Skip to main content
SpringerLink
Log in

Prediction of Formation Quality of Inconel 625 Clads Using Support Vector Regression

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

The process parameters of pulsed tungsten inert gas (PTIG) have a significant influence on the formation quality, mechanical properties and corrosion resistance of the weld overlay. The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters, which were determined by the central composite design (CCD) method. Based on the experimental results, the relationship between process parameters of PTIG and formation quality of the Inconel 625 clads was established using support vector regression (SVR) with different kernel functions, including polynomial kernel function, radial basis function (RBF) kernel function, and sigmoid kernel function. The results indicate that the kernel functions have a great influence on the prediction of height, width and dilution. The models with RBF kernel function feature the best goodness of fitting and the most accurate against the other SVR models for estimating the height and the dilution. However, the model with polynomial kernel function is superior to the other SVR models for predicting the width. Meanwhile, the prediction performance of the SVR models was compared with the general regression analysis. The results demonstrate that the optimized SVR model is much better than the general regression model in the prediction performance.

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

Similar content being viewed by others

References

  1. SILVA C C, DE MIRANDA H C, MOTTA M F, et al. New insight on the solidification path of an alloy 625 weld overlay [J]. Journal of Materials Research and Technology, 2013, 2(3): 228–237.

    Article  Google Scholar 

  2. GUO L L, ZHENG H L, LI Y Q, et al. Microstructure and performance of Inconel 625 cladding deposited by hot wire pulsed TIG [J]. China Surface Engineering, 2016, 29(2): 77–84 (in Chinese).

    Google Scholar 

  3. DINDA G P, DASGUPTA A K, MAZUMDER J. Laser aided direct metal deposition of Inconel 625 super-alloy: Microstructural evolution and thermal stability [J]. Materials Science and Engineering: A, 2009, 509(1/2): 98–104.

    Article  Google Scholar 

  4. XING X X, DI X J, WANG B S. The effect of post-weld heat treatment temperature on the microstructure of Inconel 625 deposited metal [J]. Journal of Alloys and Compounds, 2014, 593: 110–116.

    Article  Google Scholar 

  5. GUO L L, ZHENG H L, LIU S H, et al. Formation quality optimization and corrosion performance of Inconel 625 weld overlay using hot wire pulsed TIG [J]. Rare Metal Materials and Engineering, 2016, 45(9): 2219–2226.

    Article  Google Scholar 

  6. ABIOYE T E, MCCARTNEY D G, CLARE A T. Laser cladding of Inconel 625 wire for corrosion protection [J]. Journal of Materials Processing Technology, 2015, 217: 232–240.

    Article  Google Scholar 

  7. RAJANI H R Z, MOUSAVI S A A A, SANI F M. Comparison of corrosion behavior between fusion cladded and explosive cladded Inconel 625/plain carbon steel bimetal plates [J]. Materials and Design, 2013, 43: 467–474.

    Article  Google Scholar 

  8. KIM J S, PARK Y I, LEE H W. Effects of heat input on the pitting resistance of Inconel 625 welds by overlay welding [J]. Metals and Materials International, 2015, 21(2): 350–355.

    Article  Google Scholar 

  9. DUPONT J N. Solidification of an Alloy 625 weld overlay [J]. Metallurgical and Materials Transactions A, 1996, 27(11): 3612–3620.

    Article  Google Scholar 

  10. ABIOYETE, FOLKES J, CLAREAT. A parametric study of Inconel 625 wire laser deposition [J]. Journal of Materials Processing Technology, 2013, 213(12): 2145–2151.

    Article  Google Scholar 

  11. KIM J S, LEE H W. A study on effect of intergranular corrosion by heat input on Inconel 625 overlay weld metal [J]. International Journal of Electrochemical Science, 2015, 10(8): 6454–6464.

    Google Scholar 

  12. LIANG E B, HU S S, WANG Z J. Optimization of GTAW cladding process of Inconel 625 on carbon steel using response surface methodology [J]. Transactions of the China Welding Institution, 2016, 37(6): 85–88 (in Chinese).

    Google Scholar 

  13. KUMAR T S, BALASUBRAMANIAN V, SANAVULLAH M Y. Influences of pulsed current tungsten inert gas welding parameters on the tensile properties of AA 6061 aluminium alloy [J]. Materials and Design, 2007, 28(7): 2080–2092.

