| Duration: April 2019 – September 2022 |
Objective
The major goals of the SAMUEL project are to:
- improve AM processes: improve the quality of printed parts and/or reduce the cost of manufacturing.
- improve the AM workflow: increase automation and improve the repeatability of steps in the workflow and extend the knowledge of AM processes, resulting in a higher AM adoption rate by lowering the threshold for users.
- improve machine learning (ML) methods for use in AM (and in manufacturing industry in general): extend the applicability of ML to a) complex and rich real-world industrial datasets, consisting of heterogeneous data types (e.g. large volumes of sensor data, images, detailed digital representations of 3D objects), and b) the AM-specific processes in an industrial environment.
The SAMUEL project’s major innovation will be a unique set of tools and processes allowing various types of users, such as designers, process engineers and supply chain experts, to identify, understand and validate suitable design rules, adequate materials, equipment, manufacturers, process parameters, etc. in order to ensure compliance with design constraints through in-process validation at the end of the manufacturing process.
The toolset will consist of innovative data mining and machine learning modules which take into account feedback from printed 3D parts, new material definitions, equipment parameters, etc.
The tools, processes, modules and techniques developed via the SAMUEL project will be tested and demonstrated in four or five use cases provided by the project partners.
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