Skip to main content
SpringerLink
Log in

Monitoring the forming dimensions of components produced by arc-directed energy deposition based on a molten pool’s geometric characteristics

  • Research Paper
  • Published:
Welding in the World Aims and scope Submit manuscript

Abstract

A deposition size monitoring method based on a molten pool’s geometric characteristics, including image calibration, contour extraction, contour coordinate calculation, and deposition size modeling, is established in this study to observe the real-time size of components formed by arc-directed energy deposition (ADED). Image calibration was first performed using the molten pool’s imaging transformation matrix to accurately match the image’s pixels to the actual spatial position. Next, an adaptive threshold segmentation algorithm was applied to extract the contours of the calibrated molten pool’s images. The contour was further completed using breakpoint coordinates and their gradient information. The minimum bounding rectangle algorithm was used to fit the contour coordinates for determining the width and height of a single-pass deposition. A mathematical multi-pass and multi-layer depositional size model was created via the parabolic model and the equal volume algorithm for realizing the deposition size monitoring based on the molten pool images. Finally, the rocket engine shell was manufactured using ADED, and the depositional forming size was observed in real time. The overall average size deviation of the component was within ±2.41 mm, suggesting a high forming accuracy.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Omiyale BO, Olugbade TO, Abioye TE, Farayibi PK (2022) Wire arc additive manufacturing of aluminium alloys for aerospace and automotive applications: a review. Mater Sci Tech-Lond 38(7):391–408

    Article  CAS  Google Scholar 

  2. Williams SW, Martina F, Addison AC, Ding J, Pardal G, Colegrove P (2016) Wire + arc additive manufacturing. Mater Sci Tech-Lond 32(7):641–647

    Article  CAS  Google Scholar 

  3. Fang X, Zhang L, Chen G, Huang K, Xue F, Wang L, Zhao J, Lu B (2021) Microstructure evolution of wire-arc additively manufactured 2319 aluminum alloy with interlayer hammering. Mat Sci Eng A-Struct 800:140168

    Article  CAS  Google Scholar 

  4. Vimal KEK, Srinivas MN, Rajak S (2021) Wire arc additive manufacturing of aluminium alloys: a review. Mater Today: Proc 41:1139–1145

    CAS  Google Scholar 

  5. Wu B, Pan Z, Ding D, Cuiuri D, Li H, Xu J, Norrish J (2018) A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. J Manuf Process 35:127–139

    Article  Google Scholar 

  6. Rauch M, Hascoet JY, Querard V (2021) A multiaxis tool path generation approach for thin wall structures made with WAAM. J Manuf Mater Process 5(4):128

    Google Scholar 

  7. Ma G, Zhao G, Li Z, Yang M, Xiao W (2019) Optimization strategies for robotic additive and subtractive manufacturing of large and high thin-walled aluminum structures. Int J Adv Manuf Technol 101:1275–1292

    Article  Google Scholar 

  8. He T, Yu S, Huang A, Yu G (2021) Path planning and forming of wire multi-arc additive collaborative manufacture for marine propeller bracket. J Manuf Process 68:1191–1201

    Article  Google Scholar 

  9. He T, Yu S, Shi Y, Dai Y (2019) High-accuracy and high-performance WAAM propeller manufacture by cylindrical surface slicing method. Int J Adv Manuf Technol 105:4773–4782

    Article  Google Scholar 

  10. Xia C, Pan Z, Zhang S, Polden J, Wang L, Li H, Xu Y, Chen S (2020) Model predictive control of layer width in wire arc additive manufacturing. J Manuf Process 58:179–186

    Article  Google Scholar 

  11. Xia C, Pan Z, Polden J, Li H, Xu Y, Chen S, Zhang Y (2020) A review on wire arc additive manufacturing: monitoring, control and a framework of automated system. J Manuf Syst 57:31–45

    Article  Google Scholar 

  12. He T, Yu S, Shi Y, Huang A (2020) Forming and mechanical properties of wire arc additive manufacture for marine propeller bracket. J Manuf Process 52:96–105

    Article  Google Scholar 

  13. Zheng B, Yu S, Tang L, Shi Y (2022) Precision and performance of aluminum alloy rod of lattice manufactured by laser wire arc additive manufacturing. J Huazhong Univ of Sci & Tech (Natural Science Edition) 50(12):49–57

    Google Scholar 

  14. Ye X, Gu S (2019) Image recognition and adaptive extraction method for weld pool characteristics of robotic K-TIG welding. Automation & Instrumentation 7:161–164

    Google Scholar 

  15. Xia C, Pan Z, Li Y, Chen J, Li H (2022) Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. Int J Adv Manuf Technol 120:551–562

