Tribological experiments (i.e., characterizing the friction and wear behavior of materials) are crucial for determining their potential areas of application. Automating such tests could hence help speed up the development of novel materials and coatings. Here, we utilize convolutional neural networks (CNNs) to automate a common experimental setup whereby an endoscopic camera was used to measure the contact area between a rubber sample and a spherical counterpart. Instead of manually determining the contact area, our approach utilizes a U-Net-like CNN architecture to automate this task, creating a much more efficient and versatile experimental setup. Using a 5× random permutation cross validation as well as additional sanity checks, we show that we approached human-level performance. To ensure a flexible and mobile setup, we implemented the method on an NVIDIA Jetson AGX Xavier development kit where we achieved ~18 frames per second by employing mixed-precision training.

中文翻译：

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基于U-Net的摩擦学实验自动化方法

摩擦学实验（即表征 材料的摩擦和磨损行为）对于确定其潜在的应用领域至关重要。因此，使此类测试自动化可以帮助加速新型材料和涂层的开发。在这里，我们利用卷积神经网络（CNN）来自动化常见的实验设置，从而使用内窥镜相机测量橡胶样品与球形对应物之间的接触面积。我们的方法不是手动确定接触区域，而是利用类似U-Net的CNN架构来自动执行此任务，从而创建了效率更高且用途更广泛的实验设置。使用5倍随机置换交叉验证以及其他健全性检查，我们表明我们已接近人类水平的性能。为了确保设置灵活灵活，