title: LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
summary: 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.
paper_authors._id: 66cd3716fc6f5032138a44ba
paper_authors.name: Yayati Jadhav
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-27T07:38:31.891Z
paper_authors._id: 657c2a901953a4194acf93b3
paper_authors.avatarUrl: /avatars/6560aa2a3b42baa79c790e5ebdc33a0c.svg
paper_authors.fullname: yayati jadhav
paper_authors.type: user
paper_authors.user: yayati
paper_authors._id: 66cd3716fc6f5032138a44bb
paper_authors.name: Peter Pak
paper_authors._id: 66cd3716fc6f5032138a44bc
paper_authors.name: Amir Barati Farimani
paper_title: LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
paper_summary: 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.
paper_ai_summary: A process monitoring and control framework uses pre-trained Large Language Models to detect and autonomously correct defects in 3D printing.
paper_ai_keywords: Large Language Models process monitoring autonomous control defect detection corrective action plan pre-trained models