- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LIU Yuekai, GAO Hongli, GUO Liang, YOU Zhichao, LI Shichao. In-situ Roughness Evaluation of Milling Machined Surface Based on Lightweight Deep Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 193-200. doi: 10.3969/j.issn.0258-2724.20210959
|
Traditional machine learning methods (e.g., hand-coded feature extraction) are sensitive to light sources, equipment installation errors and other factors, which require repeated debugging and experiments and make it difficult to achieve automatic detection in large-scale production. Considering the above-mentioned problems, an in-situ roughness evaluation method is proposed to effectively enhance the efficiency and accuracy of the detection processes. Firstly, an enhanced candidate frame extraction operator for the histogram-of-gradient feature set with low sensitivity parameters is proposed to locate the milling workpiece, and the installation error is corrected using the point matching algorithm. Then, the focusing process of the industrial camera is optimized via the sharpness evaluation metrics. Finally, a lightweight convolutional neural network model for real-time computing at mobile terminals is constructed. The proposed method realizes the classification of surface textures of workpieces with different roughness values, and is experimentally verified on the end milling texture data set. Taking the times of multiplication and addition as the metrics, the performed experiments indicate that the number of floating-point operations (e.g., add and multiply) required for model inference is reduced by 55%, compared with the general convolutional neural network. In addition, the introduced cost-sensitive loss effectively improves the model’s stability to unbalanced data. Compared with the traditional machine learning methods, the accuracy of the proposed model is improved under the same experimental conditions (i.e., detection frame rate and image resolution), where the recall rate is increased by 21%, and the accuracy rate is enhanced by 8% simultaneously.
| [1] |
RIFAI A P, FUKUDA R, AOYAMA H. Surface roughness estimation and chatter vibration identification using vision-based deep learning[J]. Journal of the Japan Society for Precision Engineering, 2019, 85(7): 658-666. doi: 10.2493/jjspe.85.658
|
| [2] |
SHI W S, CAO J, ZHANG Q, et al. Edge computing: vision and challenges[J]. IEEE Internet of Things Journal, 2016, 3(5): 637-646. doi: 10.1109/JIOT.2016.2579198
|
| [3] |
WANG B, LEI Y G, YAN T, et al. Recurrent convolutional neural network: a new framework for remaining useful life prediction of machinery[J]. Neurocomputing, 2020, 379: 117-129. doi: 10.1016/j.neucom.2019.10.064
|
| [4] |
高宏力,李登万,许明恒. 基于人工智能的丝杠寿命预测技术[J]. 西南交通大学学报,2010,45(5): 685-691. doi: 10.3969/j.issn.0258-2724.2010.05.006
GAO Hongli, LI Dengwan, XU Mingheng. Intelligent monitoring system for screw life evaluation[J]. Journal of Southwest Jiaotong University, 2010, 45(5): 685-691. doi: 10.3969/j.issn.0258-2724.2010.05.006
|
| [5] |
ZHANG L, GUO L, GAO H L, et al. Instance-based ensemble deep transfer learning network: a new intelligent degradation recognition method and its application on ball screw[J]. Mechanical Systems and Signal Processing, 2020, 140: 106681.1-106681.14.
|
| [6] |
PAPANDREA P J, FRIGIERI E P, MAIA P R, et al. Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: a PCA-based approach[J]. Applied Acoustics, 2020, 159: 107102.1-107102.9.
|
| [7] |
PLAZA E G, NÚÑEZ LÓPEZ P J. Surface roughness monitoring by singular spectrum analysis of vibration signals[J]. Mechanical Systems and Signal Processing, 2017, 84: 516-530. doi: 10.1016/j.ymssp.2016.06.039
|
| [8] |
TANGJITSITCHAROEN S, THESNIYOM P, RATANAKUAKANGWAN S. Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio[J]. Journal of Intelligent Manufacturing, 2017, 28(1): 13-21. doi: 10.1007/s10845-014-0958-8
|
| [9] |
RIFAI A P, AOYAMA H, THO N H, et al. Evaluation of turned and milled surfaces roughness using convolutional neural network[J]. Measurement, 2020, 161: 107860.1-107860.11.
|
| [10] |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
|
| [11] |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
|
| [12] |
夏毅敏,李清友,邓朝辉,等. 基于轻量级模型的隧道岩性快速识别方法[J]. 西南交通大学学报,2021,56(2): 420-427.
XIA Yimin, LI Qingyou, DENG Chaohui, et al. Rapid identification method for lithology of tunnel based on lightweight model[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 420-427.
|
| [13] |
BHAT N N, DUTTA S, PAL S K, et al. Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images[J]. Measurement, 2016, 90: 500-509. doi: 10.1016/j.measurement.2016.05.022
|
| [14] |
DATTA A, DUTTA S, PAL S K, et al. Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method[J]. Journal of Materials Processing Technology, 2013, 213(12): 2339-2349. doi: 10.1016/j.jmatprotec.2013.07.008
|
| [15] |
CHANG S I, RAVATHUR J S. Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology[J]. Quality Engineering, 2005, 17(3): 435-451. doi: 10.1081/QEN-200059881
|
| [16] |
DUTTA S, PAL S K, SEN R. Progressive tool condition monitoring of end milling from machined surface images[J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2018, 232(2): 251-266. doi: 10.1177/0954405416640417
|
| [17] |
RIFAI A P, FUKUDA R, AOYAMA H. Image based identification of cutting tools in turning-milling machines[J]. Journal of the Japan Society for Precision Engineering, 2019, 85(2): 159-166. doi: 10.2493/jjspe.85.159
|
| [18] |
LIU Y K, GUO L, GAO H L, et al. Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: a review[J]. Mechanical Systems and Signal Processing, 2022, 164: 108068.1-108068.30.
|
| [19] |
NATHAN D, THANIGAIYARASU G, VANI K. Study on the relationship between surface roughness of AA6061 alloy end milling and image texture features of milled surface[J]. Procedia Engineering, 2014, 97: 150-157. doi: 10.1016/j.proeng.2014.12.236
|
Figures(12) / Tables(4)
DownLoad:
