The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost.

The grinding process of bearing components is a critical step in their manufacturing, as it directly impacts the functional properties of raceways and other critical surfaces. One important failure that arises during the grinding process is the appearance of waviness in the machined surface. This geometrical defect causes vibrations in operation with a consequent impact on power losses, noise and fatigue. The present work proposes an in-line detection system of waviness defects in bearing raceways. For this, the system uses accelerometers installed near the machined part and runs a detection algorithm in a local calculation unit. The results are sent over Ethernet to the central quality control of the line. The embedded algorithm uses the frequency content of the measured signal for predicting the surface quality of the final part. The prediction is performed by learning a non-parametric model describing the correspondence between the surface geometry and the measured vibration content. In order to obtain this model, a calibration process is conducted for each bearing reference, ensuring that the model accounts for the specific geometric and operational characteristics of the parts. By analyzing the correlation between accelerometer signals and harmonics, the algorithm predicts the probability of waviness occurrence. The proposed system has been implemented in a high-precision bearing production line, validating its effectiveness with multiple parts of the same reference. This approach identifies waviness during the machining process without the need for offline tests. This fact represents an improvement in the detection of defects, and it provides higher product quality and reduced operational costs.