AI based verification

The Next Level of Effectiveness

AI based verification

A major advantage of artificial intelligence is its ability to attribute evaluations to scenarios, a capability traditionally reserved for human reasoning. For quality control of electronic components, this means that components can be assessed holistically using innovative approaches. At the same time, established algorithms and measurement methods continue to deliver reliable results and remain essential in daily practice, even with the use of vAI.

Viscom’s AI based verification can significantly reduce the workload on employees while helping to avoid human errors. This optimizes the classification of inspection results cost-effectively, enhances product quality, and prevents unnecessary repairs and scrap. This saves on resources and supports a more sustainable production process.

 

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Why Choose Viscom’s AI based verification?

Viscom’s vAI solution has been trained with millions of qualified images from real electronic production environments, providing a data set far beyond what a single production site could achieve in terms of defect variation. Viscom incorporates this extensive data into the training of the AI networks and supplements it with additional customer data. Each trained model is accompanied by an individual vAI Quality Report. This statistical report reflects performance, shows potential workload reduction in the verification process, and highlights the model's strengths and areas of potential optimization.

Three incremental modes facilitate the gradual and customized introduction of AI into the verification stations in real production environments:

Mode 1: Four-Eyes Principle

 

By applying the four-eyes principle, incorrect decisions can be identified and corrected early. The operator’s reclassification is cross-checked with the AI result. If there is a discrepancy in the evaluation, a notification appears at the vVerify Station. A percentage confidence value indicates how certain the AI is in the specific case. The operator can then correct or confirm the input. This way, errors are caught and corrected early in the production process rather than being discovered later.

Mode 2: AI Decision in Case of False Alarms

If the AI can clearly identify a false alarm, it autonomously performs the reclassification. If the confidence value during operation is above a predefined threshold, the AI model independently evaluates the result; if it is below the threshold, the four-eyes principle is applied. Practical experience shows that the number of false alarms is higher than that of actual errors. Therefore, in Mode 2, the AI handles the majority of the verification, significantly reducing the workload for the personnel.

Mode 3: Fully Automated AI based verification

Transitioning smoothly to autonomous classification, the AI can make independent decisions in predefined areas. In a fully automated operation, the AI not only evaluates false alarms autonomously but also classifies actual errors. Most actual errors are straightforward and no longer need to be assessed by the operator. Mode 3 of the fully automated AI verification, further alleviates the workload of trained personnel so they can focus on the remaining components that need to be classified.

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