Skip to main content
SpringerLink
Log in

Prediction of surface roughness of titanium alloy in abrasive waterjet machining process

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

Abstract

This work presents a comparison between methods for predicting surface roughness (Ra) of titanium alloy machined by low-pressure abrasive water jet machine developed in our laboratory. Artificial neural network (ANN), support vector machine (SVM) and regression analysis (RA) models were used to analyse the machining data. The work aims at the comparison and selection of the best prediction method based on the accuracy of the predicted surface roughness. An experiment was designed using full factorial experimental design. In the experiment, machining parameters of traverse speed (V), waterjet pressure (P) and standoff distance (h) were considered as model variables. The actual surface roughness values were collected based on the designed experiment. A feed forward back propagation neural network was used, structured as one input layer, one hidden layer with 5 hidden neurons and one output layer. Both ANN and SVM models were trained and analysis of variance was used. F-test was used to validate the RA model. The results showed that the proposed methods indicated an acceptable level of accuracy for predicting the surface roughness, however, ANN model had better accuracy than SVM and RA models as it produced lower relative errors between the predicted values and experimental results.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availability

All the data is available in the manuscript.

References

  1. Husin, H., Nawi, M.N.M., Gebremariam, M.A., Azhari, A.: Investigation on the effect of abrasive waterjet parameter on machining stainless steel. In: Osman Zahid, M.N., Aziz, R.A., Yusoff, A.R., Mat Yahya, N., Abdul Aziz, F., Yazid Abu, M. (eds.) IMEC-APCOMS 2019, pp. 544–549. Springer, Singapore (2020)

    Chapter  Google Scholar 

  2. Ginting, A., Nouari, M.: Surface integrity of dry machined titanium alloys. Int. J. Mach. Tools Manuf. 49, 325–332 (2009). https://doi.org/10.1016/j.ijmachtools.2008.10.011

    Article  Google Scholar 

  3. Mebrahitom, A., Rizuan, D., Azmir, M., Nassif, M.: Effect of High-speed Milling tool path strategies on the surface roughness of Stavax ESR mold insert machining. IOP Conf. Ser. Mater. Sci. Eng. 114, 12006 (2016). https://doi.org/10.1088/1757-899x/114/1/012006

    Article  Google Scholar 

  4. Seo, Y.W., Ramulu, M., Kim, D.: Machinability of titanium alloy (Ti–6Al–4V) by abrasive waterjets. Proc. Inst Mech. Eng. Part B J. Eng. Manuf. 217, 1709–1721 (2003). https://doi.org/10.1243/095440503772680631

    Article  Google Scholar 

  5. Mokhtar, N., Gebremariam, M.A., Zohari, H., Azhari, A.: Analysis of acoustic emission during abrasive waterjet machining of sheet metals. IOP Conf. Ser. Mater. Sci. Eng. 342, 12107 (2018). https://doi.org/10.1088/1757-899x/342/1/012107

    Article  Google Scholar 

  6. Saptaji, K., Gebremariam, M.A., Azhari, M.A.B.M.: Machining of biocompatible materials: a review. Int. J. Adv. Manuf. Technol. 97, 2255–2292 (2018). https://doi.org/10.1007/s00170-018-1973-2

    Article  Google Scholar 

  7. Smeets, R., Stadlinger, B., Schwarz, F., Beck-Broichsitter, B., Jung, O., Precht, C., Kloss, F., Gröbe, A., Heiland, M., Ebker, T.: Impact of dental implant surface modifications on osseointegration. Biomed. Res. Int. (2016). https://doi.org/10.1155/2016/6285620

    Article  Google Scholar 

  8. Martinez, M.A.F., Balderrama, Í.F., Karam, P.S.B.H., de Oliveira, R.C., de Oliveira, F.A., Grandini, C.R., Vicente, F.B., Stavropoulos, A., Zangrando, M.S.R., SantAna, A.C.P.: Surface roughness of titanium disks influences the adhesion, proliferation and differentiation of osteogenic properties derived from human. Int. J. Implant Dent. 6, 46 (2020). https://doi.org/10.1186/s40729-020-00243-5

    Article  Google Scholar 

  9. Zhao, W., Ren, F., Iqbal, A., Gong, L., He, N., Xu, Q.: Effect of liquid nitrogen cooling on surface integrity in cryogenic milling of Ti–6Al–4V titanium alloy. Int. J. Adv. Manuf. Technol. 106, 1497–1508 (2020). https://doi.org/10.1007/s00170-019-04721-y

    Article  Google Scholar 

  10. Saptaji, K., Triawan, F., Sai, T.K., Gebremariam, A.: Deburring method of aluminum mould produced by milling process for microfluidic device fabrication. Indones. J. Sci. Technol. 6, 123–140 (2021). https://doi.org/10.17509/ijost.v6i1.31852

