Journals →  Chernye Metally →  2019 →  #5 →  Back

New developments of Tula State University
ArticleName Optimization of cutting speed and tool replacement in the processing of ferrous metals, taking into account the spread of tool lifetime
ArticleAuthor A. V. Antsev, N. I. Pasko, N. V. Antseva
ArticleAuthorData

Tula State University (Tula, Russia):

A. V. Antsev, Cand. Eng., Associate Prof., e-mail: a.antsev@yandex.ru
N. I. Pasko, Dr. Eng., Prof.
N. V. Antseva, Cand. Eng., Associate Prof.

Abstract

A tool life of cutting tool during ferrous metals machining is a random variable. The spread of a tool life of cutting tool can be 15-35 % within same batch. Unforeseen tool failure has a serious impact on the cost of products. Many scientific works are devoted to predicting the tool life of a cutting tool and optimizing cutting speed and the period of its replacement. However, most researchers do not take into account the stochastic nature of the process of cutting of ferrous metals. In this work it is considered that a tool life with abrasive wear due to the variation in the cutting properties of individual tools even in single batch and due to the variation in the properties of workability of workpieces (hardness, machining allowance, etc.) is a random variable with a certain distribution law. It is necessary to know the distribution law of this random variable and the dependence of the parameters of this law on the parameters of the cutting mode. As an optimization criterion, the unit costs are taken. The unit costs are the costs of replacing the tool and possible defects during machining with the failed tool per one processed part. The search for optimal values of the cutting speed and the period of the cutting tool replacement with minimum of the unit costs was performed by an enumerative technique with a small step due to the multi-extremality of the unit costs function. The article describes an example of optimization of cutting speed and tool replacement period when machining 1X18H9T (1Kh18N9T) steel with replaceable hard-alloy T30K4 plates. It is shown that taking into account stochastic nature of machining will reduce the unit costs by 86.4% with a zero reject level and 69.5% with a reject level of 0.1% while optimizing for productivity and cost, respectively, in the case when the tool life of the cutting tool has a variation with variation coefficient 0.1.
The results of the study have been published with financial support of the TSU within the framework of the scientific project No. 8705.

keywords Cutting tool, tool life, wear, tool life equation, stochastic model, wear intensity, cutting speed, replacement period, optimization
References

1. Bushuev V. V., Sabirov F. S. Lines of global machine tool industry development. Vestnik MGTU Stankin. 2010. No. 1. pp. 24–30.
2. Vasilko K. Taylor equation of durability and its modification. Manufacturing Technology. 2017. Vol. 17. pp. 393–397.
3. Prasada Rao V. D., Raghu Ram N. S., Harsha N., Navya Geethika V. Optimization of cutting parameters in CNC turning of stainless steel 304 with TiAlN nano-coated carbide cutting tool. IOP Conference Series: Materials Science and Engineering. 2018. Vol. 310. 012109.
4. Ahmed Z. J., Prickett P. W., Grosvenor R. I. The difficulties of the assessment of tool life in CNC milling. 2016 International Conference for Students on Applied Engineering, ICSAE 2016. Institute of Electrical and Electronics Engineers Inc., 2107. pp. 452–457.
5. Amorim H. J., Neto A. O. K. Study of the relationship between tool wear and surface finish in turning with carbide tool. 2014 2nd International Conference on Manufacturing Engineering and Technology for Manufacturing Growth, METMG 2014. Trans Tech Publications. 2014. Vol. 902. pp. 95–100.
6. Johansson D., Hägglung S., Bushlya V., Shâht J.-E. Sensitivity of Colding tool life equation on the dimensions of experimental dataset. Journal of Superhard Materials. 2017. Vol. 39. No. 4. pp. 271–281.
7. Karandikar J. M., Abbas A. E., Schmitz T. L. Tool life prediction using Bayesian updating. Part 1: Milling tool life model using a discrete grid method. Precision Engineering. Vol. 38. No. 1. pp. 18–27.
8. Kundrák J., Pálmai Z. Application of general tool-life function under changing cutting conditions. Acta Polytechnica Hungarica. 2014. Vol. 11. No. 2. pp. 61–76.
9. Nagasaka K., Hashimoto F. Identification of forecasting model for tool-life considering amount of tool wear. J. Japan Soc. Precis. Engng. Vol. 47. No. 2 (1981). pp. 185–190.
10. Odedeyi P. B., Abou-El-Hossein K., Liman M. An experimental study of flank wear in the end milling of AISI 316 stainless steel with coated carbide inserts. Journal of Physics: Conference Series. Institute of Physics Publishing. 2017. Vol. 843. No. 1. 012058.
11. Qehaja N., Abdullahu F., Zhujani F. Predictive mathematical modeling of tool life based on cutting parameters and workpiece material hardness using regression analysis. International Journal of Mechanical Engineering and Technology. 2017. Vol. 8. No. 8. pp. 1229–1237.
12. Lisunets N. L. Improving the efficiency of the processes of billets manufacture from rolled metal via shift cutting based on simulation. Chernye Metally. 2018. No. 6. pp. 31–35.

