Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Reuse of partially worn-out materials and parts is a philosophy now being applied in all manufacturing industries to achieve the goal of green manufacturing. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. This exercise will in turn reduce the overall cost and help achieve enhanced productivity. This paper reports the use of two soft computing techniques, namely, neuro fuzzy logic technique and support vector regression technique for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid two soft computing techniques and RUL obtained from these values are compared.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

v:

Spindle speed, rpm

f:

Feed, mm/min

d:

Depth of cut, mm

TL:

Tool life, min

RUL:

Remaining useful life, min

\(\text{ L }_\mathrm{{P}}\) :

Predicted life, min

\(\text{ L }_\mathrm{{C}}\) :

Consumed life, min

References

  • Anityasari, M., & Kaebernick, H. (2008). A concept of reliability evaluation for resue and remanufacturing. International Journal of Sustainable Manufacturing, 1(1), 3–17.

    Article  Google Scholar 

  • Antony, J., Anand, R. B., Kumar, M., & Tiwari, M. K. (2006). Multiple response optimization using Taguchi methodology and nero-fuzzy based model. Journal of Manufacturing Technology Management, 17(7), 908–925.

    Google Scholar 

  • Attarzadeh, I., & Ow, S. H. (2010). A novel algorithmic cost estimation model based on soft computing technique. International Journal of Computer Science, 6(2), 117–125.

    Google Scholar 

  • Ball, P. D., Despeisse, Evans, S., & Levers, A. (2009). Mapping manufacturing material, energy and waste process flows. In 7th Global conference on sustainable manufacturing, Chennai, India.

  • Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.

    Article  Google Scholar 

  • Caydas, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing, 23(3), 639–650.

    Article  Google Scholar 

  • Chao, P. Y., & Hwang, Y. D. (1997). An improvement neural network model for the prediction of cutting tool life. Journal of Intelligent Manufacturing, 8(2), 107–115.

    Article  Google Scholar 

  • Gajate, A, Haber, R, del Toro, R., Vega, P., & Bustillo, A. (2010). Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process. Journal of Intelligent Manufacturing, 1–14. doi:10.1007/s10845-010-0443-y.

  • Gokulachandran, J., & Mohandas, K. (2012). Predicting remaining useful life of cutting tools with regression and ANN analysis. International Journal of Productivity and Quality Management, 9(4), 502–518.

    Article  Google Scholar 

  • Hong, W.-C., & Pai, P.-F. (2006). Predicting engine reliability by support vector machines. International Journal of Advanced Manufacturing Technology, 28(1–2), 154–161.

    Article  Google Scholar 

  • Hossain, A., Rahman, A., Hossen, J., Iqbal, A. K. M. P., & Hasan, S. K. (2011). Application of fuzzy logic approach for an aircraft model with and with winglet. International Journal of Aerospace and Mechanical Engineering, 5(4), 224–232.

    Google Scholar 

  • Hossain, A., Rahman, A., Mohiuddin, A. K. M., & Aminanda, Y. (2012). Fuzzy logic system for tractive performance prediction of an intelligent air-cushion track vehicle. International Journal of Aerospace and Mechanical Engineering, 6(1), 1–7.

    Google Scholar 

  • ISO. (1993). Tool life testing with single point turning tools. ISO 3685 (2nd ed.). Geneva: International organization for Standards.

  • Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, Cybernetics, 23(5), 665–685.

    Article  Google Scholar 

  • Kanari, N., Pineau, J.-L., & Shallari, S. (2003). End-of-life vehicle recycling in the European Union. Journal of the Minerals, Metals and Materials Society, 55(5), 15–19.

    Article  Google Scholar 

  • Kim, K.-J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1–2), 307–319.

    Article  Google Scholar 

  • Klausner, M., Grimm, W., & Hendrickson, M. C. (1998). Reuse of electric motors in consumer products. Journal of Industrial Ecology, 2(2), 89–102.

    Article  Google Scholar 

  • Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., & Gostimirovic, M., (2012). Application of fuzzy logic and regression analysis for modeling surface roughness in face milling. Journal of Intelligent Manufacturing, 1–8, doi:10.1007/s10845-012-0623-z.

  • Lee, K. C., Ho, S. J., & Ho, S. Y. (2005). Accurate estimation of surface roughness from texture features of the surface image using an adaptive nero-fuzzy inference system. Precision Engineering, 29(1), 95–100.

    Article  Google Scholar 

  • MATLAB version 7.11, R2010b, Computer software. Natick, MA: The MathWorks, Inc.

  • Mazhar, M. I., Kara, S., & Kaebernick, H. (2007). Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks. Journal of Operations Management, 25(6), 1184–1193.

    Article  Google Scholar 

  • Nagpal, G. R. (2002). Tool engineering and design. India: Khanna Publishers.

    Google Scholar 

  • Nguyen, T., & Prasad, N. (2000). Fuzzy modeling and control —selected works of M.Sageno (pp. 17–145). New York: CRC Press.

