3D Printing of Congenital and Prenatal Heart Diseases

Yang, Jian; Pan, Xiangbin; Pan, Wenzhi; Yang, Meng; Liu, Jincheng; Tang, Jiayou

doi:10.1007/978-981-15-6957-9_5

Cited by 3 publications

(8 citation statements)

References 53 publications

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“…previous work [6] . The method has fully considered the resource-related factors, including material deposition paths, process parameters and machine behaviours.…”

Section: Fig 1 Flow Diagram Of 3dp Processmentioning

confidence: 78%

“…The optimisation scope has been expanded from a single objective to threedimensional resource objectives, which covers all types of resource consumptions involved in 3DP process, namely time, energy and material. In terms of prediction modelling, the HCDM method has been proposed in the authors' previous research work, which considers all resource-related factors, including CAD model, process parameters, toolpaths, machine characteristics and manufacturing environment [6] . In terms of multi-objective optimisation (MOO), meta-heuristic algorithm is used to construct the framework of optimisation algorithm, aiming at the best-performing solution of process parameters that consume the least resources.…”

Section: Background and Motivationmentioning

confidence: 99%

“…The HCDM method has been proposed in the authors' previous research work [6] . By investigating the mainstream 3DP processes in the market, five functional machine operations have been classified, namely axial moving, material processing, material feeding, unit heating and auxiliary.…”

Section: Hybrid Code-based and Data-driven Modelling Of 3dp Resource ...mentioning

confidence: 99%

“…During printing, the 3DP machine system follows the instructions from Gcodes to construct each layer until the part is delivered [5] . During post-processing, fine-finishing operations such as material removal and surface polishing are performed to achieve the desired structure [6] .…”

Section: Introductionmentioning

confidence: 99%

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Prediction-based multi-objective optimisation method for 3D Printing Resource Consumption

Yang,

Wang,

Dun

et al. 2024

Preprint

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A prediction-based multi-objective optimisation (PBMO) method is proposed for 3D printing (3DP) technology to predict and reduce resource requirements on-demand, including time, energy and material. In the authors’ previous research work, a hybrid code-based and data-driven modelling (HCDM) scheme has been proposed to forecast 3DP resource consumption. The predictive models are customised based on process parameters, material deposition paths and machine behaviours. Aiming at the appropriate process parameters that consume the least resources, this study further utilises the models as three objectives to be minimised. Meta-heuristic algorithm is adopted to construct the optimisation framework, in which the HCDM process is embedded in the fitness evaluation step. To validate the proposed method, the corresponding computing program is compiled using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and demonstrated on two material extrusion (MEX) machines. Hypervolume is used as the Lebesgue measure to evaluate the superiorities of near-optimal solutions on the non-dominated Pareto front. In three-dimensional objective space, the solution set that occupies the maximum hypervolume will be recommended as the optimal-found solutions for 3DP. In addition to 3DP, the proposed optimisation method is applicable to mainstream computer numerical control (CNC) manufacturing technologies, and will guide process design to promote resource conservation for cleaner production.

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“…previous work [6] . The method has fully considered the resource-related factors, including material deposition paths, process parameters and machine behaviours.…”

Section: Fig 1 Flow Diagram Of 3dp Processmentioning

confidence: 78%

Section: Background and Motivationmentioning

confidence: 99%

Section: Hybrid Code-based and Data-driven Modelling Of 3dp Resource ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction-based multi-objective optimisation method for 3D Printing Resource Consumption

Yang,

Wang,

Dun

et al. 2024

Preprint

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“…3D printing technology, especially additive manufacturing has been reviewed for its utility in gynecology in a variety of instances: broids, endometriosis, pelvic oor disorders, gynecological oncology, patient education for surgical procedures, student education, and simulation-based training [23]. In obstetrics 3D printing technology has been used in cases of placenta accreta, fetal congenital anomalies for presurgical planning, and interactions with visually impaired pregnant patients [24][25][26][27]. Although we are unaware of any studies examining the impact of 3D printed technology on antenatal depression or anxiety, a few studies have shown signi cant improvement in MFA with 3D printed fetal models [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

3D Printed Models In Pregnancy And Its Utility In Improving Psychological Constructs: A Case Series

Coté

Badura-Brack

2022

Preprint

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Background: Additive manufacturing is being utilized in almost every aspect of medicine. 3D printing has especially been used in conjunction with 3D ultrasonography to assist in antenatal assessment and presurgical planning with fetal malformations. As printing capabilities improve and applications are explored there may be more advantages for all parents to visualize and touch 3D printed models of their fetus. Case Presentation: We present three cases involving 3D printed models and four different but interrelated psychological constructs- antenatal depression, antenatal anxiety, maternal-fetal attachment, and paternal-fetal attachment. Each case shows for the first time possible beneficial effects within these prevalent and significant problems.Conclusions: The degree to which the anxiety, depression, and attachment scores improved after the presentation of the 3D printed models is encouraging. Randomized controlled trials utilizing 3D printed models to improve psychological constructs should be supported considering the findings within these four cases.

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3D printed models in pregnancy and its utility in improving psychological constructs: a case series

Coté

Badura-Brack

2022

3D Print Med

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Background 3D printing is being utilized in almost every aspect of medicine. 3D printing has especially been used in conjunction with 3D ultrasonography to assist in antenatal assessment and presurgical planning with fetal malformations. As printing capabilities improve and applications are explored there may be more advantages for all parents to visualize and touch 3D printed models of their fetus. Case presentation We present three cases involving 3D printed models and four different but interrelated psychological constructs- antenatal depression, antenatal anxiety, maternal-fetal attachment, and paternal-fetal attachment. Each case shows for the first time possible beneficial effects within these prevalent and significant problems. Conclusions The degree to which the anxiety, depression, and attachment scores improved after the presentation of the 3D printed models is encouraging. Randomized controlled trials utilizing 3D printed models to improve psychological constructs should be supported considering the findings within these four cases.

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3D Printing of Congenital and Prenatal Heart Diseases

Cited by 3 publications

References 53 publications

Prediction-based multi-objective optimisation method for 3D Printing Resource Consumption

Prediction-based multi-objective optimisation method for 3D Printing Resource Consumption

3D Printed Models In Pregnancy And Its Utility In Improving Psychological Constructs: A Case Series

3D printed models in pregnancy and its utility in improving psychological constructs: a case series

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