When visual inspection and process telemetry come together, they deliver Industry 4.0 smart factory capabilities

When your job is to prevent your customers and supply chain partners from dealing with defects created by your out-of-control production line you have to decide if refinement of what you are doing now is good enough, or if you should solve the problem with entirely new capabilities.

Computer vision system providers have had decades of experience on productions lines. Patents for applying machine vision to manufacturing dates from the 1960s. Technology of image formation has an even longer historyPLCs also have a long history of use in manufacturing and made possible MES and SCADA.

Perhaps its isn’t realistic it to expect your next wave of refinement of technology won’t change something that already has decades of development poured into it and it is time to try something different. Progress may lie not in improving something these technologies do well and but rather to focus on what they were never designed to do at all.

Computer Vision systems performing visual inspection workloads are primarily responsible for annunciating defects on a HMI or sending a signal to downstream equipment to deal with the problem. But they are rarely capable of explaining how the defect was created.

Process telemetry collected by a Manufacturing Execution System (MES), data loggers or process historians are designed to quickly explain what was happening when a lot, batch, or serialized part was made. But they are rarely capable of explaining what the computer vision system actually saw.

So as a process engineer, you must make up for each technology’s respective limitations. If you don’t have a smart factory then you or someone you can boss around, must:

  1. Collect samples, or images of examples of the defects
  2. Define a classification or grading score to apply to the collection
  3. Analyze the frequency of the problem
  4. Match the defect to a specific lot/batch/serial number
  5. Pull the telemetry for all those defects
  6. Define a classification or grading score to ‘bin’ the data into an event.
  7. Combine those results into a workable dataset for analysis
  8. See if there is a relationship between how the item was made and the type of defect produced.
  9. Critically review the strength of those relationships to eliminate post-hoc bias and prove true causality.

The above steps are necessary to deliver insight and vision upon which to decide and act. As described by John Boyd’s work on Organic Design for Command and Control.

“Why insight and Vision? Without insight and vision there can be no orientation to deal with both present and future”.

Either after sufficient personal experience, or sage mentoring, you know how certain defects are created and, if the universe has smiled upon you, know how to prevent them.  But knowing isn’t the same as doing. You are only half way along your OODA loop.

Now John Boyd was a colonel in the air force and OODA has been successfully applied in military strategy, but Chet Richard’s book “Certain to Win” points out the similarities to OODA principles to the TPS and that is solidly in the realm of manufacturing.

And others such as Nigel Duffy when he was Innovation Leader AI Leader at EY saw OODA’s applicability in business workflow.

Regardless if you like it or not, as a process engineer, your job is a series of never-ending OODA loops and dealing with the present and future is the next half of the OODA, the DA - Deciding and Acting part.

In manufacturing, an automated OODA loop lives in an Industry 4.0 smart factory ‘where human beings, machines and resources communicate with each other as naturally as in a social network.’

When you blend process telemetry and visual inspection together, you can have your own 4.0 smart factory OODA loop. For one of our customers their smart factory OODA loop is one where SVI and SCF work together to:

(O)bserve: using a deep-learning model that reviews the images and recognizes a defect.

(O)rient: the raw telemetry from the machines that is converted into OOC events that are matched to the type of defect the deep-learning model identified.

(D)ecide: based on the frequency of the defect, the scale of the out-of-control process events and using rules or other AI to trigger a response.

(A)ct: with a situation report with suggested remedial actions sent via email or text to the process leads in the cell identified as the cause of the defect so they can take action.

With IoT, AI and cloud being commonplace, and the first industry 4.0 documents already consigned to the archives, why do they look so unusual? With a younger workforce rightly expecting social networks to be intrinsic to their workplace experience, then why are these 4.0 smart factory OODA loops not commonplace? Perhaps the answer lies in the words of Matthew Stockwin, Manufacturing Director, Coats.

"AI will come and digitisation is an unstoppable trend, but my view is that its penetration into the deep bowels of manufacturing will take more time than we think."

Stockwin was quoted in 2019 and attributes the problem of managers failing to learn and adapt and put themselves outside of their comfort zone. They fail to acknowledge connectivity is where you start a journey that ‘ends in the prediction power of systems to see problems before they occur’, and therefore cannot advocate for capital expense of connectivity for connectivity’s sake.

Without a desire to learn and adapt you cannot create the new capabilities needed to remove defects.

But if you are ready to learn and adapt to the new capabilities of combining visual inspection and process telemetry and have your very own 4.0 Smart factory OODA loops, then we are ready to help you.

…without the whole bowel penetration thing.


Peter Darragh | EVP Product Engineering

Peter defines and executes the product roadmap for Mariner.