Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing

Journal of Intelligent Manufacturing

Volumn 28, Issue 6, 2017, Pages 1285-1301

  D’Addona, Doriana M ^a   Ullah, A M M Sharif ^b   Matarazzo, D ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b KITAMI INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Artificial neural network; DNA based computing; Nature inspired computing; Pattern recognition; Prediction; Tool wear

Indexed keywords

COMPLEX NETWORKS; DNA; FORECASTING; NEURAL NETWORKS; PATTERN RECOGNITION; WEAR OF MATERIALS;

COMPLEX PROBLEMS; COMPUTATIONAL PROBLEM; DEGREE OF SIMILARITY; DNA-BASED COMPUTING; MATERIAL REMOVAL PROCESS; NATURE INSPIRED COMPUTING; PROCESSED IMAGES; TOOL WEAR;

CUTTING TOOLS;

EID: 84944699289     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-015-1155-0     Document Type: Article

References (37)

1. Azmi, A. I. (2015). Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites. Advances in Engineering Software, 82, 53–64.
2. D’Addona, D. M., & Teti, R. (2013). Image data processing via neural networks for tool wear prediction. Procedia CIRP, 12, 252–257.
3. D’Addona, D., Segreto, T., Simeone, A., & Teti, R. (2011). ANN tool-wear modelling in the machining of nickel superalloy industrial products. CIRP Journal of Manufacturing Science and Technology, 4, 33–37.
4. D’Addona, D. M., Matarazzo, D., Di Foggia, M., Caramiello, C., & Iannuzzi, S. (2015a). Inclusion scraps control in aerospace blades production through cognitive paradigms. Procedia CIRP, 33, 322–327.
5. D’Addona, D. M., Matarazzo, D., Ullah, A. M. M. S., & Teti, R. (2015b). Tool wear control through cognitive paradigms. Procedia CIRP, 33, 221–226.
6. ISO/IEC. (1996). Information technology-vocabulary-part 16: Information theory. International Standard, ISO/IEC 2382/16:1996(E/F).
7. Jemielniak, K., Urbański, T., Kossakowska, J., & Bombiński, S. (2012). Tool condition monitoring based on numerous signal features. The International Journal of Advanced Manufacturing Technology, 59, 73–81.
8. Kassim, A. A., Mannan, M. A., & Jing, M. (2000). Machine tool condition monitoring using workpiece surface texture analysis. Machine Vision and Applications, 11, 257–263.
9. Kilundu, B., Dehombreux, P., & Chiementin, X. (2011). Tool wear monitoring by machine learning techniques and singular spectrum analysis. Mechanical Systems and Signal Processing, 25, 400–415.
10. Lanzetta, M. (2001). A new flexible high-resolution vision sensor for tool condition monitoring. Journal of Materials Processing Technology, 119, 73–82.
11. Li, X., Djordjevich, A., & Venuvinod, P. K. (2000). Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring. IEEE Transactions on Industrial Electronics, 47, 697–702.
12. Li, X., & Zhejun, Y. (1998). Tool wear monitoring with wavelet packet transform fuzzy clustering method. Wear, 219, 145–154.
13. Lou, C. W., & Dong, M. C. (2015). A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting. International Journal of Electrical Power and Energy Systems, 73, 34–44.
14. Masters, T. (1993). Practical neural network recipes in C++. San Diego: Academic Press.
15. Murata, M., Kurokawa, S., Ohnishi, O., Uneda, M., & Doi, T. (2012). Real-time evaluation of tool flank wear by in-process contact resistance measurement in face milling. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 6, 958–970.
16. Nouri, M., Fussell, B. K., Ziniti, B. L., & Linder, E. (2015). Real-time tool wear monitoring in milling using a cutting condition independent method. International Journal of Machine Tools and Manufacture, 89, 1–13.
17. Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2011). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing, 22, 491–504.
18. Patra, K., Pal, S. K., & Bhattacharyya, K. (2010). Fuzzy radial basis function (FRBF) network based tool condition monitoring system using vibration signals. Machining Science and Technology, 14, 280–300.
19. Penedo, F., Haber, R. E., Gajate, A., & del Toro, R. M. (2012). Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes. IEEE Transactions on Industrial Informatics, 8, 811–818.
20. Prakash, M., & Kanthababu, M. (2013). In-process tool condition monitoring using acoustic emission sensor in microendmilling. Machining Science and Technology, 17, 209–227.
21. Ren, Q., Balazinski, M., Baron, L., Jemielniak, K., Botez, R., & Achiche, S. (2014). Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Information Sciences, 255, 121–134.
22. Segreto, T., Simeone, A., & Teti, R. (2014). Principal component analysis for feature extraction and NN pattern recognition in sensor monitoring of chip form during turning. CIRP Journal of Manufacturing Science and Technology, 7, 202–209.
23. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423 and 623-656.
24. Takuma, M., Shibasaka, T., Yamamoto, A., & Teshima, T. (1994). A study on tool management system in turning (1st report): Estimation of cutting tool life by processing image data with neural network. Journal of the Japan Society for Precision Engineering, 60, 723–727.
25. Tengeleng, S., & Armand, N. (2014). Performance of using cascade forward back propagation neural networks for estimating rain parameters with rain drop size distribution. Atmosphere, 5, 454.
26. Teshima, T., Shibasaka, T., Takuma, M., Yamamoto, A., & Iwata, K. (1993). Estimation of cutting tool life by processing tool image data with neural network. CIRP Annals Manufacturing Technology, 42, 59–62.
27. Teti, R., Jemielniak, K., O’Donnell, G., & Dornfeld, D. (2010). Advanced monitoring of machining operations. CIRP Annals Manufacturing Technology, 59, 717–739.
28. Ullah, A. M. M. S., Arai, N., & Watanabe, M. (2013). Concept map and internet-aided manufacturing. Procedia CIRP, 12, 378–383.
29. Ullah, A. M. M. S., D’Addona, D., & Arai, N. (2014). DNA based computing for understanding complex shapes. Biosystems, 117, 40–53.
30. Ullah, A. M. M. S. (2010). A DNA-based computing method for solving control chart pattern recognition problems. CIRP Journal of Manufacturing Science and Technology, 3, 293–303.
31. Wang, W. H., Hong, G. S., Wong, Y. S., & Zhu, K. P. (2007). Sensor fusion for online tool condition monitoring in milling. International Journal of Production Research, 45, 5095–5116.
32. Wang, G., Qian, L., & Guo, Z. (2013a). Continuous tool wear prediction based on Gaussian mixture regression model. The International Journal of Advanced Manufacturing Technology, 66, 1921–1929.
33. Wang, G., & Cui, Y. (2013b). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing, 24, 1085–1094.
34. Wang, G., & Feng, X. (2013). Tool wear state recognition based on linear chain conditional random field model. Engineering Applications of Artificial Intelligence, 26, 1421–1427.
35. Wang, W. H., Wong, Y. S., & Hong, G. S. (2006a). 3D measurement of crater wear by phase shifting method. Wear, 261, 164–171.
36. Wang, W. H., Hong, G. S., & Wong, Y. S. (2006b). Flank wear measurement by a threshold independent method with sub-pixel accuracy. International Journal of Machine Tools and Manufacture, 46, 199–207.
37. Xu, J., Yamada, K., Seikiya, K., Tanaka, R., & Yamane, Y. (2014). Comparison of applying static and dynamic features for drill wear prediction. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 8, JAMDSM0056.