    Article  Google Scholar 

  11. Hascalik, A., Çaydaş, U., Gürün, H.: Effect of traverse speed on abrasive waterjet machining of Ti–6Al–4V alloy. Mater. Des. 28, 1953–1957 (2007). https://doi.org/10.1016/j.matdes.2006.04.020

    Article  Google Scholar 

  12. Aydin, G., Karakurt, I., Hamzacebi, C.: Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. Int. J. Adv. Manuf. Technol. 75, 1321–1330 (2014). https://doi.org/10.1007/s00170-014-6211-y

    Article  Google Scholar 

  13. Murugan, M., Gebremariam, M.A., Hamedon, Z., Azhari, A.: Performance analysis of abrasive waterjet machining process at low pressure. IOP Conf. Ser. Mater. Sci. Eng. 319, 12051 (2018). https://doi.org/10.1088/1757-899x/319/1/012051

    Article  Google Scholar 

  14. Çaydaş, U., Hasçalik, A.: A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J. Mater. Process. Technol. 202, 574–582 (2008). https://doi.org/10.1016/j.jmatprotec.2007.10.024

    Article  Google Scholar 

  15. Ficko, M., Begic-Hajdarevic, D., Cohodar Husic, M., Berus, L., Cekic, A., Klancnik, S.: Prediction of surface roughness of an abrasive water jet cut using an artificial neural network. Materials (Basel) (2021). https://doi.org/10.3390/ma14113108

    Article  Google Scholar 

  16. Ozcelik, B., Oktem, H., Kurtaran, H.: Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int. J. Adv. Manuf. Technol. 27, 234–241 (2005). https://doi.org/10.1007/s00170-004-2175-7

    Article  Google Scholar 

  17. Asgary, A., Naini, A.S., Levy, J.: Intelligent security systems engineering for modeling fire critical incidents: towards sustainable security. J. Syst. Sci. Syst. Eng. 18, 477–488 (2009). https://doi.org/10.1007/s11518-009-5121-2

    Article  Google Scholar 

  18. Gupta, A.K.: Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int. J. Prod. Res. 48, 763–778 (2010). https://doi.org/10.1080/00207540802452132

    Article  Google Scholar 

  19. Gebremariam, M.A., Xiang Yuan, S., Azhari, A., Lemma, T.A.: Remaining Tool Life Prediction Based on Force Sensors Signal During End Milling of Stavax ESR Steel (2017). https://doi.org/10.1115/IMECE2017-70058.

  20. Benkedjouh, T., Medjaher, K., Zerhouni, N., Rechak, S.: Health assessment and life prediction of cutting tools based on support vector regression. J. Intell. Manuf. 26, 213–223 (2015). https://doi.org/10.1007/s10845-013-0774-6

    Article  Google Scholar 

  21. Kumaran, S.T., Ko, T.J., Uthayakumar, M., Islam, M.M.: Prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis. J. Alloys Compd. 724, 1037–1045 (2017). https://doi.org/10.1016/j.jallcom.2017.07.108

    Article  Google Scholar 

  22. Tosun, N., Cogun, C.: An investigation on wire wear in WEDM. J. Mater. Process. Technol. 134, 273–278 (2003). https://doi.org/10.1016/S0924-0136(02)01045-2

    Article  Google Scholar 

Download references

Funding

This research was supported by research grant RDU1901161- FRGS/1/2019/TK03/UMP/02/25 from Ministry of Higher Education of Malaysia and Universiti Malaysia Pahang, Malaysia-

Author information

Authors and Affiliations

  1. Industrial Engineering Department, College of Engineering, Universiti Malaysia Pahang, Gambang, Malaysia

    Ho Yi Ting & Mebrahitom Asmelash

  2. Manufacturing Engineering Technology Department, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Gambang, Malaysia

    Azmir Azhari

  3. Mechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Tamiru Alemu

  4. Department of Mechanical Engineering, Faculty of Engineering and Technology, Sampoerna University, Jakarta, Indonesia

    Kushendarsyah Saptaji

Authors
  1. Ho Yi Ting
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mebrahitom Asmelash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azmir Azhari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tamiru Alemu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kushendarsyah Saptaji
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ho Yi Ting developed the methods, analysed and drafted the manuscript; Mebrahitom Asmelash as supervisor supported in shaping the research methodology and refined the manuscript; finally, Azmir Azhari, Kushendarsyah Saptaji and Tamiru Alemu as research associates, reviewed the paper.

Corresponding author

Correspondence to Mebrahitom Asmelash.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest, all authors consent to participate in this research and contribute and publish the research. There are no potential copyright/plagiarism issues involved in this research.

Human and animal rights

The article adheres to the guidelines of the Committee on Publication Ethics (COPE) and involves no studies on humans or animals.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ting, H.Y., Asmelash, M., Azhari, A. et al. Prediction of surface roughness of titanium alloy in abrasive waterjet machining process. Int J Interact Des Manuf 16, 281–289 (2022). https://doi.org/10.1007/s12008-021-00830-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-021-00830-9

Keywords

Search

Navigation