  • Ross, P. J. (2005). Taguchi techniques for quality engineering (p. 280). New York: Tata McGraw Hill.

  • Rugrungruang, F., Kara, S., & Kaebernic, H. (2009). An integrated methodology for assessing physical and technological life of products for reuse. International Journal of Sustainable Manufacturing, 1(4), 463–490.

    Google Scholar 

  • Salat, R., & Osowski, S. (2004). Accurate fault location in the power transmission line using support vector machine approach. IEEE Transactions on Power Systems, 19(2), 879–886.

    Article  Google Scholar 

  • Sathiyasekar, K., Thyagarajah, K., & Krishnan, A. (2011). Nero fuzzy based predict the insulation quality of high voltage rotating machine. Expert Systems with Applications, 38(1), 1066–1072.

    Article  Google Scholar 

  • Schneider, E. L., Kindlein, W, Jr, Souza, S., & Malfatti, C. F. (2009). Assessment and reuse of secondary batteries cells. Journal of Power Sources, 189((2)), 1264–1269.

    Article  Google Scholar 

  • Si, X. S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining useful life estimation—A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14.

    Article  Google Scholar 

  • Sidda Reddy, B., Suresh Kumar, J., Reddy, V. K., & Padmanaban, G. (2009). Application of soft computing for the prediction of warpage of plastic injection molded parts. Journal of Engineering Science and Technology Review, 2(1), 52–62.

    Google Scholar 

  • Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modeling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836.

    Article  Google Scholar 

  • Sivarao, P. B. (2009). Mamdani fuzzy inference system modeling to predict surface roughnes in laser machining. International Journal of Intelligent Information Technology Application, 2(1), 12–18.

    Google Scholar 

  • Sivarao, I. R., Castillo, W. J. G., & Taufik. (2009). Machining quality predictions: Comparative analysis of neural net work and fuzzy logic. International Journal of Electrical & Computer Sciences, 9(9), 451–456.

  • Smola, A. J., & Scholkopf, B. (1998). A tutorial on support vector regression. Technical report NC2-TR-1998-030. ESPRIT Working Group in Neural and Computational Learning.

  • Thukaram, D., Khincha, H. P., & Vijaynarasimha, H. P. (2005). Artificial neural network and support machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery, 20(2), 710–721.

    Article  Google Scholar 

  • Tomar, D., Arya, R., & Agarwal, S. (2011). Prediction of profitability of industries using weighted SVR. International Journal of Computer Science and Engineering, 3(5), 1938–1944.

    Google Scholar 

  • Vapnik, V. (1995). The nature of statistical learning theory. Berlin: Springer.

    Book  Google Scholar 

  • Vapnik, V., Golowich, S., & Smola, A. (1996). Support vector method for function approximation regression estimation and signal processing. Advances in Neural Information Processing Systems, 9, 281–287.

    Google Scholar 

  • Vass, J., Randall, R. B., Kara, S., & Kaebernick, H. (2010). Vibration based approach to life time prediction of electric motors for reuse. International Journal of Sustainable Manufacturing, 2(1), 2–29.

    Google Scholar 

  • Wang, X. (2009). Intelligent modeling and predicting surface roughness in end milling. In Fifth international conference on natural computation (pp. 521–525). Tianjin: IEEE.

  • Wang, M., & Wang, J. (2012). CHMM for tool condition monitoring and remaining useful life prediction. International Journal of Advanced Manufacturing Technology, 59(5–8), 463–471.

    Article  Google Scholar 

  • Witten, I. H., & Frank, E. (2000). Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.

    Google Scholar 

  • Wu, C.-H., Ho, J.-M., & Lee, D. T. (2004). Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 5(4), 276–281.

    Article  Google Scholar 

  • Yadav, O. P., Singh, N., Chinnam, R. B., & Goel, P. S. (2003). A fuzzy logic based approach to reliability improvement estimation during product development. Reliability Engineering and System Safety, 80(1), 63–74.

    Google Scholar 

  • Zadeh, L. A. (1975). The concept of a linguistic variables and its application to approximate reasoning-I. Information Sciences, 8, 199–249.

    Article  Google Scholar 

  • Zhang, J. H. R. (2004). A new algorithm of improving fault location based on SVM. Eighth IEE International Conference on Development in Power system Protection, 1, 204–207.

  • Zhao, F., Chen, J., & Xu, W. (2009). Condition prediction based on wavelet packet transform and least squares support vector machine methods. Proceedings of the IMech E, Part E: Journal of Process Mechanical Engineering, 223, 71–79.

    Article  Google Scholar 

Download references

Acknowledgments

The authors wish to thank the chairman, Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham for providing work shop facilities to carry out their experimental work.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India

    J. Gokulachandran & K. Mohandas

Authors
  1. J. Gokulachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Mohandas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Gokulachandran.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gokulachandran, J., Mohandas, K. Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools. J Intell Manuf 26, 255–268 (2015). https://doi.org/10.1007/s10845-013-0778-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-013-0778-2

Keywords

Search

Navigation