References
- Brandt M, editor. Laser additive manufacturing: materials, design, technologies, and applications. Amsterdam: Elsevier, Woodhead; 2017.
- Hanzl P, Zetek M, Bakša T, Kroupa T. The influence of processing parameters on the mechanical properties of SLM parts. Procedia Eng. 2015;100:1405–13. https://doi.org/10.1016/j.proeng.2015.01.510.
- Le TP, Wang X, Davidson KP, Fronda JE, Seita M. Experimental analysis of powder layer quality as a function of feedstock and recoating strategies. Addit Manuf. 2021;39:101890. https://doi.org/10.1016/j.addma.2021.101890.
- Wang D, Yu C, Ma J, Liu W, Shen Z. Densification and crack suppression in selective laser melting of pure molybdenum. Mater Des. 2017;129:44–52. https://doi.org/10.1016/j.matdes.2017.04.094.
- Scime L, Beuth J. Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm. Addit Manuf. 2018;19:114–26. https://doi.org/10.1016/j.addma.2017.11.009.
- Maamoun AH, Xue YF, Elbestawi MA, Veldhuis SC. Effect of selective laser melting process parameters on the quality of Al alloy parts: powder characterization, density, surface roughness, and dimensional accuracy. Materials (Basel). 2018;11. https://doi.org/10.3390/ma11122343.
- Chen HY, Lin CC, Horng M-H, Chang LK, Hsu JH, Chang TW, et al. Deep learning applied to defect detection in powder spreading process of magnetic material additive manufacturing. Materials (Basel). 2022;15. https://doi.org/10o3390/ma15165662.
- Li X, Shan G, Shek CH. Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability. J Mater Sci Technol. 2022;103:113–20. https://doi.org/10.1016/j.jmst.2021.05.076.
- Zhang P, Tan J, Tian Y, Yan H, Yu Z. Research progress on selective laser melting (SLM) of bulk metallic glasses (BMGs): a review. Int J Adv Manuf Technol. 2022;118:2017–57. https://doi.org/10.1007/s00170-021-07990-8.
- McCann R, Obeidi MA, Hughes C, McCarthy É, Egan DS, Vijayaraghavan RK, et al. In-situ sensing, process monitoring and machine control in laser powder bed fusion: a review. Addit Manuf. 2021;45:102058. https://doi.org/10.1016/j.addma.2021.102058.
- Craeghs T, Clijsters S, Yasa E, Kruth J. Online quality control of selective laser melting. Proc 20th Solid Freeform Fabric (SFF) Symp. Austin, TX, USA. 8–10 August 2011.
- Yin Y, Liming, DG. Research on feature extraction of local binary pattern of SLM powder bed gray image. J Phys: Conf Series. 2021;1885:32007. https://doi.org/10.1088/1742-6596/1885/3/032007.
- Scime L, Siddel D, Baird S, Paquit V. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: a machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Addit Manuf. 2020;36:101453. https://doi.org/10.1016/j.addma.2020.101453.
- Lin Z, Lai Y, Pan T, Zhang W, Zheng J, Ge X, Liu Y. A new method for automatic detection of defects in Sselective laser melting based on machine vision. Materials (Basel). 2021;14. https://doi.org/10.3390/ma14154175.
- Phuc LT, Seita M. A high-resolution and large field-of-view scanner for in-line characterization of powder bed defects during additive manufacturing. Mater Des. 2019;164:107562. https://doi.org/10.1016/j.matdes.2018.107562.
- Fischer FG, Zimmermann MG, Praetzsch N, Knaak C. Monitoring of the powder bed quality in metal additive manufacturing using deep transfer learning. Mater Des. 2022;222:111029. https://doi.org/10.1016/j.matdes.2022.111029.
- Bovik AC. Handbook of image and video processing. San Diego: Academic Press; 2000.
- Gholami R, Fakhari N. Support vector machine: principles, parameters, and applications. In: Samui P, Sekhar S, Balas VEBT-H, editors. Handbook of neural computation. Amsterdam: Elsevier, Academic Press,; 2017. p. 515–35. doi: 10.1016/B978-0-12-811318-9.00027-2
- Liu J, Ye J, Silva Izquierdo D, Vinel A, Shamsaei N, Shao S. A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing. J Intell Manuf. 2022. https://doi.org/10.1007/s10845-022-02012-0.
- Xiao L, Lu M, Huang H. Detection of powder bed defects in selective laser sintering using convolutional neural network. Int J Adv Manuf Technol. 2020;107:2485–96. https://doi.org/10.1007/s00170-020-05205-0.
- Li J, Zhou Q, Cao L, Wang Y, Hu J. A convolutional neural network-based multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. J Manuf Syst. 2022;64:429–42. https://doi.org/10.1016/j.jmsy.2022.07.007.