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Using AI driven QA to make AM more reliable and industrially scalable

Neotech AMT GmbH and the University of Hamburg to develop AI-driven quality assurance of “fully additive” 3D printed electronics.

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Neotech AMT and the TAMS-group at the University of Hamburg developed a camera-based monitoring system for additively manufactured objects with integrated electronics (Project: KAM EI). The project, funded by Central Innovation Program for SMEs (ZIM) from the German Federal Federal Ministry for Economic Affairs and Energy (BMWi), will run until September 2022. Automated Quality Assurance (QA) will be developed with which the manufacturing processes can be monitored online in the 5-axis manufacturing cell.

Dr. Martin Hedges, Managing Director of Neotech AMT commented: “This exciting development opens the way for guaranteed QA and traceability in “Fully Additive” 3D Printed Electronics whist also increasing product yields. The risk of defects only being discovered after lengthy manufacturing processes is mitigated and autocorrection will bring yield losses to an absolute minimum.”

The developed capability will record and classify the manufacturing process and automatically correct processing errors dependent on type. A vision system will record the printed electrical structures in 3D space with the images being compiled, compensating for distortion and depth of field elements. The printed tracks are then recognized by means of image processing using Artificial Intelligence (AI) to check for potential defects such as line breaks, short circuits and geometrical errors in width and thickness.

If a defect is identified the AI-trained system selects one of three options that can be executed: automated correction, correction with operator input or part rejection (abort print). With this feedback, the printing process of the conductive tracks becomes a closed-loop system. Furthermore, the data generated by the system are to be archived for documentation. In this way, a contribution can be made to support certification processes in critical applications.

Dr. Florens Wasserfall from University Hamburg concluded:  “This project is an important step towards closed-loop control for hybrid manufacturing processes. Variations in quality and accuracy have been a major limitation ever since the invention of additive technologies. Active process monitoring is a core aspect to enable reliable and scalable manufacturing of highly integrated, smart products.”

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