Each individual step in the manufacture of a product is precisely calculated, yet the smallest deviations within the tolerance limits result in quality errors and safety gaps in the end product as a whole. Predictive quality is the approach that can achieve a cost reduction in the manufacturing process by mastering complex processes.
How does predictive quality work?
Predictive quality assurance is a data-based and data-driven approach to quality management. This is known as data analytics. This involves making targeted predictions so that potential future challenges in product development can be identified at an early stage. This can reduce the need for internal and external rework within the company, while at the same time protecting reputation and recognition from potential damage.
Numerous different parameters influence the quality of a product. Even within the specified tolerance limits of individual part production, problems can arise in the product. Predictive quality automatically monitors all production parameters and predicts possible quality defects in the product based on the interactions. This means that corrections can be made before completion, resulting in cost savings for the company.
Forecast models trained with machine learning
If artificial intelligence is to make data-based predictions, an initial defect classification must be carried out. Samples are taken from the production process and analyzed by a camera system with an image processing system. In this way, the system "learns" the difference between faultless and faulty parts. By linking the neural networks of the individual processes and the data analysis, the influence of deviations on other production steps can also be monitored.
This makes it possible to intervene in error correction at an early stage. The company can thus avoid potentially cost-intensive rework. In addition, the forecasting model continues to learn retrospectively for the quality assurance of the individual production process. This can result in possible corrections to the tolerance limits of production. Predictive quality therefore enables virtual testing of the interaction of components even before final completion, while still taking real production data from the industry into account.