Understanding and classifying lithic use wear: a systematic study using controlled tribological experiments and computer vision

技术的历史对于理解是什么让我们成为人类至关重要。考古学家出土的最早的(石头)工具上存在的微观磨损痕迹，为了解古人如何加工各种材料提供了关键的见解，如木材、兽皮、象牙等。尽管存在巨大的解释潜力，考古学家遇到了与安全地从磨损痕迹中识别加工材料以及复制彼此的结果的难度和效率有关的问题。该项目将利用摩擦学和人工智能(AI)的力量来促进对磨损模式形成的理解，同时促进识别过程的便利化和民主化。除了科学出版物，研究人员还将开发一个开源工具，帮助其他研究人员在自己的工作中使用他们的算法。因此，产生的结果将对生物工程界和考古学家都有用。为了解决考古科学中的管道问题，该项目将通过邀请来自纽约市的高中生参加实验室实验来增加多样性和少数群体的代表性。该项目旨在解决考古学70年来的一个问题：由于使用不同的材料，石器表面如何形成磨损？回答这个问题将产生预测，使未来能够进行大规模的考古磨损研究。为了解决这个问题，一个由考古学家组成的跨学科团队将与机械和生物材料工程师一起，首先对木材、象牙、皮肤等自然加工材料的机械性能进行分类，然后研究人员将构建隔离这些性能(如硬度、弹性、断裂韧性等)的替代生物材料，以便在实验中单独测试它们对磨损形成的影响。每种替代材料都将使用摩擦试验机与实验生产的石块摩擦。其结果将是大量记录的磨损模式，这些模式可能与加工材料的性能之间存在因果关系。在第二个实验中，石块将被用于更现实的任务，使用力控制的机器人和有机目标材料来产生大量接近考古磨损的磨损图案。来自这两个实验的信息将被用来训练计算机，根据它们最可能接触到的材料，对考古发生的磨损模式进行分类。此外，还将比较在有和没有专家输入的情况下识别加工材料的分类算法，这一分类算法解决了人工智能在考古学中的应用，这是考古学家目前非常感兴趣的一个话题。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The history of technology is crucial to understanding what makes us human. Microscopic wear traces present on the earliest (stone) tools excavated by archaeologists, offer key insights into how ancient people worked a variety of materials, like wood, skins, ivory, etc. Despite the enormous interpretive potential, archaeologists have experienced problems related to the difficulty and efficiency of securely identifying worked materials from wear traces, as well as reproducing each other’s results. This project will harness the power of tribology and artificial intelligence (AI) to advance understanding how wear patterns form and, at the same time, facilitate and democratize the identification process. Beside scientific publications, the investigators will produce an open-source tool for helping other researchers use their algorithms in their own work. The produced results will thus be useful to both the bio-engineering community as well as to archaeologists. To address pipeline issues in archaeological science, the project will increase diversity and the representation of minority groups by engaging high school students from New York City in the lab experiments.The project seeks to solve a 70-year standing problem in archaeology: how does wear form on the surface of stone tools as a result of working different materials? Answering this question will generate predictions that will enable large-scale studies of archaeological wear to be carried out in the future. To tackle this problem, an interdisciplinary team of archaeologists, together with mechanical and bio-materials engineers will first catalogue the mechanical properties of natural worked materials, such as wood, ivory, skins, etc. The researchers will then build surrogate biomaterials isolating the role of these properties (such as hardness, elasticity, fracture toughness, etc.), so that their effect on wear formation can be tested individually in experiments. Each surrogate material will be rubbed against experimentally produced stone bits using a tribometer. The result will be a large collection of recorded wear patterns that can be causally correlated with worked material properties. In a second experiment, stone bits will be used in more realistic tasks using a force-controlled robot and organic target materials to generate a large collection of wear patterns that approximate archaeological wear. Information from both experiments will be used to train computers to classify archaeologically occurring wear patterns according to the materials they most likely came into contact with. Moreover, classification algorithms with and without expert input for identifying worked materials will be compared, which addresses the application of artificial intelligence in archaeology, a topic which is currently of great interest to archaeologists in general.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.