Quantification of defects in the powder bed fusion of metals using a laser beam by sensor data fusion and consideration of the self-healing effect

• Applicants: Professor Dr.-Ing. Matthias Weigold; Professor Dr.-Ing. Michael Friedrich Zäh
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 546347258

Additive manufacturing in the form of the powder bed fusion of metals using a laser beam (PBF-LB/M) has been established as an important manufacturing process in various areas of industry. In addition to prototypes for functional validation, this process is increasingly used to produce individual parts as well as small to medium-sized series. The PBF-LB/M process leads to purely statistical component irregularities due to the large number of influencing factors, which can only be influenced to a limited extent by parameter settings. Defects in parts lead to reduced mechanical properties (e.g. as crack initiators). Safety-relevant components must, therefore, be subjected to quality testing. However, safeguarding the properties, especially for safety-critical components, is a challenge and is described in the VDI 3405 standard Sheet 2. In addition to test methods for quality monitoring and qualification of the powder, test specimens are recommended which are produced in a production order ("build job") alongside the component to be qualified ("accompanying sample"). However, if the location, type, and shape of the defects are not known, the mere inspection of the accompanying sample only provides information about the global effect of all defects in the accompanying sample caused by the manufacturing process. However, their type, distribution, and, therefore, effect can vary in the component to be qualified. Local assurance of properties can also be provided by non-destructive testing of each component, e.g. by computer tomography scans (CT scans). These tests limit the usability of additive manufacturing due to the high time and financial outlay involved. The aim of the proposed project is the detection, classification, and quantification of defects, taking into account the self-healing effect of PBF-LB/M through process monitoring and sensor data fusion. The characterization includes the evaluation of the probability of occurrence and the quantification of the characteristics. The sensor data fusion of several optical process monitoring systems is carried out at both system and evaluation level. The classification is carried out by linking domain knowledge and characteristic data patterns of the process monitoring systems. The material 1.4404 is used because it is highly relevant, basic knowledge of defect formation is available, and the material system is not too complex.

• DFG Programme: Research Grants