Development of an automated mechanical testbed for efficient experiments on smart composites

Abstract Impact detection for carbon-fiber reinforced plastics plays an important role in structural health monitoring systems. Such systems can extend the service life of these valueable engineered materials and thus contribute to improving their viability for demanding applications such as aerospace. The proliferation of machine learning (ML) has enabled the use of low-cost micro-electromechanical systems sensors in impact monitoring. ML models trained on impact data require consistent datasets containing a large number of individual impact samples. To advance research in this area, we developed an automated testbed for collecting low-energy impact data on composite structures. Since the testbed is fully computer-controlled, the generated impacts are precise, repeatable, and inexpensive. Additionally, we developed a complete data processing pipeline that further reduces the effort required for large-scale impact experiments. Emphasizing runtime stability allows the testbed to support error recovery and collect over 1,750 impacts per hour. Consequently, we can to collect two large datasets daily, each containing over 17,500 impact samples.