پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 127 |
| تعداد شمارهها | 7,196 |
| تعداد مقالات | 77,227 |
| تعداد مشاهده مقاله | 157,217,882 |
| تعداد دریافت فایل اصل مقاله | 118,403,642 |
Ball Screw Surface Defect Impact Level Categorization Using the Collective Constraint Intelligent Prediction Technique | ||
| Journal of Information Technology Management | ||
| دوره 18، شماره 1، 2026، صفحه 34-52 اصل مقاله (1.61 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2026.106253 | ||
| نویسندگان | ||
| Soorya Prabha Mohan* 1؛ S Jaya Nirmala2 | ||
| 1Research Scholar, Department of Computer Science and Engineering, National Institute of Technology, Trichy, Tamil Nadu, India. | ||
| 2Associate Professor, Department of Computer Science and Engineering, National Institute of Technology, Trichy, Tamil Nadu, India. | ||
| چکیده | ||
| Ball screw failures often exhibit early signs of surface defects, which can arise from contamination and indicate deterioration. Identifying these surface defects is crucial for minimizing repair costs and maximizing machinery uptime. This study presents a prediction algorithm based on transfer learning methods to enhance the accuracy of ball screw surface defect detection throughout its lifecycle. We investigate four transfer learning models: CCIP-V, CCIP-D, VGG16, and DenseNet, utilizing image data mining techniques for defect classification. These models are validated using a specialized dataset of ball screw surface defects, employing Region of Interest (ROI) masking techniques to enhance image classification for each model. Our findings reveal that the proposed hybrid approach, combining CCIP and ROI, demonstrates superior performance, with the best classifier achieving an accuracy of 0.983. Notably, the CCIP classifier, when enhanced with ROI techniques, achieves an impressive accuracy of 0.985, effectively predicting defect impact levels. This research underscores the potential of transfer learning and the integration of CCIP and ROI in improving classifier performance and identifying surface defect severity in ball screws. | ||
| کلیدواژهها | ||
| Predictive Maintenance؛ Region of Interest؛ Masking؛ Transfer Learning | ||
| مراجع | ||
|
Amit, S. P., Sunnapwar, B. K., & Oza, A. D. (2022). Effective machining parameter selection through fuzzy AHP–TOPSIS for 3D finish milling of Ti6Al4V. International Journal on Interactive Design and Manufacturing.
Anderson, R. N. (2017). Petroleum analytics learning machine for optimizing the Internet of Things of today’s digital oil field-to-refinery petroleum system. In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data) (pp. 4542–4545).
Berend, N., et al. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The International Journal of Advanced Manufacturing Technology, 127, 1143–1164.
Bovic, K., Pierre, D., & Xavier, C. (2011). Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mechanical Systems and Signal Processing, 25, 400–415.
Chang-Fu, L., et al. (2018). Techniques developed for fault diagnosis of long-range running ball screw drive machines to evaluate lubrication condition. Measurement. https://doi.org/10.1016/j.measurement.2018.05.059
Deng, Y., Shichang, D., Shiyao, J., Chen, Z., & Zhiyuan, X. (2020). Prognostic study of ball screws by ensemble data-driven particle filters. Journal of Manufacturing Systems, 56, 359-372.
Denkena, B., Dittrich, M.-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The International Journal of Advanced Manufacturing Technology, 127, 1143–1164.
Gouarir, A., Garcia, M.-A., Terrazas, R. S., & Benardos, P. (2018). In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP, 27, 501–504.
Han, X., Miller, M., Vogl, G. W., Chen, G., & Jia, X. (2024). Robust feature design for early detection of ball screw preload loss. Manufacturing Letters, 41, 1225–1230.
Heng, A., Tan, A. C., Mathew, J., Montgomery, N., Banjevic, D., & Jardine, A. K. (2009). Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23, 1600–1614.
Jinwi et al. (2013). Methodology For Ball Screw Component Health Assessment And Failure Analysis. Proceedings of the ASME 2013 International Manufacturing Science and Engineering Conference
Juan et al. (2018). A new method for identifying the ball screw degradation level based on the multiple classifier systems. Measurement, 130, 118–127.
K Venkata, R., M. B.S.N., and N. Rao (2014). Prediction of cutting tool wear, surfac roughness and vibration of work piece in boring of aisi 316 steel with artificial neural network. Measurement, 63–70.
Liang et al. (2019). Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation. Shock and Vibration, DOI 10.1155/2019/7497363.
Lee, W. G., Lee, J. W., Hong, M. S., Nam, S. H., Jeon, Y., & Lee, M. G. (2015). Failure Diagnosis System for a Ball‐Screw by Using Vibration Signals. Shock and Vibration, 2015(1), 435870.
M.A. Vargas et al. (2019). A mechatronic approach for ball screw drive system: modeling, control, and validation on an FPGA-based architecture. The International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-019-03945-2
Mo¨hring et al. (2012). Integrated autonomous monitoring of ball screw drives. CIRP Annals - Manufacturing Technology, 61, 355–358
Nadeem, I., B.-A. Thorkil, E. N. Finn, and J. Karsten (2022). Outlier detection in sensor data using ensemble learning. Procedia Computer Science, 176, 1160–1169.
Nikolai, S., M. Jan, and F. Christoph M. (2018). Big data on the shop-floor: sensor-based decision-support for manual processes. Journal of Business Economics, 88, 593–616.
Ringsquandl, M., S. Lamparter, S. Brandt, T. Hubauer, and R. Lepratti (2015). Semantic-guided feature selection for industrial automation systems. In 14th International Semantic Web Conference, 225–240.
Schlagenhauf, T., Scheurenbrand, T., Hofmann, D., & Krasnikow, O. (2023). Analysis of the visually detectable wear progress on ball screws. CIRP Journal of Manufacturing Science and Technology, 40, 1-9.
Schlagenhauf, T., Sun, C., & Fleischer, J. (2020). GAN based ball screw drive picture database enlargement for failure classification. arXiv preprint arXiv:2011.10235.
Schlagenhauf, T., & Landwehr, M. (2021). Industrial machine tool component surface defect dataset. Data in Brief, 39, 107643.
Xu, Y., Zhang, K., & Wang, L. (2021). Metal surface defect detection using modified YOLO. Algorithms, 14(9), 257.
| ||
|
آمار تعداد مشاهده مقاله: 160 تعداد دریافت فایل اصل مقاله: 174 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب