Content By Jody Muelaner

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By: Jody Muelaner

In a general sense, capability is the ability to do something. Within manufacturing, capability is given a much more specific definition. It is an expression of the accuracy of a process or equipment, in proportion to the required accuracy.

This can be applied to production processes, in which case any random variation and bias in the process must be significantly smaller than the product tolerance. It can also be applied to measurements, where any uncertainties in the measurement must be significantly smaller than the product tolerance or process variation that is being measured.

Jody Muelaner’s picture

By: Jody Muelaner

Whether we like it or not, manufacturing is becoming digitized and connected. Industry increasingly connects production machinery with internet of things (IoT) devices, gathers multiple real-time sensor information into large datasets, and harnesses machine learning to make data-driven decisions. The advantages of this Fourth Industrial Revolution are expected to generate huge increases in profits during the next few years. However, these developments are not without risk.

I’m not going to discuss the existential risk of drifting into dependence on a system so complex that only machine intelligence can make any sense of it. Cybersecurity presents much more immediate risks. Industry 4.0 brings the possibility of both terrorists and state actors gaining the ability to remotely shut down and sabotage critical infrastructure and military assets.

Jody Muelaner’s picture

By: Jody Muelaner

In this article I will show that the conventional method for calculating uncertainty is not always reliable. In fact, it is generally only exact when the measurement can be represented by a simple linear equation and the input uncertainties are all normally distributed. Whenever the measurement is more complex, there will be errors in the way uncertainties are combined. Using the conventional analytic methods, these errors can be difficult to quantify, although there are some methods that can be used. I will also show how simulation can be a much more reliable approach.

Uncertainty evaluation requires a mathematical model describing how influences on the measurement combine to produce the measurement result. The uncertainty in each influence is then combined, with reference to the mathematical model, to give the uncertainty in the measurement result. This approach enables all available experimental data to be combined with other unobserved sources of uncertainty, which we know about by some other means. In a previous article I showed how gauge studies can underestimate uncertainty by failing to include these Type-B uncertainties.