central to our approach is the Prompt Evaluation Framework (PEF), which leverages strategies such as zero-shot prompting, chain-of-thought, and directional stimulus prompting to refine interactions with LLMs. This framework enables the dynamic generation of personalized explanations and resolution strategies for detected faults, thereby enhancing accessibility and usability for users across different technical backgrounds. Our experimental results indicate that this integrated methodology not only improves the accuracy of fault detection across various fault types but also significantly enhances the interpretability and usability of the outputs. These findings have considerable practical implications for quality control in additive manufacturing, highlighting the potential for broader applications in intelligent, interactive fault diagnosis systems. By leveraging the power of machine learning and LLMs, our work represents a significant advancement in methodologies for 3D print fault detection and resolution.