Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach
Abstract
:1. Introduction
2. Experimental Details
3. Design of the Artificial Neural Network
3.1. ANN Training
3.2. ANN Testing
4. Results and Discussion
5. Conclusions
- As a general recommendation, a low point-angle seems to be an appropriate selection to avoid excessive damage in the CFRP laminate.
- The effect of the drill point-angle loses relevance with the inevitable evolution of tool wear. This effect demonstrated the significance of the general drill geometry.
- When tool wear increases, the parametrical analysis of the influence of cutting parameters revealed that the thrust force is more sensitive to feed rate than to cutting speed
- Therefore, the wear level evolution is the most significant input factor of thrust force, due to the change generated in the tool geometry. However, a critical wear value exists for which the feed rate becomes more relevant than the cutting speed, and the thrust force remains almost constant.
- The feed rate is the cutting parameter that should be modified when the drill bit wear increases.
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | Definition | Values |
---|---|---|
E11,22 | Young’s modulus | 68 GPa |
G12 | Shear modulus | 5 GPa |
υ12,21 | Poisson ratio | 0.05 |
Xt = Yt | Maximum tensile strength | 795 MPa |
Xc = Yc | Maximum compressive strength | 860 MPa |
Parameter | Level | ||
---|---|---|---|
1 | 2 | 3 | |
Wear (mm) | 0 | 0.05 | 0.3 |
Point angle (°) | 90 | 118 | 140 |
Feed rate (mm/rev) | 0.05 | 0.10 | 0.15 |
Cutting speed (m/min) | 25 | 50 | 100 |
Factor | Value |
---|---|
Learning rate | 0.001 |
Learning rate increment | 10 |
MSE goal | 10−5 |
Maximum number of epochs | 10,000 |
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Feito, N.; Muñoz-Sánchez, A.; Díaz-Álvarez, A.; Loya, J.A. Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach. Materials 2019, 12, 2747. https://0-doi-org.brum.beds.ac.uk/10.3390/ma12172747
Feito N, Muñoz-Sánchez A, Díaz-Álvarez A, Loya JA. Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach. Materials. 2019; 12(17):2747. https://0-doi-org.brum.beds.ac.uk/10.3390/ma12172747
Chicago/Turabian StyleFeito, Norberto, Ana Muñoz-Sánchez, Antonio Díaz-Álvarez, and José Antonio Loya. 2019. "Analysis of the Machinability of Carbon Fiber Composite Materials in Function of Tool Wear and Cutting Parameters Using the Artificial Neural Network Approach" Materials 12, no. 17: 2747. https://0-doi-org.brum.beds.ac.uk/10.3390/ma12172747