Skip to main content
SpringerLink
Log in

Interpolation-based virtual sample generation for surface roughness prediction

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Surface roughness is an essential technical indicator for the surface quality of machined parts and significantly affects the service performance of the products. Accurate prediction of the surface roughness in the machining process can play an important role in reducing costs and increasing efficiency. However, data-based methods often require a large sample size for model training to improve prediction accuracy. Obtaining a sufficient number of training samples is challenging due to cost and efficiency constraints. To this end, an interpolation-based virtual sample generation scheme is proposed in this article, which utilizes a broad learning system (BLS) to generate virtual samples of the cutting groove surface roughness. Experimental verification was carried out in an ultra-precision machining center, where depth of cut and cutting speed were selected as inputs to the BLS. The results reveal that the proposed virtual sample generation approach can considerably improve the surface roughness prediction accuracy. Compared to other machine learning methods, BLS has the highest error reduction rate with and without virtual samples.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Benardos, P., & Vosniakos, G.-C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844.

    Article  Google Scholar 

  • Chaudhari, A., Soh, Z. Y., Wang, H., & Kumar, A. S. (2018). Rehbinder effect in ultraprecision machining of ductile materials. International Journal of Machine Tools and Manufacture, 133, 47–60.

    Article  Google Scholar 

  • Chen, C. P., & Liu, Z. (2017). Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 29(1), 10–24.

    Article  Google Scholar 

  • Chen, Y., Sun, R., Gao, Y., & Leopold, J. (2017). A nested-ann prediction model for surface roughness considering the effects of cutting forces and tool vibrations. Measurement, 98, 25–34.

    Article  Google Scholar 

  • Chen, J., & Zhao, Q. (2015). A model for predicting surface roughness in single-point diamond turning. Measurement, 69, 20–30.

    Article  Google Scholar 

  • Dong, M., & Wang, N. (2011). Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Applied Mathematical Modelling, 35(3), 1024–1035.

    Article  Google Scholar 

  • Gong, X., Zhang, T., Chen, C. P., & Liu, Z. (2021). Research review for broad learning system: Algorithms, theory, and applications. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2021.3061094.

    Article  Google Scholar 

  • Grzenda, M., & Bustillo, A. (2013). The evolutionary development of roughness prediction models. Applied Soft Computing, 13(5), 2913–2922.

    Article  Google Scholar 

  • Guo, J., Zhang, J., Wang, H., Liu, K., & Kumar, A. S. (2018). Surface quality characterisation of diamond cut v-groove structures made of rapidly solidified aluminium rsa-905. Precision Engineering, 53, 120–133.

    Article  Google Scholar 

  • He, Y.-L., Hua, Q., Zhu, Q.-X., & Lu, S. (2022). Enhanced virtual sample generation based on manifold features: Applications to developing soft sensor using small data. ISA Transactions, 126, 398–406.

    Article  Google Scholar 

  • Ho, W.-H., Tsai, J.-T., Lin, B.-T., & Chou, J.-H. (2009). Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid taguchi-genetic learning algorithm. Expert Systems with applications, 36(2), 3216–3222.

    Article  Google Scholar 

  • Huang, P. B., Zhang, H.-J., & Lin, Y.-C. (2019). Development of a grey online modeling surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 30(4), 1923–1936.

    Article  Google Scholar 

  • Kannadasan, K., Edla, D. R., Yadav, M. H., & Bablani, A. (2020). Intelligent-anfis model for predicting measurement of surface roughness and geometric tolerances in three-axis cnc milling. IEEE Transactions on Instrumentation and Measurement , 69(10), 7683–7694.

    Article  Google Scholar 

  • Kong, D., Zhu, J., Duan, C., Lu, L., & Chen, D. (2020). Bayesian linear regression for surface roughness prediction. Mechanical Systems and Signal Processing, 142, 106770.

    Article  Google Scholar 

  • Kong, D., Zhu, J., Duan, C., Lu, L., & Chen, D. (2021). Surface roughness prediction using kernel locality preserving projection and bayesian linear regression. Mechanical Systems and Signal Processing, 152, 107474.

    Article  Google Scholar 

  • Li, L., Damarla, S. K., Wang, Y., & Huang, B. (2021). A gaussian mixture model based virtual sample generation approach for small datasets in industrial processes. Information Sciences, 581, 262–277.

    Article  Google Scholar 

  • Lin, W., Lee, B., & Wu, C. (2001). Modeling the surface roughness and cutting force for turning. Journal of Materials Processing Technology, 108(3), 286–293.

