EFFECTS OF CONTROLLABLE AND UNCONTROLLABLE PARAMETERS ON DIAMOND WIRE CUTTING PERFORMANCE USING STATISTICAL ANALYSIS - A CASE STUDY
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2021-01-01 |
| Journal | Rudarsko-geoloĹĄko-naftni zbornik |
| Authors | Arezou Rasti, Hamid Ranjkesh Adarmanabadi, Mohammad Reza Sahlabadi |
| Institutions | Islamic Azad University, Science and Research Branch, New Mexico Institute of Mining and Technology |
| Citations | 6 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive SummaryâThis study investigates the influence of rock properties (uncontrollable parameters) and operational settings (controllable parameters) on the performance of diamond wire saw cutting in dimension stone quarries, focusing on Cutting Rate (CR) and Diamond Bead Wear Rate (WR).
- Performance Drivers: Cutting Rate (CR) is inversely proportional to rock strength (UCS) and hardness (Schmidt Hammer value, B). CR decreases significantly as these uncontrollable parameters increase.
- Abrasivity Effect: CR increases with increasing Los Angeles Abrasion (LA) percentage, while Wear Rate (WR) decreases with increasing LA. This suggests that highly abrasive rocks, while potentially easier to cut, cause less bead wear than expected, possibly due to effective self-sharpening mechanisms.
- Controllable Optimization: The optimal Cutting Angle (CA) for maximum productivity is 0°. Increasing the CA to 90° resulted in a CR reduction of approximately 30% due to increased pressure and reduced machine speed.
- Machine Setting Impact: Increasing the drive wheel diameter (from 70 cm to 90 cm) consistently increases the Cutting Rate across different rock types, confirming the benefit of higher wire velocity.
- Predictive Modeling: High-accuracy multiple non-linear regression models were developed. The best models achieved a coefficient of determination (R2) greater than 0.96 for predicting CR and greater than 0.88 for predicting WR, enabling reliable performance forecasting.
- Wear Rate Correlation: A strong inverse relationship (R2 > 0.7) was established between CR and WR: increasing the cutting rate decreases the diamond bead wear rate.
Technical Specifications
Section titled âTechnical Specificationsâ| Parameter | Value | Unit | Context |
|---|---|---|---|
| Main Motor Power | 50 | kW | Diamond Wire Saw Machine |
| Wire Tensioning Motor | 0.75 | kW | DC Motor |
| Rotator Diameter (Range) | 70-90 | cm | Controllable Pulley/Drive Wheel Diameter |
| Linear Wire Speed | 28-30 | m/s | Operational Velocity |
| Wire Length | 50 | m | Standard Wire Length |
| Beads per Meter | 31-34 | beads/m | Wire Assembly Density |
| Fresh Bead Diameter | 11.5 | mm | Initial Sintered Bead Size |
| UCS Range (Tested Rocks) | 58.7 - 97.5 | MPa | Uniaxial Compression Strength |
| Hardness Range (B/SH) | 49.4 - 58.6 | Rebound Value | Schmidt Hammer Hardness |
| Abrasion Range (LA) | 28.4 - 45.7 | % | Los Angeles Abrasion |
| Max CR Reduction (0° to 90°) | ~30 | % | Reduction in Cutting Rate due to angle change |
| Best CR Model R2 | > 0.96 | N/A | Multiple non-linear regression model accuracy |
| Best WR Model R2 | > 0.88 | N/A | Multiple non-linear regression model accuracy |
Key Methodologies
Section titled âKey MethodologiesâThe study utilized a combined field and laboratory approach across ten quarries cutting carbonate rocks (Limestone, Marble, Travertine).
- Field Data Collection: Primary blocks (typical dimension 9m x 9m x 8m) were cut using standard 50 kW diamond wire saw machines. Cutting time (t) and surface area (A) were recorded to calculate the Cutting Rate (CR = A/t).
- Controllable Parameter Variation: Cutting operations were systematically performed at three Cutting Angles (CA): 0°, 30°, and 90°. The effect of drive wheel diameter (70, 80, and 90 cm) on CR was also measured in specific quarries.
- Wear Rate Measurement: After cutting a specific surface area, one-quarter of the wire length was sampled. A digital micrometer was used to measure the average diameter of the worn beads (da). Wear Rate (WR) was calculated using the formula: WR = (do - da) / A, where do is the initial diameter (11.5 mm).
- Uncontrollable Parameter Testing (Laboratory): Rock samples were collected and prepared for standardized geotechnical testing:
- UCS (ASTM C170) for strength.
- Schmidt Hammer (ASTM D5873-14) for hardness (B).
- Los Angeles Abrasion (ASTM C131) for toughness and abrasion (LA).
- Statistical Modeling: Simple and multiple regression analyses (both linear and non-linear formats) were executed using Wolfram Mathematica. The goal was to establish predictive equations relating the dependent variables (CR, WR) to the independent variables (UCS, B, LA, CR, WR, and CA).
- Model Validation: The proposed models were validated by plotting predicted values against actual measured values. Models exhibiting high R2 values (CR > 0.9, WR > 0.7) and low standard errors were selected as the most reliable predictors.
Commercial Applications
Section titled âCommercial ApplicationsâThe findings provide critical engineering data for optimizing operational efficiency and cost control in the dimension stone industry.
- Quarry Optimization: Allows quarry managers to select the optimal cutting angle (0° for maximum CR) and drive wheel diameter (larger diameter for higher CR) based on the specific rock properties encountered.
- Tooling Procurement and Design: Provides quantitative metrics (WR models) linking rock characteristics (UCS, LA) to diamond bead consumption, enabling manufacturers to design beads with optimal sintering and abrasive material concentration for specific geological environments.
- Production Forecasting: The developed regression equations allow for accurate prediction of CR and WR based on pre-production geotechnical surveys, improving project planning, scheduling, and resource allocation.
- Cost Modeling: By accurately predicting the Wear Rate, companies can precisely estimate the operational cost associated with diamond wire replacement, which is a critical factor in overall quarrying economics.
- Rock Sawability Classification: The study contributes to a more robust classification system for rock sawability, moving beyond simple strength metrics to incorporate abrasion and operational factors.
View Original Abstract
Nowadays, most mining and quarrying industries utilize a diamond wire saw machine for bench cutting operations. This method uses a metal wire or cable assembled by diamond beads to cut the hard stone into large blocks. Many parameters classified into controllable and uncontrollable parameters affect the performance of the diamond wire saw cutting method. The uncontrollable parameters are related to rock engineering properties, and controllable parameters are related to operational aspects and machine performance. The diamond wire sawing processâs production rate is one of the most critical parameters influencing the design optimization and quarrying cost estimation. The cutting rate and wear rate of diamond beads are the most important factors to evaluate quarriesâ production performance. This study aims to determine the effects of different controllable and uncontrollable parameters on different quarriesâ production rates. Rock engineering properties like strength, hardness, and abrasivity, and operational aspects, such as cutting angle and drive wheel diameters, are considered as the main factors affecting the production performance of the diamond wire saw method. To discover the influence of these parameters, a detailed investigation in ten quarry operations was carried out. The relation between cutting rate and diamond bead wear with different parameters is estimated. It was observed that different controllable and uncontrollable parameters could increase or decrease the cutting rate and diamond bead wearing. Furthermore, using simple and multiple regression analysis, performance prediction of the cutting rate and wearing of diamond beads was developed, and the best equations were proposed.