Application of additive manufacturing (AM) technology is rapidly spreading throughout several diverse industrial sectors. The appeal of this manufacturing process includes its ability to make complex 3D geometries in a single process from a digital design. The ability to closely monitor the building process, detect dimensional deviations, and identify damaging discontinuities early during building is a very attractive prospect. The addition of various noninvasive sensors and monitoring capabilities to additive machines has been investigated by several research groups. Examples of these sensors are high-speed/high-resolution optical or infrared (IR) imaging devices, pyrometers, photodetectors, acoustic microphones, and ultrasound sensors. In contrast, the present work is devoted to the integration of a suite of IR, optical, and acoustic emission (AE) sensors into a commercial laser powder bed fusion system with a focus on the synchronization of their signals. Data from several builds that developed critical discontinuities during their manufacture have been collected and analyzed. The data from the AE sensors indicated in real time which specific types of subsurface discontinuities were forming in the part. At the same time, the IR imaging provided instantaneous information on the laser beam position and its interaction with the melting powder. In each layer, both IR and high-resolution optical images of the powder spread quality were gathered, including detection of uneven spreading and surface distortion. Combined data from the sensor suite provide a unique potential to detect most major build discontinuities during the AM process. The challenges associated with the large data sets and formatting from multiple sources are discussed.
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