High pressure die casting (HPDC) has been developed since the late nineteenth century for a breadth of manufacturing applications. The process forms molten metal into molds at high temperatures given a complex array of parameters and variables that are challenging to observe. We used a set of thermal cameras to capture imagery of the die used as a mold during its cooling process between part productions. This data was used to train a convolution neural network to assess the quality of the part just produced based on the thermal characteristics of the surface of the die. The system achieved 90% accuracy when distinguishing between parts that met quality standards and parts that did not.
Kelly Cashion, Nilesh Powar, Robert De Neff, Robert Kress, "Part Quality Assessment using Convolution Neural Networks in High Pressure Die Casting" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision, 2018, pp 277-1 - 277-6, https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-277