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 present paper shows the capabilities of a distributed real-time co-simulation environment merging simulation models and testing facilities for developing and verifying electric vehicles. This environment has been developed in the framework of the XILforEV project and the presented case is focused on a ride control with a real suspension installed on a test bench in Spain, which uses real-time information from a complete vehicle model in Germany. Given the long distance between both sites, it has been necessary to develop a specific delay compensation algorithm. This algorithm is general enough to be used in other real-time co-simulation frameworks. In the present work, the system architecture including the communication compensation is described and successfully experimentally validated.

Cell culture of bone and tendon tissues requires mechanical stimulation of the cells in order to mimic their physiological state. In the present work, a device has been conceived and developed to generate a controlled magnetic field with a homogeneous gradient in the working space. The design requirement was to maximize the magnetic flux gradient, assuring a minimum magnetizing value in a 15 mm × 15 mm working area, which highly increases the normal operating range of this sort of devices. The objective is to use the machine for two types of biological tests: magnetic irradiation of biological samples and force generation on paramagnetic particles embedded in scaffolds for cell culture. The device has been manufactured and experimentally validated by evaluating the force exerted on magnetic particles in a viscous fluid. Apart from the magnetic validation, the device has been tested for irradiating biological samples. In this case, viability of human dental pulp stem cells has been studied in vitro after electromagnetic field exposition using the designed device. After three days of irradiation treatment, cellular microtissues showed a 59% increase in the viable cell number. Irradiated cells did not show morphological differences when compared with control cells.

The control of vehicles towing trailers is of significant interest to industry due to their wide-ranging applications across various sectors. Trailers play essential roles in logistics, mining, and other fields. This study focuses on the control of a dumper with a trailer specifically used for the monitoring of terrain stability in mining operations. The trailer is equipped with a radar system for detecting potential ground shifts that could jeopardize fieldwork safety. While numerous studies have addressed the control of Ackerman vehicles and trailers, this dumper presents a unique challenge due to its rear-axle steering mechanism. Due to this configuration, which has not been extensively studied in the literature, although the differential flatness of the system is proven, computation of the flat outputs leads to a system of partial differential equations that cannot be solved analytically. For this reason, this paper examines partial feedback linearization to facilitate control and proposes a solution for trajectory tracking that also stabilizes jack-knifing tendencies between the vehicle and trailer. The designed control system was successfully validated in a virtual environment.

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.