FieldMade visit to AMiquam
Had a great meeting yesterday discussing the future of quality control and monitoring in metal additive manufacturing with our colleagues from Fieldmade AS, armasuisse Wissenschaft und Technologie, and HES-SO Valais-Wallis. The AMiquam team presented a new design of the in situ NDT/monitoring system installed in a Nikon SLM Solutions machine. Based on this design, the…
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AMiquam to contribute to the Joint ISO ASTM board for standardisation of additive manufacturing
Glad to participate again to these working groups creating the future standards of our industry.
Paper published about the influence of temperature on insitu eddy currents measurements
This paper describes and analyses the impact of the temperature on the insitu eddy current measurements during PBF-LB manufacturing. The research has been conducted by AMiquam, ETHZ, and inspire in a partnership supported by Innosuisse. In a nutshell: yes, as expected, temperature affects the eddy current response because of the change in the electrical conductivity…
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AMiquam invited to WAMS24, the joint ESA-NASA conference on advanced manufacturing
Proud to be invited to this conference to present our latest technology for in machine defect detection and part certification. Join us next week in Orlando, Florida !
In-process monitoring of metal AM parts for commercial aviation: from confidence to evidence
We are thrilled to announce that AMiquam has been featured in the latest report by industry experts Donald G Godfrey and Fernando Lartategui, focusing on acceptance of in-process monitoring for metal additive manufacturing (AM) in commercial aviation. This acknowledgment underscores our relentless dedication to pioneering innovation and maintaining excellence within the industry. One statement from the report summarizes…
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Machine learning applied to Eddy Current data collected during LPBF manufacturing
In the frame of a partnership with the University of Applied Sciences and Arts Western Switzerland (HES-SO), a paper has been recently published on the application of machine learning to detect process degradation during the LPBF process monitored with Eddy Current sensors. The empirical approach is achieved by setting up a trained AI algorithm for…
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AMiquam mentioned in an important report about AM in aviation
AMiquam solutions are recognised as a possible route to strengthen certification of part using in situ inspections.