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18-06-2019 | Manufacturing | News | Article

Additive machines discover superalloys

Author:
Leyla Buchholz

Scientists at the Fraunhofer Institute for Material and Beam Technology IWS in Dresden have developed innovative methods enabling more materials to be processed in additive manufacturing than ever before. For example, additive manufacturing systems could facilitate better future aircraft engines with lower fuel consumption. ​​​​​​​

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Fraunhofer IWS engineers have refined laser powder buildup welding over decades in order to allow more materials to be applied in additive manufacturing. In this procedure, a system feeds various filler powders into a process zone. There, a laser melts the powder and deposits it on a workpiece surface. As a result, the desired part is generated in a layer by layer process. "One of the advantages of this additive procedure is that we can adapt the process very flexibly to the requirements of high-performance materials," explains Fraunhofer IWS project administrator Michael Müller. In this way it is also possible, for example, to print nickel-based alloys that are difficult to weld and process using traditional methods. However, this only works if the temperature, powders, feed rate and other parameters are correct. "We have to adjust all the set screws precisely," explains Michael Müller. "This is the only way we can find the right recipe." Within the framework of the Fraunhofer lighthouse project "futureAM - Next Generation Additive Manufacturing", IWS engineers are recording numerous sensor data with very high sampling rates for this purpose. However, this generates large amounts of data ("big data") that are difficult for people to understand.

Nevertheless, Fraunhofer experts use advanced methods of "artificial intelligence" (AI) and "machine learning", which are also researched under the catchword "Big Data" in a working group led by Prof. Karol Kozak, Head of Image Processing and Data Management at Fraunhofer IWS, to find hidden connections in these signal floods. For example, special analysis algorithms link the measured sensor values with the institute's powder database and evaluate further process parameters. Gradually, the machines learn to make their own decisions. For example, they can determine for themselves whether a slight rise in temperature in the welding process can be tolerated or whether they have to take immediate countermeasures before the entire component ends up as waste. "Industry is looking for ever more and ever different materials which are, however, often difficult to process," emphasizes Prof. Frank Brückner, Business Unit Manager Generation and Printing at Fraunhofer IWS.

 

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Background information for this content

2019 | Book

Mechanics of Additive and Advanced Manufacturing, Volume 8

Proceedings of the 2018 Annual Conference on Experimental and Applied Mechanics

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