Otwarty dostęp

Application of machine vision for the detection of powder bed defects in additive manufacturing processes


Zacytuj

Brandt M, editor. Laser additive manufacturing: materials, design, technologies, and applications. Amsterdam: Elsevier, Woodhead; 2017. Brandt M , editor. Laser additive manufacturing: materials, design, technologies, and applications . Amsterdam : Elsevier, Woodhead ; 2017 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. Chen HY Lin CC Horng M-H Chang LK Hsu JH Chang TW Deep learning applied to defect detection in powder spreading process of magnetic material additive manufacturing . Materials (Basel) . 2022 ; 15 . https://doi.org/10o3390/ma15165662 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. McCann R Obeidi MA Hughes C McCarthy É Egan DS Vijayaraghavan RK 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

Bovik AC. Handbook of image and video processing. San Diego: Academic Press; 2000. Bovik AC. Handbook of image and video processing . San Diego : Academic Press ; 2000 . Search in Google Scholar

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 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 Open DOISearch in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

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. 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 . Search in Google Scholar

eISSN:
2083-134X
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Materials Sciences, other, Nanomaterials, Functional and Smart Materials, Materials Characterization and Properties