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Quality control with image recognition
Manual quality control in high-volume manufacturing can be unwieldy and time-consuming and can be a significant driver of costs. An AI-based approach can alleviate a major portion of the issues associated with performing quality control in a high-volume production environment.
How can image recognition help you automate the process of quality control?
Based on an annotated set of images of a product of interest, an Artificial Intelligence model (neural network) can be trained to automatically detect patterns of faulty equipment. This model can then be embedded in the quality control process so products that do not meet the requirements can be detected and singled out. Of course, the model in itself can only detect faulty products, we do however need either a robot or a human to actually remove the faulty product out of the production chain.
Such systems yield improvements in terms of both speed and efficiency of a quality control process, while significantly reducing the operational costs. Additionally, it can eliminate the human mistakes associated with a manual approach and offer the possibility of an early detection of potential problems.
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