SpringerLink
Log in

Artifical intelligence inspired approach to numerically investigate chip morphology in machining AISI630

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

This study examines AISI630 steel to gain insight into the chip morphology. Chip morphology provides useful information about the plastic shear deformation involved in the machining process. The presented study aims to integrate the innovative artificial intelligence driven method to efficiently analyse the saw-tooth chip geometry. The paper used an artificial intelligence inspired algorithm based on the image processing of segmented chips that quantifies various output responses, including peak, valley, pitch, chip segmentation ratio, chip compression ratio, and shear angle. In order to generate chips related data, a finite element-based machining model was prepared for the stainless steel AISI630 workpiece. The performance of data collected using artificial intelligence (AI) inspired technique has been re-evaluated using manual image processing tool Image J software. The peaks and valleys of the chip’s morphology are used to calculate the chip segmentation ratio. The study showed valleys can introduce average percentage errors of 7.3%. The shear angle is computed using the chip compression ratio, with an average percentage error of 2.4%. In addition, pitch values obtained from chip morphology have a percentage error of 5.2%.

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
EUR 32.99 /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 includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bermingham, M.J., Palanisamy, S., Kent, D., Dargusch, M.S.: A comparison of cryogenic and high pressure emulsion cooling technologies on tool life and chip morphology in Ti-6Al-4V cutting. J. Mater. Process. Technol. 212(4), 752–765 (2012). https://doi.org/10.1016/j.jmatprotec.2011.10.027

    Article  Google Scholar 

  2. Yildirim, Ã.V., Kivak, T., Sarikaya, M., Şirin, Å.: Evaluation of tool wear, surface roughness/topography and chip morphology when machining of Ni-based alloy 625 under MQL, cryogenic cooling and CryoMQL. J. Mater. Res. Technol. 9(2), 2079–2092 (2020). https://doi.org/10.1016/j.jmrt.2019.12.069

    Article  Google Scholar 

  3. Saad Elmunafi, M.H., Noordin, M.Y., Elshwain, A.E., Kurniawan, D.: Influence of cutting condition on chip morphology when turning hardened stainless steel using coated carbide cutting tools under minimum quantity of lubrication. AIP Conf. Proc. vol. 2262, (2020). https://doi.org/10.1063/5.0015828

  4. Mohan, R., Harshavardhana, N., Chaudhari, M., Jeyanthi, S., Abimannan, G.: “Analysis on surface finish and chip morphology during dry turning process,” in Materials Today: Proceedings, vol. 46, pp. 999–1002, doi: (2021). https://doi.org/10.1016/j.matpr.2021.01.137

  5. Sampaio, M.A., Machado, Ã.R., Laurindo, C.A.H., Torres, R.D., Amorim, F.L.: Influence of minimum quantity of lubrication (MQL) when turning hardened SAE 1045 steel: A comparison with dry machining. Int. J. Adv. Manuf. Technol. 98, 1–4 (2018). https://doi.org/10.1007/s00170-018-2342-x

    Article  Google Scholar 

  6. Cagan, S.C., Venkatesh, B., Buldum, B.B.: “Investigation of surface roughness and chip morphology of aluminum alloy in dry and minimum quantity lubrication machining,” Mater. Today Proc, vol. 27, no. xxxx, pp. 1122–1126, doi: (2020). https://doi.org/10.1016/j.matpr.2020.01.547

  7. Behera, B.C., Alemayehu, H., Ghosh, S., Rao, P.V.: A comparative study of recent lubri-coolant strategies for turning of Ni-based superalloy. J. Manuf. Process. 30, 541–552 (2017). https://doi.org/10.1016/j.jmapro.2017.10.027

    Article  Google Scholar 

  8. Sivaiah, P., Chakradhar, D.: Effect of cryogenic coolant on turning performance characteristics during machining of 17 – 4 PH stainless steel: A comparison with MQL, wet, dry machining. CIRP J. Manuf. Sci. Technol. 21, 86–96 (2018). https://doi.org/10.1016/j.cirpj.2018.02.004

    Article  Google Scholar 

  9. **uli, F., Wenxing, L., Yongzhi, P., Wentao, L.: Morphology evolution and micro-mechanism of chip formation during high-speed machining. Int. J. Adv. Manuf. Technol. 98, 1–4 (2018). https://doi.org/10.1007/s00170-017-0411-1

    Article  Google Scholar 

  10. Li, G., Cai, Y., Qi, H.: Prediction of the critical cutting conditions of serrated chip in high speed machining based on linear stability analysis. Int. J. Adv. Manuf. Technol. 94, 1–4 (2018). https://doi.org/10.1007/s00170-017-0958-x

    Article  Google Scholar 

  11. Li, G., Smith, J., Liu, W.K.: Finite element simulation of saw-tooth chip in high-speed machining based on multiresolution continuum theory. Int. J. Adv. Manuf. Technol. 101, 5–8 (2019). https://doi.org/10.1007/s00170-018-3078-3

