Affiliations 

  • 1 Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India. ssrajpurohit@hotmail.com
  • 2 Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan. yowagaye@gmail.com
  • 3 Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
  • 4 Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland
  • 5 Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan
  • 6 Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, Saudi Arabia
  • 7 Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden. taoufik.najeh@ltu.se
  • 8 Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
  • 9 Faculty of Engineering, Hokkaido University, Kita 8, Nishi 5, Kita-ku, Sapporo, 0608628, Japan
Sci Rep, 2024 Feb 26;14(1):4590.
PMID: 38409139 DOI: 10.1038/s41598-024-54625-5

Abstract

This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment's wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.