Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
Abstract
:1. Introduction
2. Detection Process of Surface Defects in SiCp/Al Composites
2.1. Establishment of the Image Dataset for Machined Surface Defects
2.2. Detection Model for SiCp/Al Composite Surface Defects
2.3. Training and Evaluation of Defect Detection Model
2.3.1. Environment Configuration and Evaluation Criterion
2.3.2. Training Process
3. Extraction of Characteristic Parameters of Surface Defects
3.1. Edge Detection
3.2. Morphological Manipulation of Defect Region Images
3.3. Defect Characteristic Parameters
4. Influence of Machining Parameters on Surface Quality
4.1. Analysis of the Effect of Grinding Parameters
4.2. Analysis of the Effect of Tool Parameters
5. Conclusions
- A deep learning-based surface defect detection model is developed for grinding SiCp/Al composites. After training the defect detection model, the average accuracy of the defect detection is 97.4%, and the average detection time for a single image is 47 ms, which meets the requirements for accurate and fast defect detection.
- OpenCV is used to perform a series of image processing steps such as cropping, edge detection, and morphological operations on the detected defect regions, which achieves the accurate extraction of the characteristic parameters of surface defects. A surface quality evaluation method using the number of defects and the total area of defects in the same machining area as indicators was established.
- The number and total area of surface defects in SiCp/Al composite grinding increase with the increasing feed rate, tool diameter, and abrasive size, and they decrease with the increasing spindle speed and ultrasonic vibration amplitude. The grinding depth has little effect on the number of surface defects and the total area, as it is less than 20 μm. When it is greater than 20 μm, an increase in the grinding depth leads to a rapid deterioration in the machined surface quality.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Process Variables | Levels |
---|---|
Spindle speed n (rmp) | 3000, 6000, 9000, 12,000, 15,000 |
Feed rate vf (mm/min) | 15, 20, 25, 30, 35 |
Cutting depth ap (μm) | 5, 10, 15, 20, 25 |
Ultrasonic vibration amplitude A (μm) | 0, 1.25, 2.5, 3.75, 5 |
Tool Diameter d (mm) | 2, 4, 6, 8 |
Abrasive size Sa (μm) | 46, 54, 64, 76 |
Material Properties | Al2024 | SiC Particle |
---|---|---|
Density (g/cm3) | 2.77 | 3.2 |
Young’s modulus (GPa) | 71 | 3.2 |
Specific heat (J/g k) | 0.8875 | 0.427 |
Thermal conductivity (W/m K) | 180 | 81 |
Coefficient of thermal expansion (K−1) | 23.6 × 10−6 | 4.9× 10−6 |
Poisson’s ratio | 0.34 | 0.183 |
Feature Map | Anchors |
---|---|
76 × 76 | (34, 43) (66, 79) (81, 144) |
38 × 38 | (153, 125) (130, 245) (234, 210) |
19 × 19 | (239, 351) (177, 413) (492, 227) |
Magnifying Power | d (μm) | np (Pixel) |
---|---|---|
100× | 100 | 52 |
300× | 100 | 148 |
600× | 100 | 297 |
1000× | 100 | 476 |
No. | n (rmp) | vf (mm/min) | ap (μm) | A (μm) | d (mm) | Sa (μm) | At (μm2) | Nd |
---|---|---|---|---|---|---|---|---|
1 | 3000 | 25 | 10 | 5 | 4 | 54 | 57,222.57 | 7 |
2 | 6000 | 53,182.92 | 9 | |||||
3 | 9000 | 38,162.55 | 6 | |||||
4 | 12,000 | 27,794.07 | 4 | |||||
5 | 15,000 | 11,264.24 | 2 | |||||
6 | 12,000 | 15 | 10 | 5 | 4 | 54 | 18,171.58 | 3 |
7 | 20 | 25,998.80 | 4 | |||||
8 | 25 | 27,794.07 | 4 | |||||
9 | 30 | 26,990.74 | 4 | |||||
10 | 35 | 50,823.62 | 5 | |||||
11 | 12,000 | 25 | 5 | 5 | 4 | 54 | 19,626.48 | 3 |
12 | 10 | 27,794.07 | 4 | |||||
13 | 15 | 17,311.28 | 3 | |||||
14 | 20 | 28,022.15 | 4 | |||||
15 | 25 | 39,889.16 | 5 | |||||
16 | 12,000 | 25 | 10 | 0 | 4 | 54 | 39,159.21 | 5 |
17 | 1.25 | 42,518.35 | 6 | |||||
18 | 2.5 | 42,178.25 | 5 | |||||
19 | 3.75 | 27,794.07 | 4 | |||||
20 | 5 | 22,950.30 | 3 | |||||
21 | 12,000 | 25 | 10 | 5 | 2 | 54 | 18,920.75 | 2 |
22 | 4 | 27,794.07 | 4 | |||||
23 | 6 | 37,205.05 | 5 | |||||
24 | 8 | 34,603.97 | 4 | |||||
25 | 12,000 | 25 | 10 | 5 | 4 | 46 | 23,465.70 | 3 |
26 | 54 | 27,794.07 | 4 | |||||
27 | 64 | 31,812.16 | 4 | |||||
28 | 76 | 49,643.07 | 6 |
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Wang, H.; Zhang, H.; Zhou, M.; Gu, C.; Bai, S.; Lin, H. Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites. Appl. Sci. 2023, 13, 11961. https://doi.org/10.3390/app132111961
Wang H, Zhang H, Zhou M, Gu C, Bai S, Lin H. Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites. Applied Sciences. 2023; 13(21):11961. https://doi.org/10.3390/app132111961
Chicago/Turabian StyleWang, Haotao, Haijun Zhang, Ming Zhou, Chengbo Gu, Sutong Bai, and Hao Lin. 2023. "Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites" Applied Sciences 13, no. 21: 11961. https://doi.org/10.3390/app132111961
APA StyleWang, H., Zhang, H., Zhou, M., Gu, C., Bai, S., & Lin, H. (2023). Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites. Applied Sciences, 13(21), 11961. https://doi.org/10.3390/app132111961