Nghiên cứu thực nghiệm và phát triển khung học sâu để dự đoán độ nhám bề mặt do quá trình sản xuất gây ra ở hợp kim nhôm được chế tạo theo phương pháp in 3D

Engler O, Hirsch J (2002) Texture control by thermomechanical processing of AA6xxx Al-Mg-Si sheet alloys for automotive applications - a review. Mater Sci Eng A 336(1–2):249–262 McQueen HJ, Spigarelli S, Kassner ME, Evangelista E (2011) Hot deformation and processing of aluminum alloys, 1st edn. CRC Press Gu DD, Meiners W, Wissenbach K, Poprawe R (2012) Laser additive manufacturing of metallic components: materials, processes and mechanisms. Int Mater Rev 57(3):133–164 Gao W, Zhang Y, Ramanujan D, Ramani K, Chen Y, Williams CB, Wang CCL, Shin YC, Zhang S, Zavattieri PD (2015) The status, challenges, and future of additive manufacturing in engineering. CAD Comput Aided Des 69:65–89 Song B, Zhao X, Li S, Han C, Wei Q, Wen S, Liu J, Shi Y (2015) Differences in microstructure and properties between selective laser melting and traditional manufacturing for fabrication of metal parts: a review. Front Mech Eng 10(2):111–125 Pauly S, Wang P, Kühn U, Kosiba K (2018) Experimental determination of cooling rates in selectively laser-melted eutectic Al-33Cu. Addit Manuf 22:753–757 Zhi Wang L, Wang S, Jiao Wu J (2017) Experimental investigation on densification behavior and surface roughness of AlSi10Mg powders produced by selective laser melting. Opt Laser Technol 96:88–96 Read N, Wang W, Essa K, Attallah MM (2015) Selective laser melting of AlSi10Mg alloy: process optimisation and mechanical properties development. Mater Des 65:417–424 Du Plessis A, Beretta (2020) Killer notches: the effect of as-built surface roughness on fatigue failure in AlSi10Mg produced by laser powder bed fusion. Addit Manuf 35(June):101424, 2020 Mohammadi M, Asgari H (2018) Achieving low surface roughness AlSi10Mg_200C parts using direct metal laser sintering. Addit Manuf 20:23–32 Poncelet O, Marteleur M, van der Rest C, Rigo O, Adrien J, Dancette S, Jacques PJ, Simar A (2021) Critical assessment of the impact of process parameters on vertical roughness and hardness of thin walls of AlSi10Mg processed by laser powder bed fusion. Addit Manuf 38(July 2020):101801 Calignano F, Manfredi D, Ambrosio EP, Iuliano L, Fino P (2013) Influence of process parameters on surface roughness of aluminum parts produced by DMLS. Int J Adv Manuf Technol 67(9–12):2743–2751 Yang T, Liu T, Liao W, MacDonald E, Wei H, Chen X, Jiang L (2019) The influence of process parameters on vertical surface roughness of the AlSi10Mg parts fabricated by selective laser melting. J Mater Process Technol 266 26–36 Yang T, Liu T, Liao W, Wei H, Zhang C, Chen X, Zhang K (2021) Effect of processing parameters on overhanging surface roughness during laser powder bed fusion of AlSi10Mg. J Manuf Process 61(November 2019):440–453 Han Q, Jiao Y (2019) Effect of heat treatment and laser surface remelting on AlSi10Mg alloy fabricated by selective laser melting. Int J Adv Manuf Technol 102(9–12):3315–3324 Yu W, Sing SL, Chua CK, Tian X (2019) Influence of re-melting on surface roughness and porosity of AlSi10Mg parts fabricated by selective laser melting. J Alloys Compd 792:574–581 Boschetto A, Bottini L, Pilone D (2021) Effect of laser remelting on surface roughness and microstructure of AlSi10Mg selective laser melting manufactured parts. Int J Adv Manuf Technol 113(9–10):2739–2759 Sanaei N, Fatemi A (2021) Defects in additive manufactured metals and their effect on fatigue performance: a state-of-the-art review. Prog Mater Sci 117(August 2020):100724 Jenab A, Sari Sarraf I, Green DE, Rahmaan T, Worswick MJ (2016) The use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-sheets. Mater Des 94:262–273 Montes de Oca Zapiain D, Shanker A, Kalidindi SR (2022) Convolutional neural networks for the localization of plastic velocity gradient tensor in polycrystalline microstructures. J Eng Mater Technol 144(1):011004 Derogar A, Djavanroodi F (2011) Artificial neural network modeling of forming limit diagram. Mater Manuf Process 