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Using Computational Intelligence for Smart Device Operation Monitoring

Digital Manufacturing is at the core of the industry 4.0 revolution, owing to smart manufacturing and smart factories, which would allow an unprecedented degree of flexibility and visibility into manufacturing processes from how parts are designed to how they are manufactured, assembled, shipped and used. HP Inc. with the introduction of Multi Jet Fusion (MJF) technology is at the fore-front of this revolution with technology that enables great versatility around material, form factor, color etc. at industrial scale. A key challenge with 3D Printing is being able to control the process for output consistency and in-spec operation. In this paper, we detail how HP is using Big Data and Machine Learning techniques to analyze the swaths of data from our 3D Printing devices to be able to tell, with minimal supervision or context, when a machine might stray into unchartered territory that may impact machine operation and/or quality of outcome. We demonstrate using Auto-encoder based deep learning models, operating on both structured and unstructured data, to flag if the values for the parameter values or imagery (in unstructured case) is deviating from what we saw before (for the same parameter value). The work described would be candidate for rolling into a 3D Print device process monitoring solution.