    Article  Google Scholar 

  14. GUO L L, HE Y T, JU L Y, et al. Progress in additive manufacturing technique based on pulsed TIG [J]. Journal of Materials Engineering, 2018, 46(12): 10–17 (in Chinese).

    Google Scholar 

  15. MADADI F, ASHRAFIZADEH F, SHAMANIAN M. Optimization of pulsed TIG cladding process of stellite alloy on carbon steel using RSM [J]. Journal of Alloys and Compounds, 2012, 510(1): 71–77.

    Article  Google Scholar 

  16. GHOSH P K, KUMAR R. Surface modification of micro-alloyed high-strength low-alloy steel by controlled TIG arcing process [J]. Metallurgical and Materials Transactions A, 2015, 46(2): 831–842.

    Article  Google Scholar 

  17. GUO L L, ZHENG H L, LIU S H, et al. Effect of heat treatment temperatures on microstructure and corrosion properties of Inconel 625 weld overlay deposited by PTIG [J]. International Journal of Electrochemical Science, 2016, 11(7): 5507–5519.

    Article  Google Scholar 

  18. QI B J, YANG M X, CONG B Q, et al. The effect of arc behavior on weld geometry by high-frequency pulse GTAW process with 0Cr18Ni9Ti stainless steel [J]. The International Journal of Advanced Manufacturing Technology, 2013, 66: 1545–1553.

    Article  Google Scholar 

  19. WEN P, FENG Z H, ZHENG S Q. Formation quality optimization of laser hot wire cladding for repairing martensite precipitation hardening stainless steel [J]. Optics and Laser Technology, 2015, 65: 180–188.

    Article  Google Scholar 

  20. SHAHI A S, PANDEY S. Modelling of the effects of welding conditions on dilution of stainless steel claddings produced by gas metal arc welding procedures [J]. Journal of Materials Processing Technology, 2008, 196(1/2/3): 339–344.

    Article  Google Scholar 

  21. PETKOVIC D. Prediction of laser welding quality by computational intelligence approaches [J]. Optik, 2017, 140: 597–600.

    Article  Google Scholar 

  22. ALAMANIOTIS M, MATHEW J, CHRONEOS A, et al. Probabilistic kernel machines for predictive monitoring of weld residual stress in energy systems [J]. Engineering Applications of Artificial Intelligence, 2018, 71: 138–154.

    Article  Google Scholar 

  23. ZHANG Z F, ZHONG J Y, CHEN Y X, et al. Feature extraction and modeling of welding quality monitoring in pulsed gas touch argon welding based on arc voltage signal [J]. Journal of Shanghai Jiao Tong University (Science), 2014, 19(1): 11–16.

    Article  Google Scholar 

  24. YOUSEFIEH M, SHAMANIAN M, SAATCHI A. Optimization of experimental conditions of the pulsed current GTAW parameters for mechanical properties of SDSS UNS S32760 welds based on the Taguchi design method [J]. Journal of Materials Engineering and Performance, 2012, 21(9): 1978–1988.

    Article  Google Scholar 

  25. GIRIDHARAN P K, MURUGAN N. Optimization of pulsed GTA welding process parameters for the welding of AISI 304L stainless steel sheets [J]. The International Journal of Advanced Manufacturing Technology, 2009, 40: 478–489.

    Article  Google Scholar 

  26. BABU S, KUMAR T S, BALASUBRAMANIAN V. Optimizing pulsed current gas tungsten arc welding parameters of AA6061 aluminium alloy using Hooke and Jeeves algorithm [J]. Transactions of Nonferrous Metals Society of China, 2008, 18(5):1028–1036.

    Article  Google Scholar 

  27. GOMES J H F, PAIVA A P, COSTA S C, et al. Weighted Multivariate Mean Square Error for processes optimization: A case study on flux-cored arc welding for stainless steel claddings [J]. European Journal of Operational Research, 2013, 226(3): 522–535.

    Article  Google Scholar 

  28. DAS B, PAL S, BAG S. Torque based defect detection and weld quality modelling in friction stir welding process [J]. Journal of Manufacturing Processes, 2017, 27: 8–17.

