Page 86 - ESAM-1-1
P. 86

Engineering Science in
            Additive Manufacturing                                                       ML in additive manufacturing



               doi: 10.1016/j.mtcomm.2024.110294                  manufacturing process-a review. J Braz Soc Mech Sci Eng.
                                                                  2023;45(10):535.
            32.  Farrag A, Yang Y, Cao N, Won D, Jin Y. Physics-informed
               machine learning for metal additive manufacturing.  Prog      doi: 10.1007/s40430-023-04425-1
               Addit Manuf. 2024;10:110008.
                                                               43.  Saboori A, Aversa A, Marchese G, Biamino S, Lombardi M,
               doi: 10.1007/s40964-024-00612-1                    Fino P. Application of directed energy deposition-based
            33.  Castillo  M,  Monroy  R,  Ahmad  R.  Scientometric  analysis   additive manufacturing in repair. Appl Sci. 2019;9(16):3316.
               and systematic review of smart manufacturing technologies      doi: 10.3390/app9163316
               applied to the 3D printing polymer material extrusion
               system. J Intell Manuf. 2024;35(1):3-33.        44.  Liu WW, Tang ZJ, Liu XY, Wang HJ, Zhang HC. A review on
                                                                  in-situ monitoring and adaptive control technology for laser
               doi: 10.1007/s10845-022-02049-1                    cladding remanufacturing. Proced Cirp. 2017;61:235-240.
            34.  Moradi A, Tajalli S, Mosallanejad MH, Saboori A. Intelligent      doi: 10.1016/j.procir.2016.11.217
               laser-based metal additive manufacturing: A  review on
               machine learning for process optimization and property   45.  Inayathullah S, Buddala R. Review of machine learning
               prediction. Int J Adv Manuf Technol. 2024;136:527-560.  applications in additive manufacturing.  Results Eng.
                                                                  2024;25:103676.
               doi: 10.1007/s00170-024-14858-0
                                                                  doi: 10.1016/j.rineng.2024.103676
            35.  Su J, Jiang F, Teng J, et al. Recent innovations in laser additive
               manufacturing of titanium alloys.  Int J Extrem Manuf.   46.  Tapia G, Khairallah S, Matthews M, King WE, Elwany A.
               2024;6(3):032001.                                  Gaussian process-based surrogate modeling framework
                                                                  for process planning in laser powder-bed fusion additive
               doi: 10.1088/2631-7990/ad2545                      manufacturing of 316L stainless steel.  Int J Adv Manuf
            36.  Hassan M, Misra M, Taylor GW, Mohanty AK. A review of AI   Technol. 2018;94(9):3591-3603.
               for optimization of 3D printing of sustainable polymers and      doi: 10.1007/s00170-017-1045-z
               composites. Compos Part C Open Access. 2024;15:100513.
                                                               47.  Kamath C, Fan YJ. Regression with small data sets: A case
               doi: 10.1016/j.jcomc.2024.100513                   study using code surrogates in additive manufacturing.
            37.  Bin Abu  Sofian ADA,  Lim  HR, Chew KW, Show  PL.   Knowl Inf Syst. 2018;57:475-493.
               Advancing 3D printing through integration of machine      doi: 10.1007/s10115-018-1174-1
               learning with algae‐based biopolymers.  ChemBioEng Rev.
               2024;11(2):406-425.                             48.  Kumar HA, Kumaraguru S, Paul C, Bindra K. Faster
                                                                  temperature prediction in the powder bed fusion process
               doi: 10.1002/cben.202300054                        through the development of a surrogate model. Optics Laser
            38.  Karimzadeh M, Basvoju D, Vakanski A, Charit I, Xu F,   Technol. 2021;141:107122.
               Zhang X. Machine learning for additive manufacturing      doi: 10.1016/j.optlastec.2021.107122
               of functionally graded materials.  Materials  (Basel).
               2024;17(15):3673.                               49.  Chen L, Bi G, Yao X, et al. Multisensor fusion-based digital
                                                                  twin for localized quality prediction in robotic laser-
               doi: 10.3390/ma17153673                            directed energy deposition.  Robot Comput Integr Manuf.
            39.  Garg A, Sharma A, Zheng W, Li L. A review on artificial   2023;84:102581.
               intelligence-enabled  mechanical  analysis  of  3D  printed      doi: 10.1016/j.rcim.2023.102581
               and FEM-modelled auxetic metamaterials.  Virtual Phys
               Prototyp. 2025;20(1):e2445712.                  50.  Huang X, Xie T, Wang Z, Chen L, Zhou Q, Hu Z. A transfer
                                                                  learning-based multi-fidelity point-cloud neural network
               doi: 10.1080/17452759.2024.2445712                 approach for melt pool modeling in additive manufacturing.
            40.  Nam J, Kim M. Advances in materials and technologies   ASCE ASME J Risk Uncertain Eng Syst Part  B Mech Eng.
               for digital light processing 3D printing.  Nano Converg.   2022;8(1):011104.
               2024;11(1):45.                                     doi: 10.1115/1.4051749
               doi: 10.1186/s40580-024-00452-3                 51.  Wang Y, Zheng P, Xu X, Yang H, Zou J. Production planning for
            41.  Yuan K, Xu Y, Wang X, Ma X, Wang Q, Zhang H. Key   cloud-based additive manufacturing-a computer vision-based
               technologies and research progress in robotic arc additive   approach. Robot Comput Integr Manuf. 2019;58:145-157.
               remanufacturing. Sens Actuators A Phys. 2024;376:115547.     doi: 10.1016/j.rcim.2019.03.003
               doi: 10.1016/j.sna.2024.115547
                                                               52.  Safdar M, Paul PP, Lamouche G,  et al. Fundamental
            42.  Selot A, Dwivedi R. Machine learning and sensor-  requirements of a machine  learning operations  platform
               based approach for defect detection in MEX additive   for industrial metal additive manufacturing.  Comput  Ind.


            Volume 1 Issue 1 (2025)                         19                         doi: 10.36922/ESAM025040004
   81   82   83   84   85   86   87   88   89   90   91