Transfer of expertise
The accumulated knowledge can be transferred to other production lines.
Nowadays, it is impossible to imagine our daily business without data collection and tracking. But data alone only provides limited benefits. It’s a matter of using it wisely. In collaboration with Leftshift One, we have dedicated ourselves to exactly this challenge. With autfactory, we deliver production data of exceptionally high quality. This makes it possible to optimally feed the AI system AIOS from Leftshift One with data.
Together, we were able to create a dashboard for the continuous identification of anomalies and quality deviations. Corresponding corrective measures are subsequently derived from this. Detecting and analyzing patterns in the data becomes much easier and clearer for the user thanks to AI. Clustering of relevant data also becomes possible, for example for more precise technical inspection, or for detailed analysis of borderline components.
Artificial intelligence and deep learning on the shopfloor are no longer dreams of the future. We are also intensively involved with these technologies and were able to gain interesting insights for quality assurance in practice as part of a research project. In collaboration with Leftshift One, we were able to show:
We know which of the components are NOK before they are measured or by the EOL test bench!I want to know more
AI-based solutions enable you to detect defective products at an early stage and remove them from production. Thus, defective end products can be reduced. The sample size for EOL quality checks can also be minimized, as AI can identify potential errors earlier than the current quality assurance process.
The use of artificial intelligence in production leads to significant cost savings. Thanks to early detection of defective parts, scrap and quality inspection costs can be minimized. In addition, the time spent on rework can be significantly reduced.
The near real-time analysis of production data performed by AI allows experienced users to focus on more important tasks (e.g., process and work improvements) instead of wasting their time on repetitive data analysis. The reconfiguration of the manufacturing process can also be performed by “non-experts” supported by AI guidance.