    Article  Google Scholar 

  29. HUANG X X, SHI F H, GU W, et al. SVM-based fuzzy rules acquisition system for pulsed GTAW process [J]. Engineering Applications of Artificial Intelligence, 2009, 22(8): 1245–1255.

    Article  Google Scholar 

  30. DAS B, PAL S, BAG S. A combined wavelet packet and Hilbert-Huang transform for defect detection and modelling of weld strength in friction stir welding process [J]. Journal of Manufacturing Processes, 2016, 22: 260–268.

    Article  Google Scholar 

  31. KOO Y D, YOO K H, NA M G. Estimation of residual stress in welding of dissimilar metals at nuclear power plants using cascaded support vector regression [J]. Nuclear Engineering and Technology, 2017, 49(4): 817–824.

    Article  Google Scholar 

  32. CORTES C, VAPNIK V. Support-vector networks [J]. Machine Learning, 1995, 20(3): 273–297.

    MATH  Google Scholar 

  33. EDWIN RAJA DHAS J, KUMANAN S. Evolutionary fuzzy SVR modeling of weld residual stress [J]. Applied Soft Computing, 2016, 42: 423–430.

    Article  Google Scholar 

  34. MA H B, WEI S C, LIN T, et al. Mixed logical dynamical model for back bead width prediction of pulsed GTAW process with misalignment [J]. Journal of Materials Processing Technology, 2010, 210(14): 2036–2044.

    Article  Google Scholar 

  35. PAI P F, HONG W C. Software reliability forecasting by support vector machines with simulated annealing algorithms [J]. Journal of Systems and Software, 2006, 79(6): 747–755.

    Article  Google Scholar 

  36. PAI P F. System reliability forecasting by support vector machines with genetic algorithms [J]. Mathematical and Computer Modelling, 2006, 43(3/4): 262–274.

    Article  MathSciNet  Google Scholar 

  37. KEERTHI S S, LIN C J. Asymptotic behaviors of support vector machines with Gaussian kernel [J]. Neural Computation, 2003, 15(7): 1667–1689.

    Article  Google Scholar 

  38. EL-ABBASY M S, SENOUCI A, ZAYED T, et al. Artificial neural network models for predicting condition of offshore oil and gas pipelines [J]. Automation in Construction, 2014, 45: 50–65.

    Article  Google Scholar 

  39. KARTHIKEYAN R, BALASUBRAMANIAN V. Predictions of the optimized friction stir spot welding process parameters for joining AA2024 aluminum alloy using RSM [J]. The International Journal of Advanced Manufacturing Technology, 2010, 51: 173–183.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering College, Xi’an Shiyou University, Xi’an, 710065, China

    Longlong Guo  (郭龙龙), Zebing Wu  (吴泽兵), Yutian He  (贺雨田), Wenlan Wei  (魏文澜), Shengyong Xia  (夏胜勇), Luyan Ju  (鞠录岩) & Yong Zhang  (张勇)

  2. Chongqing Engineering Technology Research Center for Light Alloy Materials and Processing, Chongqing, 404000, China

    Longlong Guo  (郭龙龙)

  3. Refining and Chemical Plant of Yumen Oilfield Company, Jiuquan, Gansu, 735000, China

    Bo Wang  (王博)

Authors
  1. Longlong Guo  (郭龙龙)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zebing Wu  (吴泽兵)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yutian He  (贺雨田)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenlan Wei  (魏文澜)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shengyong Xia  (夏胜勇)
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luyan Ju  (鞠录岩)
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bo Wang  (王博)
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yong Zhang  (张勇)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Longlong Guo  (郭龙龙).

Additional information

Foundation item: the Natural Science Basic Research Plan in Shaanxi Province of China (Nos. 2020JQ-780 and 2017JQ5106), the Open Foundation of Chongqing Engineering Technology Research Center for Light Alloy Materials and Processing (No. GCZX202001), and the Young Teacher Research Project of Xi’an Shiyou University (No. 0104-134010025)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, L., Wu, Z., He, Y. et al. Prediction of Formation Quality of Inconel 625 Clads Using Support Vector Regression. J. Shanghai Jiaotong Univ. (Sci.) 25, 746–754 (2020). https://doi.org/10.1007/s12204-020-2225-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-020-2225-9

Key words

CLC number

Document code

Search

Navigation