AM ResearchElectronics

Neotech AMT and University of Hamburg develop AI-driven AM quality assurance

Successfully implementing a camera-based monitoring system for additively manufactured objects with integrated electronics

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Neotech AMT, a leading company developing manufacturing technologies for 3D printed electronics, and the TAMS group at the University of Hamburg have successfully implemented a camera-based monitoring system for additively manufactured objects with integrated electronics (Project: KAM EI).

Neotech AMT and University of Hamburg develop AI-driven AM quality assurance for additively manufactured objects with integrated electronics. The project, funded by the Central Innovation Program for SMEs (ZIM) from the German Federal Federal Ministry for Economic Affairs and Energy (BMWi), started in September 2020. The Automated Quality Assurance (AQA) system has been developed to monitor the manufacturing processes, online, in the 5-axis manufacturing cell. The AQA system records and classifies the manufacturing process and automatically corrects processing errors – dependent on type.

A vision system records the printed electrical structures in 3D space – compensating for optical distortion and depth of field elements. The printed tracks are then recognized using image processing, using AI, to check for potential defects. If a defect is identified, the system automatically corrects it.

With this feedback, the printing process of the conductive tracks becomes a closed-loop system. Furthermore, the data generated by the system are archived for documentation. In this way, a contribution can be made to support certification processes in critical applications.

“This exciting development opens the way for guaranteed QA and traceability in ‘fully additive’ 3D printed electronics whilst 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,” said Dr. Martin Hedges, Managing Director of Neotech AMT.

“This project is an important step towards closed-loop control for hybrid manufacturing processes. The developed system can work with a variety of substrate types and has shown segmentation accuracy over 99%, even with limited training data. Successful active process monitoring is a core aspect to enable reliable and scalable manufacturing of highly integrated, smart products,” said Daniel Ahlers from the University of Hamburg.

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Edward Wakefield

Edward is a freelance writer and additive manufacturing enthusiast looking to make AM more accessible and understandable.

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