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Volume: 30 | Article ID: art00014
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Part Quality Assessment using Convolution Neural Networks in High Pressure Die Casting
  DOI :  10.2352/ISSN.2470-1173.2018.09.IRIACV-277  Published OnlineJanuary 2018
Abstract

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.

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Kelly Cashion, Nilesh Powar, Robert De Neff, Robert Kress, "Part Quality Assessment using Convolution Neural Networks in High Pressure Die Castingin 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

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