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Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning

Author

Listed:
  • Chunyang Xia

    (University of Wollongong
    Shanghai Jiao Tong University)

  • Zengxi Pan

    (University of Wollongong)

  • Joseph Polden

    (University of Wollongong)

  • Huijun Li

    (University of Wollongong)

  • Yanling Xu

    (Shanghai Jiao Tong University)

  • Shanben Chen

    (Shanghai Jiao Tong University)

Abstract

WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different machine learning models, including ANFIS, ELM and SVR, were developed to predict the surface roughness. Furthermore, the ANFIS model was optimized by GA and PSO algorithms. Full factorial experiments were conducted to obtain the training data, and the K-fold Cross-validation strategy was applied to train and validate machine learning models. The comparison results indicate that GA–ANFIS has superiority in predicting surface roughness. The RMSE, $$ R^{2} $$ R 2 , MAE and MAPE for GA–ANFIS were 0.0694, 0.93516, 0.0574, 14.15% respectively. This study could also provide inspiration and guidance for surface roughness modelling in multipass arc welding and cladding.

Suggested Citation

  • Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01725-4
    DOI: 10.1007/s10845-020-01725-4
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    References listed on IDEAS

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    1. Shifei Ding & Nan Zhang & Xinzheng Xu & Lili Guo & Jian Zhang, 2015. "Deep Extreme Learning Machine and Its Application in EEG Classification," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, May.
    2. Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    3. Zeqi Hu & Xunpeng Qin & Yifeng Li & Jiuxin Yuan & Qiang Wu, 2020. "Multi-bead overlapping model with varying cross-section profile for robotic GMAW-based additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1133-1147, June.
    4. Maraboina Raju & Munish Kumar Gupta & Neeraj Bhanot & Vishal S. Sharma, 2019. "A hybrid PSO–BFO evolutionary algorithm for optimization of fused deposition modelling process parameters," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2743-2758, October.
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    Cited by:

    1. Jyothi Padmaja Koduru & T. Vijay Kumar & Kedar Mallik Mantrala, 2024. "A review of wire and arc additive manufacturing using different property characterization, challenges and future trends," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4563-4581, September.
    2. Pengfei Wang & Jinkun Deng & Xu Li & Changchun Hua & Lihong Su & Guanyu Deng, 2024. "A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3559-3576, October.

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