4. Sorting products by size and class. For example, crushed stone size or grain class.
Control of the evenness of the cut on the line for the production of aerated concrete blocks
These are just some examples of using machine learning systems to control product quality. Depending on the specific conditions that apply to production, other machine learning methods for quality control can be used.
2. Detection and elimination of problems in the production process - machine learning systems can be used to analyze data on production parameters and identify possible problems that may affect product quality. For example, machine learning systems can analyze data on temperature, pressure, and other factors that can affect the quality of the final product and determine the optimal values for these parameters.
3. Optimization of production processes - machine learning systems can be used to analyze data on previously produced products and determine the optimal production parameters. For example, they can analyze data on temperature, pressure and other parameters that have been used to produce high quality products and use this data to optimize future manufacturing processes.
1. Automated quality control on the production line - machine learning systems can be used to automatically recognize defects on the surface of products in real time. For example, computer vision systems can detect cracks and chips in materials, analyze the color, size and shape of products, and detect other anomalies.