Possible usage of computer vision technology for ceramic quality check

Currently, the majority of the ceramic industry ensure their product quality on the assembly line by employing numeral human worker to visually check for anomalies or utilizing machines that can only deal with one model and may even damage the ceramics. Nowadays, the majority of the ceramic industries adopt the assembly line production. In order to maintain the high quality of their products, some ceramic industries employ workers to check or utilize machines which could only deal with one model at a time. In this paper, the author discusses the possibility of introducing a more efficient approach using cameras while driven by machine learning. To find a more efficient approach, the author discusses the possibility of utilizing the cameras which is driven by machine learning. By looking at a project with functions similar to our requirement, used by a company in their solar panel production, the author analyses how to adapt the technique used there to classify ceramics by their quality. The result shows that it is possible to train a reinforcement learning agent capable of classifying ceramics under a certain model by recognizing deformation, false coloring and small cracks or burns. But a large data set consists of pictures of many products with all the possible faults that may happen to the said model and examples of good products are required to ensure the accuracy of the trained agent.