The topic of “Predictive Quality Analytics” plays an important role in the planning of many companies. When it comes to the concrete implementation of a solution in the customer environment, not only the technology but also a deep understanding of processes by all those involved is required.
Below we present a typical use case as an example:
In the past, individual inspection orders were generated on the basis of predefined static inspection plans. In the SPC data collection, either parts are acknowledged as “OK” or “nOK” in relation to the respective inspection order. In order to obtain an overview of the reported defects, all defect information including images are recorded and displayed. In the actual customer case, more than 10 employees are permanently engaged in ultrasonic testing.
In a joint project from 2019, the basis for the realization of an AI-based process optimization was set up. One of the goals was to support anomaly detection with a defined algorithm and to reduce the inspection scope. For this purpose, the process data is compared with other welding information already in the system and thus it can be decided in real time whether the welded spot which was classified as “not OK” is relevant for a manual re-inspection and thus has to be re-inspected by an inspector. In this case, the body is directed to a designated inspection station and the inspector, using an overview, can recheck all the anomaly points collected.
The number and the effort of manual ultrasonic inspections could be reduced by more than half by using the “Predictive Quality Analytics” approach.
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Director QDA SOLUTIONS