LLM-3D print: Large Language Models to monitor and control 3D printing

Abstract

Industry 4.0 has revolutionized manufacturing by driving digitization and shifting the paradigm toward additive manufacturing (AM). Material extrusion (MEX), a core AM method, produces customized and cost-effective products with minimal waste, challenging traditional subtractive manufacturing. Despite its advantages, MEX remains susceptible to defects that can compromise part quality and function, often requiring expert intervention. Existing rule-based and machine learning approaches struggle to generalize across different printers and sensors, while deep learning methods depend on large labeled datasets, limiting their scalability and adaptability. To address these challenges, we introduce a process monitoring and control framework that employs Large Language Models (LLMs) as autonomous controllers for additive manufacturing. Unlike rule-based or heavily data-dependent approaches, our method requires no domain-specific fine-tuning or training. Instead, the LLM leverages in-context learning, self-prompting, and iterative prompt-reason refinement to evaluate print quality from sequential image captures, detect and classify emerging failure modes, and query and modify the printer for relevant operating parameters. Through this adaptive reasoning process, the LLM not only interprets defects but also improves its own decision-making logic, autonomously formulating and executing corrective actions. This demonstrates a rule-free, self-improving approach to process control that extends beyond traditional quality assurance methods. We validated the effectiveness of the proposed framework by comparing it with a control group of engineers with different levels of AM expertise. The evaluation showed that LLM-based agents not only reliably identified common 3D printing errors such as inconsistent extrusion, stringing, warping, and poor layer adhesion, but also determined their causes and corrected them without human intervention. In addition to matching expert-level accuracy, the LLM was able to recognize emerging print errors earlier than human experts, highlighting its value as a proactive controller. To further demonstrate generalizability, we deployed and tested the framework on two different 3D printers with distinct sensor setups, confirming its adaptability across hardware. We also performed compression tests on baseline prints and on prints optimized by the LLM, with the optimized parts showing clear improvements in mechanical performance. LLMs in continuous improvement cycle LLM-based supervisor agents can be employed at each step of the continuous improvement cycle. The cycle involves evaluating print quality, identifying failure modes, gathering relevant information, and planning and solving the issues by adjusting the print parameters, ensuring high-quality defect-free parts.

Topics & Concepts