    Article  Google Scholar 

  • Li, B., & Tian, X. (2021). An effective pso-lssvm-based approach for surface roughness prediction in high-speed precision milling. Ieee Access, 9, 80006–80014.

    Article  Google Scholar 

  • Liu, N., Wang, S., Zhang, Y., & Lu, W. (2016). A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling al-7075. International Journal of Mechanical Sciences, 118, 13–20.

    Article  Google Scholar 

  • Liu, L., Zhang, X., Wan, X., Zhou, S., & Gao, Z. (2022). Digital twin-driven surface roughness prediction and process parameter adaptive optimization. Advanced Engineering Informatics, 51, 101470.

    Article  Google Scholar 

  • Lo, S.-P. (2003). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 142(3), 665–675.

    Article  Google Scholar 

  • Lu, C. (2008). Study on prediction of surface quality in machining process. Journal of Materials Processing Technology, 205(1–3), 439–450.

    Article  Google Scholar 

  • Mahesh, G., Muthu, S., & Devadasan, S. (2015). Prediction of surface roughness of end milling operation using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 77(1), 369–381.

    Article  Google Scholar 

  • Manjunath, K., Tewary, S., Khatri, N., & Cheng, K. (2021). Monitoring and predicting the surface generation and surface roughness in ultraprecision machining: A critical review. Machines, 9(12), 369.

    Article  Google Scholar 

  • Özel, T., & Karpat, Y. (2005). Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture, 45(4–5), 467–479.

    Article  Google Scholar 

  • Pan, Y., Kang, R., Dong, Z., Du, W., Yin, S., & Bao, Y. (2020). On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning. Journal of Intelligent Manufacturing, 33, 675–685.

    Article  Google Scholar 

  • Plaza, E. G., & López, P. N. (2018). Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in cnc turning. Mechanical Systems and Signal Processing, 98, 634–651.

    Article  Google Scholar 

  • Plaza, E. G., & López, P. N. (2018). Application of the wavelet packet transform to vibration signals for surface roughness monitoring in cnc turning operations. Mechanical Systems and Signal Processing, 98, 902–919.

    Article  Google Scholar 

  • Rifai, A. P., Aoyama, H., Tho, N. H., Dawal, S. Z. M., & Masruroh, N. A. (2020). Evaluation of turned and milled surfaces roughness using convolutional neural network. Measurement, 161, 107860.

    Article  Google Scholar 

  • Salgado, D. R., Alonso, F., Cambero, I., & Marcelo, A. (2009). In-process surface roughness prediction system using cutting vibrations in turning. The International Journal of Advanced Manufacturing Technology, 43(1), 40–51.

    Article  Google Scholar 

  • Samanta, B. (2009). Surface roughness prediction in machining using soft computing. International Journal of Computer Integrated Manufacturing, 22(3), 257–266.

    Article  Google Scholar 

  • Sarkheyli, A., Zain, A. M., & Sharif, S. (2015). Robust optimization of anfis based on a new modified ga. Neurocomputing, 166, 357–366.

    Article  Google Scholar 

  • Sharkawy, A. B., El-Sharief, M. A., & Soliman, M.-E.S. (2014). Surface roughness prediction in end milling process using intelligent systems. International Journal of Machine Learning and Cybernetics, 5(1), 135–150.

    Article  Google Scholar 

  • Svalina, I., Šimunović, G., Šarić, T., & Lujić, R. (2017). Evolutionary neuro-fuzzy system for surface roughness evaluation. Applied Soft Computing, 52, 593–604.

    Article  Google Scholar 

  • Tian, W., Zhao, F., Min, C., Feng, X., Liu, R., Mei, X., & Chen, G. (2022). Broad learning system based on binary grey wolf optimization for surface roughness prediction in slot milling. IEEE Transactions on Instrumentation and Measurement, 71, 1–10.

    Google Scholar 

  • Tian, W., Zhao, F., Sun, Z., Zhang, J., Gong, C., Mei, X., et al. (2022). Prediction of surface roughness using fuzzy broad learning system based on feature selection. Journal of Manufacturing Systems, 64, 508–517.

    Article  Google Scholar 

  • Tsai, Y.-H., Chen, J. C., & Lou, S.-J. (1999). An in-process surface recognition system based on neural networks in end milling cutting operations. International Journal of Machine Tools and Manufacture, 39(4), 583–605.

    Article  Google Scholar 

  • Tseng, T.-L., Konada, U., & Kwon, Y. (2016). A novel approach to predict surface roughness in machining operations using fuzzy set theory. Journal of Computational Design and Engineering, 3(1), 1–13.

