Quality control through predictive analytics in manufacturing industry

Predictive Analytics in Manufacturing Plant

Fundamentals of Machine Learning for Predictive Data Analytics.

Modern prediction systems based on machine-learning models provide information about the expected quality of a future product. This highly valuable information lets us either adjust process parameters or cancel passing through the entire value chain of a particular batch, saving time and reducing costs.

The typical modern manufacturing process is a chain of an automated complex of equipment. The quality of the final product depends on processing parameters. The bottleneck here is that intermediate quality tests are limited to a few parameters and don’t allow full physical inspections. The task for the machine-learning model is to detect complex patterns in measurements from distributed sensors at early processing stages and recognize those patterns that are typical for cases when the quality of the final product drops below a defined level. Early detection of a defective batch is one of the most effective methods of saving resources and increasing manufacturing efficiency. Now for a big number of manufacturing processing types, early detection of hidden defects is only possible with methods of machine-learning predictions.

Machine Learning for predictive analytics

For each case, we select the most suitable and effective machine learning approach:

  • Random Forests
  • XGBoost
  • Support vector machines (SVM)
  • Artificial neural network

How BitRefine Group can help you with Predictive Analytics?

Data-driven technology based on machine learning is an innovative approach in the area of quality control. BitRefine offers a real-time system that identifies quality deviations at all stages of the process without installing any additional sensors.

Each offered system is always a customized solution based on client’s particular process and client’s particular dataset. Our prediction systems are easy to integrate into existing process and don’t require complex training for the staff.

Related Solutions

For more details, please contact us:

Thank You! Your message has been sent. Something went wrong, please try again later. Please enter a correct Captcha answer.