26(11):1415–1422 Gorji MB, Mozaffar M, Heidenreich JN, Cao J, Mohr D (2020) On the potential of recurrent neural networks for modeling path dependent plasticity. J Mech Phys Solids 143:103972 Figueira Pujol JC, Andrade Pinto JM (2011) A neural network approach to fatigue life prediction. Int J Fatigue 33(3):313–322 Ibragimova O, Brahme A, Muhammad W, Lévesque J, Inal K (2021) A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths. Int J Plast 144:103059 Chen D, Skouras M, Zhu B, Matusik W (2018) Computational discovery of extremal microstructure families. Sci Adv 4(1):1–8 Gu GX, Chen C-T, Richmond DJ, Buehler MJ (2018) Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater Horizons 5(5):939–945 Johnson NS, Vulimiri PS, To AC, Zhang X, Brice CA, Kappes BB, Stebner AP (2020) Invited review: machine learning for materials developments in metals additive manufacturing. Addit Manuf 36:101641 Sosnovik I, Oseledets I (2019) Neural networks for topology optimization. Russ J Numer Anal Math Model 34(4):215–223 Zhang Y, Yang S, Dong G, Zhao YF (2021) Predictive manufacturability assessment system for laser powder bed fusion based on a hybrid machine learning model. Addit Manuf 41:101946 Tapia G, Elwany AH, Sang H (2016) Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit Manuf 12:282–290 Westphal E, Seitz H (2021) A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks. Addit Manuf 41:101965 Zhu Y, Wu Z, Hartley WD, Sietins JM, Williams CB, Yu HZ (2020) Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning. Addit Manuf 34:101183 Petrich J, Snow Z, Corbin D, Reutzel EW (2021) Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing. Addit Manuf 48:102364 Gaikwad A, Giera B, Guss GM, Forien J-B, Matthews MJ, Rao P (2020) Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion – A single-track study. Addit Manuf 36:101659 Westphal E, Seitz H (2022) Machine learning for the intelligent analysis of 3D printing conditions using environmental sensor data to support quality assurance. Addit Manuf 50:102535 Mozaffar M, Paul A, Al-Bahrani R, Wolff S, Choudhary A, Agrawal A, Ehmann K, Cao J (2018) Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks. Manuf Lett 18:35–39 Zhang Z, Liu Z, Wu D (2021) Prediction of melt pool temperature in directed energy deposition using machine learning. Addit Manuf 37:101692 Aoyagi K, Wang H, Sudo H, Chiba A (2019) Simple method to construct process maps for additive manufacturing using a support vector machine. Addit Manuf 27:353–362 Sessions D, Meenakshisundaram V, Gillman A, Cook A, Fuchi K, Buskohl PR, Huff GH (2022) Mapping geometric and electromagnetic feature spaces with machine learning for additively manufactured RF devices. Addit Manuf 50:102549 Xiong J, Zhang G, Hu J, Wu L (2014) Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis. J Intell Manuf 25(1):157–163 Ye D, Hsi Fuh JY, Zhang Y, Hong GS, Zhu K (2018) In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks. ISA Trans 81:96–104 Khanzadeh M, Chowdhury S, Tschopp MA, Doude HR, Marufuzzaman M, Bian L (2019) In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Trans 51(5):437–455 Grasso M, Colosimo BM (2017) Process defects and in situ monitoring methods in metal powder bed fusion: a review. Meas Sci Technol 28(4):1–25 Scime L, Beuth J (2019) Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit Manuf 25:151–165 Muhammad W, Brahme AP, Ibragimova O, Kang J, Inal K (2021) A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys. Int J Plast 136:102867 Aboulkhair NT, Tuck C, Ashcroft I, Maskery I, Everitt NM (2015) On the precipitation hardening of selective laser melted AlSi10Mg. Metall Mater Trans A 46(8):3337–3341 Volpp J (2020) Spattering effects during selective laser melting. J Laser Appl 32(2):22023 LeCun YA, Bottou L, Orr GB, Müller K-R (2012) Efficient BackProp BT - neural networks: tricks of the trade: second edition. Montavon G, Orr GB, Müller K-R, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 9–48. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning. vol. 1. MIT press Cambridge