Published January 19, 2023 | Version v1
Journal article Open

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

Creators

  • 1. Hanoi University of Industry

Description

With modern production, Minimum Quantity Lubricant (MQL) technology has emerged as an alternative to conventional liquid cooling. The MQLs is an environmentally friendly lubricant method with low cost while meeting the requirements of machining conditions. In this study, the experimental and analytical results show that the obtained acoustic emission (AE) and vibration signal components can effectively monitor various circumstances in the SCM440 steel turning process with MQL, such as surface quality and chip formation as cutting tool conditions. The AE signals showed a significant response to the tool wear processes. In contrast, the vibration signal showed an excellent ability to reflect the surface roughness during turning with MQL. The chip formation process through the cutting mode parameters (cutting speed, feed and depth of cut) was detected through analysis amplitude of the vibration components Ax, Ay and Az and the AE signal. Finally, Gaussian process regression and adaptive neuro-fuzzy inference systems (GPR-ANFIS) algorithms were combined to predict the surface quality and tool wear parameters of the MQL turning process. Tool condition monitoring devices assist the operator in monitoring tool wear and surface quality limits, stopping the machine in case of imminent tool breakage or lower surface quality. With the unique combination of AE and vibration analysis model and the training and testing samples established by the experimental data, the corresponding average prediction accuracy is 97.57 %. The highest prediction error is not more than 3.8 %, with a confidence percentage of 98 %. The proposed model can be used in industry to predict surface roughness and wear of the tools directly during turning

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References

  • Zhang, N., Komoda, R., Yamada, K., Kubota, M., Staykov, A. (2022). Ammonia mitigation and induction effects on hydrogen environment embrittlement of SCM440 low-alloy steel. International Journal of Hydrogen Energy, 47 (33), 15084–15093. doi: https://doi.org/10.1016/j.ijhydene.2022.03.006
  • Kwak, J.-S., Sim, S.-B., Jeong, Y.-D. (2006). An analysis of grinding power and surface roughness in external cylindrical grinding of hardened SCM440 steel using the response surface method. International Journal of Machine Tools and Manufacture, 46 (3-4), 304–312. doi: https://doi.org/10.1016/j.ijmachtools.2005.05.019
  • Thien, N. V., Trung, D. D. (2021). Study on model for cutting force when milling SCM440 steel. EUREKA: Physics and Engineering, 5, 23–35. doi: https://doi.org/10.21303/2461-4262.2021.001743
  • Tazoe, K., Hamada, S., Noguchi, H. (2017). Fatigue crack growth behavior of JIS SCM440 steel near fatigue threshold in 9-MPa hydrogen gas environment. International Journal of Hydrogen Energy, 42 (18), 13158–13170. doi: https://doi.org/10.1016/j.ijhydene.2017.03.223
  • Chen, C.-C., Liu, N.-M., Chiang, K.-T., Chen, H.-L. (2012). Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis. The International Journal of Advanced Manufacturing Technology, 63 (5-8), 797–815. doi: https://doi.org/10.1007/s00170-012-3943-4
  • Thirumalai, R., Srinivas, S., Vinodh, T., Kowshik Kumar, A. L., Kumar, M. K. (2014). Optimization of Surface Roughness and Flank Wear in Turning SCM440 Alloy Steel Using Taguchi Method. Applied Mechanics and Materials, 592-594, 641–646. doi: https://doi.org/10.4028/www.scientific.net/amm.592-594.641
  • Thamizhmanii, S., Hasan, S. (2009). Effect of tool wear and forces by turning process on hard AISI 440 C and SCM 440 materials. International Journal of Material Forming, 2 (S1), 531–534. doi: https://doi.org/10.1007/s12289-009-0429-5
  • Jeong, J.-I., Kim, J.-H., Choi, S.-G., Cho, Y. T., Kim, C.-K., Lee, H. (2021). Mechanical Properties of White Metal on SCM440 Alloy Steel by Laser Cladding Treatment. Applied Sciences, 11 (6), 2836. doi: https://doi.org/10.3390/app11062836
  • Kong, Y. S., Cheepu, M., Lee, J.-K. (2021). Evaluation of the mechanical properties of Inconel 718 to SCM 440 dissimilar friction welding through real-time monitoring of the acoustic emission system. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 235 (5), 1181–1190. doi: https://doi.org/10.1177/1464420721993838
  • Furuya, Y., Matsuoka, S., Abe, T. (2003). A novel inclusion inspection method employing 20 kHz fatigue testing. Metallurgical and Materials Transactions A, 34 (11), 2517–2526. doi: https://doi.org/10.1007/s11661-003-0011-6
  • Panda, D., Kumari, K., Dalai, N. (2022). Performance of Minimum Quantity Lubrication (MQL) and its effect on Dry Machining with the addition of Nano-particle with the biodegradable base fluids: A review. Materials Today: Proceedings, 56, 1298–1301. doi: https://doi.org/10.1016/j.matpr.2021.11.275
  • Gaurav, G., Sharma, A., Dangayach, G. S., Meena, M. L. (2020). Assessment of jojoba as a pure and nano-fluid base oil in minimum quantity lubrication (MQL) hard-turning of Ti–6Al–4V: A step towards sustainable machining. Journal of Cleaner Production, 272, 122553. doi: https://doi.org/10.1016/j.jclepro.2020.122553
  • Özbek, N. A., Çiçek, A., Gülesin, M., Özbek, O. (2016). Effect of cutting conditions on wear performance of cryogenically treated tungsten carbide inserts in dry turning of stainless steel. Tribology International, 94, 223–233. doi: https://doi.org/10.1016/j.triboint.2015.08.024
  • Tran, N.-H., Park, H.-S., Nguyen, Q.-V., Hoang, T.-D. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9 (16), 3325. doi: https://doi.org/10.3390/app9163325
  • Hozdić, E. (2015). Smart factory for industry 4.0: A review. Journal of Modern Manufacturing Systems and Technology, 7 (1), 28–35. Available at: https://www.researchgate.net/publication/282791888_Smart_factory_for_industry_40_A_review
  • Usca, Ü. A., Uzun, M., Şap, S., Kuntoğlu, M., Giasin, K., Pimenov, D. Y., Wojciechowski, S. (2022). Tool wear, surface roughness, cutting temperature and chips morphology evaluation of Al/TiN coated carbide cutting tools in milling of Cu–B–CrC based ceramic matrix composites. Journal of Materials Research and Technology, 16, 1243–1259. doi: https://doi.org/10.1016/j.jmrt.2021.12.063
  • Wu, Q., Chen, G., Liu, Q., Pan, B., Chen, W. (2022). Investigation on the Micro Cutting Mechanism and Surface Topography Generation in Ultraprecision Diamond Turning. Micromachines, 13 (3), 381. doi: https://doi.org/10.3390/mi13030381
  • Orhan, S., Er, A. O., Camuşcu, N., Aslan, E. (2007). Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT & E International, 40 (2), 121–126. doi: https://doi.org/10.1016/j.ndteint.2006.09.006
  • Nguyen, D., Yin, S., Tang, Q., Son, P. X., Duc, L. A. (2019). Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precision Engineering, 55, 275–292. doi: https://doi.org/10.1016/j.precisioneng.2018.09.018