    Article  Google Scholar 

  • Wang, X., Liu, H., Li, L., & Zhang, Y. (2022). Dual adversarial learning-based virtual sample generation method for data expansion of soft senors. Measurement, 11198, 1409.

    Google Scholar 

  • Wang, Y., Niu, M., Liu, K., Shen, M., Qin, B., & Wang, H. (2022). A novel data augmentation method based on coralgan for prediction of part surface roughness. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2021.3137172.

    Article  Google Scholar 

  • Wang, S., Xia, S., Wang, H., Yin, Z., & Sun, Z. (2020). Prediction of surface roughness in diamond turning of al6061 with precipitation effect. Journal of Manufacturing Processes, 60, 292–298.

    Article  Google Scholar 

  • Wen, L., Li, X., Gao, L., & Yi, W. (2016). Surface roughness prediction in end milling by using predicted point oriented local linear estimation method. The International Journal of Advanced Manufacturing Technology, 84(9), 2523–2535.

    Article  Google Scholar 

  • Yu, W., Lu, Y., & Wang, J. (2021). Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis. Expert Systems with Applications, 183, 115397.

    Article  Google Scholar 

  • Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using artificial neural network. Expert Systems with Applications, 37(2), 1755–1768.

    Article  Google Scholar 

  • Zhang, Y., Lee, Y. J., Chang, S., Chen, Y., Bai, Y., Zhang, J., & Wang, H. (2022). Microstructural modulation of tial alloys for controlling ultra-precision machinability. International Journal of Machine Tools and Manufacture, 174, 103851.

    Article  Google Scholar 

  • Zhang, J., Lee, Y. J., & Wang, H. (2021). Surface texture transformation in micro-cutting of aa6061-t6 with the rehbinder effect. International Journal of Precision Engineering and Manufacturing-Green Technology, 8(4), 1151–1162.

    Article  Google Scholar 

  • Zhang, S., To, S., Wang, S., & Zhu, Z. (2015). A review of surface roughness generation in ultra-precision machining. International Journal of Machine Tools and Manufacture, 91, 76–95.

    Article  Google Scholar 

  • Zhu, Q.-X., Chen, Z.-S., Zhang, X.-H., Rajabifard, A., Xu, Y., & Chen, Y.-Q. (2020). Dealing with small sample size problems in process industry using virtual sample generation: A kriging-based approach. Soft Computing, 24(9), 6889–6902.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Mr. Nelson Yeo Eng Huat for his assistance in the experimental work.

Funding

This work was supported by the National Key Research and Development Program of China under grant number 2018AAA0101802, the Science and Technology Major Project of Shaanxi Province under grant number 2019ZDLGY01-05HZ, and the Singapore Ministry of Education under grant numbers MOE2018-T2-1-140, MOE-T2EP50120-0010.

Author information

Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Shaanxi Key Laboratory of Intelligent Robots, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Wenwen Tian, Fei Zhao, Xiaobing Feng & Xuesong Mei

  2. Department of Mechanical Engineering,College of Design and Engineering, National University of Singapore, Singapore, 117575, Singapore

    Jiong Zhang & Hao Wang

  3. School of Physics, Xi’an Jiaotong University, Xi’an, 710049, China

    Guangde Chen

Authors
  1. Wenwen Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaobing Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuesong Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guangde Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: Wenwen Tian, Hao Wang and Xuesong Mei; Resources: Hao Wang, Xuesong Mei; Data curation: Jiong Zhang, Fei Zhao; Methodology: Wenwen Tian, Jiong Zhang and Hao Wang; Formal analysis and investigation: Hao Wang, Xiaobing Feng and Guangde Chen; Writing - original draft preparation: Wenwen Tian, Jiong Zhang; Visualization and Software: Wenwen Tian, Jiong Zhang and Xiaobing Feng; Validation: Jiong Zhang, Fei Zhao; Writing - review and editing: Hao Wang, Fei Zhao; Funding acquisition: Fei Zhao, Hao Wang; Project administration: Xuesong Mei, Guangde Chen; Supervision: Hao Wang, Xuesong Mei and Guangde Chen. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xuesong Mei or Hao Wang.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest in the implementation of this work.

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for publication

The authors consent that the work entitled as “Interpolation-based virtual sample generation for surface roughness prediction” for possible publication in Journal of Intelligent Manufacturing. The authors certify that this manuscript is original and has not been published in whole or in part nor is it being considered for publication elsewhere.

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

Tian, W., Zhang, J., Zhao, F. et al. Interpolation-based virtual sample generation for surface roughness prediction. J Intell Manuf 35, 343–353 (2024). https://doi.org/10.1007/s10845-022-02054-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-022-02054-4

Keywords

Search

Navigation