57 - Forgetti: 3D Printing Spaghetti Made Right - Using Artificial Intelligence to Identify Real Time Mistake During Additive Manufacturing

Description/Abstract/Artist Statement

Fused deposition modeling (FDM) 3D printing is a popular additive manufacturing process, but it often faces challenges such as print failures, which can result in wasted time and materials. Existing failure detection solutions offer limited or proprietary models that hinder open accessibility and customization. This paper introduces Forgetti, an open-source, AI-based software solution designed to detect and respond to 3D print failures. Drawing inspiration from projects such as Pytorch-Wildlife, Forgetti aims to address the lack of public datasets by allowing for the fine-tuning of detection models on diverse failure scenarios. The paper outlines the methodology used to collect, label, and train a model for failure detection, utilizing Ultralytics and Svelte. The software integrates with a web interface to provide real-time failure alerts and system diagnostics, enhancing operational efficiency and security in both hobbyist and industrial environments. This work contributes to the growing need for accessible, customizable, and community-driven solutions in the 3D printing industry.

Presenting Author Name/s

Both

Faculty Advisor/Mentor

Li, Yaohang

Faculty Advisor/Mentor Department

Computer Science

College Affiliation

College of Sciences

Presentation Type

Poster

Disciplines

Artificial Intelligence and Robotics

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57 - Forgetti: 3D Printing Spaghetti Made Right - Using Artificial Intelligence to Identify Real Time Mistake During Additive Manufacturing

Fused deposition modeling (FDM) 3D printing is a popular additive manufacturing process, but it often faces challenges such as print failures, which can result in wasted time and materials. Existing failure detection solutions offer limited or proprietary models that hinder open accessibility and customization. This paper introduces Forgetti, an open-source, AI-based software solution designed to detect and respond to 3D print failures. Drawing inspiration from projects such as Pytorch-Wildlife, Forgetti aims to address the lack of public datasets by allowing for the fine-tuning of detection models on diverse failure scenarios. The paper outlines the methodology used to collect, label, and train a model for failure detection, utilizing Ultralytics and Svelte. The software integrates with a web interface to provide real-time failure alerts and system diagnostics, enhancing operational efficiency and security in both hobbyist and industrial environments. This work contributes to the growing need for accessible, customizable, and community-driven solutions in the 3D printing industry.