Written by:  

Bram Erven

Reducing waste of materials by automating quality control


As companies embark on Industry 4.0 and digital transformation initiatives, it's important to avoid getting caught up in the hype and buzzwords, and instead focus on turning abstract visions into tangible factory improvements. 

One of our customers asked us: can a combination of advanced technologies such as artificial intelligence, the Internet of Things, and robotics be used to optimize processes and reduce costs? First focus was a quality control challenge: addressing defects in a production batch. Even a single defect can result in significant material loss and lower production time. But more importantly: it can lead to material loss of possibly the complete batch. Materials that in the high tech industry might have travelled a long way, were difficult to mine, or are made of scarce rare earth elements.


We like to take a practical approach and focus on engineering simple yet smart solutions on the shop floor, connecting digital expertise with manufacturing know-how. With our customer, we focused on using data analytics to minimize the impact of defects. We used our Itility data platform to automate data collection and processing of data of the machine line, and to return predefined visualization options that heavily speed up the detection of failure modes and root cause analysis. 


This enabled quality engineers to reduce their analysis time in case of one defect from hours to a matter of minutes. While at the same time unlocking options to recognize patterns across similar products, and with that insight be able to stop a production run early in the process. A practical step to enhance the ability to determine defects' root causes and detect scraps. 
Next step is to actually predict defects ahead of time, to bring zero-defect manufacturing closer within reach.

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