Heuristic evaluation for progressive additive manufacturing of industrial bending tubes based on reconfigurable transfer learning

Xu, Jinghua; Wang, Linxuan; Gao, Mingyu; Zhan, Jiangtao; Zhang, Shuyou; Tan, Jianrong; Fei, Shaomei

doi:10.1007/s40964-023-00495-8

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…Aiming at high quality fabricating for functional structure, considering the work [33][34][35], this paper proposes a towards extreme design of manifold structure for additive manufacturing based on multi-layer transfer learning.…”

Section: Introductionmentioning

confidence: 99%

Thermal deformation prediction for additive manufacturing of thin-walled components based on multi-layer transfer learning

WANG,

XU,

ZHANG

et al. 2024

Preprint

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This paper presents a thermal deformation prediction method for additive manufacturing of thin-walled components based on multi-layer transfer learning (MTL). The printability is forwardly designed via multi-objective optimization (MOO) by evaluating scanning length, spot amount and segment amount, accompanied by support material. To avoid the burdened and time-consuming simulation of FEM for various geometric characteristics of thin-walled components, the feed-forward multi-layer perceptron was constructed as the main structure of MTL to rapidly obtain temperature and deformation distributions of manufactured parts. The proposed method is verified by the SLM of mechanical unshrouded turbine. The metallographic diagrams of manufactured components were generated to observe the fabricating quality and verify the effectiveness of the MTL-based method. The metallographic experiment of the fabricated piece proves that the main microstructure of the cross-section of molten pool is spindly columnar crystals. The cross-section morphology and size of the molten pool is different due to different process parameters, making the width of grain is about 1µm. The proposed method is especially useful for metal 3D printing under uncertainty.

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Section: Introductionmentioning

confidence: 99%

Thermal deformation prediction for additive manufacturing of thin-walled components based on multi-layer transfer learning

WANG,

XU,

ZHANG

et al. 2024

Preprint

View full text Add to dashboard Cite

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Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

Xu,

Wang,

Gao

et al. 2024

Chin. J. Mech. Eng.

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This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

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Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

Wang,

Xu,

Zhang

et al. 2024

Vis. Comput. Ind. Biomed. Art

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This study presents an energy consumption (EC) forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning (FTL). To predict the EC of manufacturing products, particularly from scale-down to scale-up, a general paradigm was first developed by categorizing the overall process into three main sub-steps. The operating electrical power was further formulated as a combinatorial function, based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC. Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power. Considering the interconnections among these factors, the outputs of the neural networks were blended and fused to jointly predict the electrical power. Most innovatively, large artifacts can be decomposed into time-dependent laser-scanning trajectories, which can be further transformed into fusionable information via neural networks, inspired by large language model. Accordingly, transfer learning can deal with either scale-down or scale-up forecasting, namely, FTL with scalability within artifact structures. The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion. The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%. The melting fusion quality was examined using metallographic diagrams. The proposed FTL framework can forecast the EC of scaled structures, which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

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Heuristic evaluation for progressive additive manufacturing of industrial bending tubes based on reconfigurable transfer learning

Cited by 3 publications

References 43 publications

Thermal deformation prediction for additive manufacturing of thin-walled components based on multi-layer transfer learning

Thermal deformation prediction for additive manufacturing of thin-walled components based on multi-layer transfer learning

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

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