Automated metrology enables additive manufacturing process insights and predictions

Applications of additive manufacturing (AM) are rapidly increasing because of advancements in AM processes, materials, and digital design tools. However, the inspection, measurement, and qualification of AM parts remain key challenges. Many production AM parts undergo expensive, manual inspection that limits economic viability. This presentation describes the development of automated metrology methods and the resulting process insights. These process-agnostic methods extract information from a variety of image data, including photographs, point clouds, and volumetric X-ray CT.

To demonstrate the system, we manufactured 48 parts using 11 polymer materials and three different AM processes. Parts were imaged using X-ray CT, and over 100,000 key internal and external dimensions were automatically measured. We find that across all AM processes and materials explored, part-to-part dimensional repeatability is good, while accuracy is poor. In fact, the accuracy of external feature dimensions does not correlate with internal feature accuracy.

Understanding the relationships between part design, dimensional accuracy, and manufacturing decisions is critical to producing high quality AM parts; however, quantifying these relationships requires detailed measurements. The automated metrology methods presented here convert image data into accessible measurement information and enable scalable inspection of complex geometries typical of AM. These methods can generate insights into AM production and lead to improved quality inspection systems.