Evaluation of Tool Wear in Polymer Milling Process Using Non-Invasive Open-Source Monitoring System

Roger Nabeyama Michels; Janaína Fracaro de Souza Gonçalves; Mayther Freire Gimenez; Rafael Tanganini Boa Sorte; Elizabeth Mie Hashimoto

doi:10.5433/1679-0375.2024.v45.49800

Evaluation of Tool Wear in Polymer Milling Process Using Non-Invasive Open-Source Monitoring System

Authors

Roger Nabeyama Michels Universidade Tecnológica Federal do Paraná https://orcid.org/0000-0001-8031-4402
Janaína Fracaro de Souza Gonçalves Universidade Tecnológica Federal do Paraná https://orcid.org/0000-0002-1920-3591
Mayther Freire Gimenez Universidade Tecnológica Federal do Paraná https://orcid.org/0009-0005-0287-3192
Rafael Tanganini Boa Sorte Universidade Tecnológica Federal do Paraná https://orcid.org/0009-0007-4340-0206
Elizabeth Mie Hashimoto Universidade Tecnológica Federal do Paraná https://orcid.org/0000-0003-0949-2358

DOI:

https://doi.org/10.5433/1679-0375.2024.v45.49800

Keywords:

Cutting fluid, Arduino, Open source, Acrylic, PVC

Abstract

This study presents a non-invasive and affordable monitoring system to estimate the electrical current demand during the milling of acrylic and expanded PVC, with and without the use of cutting fluid. The proposed system consists of an Arduino® board integrated with an SCT-013-000 electrical current sensor, an RTC-DS3231 shield, and an SC Card shield, which perform the measurement and storage of the electrical current, values collected directly from the power cable of the Router Spindle TVS.1ZM3.12. Data were collected in a machinability test, where different concentrations of cutting fluid and cutting parameters (cutting speed and feed rate) were evaluated. Outputs included surface roughess, electrical current consumption, chip shape and tool wear. The results were statistically analyzed using analysis of variance (ANOVA), revealing that the different factors have individual effects on electrical current consumption. It was observed that tool wear contributed to an increase in the main motor’s consumption. Additionally, the reduction in electrical current consumption with the use of cutting fluid indicates a decrease in friction between the tool and the workpiece.

Author Biographies

Roger Nabeyama Michels, Universidade Tecnológica Federal do Paraná

Prof. Dr. Dept of Mechanical Engineering, UTFPR, Londrina, Paraná, Brazil

Janaína Fracaro de Souza Gonçalves, Universidade Tecnológica Federal do Paraná

Prof. Dr. Dept of Mechanical Engineering, UTFPR, Londrina, Paraná, Brazil.

Mayther Freire Gimenez, Universidade Tecnológica Federal do Paraná

Student. Dept of Mechanical Engineering, UTFPR, Londrina, Paraná, Brazil.

Rafael Tanganini Boa Sorte, Universidade Tecnológica Federal do Paraná

Student. Dept of Mechanical Engineering, UTFPR, Londrina, Paraná, Brazil.

Elizabeth Mie Hashimoto, Universidade Tecnológica Federal do Paraná

Prof. Dr. Dept of Mathematics, UTFPR, Londrina, Paraná, Brazil.

References

Campos, J. C. R., Panzera, T. H., & Scarpa, F. (2015). Machining behaviour of three high-performance engineering plastics. Proceedings of the Institution of Mechanical Engineers, Part B Journal of Engineering Manufacture, 229(1), 28–37.

Gnatowski, A., Golebski, R., Sikora, P., Petru, J., & Hajnys, J. (2023). Analysis of the impact of changes in thermomechanical properties of annealed semi-crystalline plastic on the surface condition after the machining process. Materials, 16(13), 1–16.

Hill, J. L., Prickett, P. W., Grosvenor, R. I., & Hankins, G. (2019). The practical exploitation of machine tool intelligence. The International Journal of Manufacturing Technology, 104, 1693–1707.

Kuntoğlu, M., Aslan, A., Pimenov, D. Y., Usca, Ü. A., Salur, E., Gupta, M. K., Mikolajczyk, T., Giasin, K., Kaplonek, W., & Sharma, S. (2021). A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors, 21(1), 1–32.

Low, K. O., & Wong, K. J. (2011). Influence of ball burnishing on surface quality and tribological characteristics of polymers under dry sliding conditions. Tribology International, 44(2), 144–153.

Montgomery, D. C. (2013). Design and analysis of experiments. John Wiley & Sons.

Patra, K., Pal, S. K., & Bhattacharyya, K. (2007). Artificial neural network based prediction of drill flank wear from motor current signals. Applied Soft Computing, 7(3), 929–935.

Prickett, P. W., & Johns, C. (1999). An overview of approaches to end milling tool monitoring. International Journal of Machine Tools & Manufacture, 39(1), 105–122.

Reñones, A., Rodríguez, J., & Miguel, L. J. d. (2010). Industrial application of a multitooth tool breakage detection system using spindle motor electrical power consumption. The International Journal of Advanced Manufacturing Technology, 46, 517–528.

Sales, W. F., Diniz, A. E., & Machado, Á. R. (2001). Application of cutting fluids in machining processes. Journal of the Brazilian Society of Mechanical Sciences, 23(2).

Valentim, M. d. M., Santos, A. L. d., Sichieri, V. J., & Gonçalves, J. F. d. S. (2023). Evaluation of Intergranular Corrosion in 7050-T7451 Aluminum Structures Influenced by Temperature and Contact with Cutting Fluid. Semina Ciências Exatas e Tecnológicas, 44, 47981.

Volpato, N., & Amorim, J. R. d. (2011). A procedure for dealing with milling limitations in machined prototype tooling. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(12), 2163–2176.

Wang, Q., Wang, H., Hou, L., & Ui, S. (2021). Overview of tool wear monitoring methods based on convolutional neural network. Applied Sciences, 11(24), 12041.

Xiao, K. Q., & Zhang, L. C. (2002). The role of viscous deformation in the machining of polymers. International Journal of Mechanical Sciences, 44(11), 2317–2336.

Yan, P., Wang, Y., Jin, X., Zhu, J., Jiao, L., Qiu, T., & Wang, X. (2022). Effect of cutting fluid on high strain rate dynamic mechanical property and cutting performance of nickel based superalloy. Journal of Materials Research and Technology, 17, 1146–1158.