Laboratory-Scale Limestone Rock Linear Cutting Tests with a Conical Pick: Predicting Optimal Cutting Conditions from Tool Forces
Auteur(s): |
Han-eol Kim
Sung-pil Hwang Wan-kyu Yoo Woo-Seok Kim Chang-Yong Kim Han-Kyu Yoo |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 25 août 2024, n. 9, v. 14 |
Page(s): | 2772 |
DOI: | 10.3390/buildings14092772 |
Abstrait: |
This study introduces a simplified method for predicting the optimal cutting conditions to maximize excavation efficiency based on tool forces. A laboratory-scale linear rock-cutting test was conducted using a conical pick on Finike limestone. The tool forces and their ratios were analyzed in relation to cutting parameters such as penetration depth and spacing. While the cutting force (FC) and normal force (FN) increased with the penetration depth and spacing, this relationship could not predict the optimal cutting conditions. The ratio of the mean normal force to the mean cutting force (FNm/FCm) increased with the penetration depth and the ratio of spacing to penetration depth (s/d). However, even while including this relationship, predicting optimal cutting conditions remained challenging. The ratio of the peak cutting force to the mean cutting force (FCp/FCm) reached a maximum value at a specific s/d, which is similar to the relationship between the specific energy (SE) and s/d. The optimal s/d obtained through the SE methodology was found to be between 3 and 5, and FCp/FCm reached a maximum at s/d. The error between the optimal s/d and the s/d in which FCp/FCm was maximized was less than 5%. Therefore, it was confirmed that the optimal cutting conditions could be predicted through the relationship between FCp/FCm and s/d. Additionally, by using the results from previous studies, the optimal cutting conditions obtained from the SE methodology and the proposed methodology were found to agree within a margin of error of 20%. The proposed methodology can be beneficial for the design of cutter heads and the operation of excavation machines. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10799919 - Publié(e) le:
23.09.2024 - Modifié(e) le:
23.09.2024