Selective Laser Melting

Predictive Manufacturing

Predictive Manufacturing

An intelligent monitoring system to detect anomalies in 3D printing
Benjamin Uhrich, Martin Schäfer, Miriam Louise Carnot, Shirin Lange
In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 27-31 | DOI 10.30844/I4SE.23.1.88
Process-Specific Topology Optimization

Process-Specific Topology Optimization

A method for lightweight designs manufactured by selective laser melting
Jan Holoch, Steffen Czink, Markus Spadinger, Stefan Dietrich, Volker Schulze, Albert Albers
By integrating specific material properties through the manufacturing process Selective Laser Melting (SLM) into a topology optimization, the product engineer can be supported by simulation in the design process. For this purpose, a topology optimization method is being developed which takes the process-specific material properties of the SLM into account during the optimization process. This method is part of a project funded by the German Research Foundation (DFG). In this article, the influence of these specific material properties on the design is presented.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 4 | Pages 45-49
Cluster Identification of Sensor Data

Cluster Identification of Sensor Data

A Predictive Maintenance Approach for Selective Laser Melting Machines
Eckart Uhlmann ORCID Icon, Sven Pavliček, Rodrigo Pastl-Pontes, Claudio Geisert
Existing selective laser melting (SLM) machine tools are not equipped with analytics tools. This paper describes an approach to analyze offline data, based on machine learning algorithms, to identify clusters. Normal states and error cases can be identified. The results can be used to develop condition monitoring systems that provide predictive maintenance for SLM machine tools.
Industrie 4.0 Management | Volume 35 | 2019 | Edition 1 | Pages 6-10