New Trends of Quality Detection in Additive Manufacturing

Dear Colleagues,

Modern industry demands new technologies in order to use resources more efficiently and to contribute to a resilient society. Apart from a better performance of parts due to the use of advanced materials and new technologies, process optimization and a substantial increase in overall quality are crucial for achieving that goal. Additive manufacturing (AM) is a fast-growing industry that could lead to a revolution in the design of high-performance and lightweight parts. Regarding the AM process, a variety of different defects, such as a lack of fusion or delamination, can occur. If these are not detected properly and the related mechanisms remain incompletely understood, detrimental mechanical behaviour can lead to the critical failure of the part. Optimized part design, process optimization and post-processing quality testing can be applied as tools to avoid part failure. Due to advanced knowledge about the mechanisms of defect formation, parts can be designed so that defects in critical positions can be avoided. Furthermore, the number of defects can be reduced by optimizing the whole AM process. Concerning new trends in quality detection in AM, the nondestructive testing  (NDT) of AM parts and in situ monitoring are gaining relevance, accelerating the industrialization of AM and paving the way to resource-friendly AM manufacturing. NDT, especially computer tomography (CT), is often used during process development to determine the optimal process parameters. In production, NDT can be used to control the quality of the generated parts; thereby, optimal mechanical behaviour and good life expectancy can be realized. It is often more economical to use faster NDT methods such as X-ray radiography, but the lack of accuracy and detail of the information must be taken into account. In addition, the application of artificial intelligence to quality management and the correlation of nondestructive part testing with in situ process monitoring data open up new possibilities for quality control in additive manufacturing.

Prof. Dr. Christoph Leyens
Dr. Elena Lopez
Guest Editors

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• in situ monitoring
• NDT
• artificial intelligence in quality management
• additive manufacturing
• quality management