Methodology to Determine Melt Pool Anomalies in Powder Bed Fusion of Metals Using a Laser Beam by Means of Process Monitoring and Sensor Data Fusion

Materials

Michael K. Kick¹  Siegfried Baehr¹  Michael F. Zaeh¹  Andreas Wimmer¹  David L. Wenzler¹  Holger Merschroth²  Matthias Weigold²  Jana Harbig² 

[1] Institute for Machine Tools and Industrial Management, Technical University of Munich, 85748 Garching, Germany;Institute for Production Management, Technology and Machine Tools, Technical University of Darmstadt, 64289 Darmstadt, Germany;

关键词: additive manufacturing;    multi-monitoring;    PBF-LB/M;    spatter;   

DOI  :  10.3390/ma15031265

来源: DOAJ

【 摘 要 】

Additive manufacturing, in particular the powder bed fusion of metals using a laser beam, has a wide range of possible technical applications. Especially for safety-critical applications, a quality assurance of the components is indispensable. However, time-consuming and costly quality assurance measures, such as computer tomography, represent a barrier for further industrial spreading. For this reason, alternative methods for process anomaly detection using process monitoring systems have been developed. However, the defect detection quality of current methods is limited, as single monitoring systems only detect specific process anomalies. Therefore, a new methodology to evaluate the data of multiple monitoring systems is derived using sensor data fusion. Focus was placed on the causes and the appearance of defects in different monitoring systems (photodiodes, on- and off-axis high-speed cameras, and thermography). Based on this, indicators representing characteristics of the process were developed to reduce the data. Finally, deterministic models for the data fusion within a monitoring system and between the monitoring systems were developed. The result was a defect detection of up to 92% of the melt track defects. The methodology was thus able to determine process anomalies and to evaluate the suitability of a specific process monitoring system for the defect detection.

【 授权许可】

Unknown