Applying machine learning to quality control in manufacturing

Errors in production can lead to serious consequences such as defects in the final product, which can reduce consumer confidence in the brand, and can also cause equipment damage and failure.
Machine learning technologies and neural networks can solve these problems and improve quality control in production

How it works

Machine learning and neural networks can be used to monitor the quality of products at various stages of production. One example would be the use of computer vision to automatically recognize defects on the surfaces of products, which makes it possible to identify errors in the manufacturing process and correct them in a timely manner.

You can also use neural networks to analyze data about production parameters and identify possible problems that may affect product quality. For example, neural networks can analyze data about temperature, pressure, and other factors that can affect the quality of the final product.

In general, machine learning and neural networks can help improve product quality control, speed up the process of detecting and correcting errors in production, and reduce scrap and improve the level of the final product.
Computer vision is a technology that is used to detect defects on the surface of products. Computer vision systems can analyze digital images of products and identify deviations and defects on them.

Data Analysis - Machine learning systems can be used to analyze large amounts of data to identify potential problems in manufacturing processes. This can help operators and engineers identify and fix problems early.

These are just some of the technologies that can be used to control product quality using machine learning systems. The choice of specific technologies depends on the type of product, production processes and product quality requirements.

Signal Processing - This technology is used to analyze audio or visual signals that may indicate problems in manufacturing processes. Machine learning systems can use signal processing techniques to detect deviations and defects in products.


Machine learning systems can use various technologies and methods to control product quality. Some of them include:

Examples of using

Crack and chip control

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

Object Geometry Control
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.

Human posture recognition

Fixing the movement of people