    Article  Google Scholar 

  16. Xiong J, Zhang G, Qiu Z, Li Y (2013) Vision-sensing and bead width control of a single-bead multi-layer part: material and energy savings in GMAW-based rapid manufacturing. J Clean Prod 41:82–88

    Article  Google Scholar 

  17. Wang W, Yamane S, Koike T, Touma J, Hosoya K, Nakajima T, Yamamoto H (2016) Image processing method for automatic tracking of the weld line in plasma robotic welding. Int J Adv Manuf Technol 86:1865–1872

    Article  Google Scholar 

  18. Rathod VR, Anand RS, Ashok A (2012) Analysis of radiographical weld flaws using image-processing approach. Int J Signal Imaging 4(2):228–237

    Google Scholar 

  19. Pi Y, Xiong J, Zhao H, Chen H (2019) Tracking algorithm for solid-liquid separation point of molten pool tail in GTAW-based additive manufacturing. Trans China Weld Inst 40(2):104–109

    Google Scholar 

  20. Wang D, Wen Z, Nie L, Xiong J, Zhao P, Yang J (2019) Real-time detection for back weld width and position of back weld pool of aluminum alloy during GTAW. Dongfang Turbine 4:38–42

    Google Scholar 

  21. Wang Z, Shi H, Lin D (2020) Effective factors analysis on camera calibration, in: 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), Kuala, Lumpur, Malaysia. 331–334. https://doi.org/10.1109/CISCE50729.2020.00073

  22. Su J, Qi X, Duan X (2017) Plane pose measurement method based on monocular vision and checkerboard target. Acta Optica Sinica 37(8):218–228

    Google Scholar 

  23. Elsheikh A, Abu-Nabah BA, Hamdan MO, Tian G (2023) Infrared camera geometric calibration: a review and a precise thermal radiation checkerboard target. Sensors-Basel 23(7):3479

    Article  PubMed  PubMed Central  Google Scholar 

  24. Li X, Plataniotis KN (2015) A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics. IEEE T Bio-Med Eng 62(7):1862–1873

    Article  Google Scholar 

  25. Liang W, Shi Y, Zhang X (2006) Digital image processing used in jointing. Comp Eng Design 27(19):3598–3600

    Google Scholar 

  26. Li Y, Mu H, Polden J, Li H, Wang L, Xia C, Pan Z (2022) Towards intelligent monitoring system in wire arc additive manufacturing: a surface anomaly detector on a small dataset. Int J Adv Manuf Technol 120:5225–5242

    Article  Google Scholar 

  27. Wang Y, Hu Y, Sun X, He F (2021) Research on weld recognition based on MESR adaptive threshold algorithm, in: 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China. 1136–1142. https://doi.org/10.1109/ICAICA52286.2021.9497900

  28. Jin C, Kong X, Chang J, Cheng H, Liu X (2020) Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM. Int J Adv Manuf Technol 108:3313–3322

    Article  Google Scholar 

  29. Naji OAAM, Shah HNM, Anwar NSN, Johan NF (2023) Square groove detection based on Förstner with Canny edge operator using laser vision sensor. Int J Adv Manuf Technol 125:2885–2894

    Article  Google Scholar 

  30. Chen Y, Shi Y, Cui Y, Chen X (2021) Narrow gap deviation detection in Keyhole TIG welding using image processing method based on Mask-RCNN model. Int J Adv Manuf Technol 112:2015–2025

    Article  Google Scholar 

Download references

Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable

Funding

This work was supported by the National Key R&D Program of China (No. 2017YFB1103200).

Author information

Authors and Affiliations

  1. State Key Laboratory of Materials Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Shengfu Yu, Runzhen Yu, Fangbin Deng & Jia Ren

Authors
  1. Shengfu Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Runzhen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangbin Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RY: designing and conducting the experiments, data analyzing, and writing the paper. JR: revising the paper and assisting in the experiments. FD: assisting in the experiments. SY: supervising the experiments and assisting in the experiments.

Corresponding author

Correspondence to Shengfu Yu.

Ethics declarations

Ethics approval

Not applicable

Consent to participate

All authors have approved to participate.

Consent for publication

The manuscript is approved by all authors for publication.

Conflict of interest

The authors declare no competing interests.

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

Yu, S., Yu, R., Deng, F. et al. Monitoring the forming dimensions of components produced by arc-directed energy deposition based on a molten pool’s geometric characteristics. Weld World 68, 793–804 (2024). https://doi.org/10.1007/s40194-023-01640-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40194-023-01640-1

Keywords

Search

Navigation