Supervised Machine Learning to Predict Drilling Temperature of Bone
Abstract
:1. Introduction
- To investigate the crucial machinability factors affecting the bone drilling process, with a particular emphasis on the temperature distribution. By exploring the thermo-mechanical properties of these factors, we aim to enhance our understanding and improve the efficiency of bone drilling procedures.
- To evaluate the performance of bovine bone under varying machining conditions, specifically focusing on the effects of the spindle speed, feed rate, and drill bit diameter. Additionally, we seek to analyze the thermo-mechanical interactions occurring during the drilling process.
- To enhance the machining parameters and determine the best combinations of the spindle speed, feed rate, and drill bit diameter to reduce thermal osteonecrosis in bone by utilizing the Response Surface Methodology (RSM).
- To deploy ML models, particularly random forest (RF) and support vector machine (SVM), to forecast the highest temperature increase. To assess the model’s accuracy and performance in predicting the machining results based on the input variables and confirm the precision and dependability of the ML models.
2. Materials and Methods
2.1. Response Surface Methodology
2.2. Support Vector Machine for Regression (SVR)
2.3. Random Forest Regression (RFR)
2.4. Data Pre-Processing and Performance Evaluation Metrics
3. Results and Discussion
3.1. Optimization Using RSM
3.2. Prediction of Response Variables by SVM and RF
3.3. Comparison of Machine Learning and RSM Models
4. Conclusions
- The RSM method found that the best operating conditions were achieved with a tool diameter of 2 mm, a feed rate of 40 mm/min, and a spindle speed of 1000 rpm.
- This study found that the RFR model outperformed the SVM regression model and RSM, as evidenced by the lower errors in the performance metrics when comparing the three methods.
- The precision in predicting the bone drilling temperatures using the RSM method is 79%, while it is 97.3% with RFR and 25.7% with SVR models.
- The machine learning RFR model enhanced the drilling efficiency and reduced the risk of bone thermal damage by accurately predicting the temperature rises.
4.1. Significance of the Study
4.2. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Machine: Sainsmart Genmitsu 3018-PROVer CNC (Sainsmart, Las Vegas, NV, USA) | |||
---|---|---|---|
Cutting Tool: HSS 118° | |||
Process parameter | Spindle speed (rpm) | Feed rate (mm/min) | Drill bit diameter (mm) |
1000 | 20 | 2 | |
1500 | 30 | 3 | |
2000 | 40 | 4 |
Spindle Speed (rpm) | Feed Rate (mm/min) | Drill Bit Diameter (mm) | Average Tmax (°C) |
---|---|---|---|
1000 | 20 | 4 | 36.4 |
1000 | 30 | 3 | 35.9 |
1000 | 40 | 3 | 47.7 |
2000 | 40 | 2 | 45.2 |
1000 | 40 | 4 | 43.9 |
1500 | 40 | 3 | 49.5 |
1000 | 30 | 2 | 36.8 |
1500 | 30 | 2 | 42.5 |
1000 | 20 | 2 | 36 |
1000 | 30 | 4 | 37.5 |
2000 | 30 | 3 | 54.5 |
2000 | 20 | 3 | 49.9 |
2000 | 40 | 4 | 62.5 |
1500 | 30 | 4 | 41.7 |
2000 | 20 | 2 | 44 |
2000 | 20 | 4 | 53.6 |
1500 | 20 | 2 | 37.5 |
2000 | 40 | 3 | 63.8 |
1500 | 20 | 3 | 43.4 |
1500 | 40 | 4 | 48.6 |
1500 | 40 | 2 | 37.5 |
1000 | 40 | 2 | 35.5 |
1500 | 20 | 4 | 42.9 |
1500 | 30 | 3 | 46.5 |
1000 | 20 | 3 | 35.7 |
2000 | 30 | 2 | 49 |
2000 | 30 | 4 | 55.6 |
Optimization | Spindle Speed (rpm) | Feed Rate (mm/min) | Dill Bit Diameter (mm) | Average Tmax (°C) |
---|---|---|---|---|
Initial drilling parameters | 1000 | 20 | 2 | 36 |
Model prediction | 1000 | 40 | 2 | 35.05 |
Experimentation | 1000 | 40 | 2 | 35.50 |
Error percentage | - | - | - | 0.45% |
Experimental Temperature (°C) | RFR Model Predicted Temperature (°C) | SVR Model Predicted Temperature (°C) |
---|---|---|
35.7 | 38.442 | 42.360 |
49.9 | 51.764 | 45.013 |
54.5 | 52.77 | 45.013 |
36 | 36.976 | 42.360 |
36.4 | 38.538 | 42.360 |
36.8 | 37.726 | 42.360 |
42.9 | 43.051 | 43.585 |
43.9 | 44.299 | 42.361 |
Bone Drilling Temperature Predictor Model Errors | ||||
---|---|---|---|---|
Test Errors | Training Errors | |||
SVR model | RFR model | SVR model | RFR model | |
R2 | 0.249 | 0.942 | 0.257 | 0.973 |
MAPE | 0.125 | 0.033 | 0.115 | 0.026 |
MSE | 33.496 | 2.568 | 32.349 | 2.148 |
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Islam, M.A.; Kamarrudin, N.S.B.; Ijaz, M.F.; Daud, R.; Basaruddin, K.S.; Abdullah, A.N.; Takemura, H. Supervised Machine Learning to Predict Drilling Temperature of Bone. Appl. Sci. 2024, 14, 8001. https://doi.org/10.3390/app14178001
Islam MA, Kamarrudin NSB, Ijaz MF, Daud R, Basaruddin KS, Abdullah AN, Takemura H. Supervised Machine Learning to Predict Drilling Temperature of Bone. Applied Sciences. 2024; 14(17):8001. https://doi.org/10.3390/app14178001
Chicago/Turabian StyleIslam, Md Ashequl, Nur Saifullah Bin Kamarrudin, Muhammad Farzik Ijaz, Ruslizam Daud, Khairul Salleh Basaruddin, Abdulnasser Nabil Abdullah, and Hiroshi Takemura. 2024. "Supervised Machine Learning to Predict Drilling Temperature of Bone" Applied Sciences 14, no. 17: 8001. https://doi.org/10.3390/app14178001
APA StyleIslam, M. A., Kamarrudin, N. S. B., Ijaz, M. F., Daud, R., Basaruddin, K. S., Abdullah, A. N., & Takemura, H. (2024). Supervised Machine Learning to Predict Drilling Temperature of Bone. Applied Sciences, 14(17), 8001. https://doi.org/10.3390/app14178001