SLM

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.

Additive manufacturing, i.e. the printing of three- dimensional workpieces from different materials, offers the possibility of quickly producing functional prototypes. Digital optimisation is an important building block for the rapid implementation of functional product ideas. Based on digital models, the product is virtually optimised and continuously improved. Once the product has been digitally optimised in terms of its properties, it is checked and, if necessary, adapted for additive manufacturing. The product is then manufactured, reworked and finally tested. The article shows the optimisation possibilities using the example of a dispenser from the food industry. An existing component is digitised, a flow optimisation is carried out on the digital model and the improved product is additively manufactured.