LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
Industry 4.0 has revolutionized manufacturing by driving digitalization and
shifting the paradigm toward additive manufacturing (AM). Fused Deposition
Modeling (FDM), a key AM technology, enables the creation of highly customized,
cost-effective products with minimal material waste through layer-by-layer
extrusion, posing a significant challenge to traditional subtractive methods.
However, the susceptibility of material extrusion techniques to errors often
requires expert intervention to detect and mitigate defects that can severely
compromise product quality. While automated error detection and machine
learning models exist, their generalizability across diverse 3D printer setups,
firmware, and sensors is limited, and deep learning methods require extensive
labeled datasets, hindering scalability and adaptability. To address these
challenges, we present a process monitoring and control framework that
leverages pre-trained Large Language Models (LLMs) alongside 3D printers to
detect and address printing defects. The LLM evaluates print quality by
analyzing images captured after each layer or print segment, identifying
failure modes and querying the printer for relevant parameters. It then
generates and executes a corrective action plan. We validated the effectiveness
of the proposed framework in identifying defects by comparing it against a
control group of engineers with diverse AM expertise. Our evaluation
demonstrated that LLM-based agents not only accurately identify common 3D
printing errors, such as inconsistent extrusion, stringing, warping, and layer
adhesion, but also effectively determine the parameters causing these failures
and autonomously correct them without any need for human intervention.
66cd3716fc6f5032138a44ba
Yayati Jadhav
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2024-08-27T07:38:31.891Z
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yayati jadhav
user
yayati
66cd3716fc6f5032138a44bb
Peter Pak
66cd3716fc6f5032138a44bc
Amir Barati Farimani
LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
Industry 4.0 has revolutionized manufacturing by driving digitalization and
shifting the paradigm toward additive manufacturing (AM). Fused Deposition
Modeling (FDM), a key AM technology, enables the creation of highly customized,
cost-effective products with minimal material waste through layer-by-layer
extrusion, posing a significant challenge to traditional subtractive methods.
However, the susceptibility of material extrusion techniques to errors often
requires expert intervention to detect and mitigate defects that can severely
compromise product quality. While automated error detection and machine
learning models exist, their generalizability across diverse 3D printer setups,
firmware, and sensors is limited, and deep learning methods require extensive
labeled datasets, hindering scalability and adaptability. To address these
challenges, we present a process monitoring and control framework that
leverages pre-trained Large Language Models (LLMs) alongside 3D printers to
detect and address printing defects. The LLM evaluates print quality by
analyzing images captured after each layer or print segment, identifying
failure modes and querying the printer for relevant parameters. It then
generates and executes a corrective action plan. We validated the effectiveness
of the proposed framework in identifying defects by comparing it against a
control group of engineers with diverse AM expertise. Our evaluation
demonstrated that LLM-based agents not only accurately identify common 3D
printing errors, such as inconsistent extrusion, stringing, warping, and layer
adhesion, but also effectively determine the parameters causing these failures
and autonomously correct them without any need for human intervention.
A process monitoring and control framework uses pre-trained Large Language Models to detect and autonomously correct defects in 3D printing.
Large Language Models process monitoring autonomous control defect detection corrective action plan pre-trained models