    Article  Google Scholar 

  12. Chandra Behera, B., Sudarsan Ghosh, C., Paruchuri, V.R.: Study of saw-tooth chip in machining of Inconel 718 by metallographic technique. Mach. Sci. Technol. 23(3), 431–454 (2019). https://doi.org/10.1080/10910344.2019.1575397

    Article  Google Scholar 

  13. Chen, X., Tang, J., Ding, H., Liu, A.: A new geometric model of serrated chip formation in high-speed machining. J. Manuf. Process. vol 62(no February 2020), 632–645 (2021). https://doi.org/10.1016/j.jmapro.2020.12.053

    Article  Google Scholar 

  14. Xu, Z., Zheng, G., Cheng, X., Xu, R., Zhao, G., Tian, Y.: Fractal characteristics of chip morphology and Tool. Mater. (Basel). no. 13(4), 1020 (2020)

    Article  Google Scholar 

  15. Singh, B.K., Roy, H., Mondal, B., Roy, S.S., Mandal, N.: Measurement of chip morphology and multi criteria optimization of turning parameters for machining of AISI 4340 steel using Y-ZTA cutting insert. Measurement. no. 142, 181–194 (2019). https://doi.org/10.1016/j.measurement.2019.04.064

    Article  Google Scholar 

  16. Devotta, A.M., Sivaprasad, P.V., Beno, T., Eynian, M.: Predicting continuous chip to segmented chip transition in orthogonal cutting of C45E steel through damage modeling. Metal (Basel) 10(4), 789 (2020). https://doi.org/10.3390/met10040519

    Article  Google Scholar 

  17. Devotta, A., Beno, T., Löf, R., Espes, E.: Quantitative characterization of chip morphology using computed tomography in orthogonal turning process. Procedia CIRP. no. 33, 299–304 (2015). https://doi.org/10.1016/j.procir.2015.06.053

    Article  Google Scholar 

  18. Pimenov, D.Y., Bustillo, A., Wojciechowski, S., Sharma, V.S., Gupta, M.K., Kuntoğlu, M.: Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review. J. Intell. Manuf. no. (2022). https://doi.org/10.1007/s10845-022-01923-2

    Article  Google Scholar 

  19. Abbas, A.T., Pimenov, D.Y., Erdakov, I.N., Taha, M.A., Rayesel, M.M., Soliman, M.S.: Artificial intelligence monitoring of hardening methods and cutting conditions and their effects on surface roughness, performance, and finish turning costs of solid-state recycled aluminum alloy 6061 chips. Metal (Basel) 8(6), 214 (2018). https://doi.org/10.3390/met8060394

    Article  Google Scholar 

  20. Ngerntong, S., Butdee, S.: “Surface roughness prediction with chip morphology using fuzzy logic on milling machine,” Mater. Today Proc, vol. 26, no. xxxx, pp. 2357–2362, doi: (2019). https://doi.org/10.1016/j.matpr.2020.02.506

  21. Hrechuk, A., Bushlya, V., M’Saoubi, R., Ståhl, J.E.: Quantitative analysis of chip segmentation in machining using an automated image processing method. Procedia CIRP. no. 82, 314–319 (2019). https://doi.org/10.1016/j.procir.2019.03.272

    Article  Google Scholar 

  22. ThirdWaveSystems: Third Wave AdvantEdgeTM User’s Manual Version 7.3. (2017)

  23. Man, X., Ren, D., Usui, S., Johnson, C., Marusich, T.D.: Validation of finite element cutting force prediction for end milling. Procedia CIRP. no. 1(1), 663–668 (2012). https://doi.org/10.1016/j.procir.2012.04.119

    Article  Google Scholar 

  24. Maranhão, C., Davim, J.P.: Finite element modelling of machining of AISI 316 steel : Numerical simulation and experimental validation. Simul. Model. Pract. Theory. no. 18(2), 139–156 (2010). https://doi.org/10.1016/j.simpat.2009.10.001

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thank the support offered by Third Wave System by providing complementary research license for the AdvantEdge software.

Author information

Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Rochester Institute of Technology - Dubai Campus, P.O. Box 341055, Dubai, United Arab Emirates

    Shafahat Ali, Assem Alshibi, Abdelrahman Nasreldin & Salman Pervaiz

Authors
  1. Shafahat Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Assem Alshibi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelrahman Nasreldin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Salman Pervaiz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salman Pervaiz.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, S., Alshibi, A., Nasreldin, A. et al. Artifical intelligence inspired approach to numerically investigate chip morphology in machining AISI630. Int J Interact Des Manuf (2023). https://doi.org/10.1007/s12008-023-01340-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12008-023-01340-6

Keywords

Search

Navigation