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The predominant business case for the Industrial Internet of Things (IIoT) in the industrial machinery market centers on improving aftermarket services using remote monitoring and predictive maintenance algorithms. However, manufacturers can also realize improvements in production quality by leveraging the machine learning capabilities achievable with cloud analytics and connected machines.
Manufacturers desperately need technology suppliers to develop para-metric models that can predict the quality of a production workpiece that would otherwise be difficult to inspect in a nondestructive manner. As more digitally controlled machines become connected via the IIoT, real-time process parameters can be extracted from machines located in many locations. Production machinery process models can be developed and continuously improved upon to provide manufacturers mathematical models that predict the finish quality of a production workpiece.
Die press and forging machinery are applications that are both well-suited to realize the benefits achievable with IIoT and Big Data. While distinctly different, these applications share similar challenges. Machine operators do not fully understand the sensitivity each process parameter has on the quality of production output. The precise relationship be-tween the process variables and specific qualities of the finished product is often poorly understood.
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Keywords: Die Press, Forging, Machining, Cloud, Modeling, Big Data, Discrete Manufacturing, Industrial Internet of Things (IIoT), ARC Advisory Group.