A Method for 3D Printing Defect Detection Based on Multimodal Large Language Models

Le résumé fourni par la source

With the widespread application of 3D printing in industries such as industrial manufacturing, aerospace, and construction, increasing attention is being paid to defects that arise during the printing process. To address defect detection, academia and industry have proposed numerous solutions, yet these still exhibit several limitations: 1) Traditional defect detection methods often require the collection, fine-tuning, and training of various types of defects. Given the vast amount of data in the 3D printing process, this demands significant time and memory consumption; 2) When dealing with complex defect issues, traditional models struggle to achieve desired results due to limitations in their structure and parameter scale; 3) For different printing tasks, traditional defect detection methods necessitate extensive fine-tuning and processing before defect recognition knowledge can be transferred. To tackle these issues, this study introduces a 3D printing defect detection method based on Multimodal Large Language Models (MLLM) and Retrieval Augmented Generation (RAG). Leveraging the vast parameter count and computational power of the large model, coupled with Prompt Engineering technology, a knowledge base pertaining to 3D printing defects is constructed. Examples composed of partial printed defect data are created to train the model, enabling it to precisely articulate defect issues. Ultimately, it continuously learns from existing data to accurately describe unknown defect problems. Through database verification and manual evaluation of the large model, we have confirmed the effectiveness of its question-answering results in 3D printing defect detection.

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