Quality Assurance in Industrial Production

The requirements for quality in industrial production are constantly increasing, and they are increasing fast. For many industries, especially in Europe, this represents a very serious challenge. After all, it is not only the ever-increasing global competition that demands better solutions. Probably even more perilous for many companies is the regulatory pressure exerted by European institutions: MDR, CLP, POP, Biocides Regulation, REACH.... . Often enough, auditors, testing laboratories and Notified Bodies do not have the capacity to meet the increased demand for audits. What's more, ‘grace periods’ are usually so tight that manufacturers have no choice but to abandon parts of their product portfolios. And it never ends: Once a company has managed to get certified for compliance with a certain set of rules, new regulations are already lurking around the next corner and a certificate becomes wastepaper again. This is where solutions are needed that help to overcome the challenges described:
quickly, effectively and efficiently.

Sampling is out

The silver bullet for the production challenges described above is automated 100% inline quality control using artificial intelligence. In most industries and in many product categories, manual or optical inspection of product samples does no longer meet regulatory requirements.
However there is a problem: In most companies, small or large, it is simply impossible to use algorithms in a targeted, sensible way, because there are no suitable data available. There is a serious lack of homogeneously structured machine, camera and sensor data on which AI driven assurance solutions could be applied in a targeted manner.

No AI without Data:
100% inline - Quality Control Revisited

Without suitable data, no AI can distinguish between "OK" and "not OK". And if an AI-capable data foundation needs to be painstakingly built up again and again for every new quality assurance solution, essential cost and time advantages of the use of AI go down the drain.
With PANDA, the lengthy, tedious and very expensive data engineering hassle is a thing of the past. PANDA offers AI providers and customers an AI-ready data solution, based on proprietary machine and sensor data, that is structured and homogeneous to meet all the required assurance targets by means of AI. Thus, with PANDA any AI driven solution fit to meet your quality assurance targets is ready for immediate use, helping to cut cost and time in a significant way.
With PANDA's smart data setup PANDA | IDI (Intelligent Data Infrastructure), industrial customers will become independent of individual solution providers. Any AI solution will be ready for use. A second specific benefit for industrial users is the connectivity of different solutions from different vendors via PANDA | IDI. Quality assurance managers no longer need to operate with isolated, unconnected niche solutions that time and again require anew particular data setups.
At the same time, PANDA | IDI's intelligent data infrastructure is also very helpful for solution providers: now they can focus on what they do best and sell more, being finally able to leave all the tedious data engineering aside.

Samples from our Client Database

  • Detection of cannula malformations by means by means of a deep learning application to reduce follow-up cost
  • Setup of a quality assurance  system to ensure an automated 100% identification of  of cracks on formed parts with ensuing intervention in such an event
  • Automated identification of bubble formation in adhesive solutions based on an vision system and a deep learning applications
  • Condition monitoring of machine conditions in crash systems based on vibration sensors, a vision system and NIR spectrometers for assessing oil conditions
  • Development of a quality assurance  system to reduce scrap in conjunction with a dependency mining application to reduce fault-producing system states.

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We understand the issues and needs related to regulatory compliance and conformity and we do speak your technical 'language'.
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