Statistical process control, Process optimization

In the arena of Industry 4.0, PBF is often recognized as a key element for the manufacturing revolution. While the technology has come a long way in recent years, the PBF process chain's reliability and quality still largely hinge on operator skill, thus leading to potential inconsistencies in product output.

Our expert team of researchers seeks to address these challenges by reducing operator-induced variability and enhancing part quality consistency. The core of our approach involves the implementation of an intelligent feedback control system, rooted in computer vision, sensor fusion, and dynamic process control.

Utilizing computer vision, our research incorporates the use of a high-resolution camera to scrutinize the PBF manufacturing process in intricate detail. We then apply sophisticated AI-driven image analysis techniques to evaluate the material deposition layer by layer, identifying any potential defects or deviations in real-time.

In conjunction with this visual data, we employ sensor fusion, the integration of data from multiple sensors such as temperature, pressure, and vibration sensors. This method furnishes us with a more thorough understanding of the manufacturing process and heightens the accuracy of our defect detection capabilities. By blending multiple sensor inputs, we can achieve a comprehensive view of the PBF process and pinpoint issues that might otherwise elude detection.

This wealth of collected data is fed into our intelligent control system, a vital component of the feedback loop. This system is designed to adapt the local scanning strategy and tweak the process parameters for optimal results, swiftly rectifying detected issues without requiring manual intervention.

Our research and expertise lie in using this technology-rich feedback control system to bring heightened reliability, efficiency, and quality to the PBF process chain. We believe that our system will not only augment the accuracy of PBF but will also play a pivotal role in propelling Industry 4.0 forward, thus leading the way towards fully autonomous and smart manufacturing processes.