Improved Acoustic Thermometry for Long-Distance Temperature Measurements

Accurate measurements of long distances (in the order of tens of meters or more) are necessary in manufacturing processes of large structures, as, for example, in the aerospace industry. In the most demanding applications, the goal is to achieve a relative accuracy of 10^-7 in the measurement of distances (e.g., 1 µm over 10 m). This goal can be obtained with laser interferometers whose accuracy is based on knowledge of the speed of light, which, in turn, depends on the temperature of air. A thermometer based on the measurement of the speed of sound in air has been realized at INRIM. Its purpose is the measurement of the air temperature along the measurement path of the interferometer with an accuracy of 0.1 °C at distances up to 11 m. The paper describes the principle and the experimental setup of the acoustic thermometer and demonstrates its performance by comparison with calibrated reference platinum resistance thermometers. Furthermore, we demonstrate the potentiality of the method to measure the vertical temperature gradient, which is the main error source in triangulation measurements when using laser trackers.

Safety considerations for autonomous, modular robotics in aerospace manufacturing

Industrial robots are versatile machines that can be used to implement numerous tasks. They have been successful in applications where-after integration and commissioning-a more or less static and repetitive behaviour in conjunction with closed work cells is sufficient. In aerospace manufacturing, robots still struggle to compete against either specialized machines or manual labour. This can be attributed to complex or custom parts and/or small batch sizes. Here, applicability of robots can be improved by enabling collaborative use-cases. When fixed protective fences are not desired due to handling problems of the large parts involved, sensor-based approaches like speed and separation monitoring (SSM) are required. This contribution is about how to construct dynamic volumes of space around a robot as well as around a person in the way that their combination satisfies required separation distance between robot and person. The goal was to minimize said distance by calculating volumes both adaptively and as precisely as possible given the available information. We used a voxel-based method to compute the robot safety space that includes worst-case breaking behaviour. We focused on providing a worst-case representation considering all possible breaking variations. Our approach to generate the person safety space is based on an outlook for 2D camera, AI-based workspace surveillance.