AI Process Control for Additive Manufacturing Layer Errors
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Solution Overview
Problem
Current additive manufacturing systems lack precise feedback mechanisms and corrective actions during the printing process, leading to inconsistencies in achieving desired mechanical, optical, and electrical properties of printed objects.
Innovation Solution
Implementing artificial intelligence process control (AIPC) using image sensors, classifiers, and a hardware processor to analyze printed layers, identify errors, and adjust print head parameters in real-time to correct deviations from the production design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback is provided during the printing process, then the ability to take corrective action is improved, but the precision and timeliness of feedback remain insufficient
Solution Approach 1:
The patent implements a feedback mechanism where an image sensor captures images of printed layers, the hardware processor analyzes these images to detect errors and anomalies, and the system adjusts printing parameters in real-time based on the analysis results. This closed-loop feedback system enables timely corrective actions while maintaining high measurement precision through advanced image processing algorithms.
2Manufacturing precision
If image sensors and AI analysis are implemented, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system incorporates an integrated self-diagnosis and self-correction mechanism where the image sensor continuously monitors printed layers, the hardware processor automatically analyzes images to detect errors, and the control system autonomously adjusts printing parameters without external intervention. This self-service capability improves manufacturing precision while the modular architecture manages device complexity through functional integration.
3Manufacturing precision
If real-time analysis of each printed layer is performed, then quality control is improved, but processing time increases
Solution Approach 1:
The system performs preliminary error detection and analysis on each printed layer immediately after deposition, before proceeding to the next layer. By implementing real-time monitoring and analysis during the printing process rather than post-processing, the system maintains high quality control while minimizing time loss through parallel processing and automated decision-making algorithms.
Data Source
AI summary
Systems, methods, and media for additive manufacturing are provided. In some embodiments, an additive manufacturing system comprises: a hardware processor that is configured to: receive a captured image; apply a trained failure classifier to a low-resolution version of the captured image; determine that a non-recoverable failure is not present in the printed layer of the object; generate a cropped version of the low-resolution version of the captured image; apply a trained binary error classifier to the cropped version of the low-resolution version of the captured image; determine that an error is present in the printed layer of the object; apply a trained extrusion classifier to the captured image, wherein the trained extrusion classifier generates an extrusion quality score; and adjust a value of a parameter of the print head based on the extrusion quality score to print a subsequent layer of the printed object.