Abstract:

The study at hand is an implementation of a deep learning strategy on a quality control scheme. The quality control process is a substantial part of product manufacturing. It fundamentally targets to detect and eliminate defective products so that the erroneous ones will not be delivered to the customers. Final product control has been usually performed by experts. Generally, those experts can easily distinguish defective and trouble-free products. On the other hand, growing product lines and human-based natural problems may affect the efficiency of that quality control process. Therefore, there is an increasing demand for computer-aided software that will take the place of those experts. This software or algorithm typically increases the product control rate. Besides, they make it possible to avoid from human-driven faults. The algorithms run at high speed and efficacy under conditional situations i.e. perfectly lightening environment. However, they may easily fail when small changes occur in the environment or the product for some duties that humans can easily achieve. These robustness problems make them not preferable, although they have numerous advantages. At this point, deep learning-based artificial intelligence algorithms have made a significant enhancement. The general development and achievable prices of GPUs pave the way for using numerous training examples so that better networks, meaning more robust, can be created for the applications. To this end, we carried on an experiment that could realize the deep learning strategy on the quality control scheme. For this purpose, the developed algorithms applied to the inverters conveying on a product line to confirm whether they are erroneous or not. Results show that developed strategy could detect defective products similar to the human being.

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