13. Sarancha S. Yu., Levandovskiy S. A., Statsenko J. S. et al. Optimization of long products rolling and cuttung technology based on modern it. CIS Iron and Steel Review. 2014. Vol. 9. pp. 44–49.
14. Aleshchenko А. S., Gamin Yu. V., Chan B. Kh., Tsyutsyura V. Yu. Wear features of working tools during piercing of high-temperature alloys. Chernye Metally. 2018. No. 8. pp. 63–70.
15. Kuts V., Ivakhnenko A., Khandozhko A. Investigations of cutting force effect upon shaping error of surfaces with double curvature in technological systems with mechanisms of parallel structure. Proceedings of 2015 International Conference on Mechanical Engineering, Automation and Control Systems, MEACS 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7414887.
16. Bannikov A. I., Kurchenko A. I., Makarova O. A. et al. Tool wear in turning corrosion-resistant steel. Russian Engineering Research. 2016. Vol. 30. pp. 734–735.
17. Muratov K. R. Influence of rigid and frictional kinematic linkages in tool-workpiece contact on the uniformity of tool wear. Russian Engineering Research. 2016. Vol. 36. pp. 321–323.
18. Chernikov P. P., Sharipov B. U. Influence of metal lubricant on tool wear. Russian Engineering Research. 2008. Vol. 28. pp. 194–195.
19. Protasyev V. B., Plakhotnikova Е. V., Litvinova I. V. Assessment technique for the state of production systems for the signal/noise criterion on the example of manufacturing technological processes from bar billets. Chernye Metally. 2018. No. 6. pp. 20–25.
20. Martinov G. M., Grigoryev A. S. Diagnostics of cutting tools and prediction of their life in numerically controlled systems. Russian Engineering Research. 2013. Vol. 33. pp. 433–437.
21. GOST 25413-82. Hard metal brazed tips, type 34. Design and dimensions. Introduced: 01.07.1983.
22. Katsev P. G. Statistical methods for cutting tool research. Moscow: Mashinostroenie, 1968. 156 p.
23. Pasko N. I., Antsev А. V. et. al. Generalized stochastic model of cutting tool failures and its application. Tula: Izdatelstvo TulGU, 2016. 174 p.
24. Pasko N. I., Antsev A. V., Antseva N. V., Fedorov V. P. The generalized mathematical model of the failure of the cutting tool. IOP Conf. Series: Materials Science and Engineering. 2017. Vol. 177. 012052.
25. Antsev A. V., Pasko N. I., Antseva N. V. Assessment of wear dependence parameters in complex model of cutting tool wear. IOP Conf. Series: Materials Science and Engineering. 2018. Vol. 327. 042005.

Language of full-text russian
Full content Buy
Back