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Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data

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Abstract  

It is difficult to measure the surface roughness of large-pitch internal threads; predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in the scope of use. The homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using the Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and radial basis function neural networks (RBF-NNs) were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. It was verified that the absolute error of the SVM model was less than 0.05 μm, and the relative error was less than 4% for turning both left and right threaded surfaces, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness is difficult to measure due to structural characteristics.

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References

  1. Mackenzie A (2015) The production of prediction: what does machine learning want? Eur J Cult Stud 18:429–445

    Article  Google Scholar 

  2. Chan TC, Lin HH, Reddy SVVS (2022) Prediction model of machining surface roughness for five-axis machine tool based on machine-tool structure performance. Int J Adv Manuf Tech 120:237–249

    Article  Google Scholar 

  3. Yang A, Han Y, Pan Y, Xing H, Li J (2017) Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results Phys 7:1046–1050

    Article  Google Scholar 

  4. Kong D, Zhu J, Duan C, Lu L, Chen D (2020) Bayesian linear regression for surface roughness prediction. Mech Syst Signal Pr 142:106770

    Article  Google Scholar 

  5. Vahabli E, Rahmati S (2016) Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. Int J Adv Manuf Tech 17:1589–1603

    Google Scholar 

  6. Majumder H, Paul TR, Dey V, Dutta P, Saha A (2017) Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement 107:19–30

    Article  Google Scholar 

  7. Frigieri EP, Brito TG, Ynoguti CA, Paiva AP, Ferreira JR, Balestrassi PP (2017) Pattern recognition in audible sound energy emissions of AISI 52100 hardened steel turning: a MFCC-based approach. Int J Adv Manuf Tech 88:1383–1392

    Article  Google Scholar 

  8. Plaza EG, López PN (2018) Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning. Mech Syst Signal Pr 98:634–651

    Article  Google Scholar 

  9. Arulraj JGA, Wins KLD, Raj A (2014) Artificial neural network assisted sensor fusion model for predicting surface roughness during hard turning of H13 steel with minimal cutting fluid application. Procedia Mater Sci 5:2338–2346

    Article  Google Scholar 

  10. Salgado DR, Cambero I, Marcelo A, Alonso FJ (2009) Surface roughness prediction based on the correlation between surface roughness and cutting vibrations in dry turning with TiN-coated carbide tools. Proc Inst Mech Eng, Part B 223:1193–1205

    Article  Google Scholar 

  11. Rosenfeld MG, Lin CR, Amara SG (1982) Calcitonin mRNA polymorphism: peptide switching associated with alternative RNA splicing events. P Natl Acad Sci 79:1717–1721

    Article  Google Scholar 

  12. Fu X, Li Z, Zheng M, Wang X, Li J (2019) Cutting stability and optimization of process parameters for the large-pitch screw high-feed turning. Proc Inst Mech Eng, Part C 233:6851–6865

    Article  Google Scholar 

  13. Urbanowicz RJ, Meeker M, La Cava W (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189–203

    Article  Google Scholar 

  14. Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech Syst Signal Pr 21:2280–2294

    Article  Google Scholar 

  15. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349:255–260

    Article  MathSciNet  MATH  Google Scholar 

  16. Sánchez-Fernández M, de-Prado-Cumplido M, Arenas-García J, Pérez-Cruz F (2004) SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE T Signal Proces 52:2298–2307

    Article  MathSciNet  MATH  Google Scholar 

  17. Shen W, Guo X, Wu C (2003) Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl-Based Syst 24:378–385

    Article  Google Scholar 

  18. Wang W, Xu Z, Lu W, Zhang X (2003) Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing 55:643–663

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the Harbin University of Science and Technology, People’s Republic of China, for providing funds and facilities under research grants KYYWF-0349 and 51575148 to conduct this research.

Funding

This work is supported by the Fundamental Research Fundation for Universities of Heilongjiang Province (KYYWF-0349) and the National Nature Science Foundation of China (51575148).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, People’s Republic of China

    Zhe Li, Xiaobo Qin, Xiangfu Fu, Bin Jiang, Weiqi Cong & Quanjian Zhang

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  1. Zhe Li
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  2. Xiaobo Qin
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  3. Xiangfu Fu
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  4. Bin Jiang
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  5. Weiqi Cong
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  6. Quanjian Zhang
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Contributions

Qin was responsible for the literature study, data analysis, and writing the paper. Li and Fu were the supervisors of this work. They proposed the research idea, technical scheme, and all needed support conditions. They also participated in the data analysis and were responsible for completing the article. Jiang was involved in the discussion and data analysis. Cong and Zhang were involved in the discussion and significantly contributed to making the final draft of the article. All the authors read and approved the final manuscript.

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Correspondence to Xiangfu Fu.

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Li, Z., Qin, X., Fu, X. et al. Prediction for surface roughness of the large-pitch internal thread based on homologous isomerism data. Int J Adv Manuf Technol 126, 2053–2064 (2023). https://doi.org/10.1007/s00170-023-11232-4

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  • DOI: https://doi.org/10.1007/s00170-023-11232-4

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