Influence of force and duration on stone tool wear: results from experiments with a force-controlled robot
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Date
2019-11
Publication Type
Journal Article
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yes
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Abstract
Many studies have shown that micro-wear analysis can identify some parameters such as worked material and motion direction with varying degrees of success. However, because experiments have traditionally been carried out by un-monitored humans, we do not fully understand the role of force in wear formation. Here, we compare the amount of wear produced by duration vs. applied force in a controlled experiment and using both the inspection of optical images and quantitative parameters describing surface topography. We used flint flakes attached to a force/torque controllable robot arm to scrape standardized beech wooden planks under constant force profiles. The force profiles were obtained by previous experiments in scraping described by Pfleging et al. (PLoS One 10, Pfleging et al. 2015). We varied the force level and use duration among the experiments. Worn pieces were imaged with an Alicona InfiniteFocus G4 microscope and the polished parts of the flakes were analyzed using areal field parameters from metrology. The data is publicly available on the internet. Results indicate that use duration contributes more significantly to polish formation than force, confirming assumptions made in human experiments performed in the 1980s. Moreover, simple metrological height parameters appear inadequate for capturing the degree of polish. We conclude that more sophisticated quantitative methods are required to go beyond the subjective human evaluation of optical images to reconstruct past human action.
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published
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Journal / series
Volume
11 (11)
Pages / Article No.
5921 - 5935
Publisher
Springer
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Edition / version
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Software
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Date collected
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Subject
Controlled experiment; Micro-wear; Robot; Metrology; Height parameters; Fractal analysis
Organisational unit
03965 - Buchli, Jonas (SNF-Professur) (ehem.) / Buchli, Jonas (SNF-Professur) (former)
Notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
Funding
166163 - Learning Robust Control for Autonomous Robots (SNF)