Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2024-02-26 |
| Journal | Scientific Reports |
| Authors | Sohan Singh Rajpurohit, Yewuhalashet Fissha, Rabindra Kumar Sinha, Mujahid Ali, Hajime Ikeda |
| Institutions | Indian Institute of Technology Dhanbad, King Khalid University |
| Citations | 3 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive Summaryâ- Objective: Comprehensive evaluation of wear and cutting performance (CR and WR) of multi-blade circular saws utilizing Iron (Fe) based multi-layer diamond segments on nine varieties of granite.
- Methodology: Combined experimental sawing tests, detailed rock property quantification (14 parameters), and advanced statistical machine learning (PCA, Random Forest, Multiple Linear Regression, MLR).
- Key Performance Predictors: The most significant rock properties influencing sawing performance were identified as Point Load Strength Index (PLSI), Modulus of Elasticity (E), Brazilian Tensile Strength (BTS), Cerchar Hardness Index (CHI), and Cerchar Abrasivity Index (CAI).
- Cutting Rate (CR) Model: CR is negatively influenced by high strength (PLSI, CHI) but positively correlated with deformability (E, BTS). The MLR model achieved high accuracy (Adjusted R2 = 0.9243).
- Wear Rate (WR) Model: WR is strongly and positively correlated with rock abrasivity (CAI) and strength (PLSI). The MLR model for WR demonstrated exceptional accuracy (Adjusted R2 = 0.9894).
- Wear Mechanism: FESEM analysis confirmed that the primary modes of diamond segment wear are abrasion, erosion (due to slurry flow), and impact fatigue, leading to micro-fractures and grain pull-out.
- Tool Design Implication: High rock hardness and abrasivity necessitate increased diamond grain hardness and a metal matrix with higher abrasion resistance to ensure proportionate wear of both diamond and matrix.
Technical Specifications
Section titled âTechnical Specificationsâ| Parameter | Value | Unit | Context |
|---|---|---|---|
| Cutting Rate (CR) Range | 4.94 to 11.11 | m2/h | Observed performance across 9 granite types |
| Wear Rate (WR) Range | 5.069 to 13.462 | ”m/m2 | Observed segment wear across 9 granite types |
| UCS Range | 96.48 to 191.52 | MPa | Uniaxial Compressive Strength of granite samples |
| PLSI Range | 4.52 to 12.51 | MPa | Point Load Strength Index of granite samples |
| CAI Range | 2.10 to 3.32 | Index | Cerchar Abrasivity Index of granite samples |
| E Range | 35.67 to 68.15 | GPa | Modulus of Elasticity of granite samples |
| Diamond Segment Dimensions | 24 x 15 x 8.6 | mm | Linear dimensions of fresh multi-layer segment |
| Saw Blade Diameter Range | 500 to 2300 | mm | Combination of 10 circular saw blades used |
| Main Spindle Rotation Speed | 343 | rpm | Constant operational parameter |
| Feed Speed Range | 50 to 100 | mm/s | Operational parameter during sawing |
| Cutting Depth per Pass | 20 to 25 | mm | Operational parameter (up-cutting and down-cutting) |
| CR MLR Model Accuracy | 0.9243 | Adjusted R2 | Predictive model using PLSI, E, BTS, CHI |
| WR MLR Model Accuracy | 0.9894 | Adjusted R2 | Predictive model using PLSI, CAI, UCS, E |
| Diamond Segment Strength | 130 to 140 | Newtons | Crucial factor for cutting ability |
Key Methodologies
Section titled âKey Methodologiesâ- Material Characterization: Nine large granite blocks (S1-S9) were selected. 14 physico-mechanical rock properties (including UCS, BTS, PLSI, E, CAI, CHI, Vp, Vs) were quantitatively determined in the laboratory using ISRM and standard test methods.
- Tool Fabrication and Analysis: Iron (Fe) based multi-layer diamond segments (24 x 15 x 8.6 mm) were brazed onto the saw core. The segments comprised primary self-sharpening cutting layers (40/50 US mesh diamond) and softer intermediate layers (iron-based alloys).
- Segment Composition Verification: Energy Dispersive X-Ray (EDS) analysis confirmed the metallic composition difference between the soft layer (high Fe, low Cu) and the cutting layer (lower Fe, high Cu).
- Sawing Experimentation: A commercial 65 kW multi-blade circular saw (10 blades, 500-2300 mm diameter) was used. Sawing was performed in alternate up-cutting and down-cutting modes at a constant 343 rpm spindle speed, 50-100 mm/s feed speed, and 20-25 mm depth of cut per pass.
- Performance Metrics: Cutting Rate (CR, m2/h) was measured as total areal production per unit time. Wear Rate (WR, ”m/m2) was measured as the macroscopic linear wear progression of four marked diamond segments relative to the sawn area.
- Wear Morphology Study: Segment wear progression was investigated using Field Emission Scanning Electron Microscopy (FESEM) to observe diamond grain status (emerging, polished, fractured, pulled out) and matrix wear (cavities, erosion).
- Statistical Modeling:
- Correlation and Principal Component Analysis (PCA) were used to assess collinearity and variance contribution of rock properties.
- Random Forest (RF) regression determined the variable importance (nodeIncPurity) of input parameters.
- Multiple Linear Regression (MLR) using the best subset selection method developed highly predictive equations for CR and WR based on the most significant rock properties.
Commercial Applications
Section titled âCommercial Applicationsâ- Optimized Tool Selection: Enables diamond tool manufacturers and stone processors to select or design segments (e.g., matrix hardness, diamond concentration) specifically matched to the quantitative strength (PLSI, UCS) and abrasivity (CAI) profile of the granite workpiece.
- Production Forecasting: The high-accuracy MLR models (R2 > 0.92) allow engineers to accurately predict the achievable Cutting Rate (CR) and expected Wear Rate (WR) for new granite varieties, improving production scheduling and cost estimation.
- Process Control Optimization: Provides quantitative justification for adjusting operational parameters (feed speed, depth of cut) based on rock properties. For instance, high CHI (hardness) suggests lower diamond penetration, requiring operational adjustments or tool changes to prevent polishing.
- Material Classification: Offers a quantitative framework for classifying granite based on sawing difficulty (e.g., classifying S9 as hard and abrasive, requiring specific high abrasion resistance tooling), moving beyond qualitative geological descriptions.
- Advanced Tribological Design: Provides empirical data on the wear mechanisms (abrasion, erosion, impact) of Fe-based metal matrix composites (MMC) under granite sawing conditions, guiding future development of more durable and self-sharpening segment designs.
Tech Support
Section titled âTech SupportâOriginal Source
Section titled âOriginal SourceâReferences
Section titled âReferencesâ- 2017 - Tribology: Friction and Wear of Engineering Materials