Data-driven simulation for fast prediction of pull-up process in bottom-up stereo-lithography

Wang, Jun; Das, Sonjoy; Rai, Rahul; Zhou, Chi

doi:10.1016/j.cad.2018.02.002

Cited by 25 publications

(9 citation statements)

References 35 publications

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“…This process is time-consuming and causes the formation of adhesion forces which may damage the part, hence the need for careful choice of model manufacturing orientation and modelling the support to allow for proper model growth. and (Liravi et al, 2015;Melchels et al, 2010;Pan et al, 2017;Wang et al, 2018). Greater mechanical strength is achieved by curing by treating the manufactured part with light with a wavelength initiating the photopolymerisation at the end of the printing process (Sun et al, 2008;Zguris, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the influence of UV light exposure time on hardness and density properties of SLA models

Dzadz

Pszczółkowski²

2020

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The article analysis the effect of exposure to ultraviolet light on the hardening process of the model made in the SLA technology. Research samples were created with the SLA additive technique using a 10s exposure time. In this experiment, the change in item hardness and density over a 96-hour period was analysed. Light exposure time for details of an item made in SLA technology results in an increase in hardness. At the same time are observed, changes in density and stabilization of both parameters with increasing exposure time to UV light. .

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Section: Introductionmentioning

confidence: 99%

Analysis of the influence of UV light exposure time on hardness and density properties of SLA models

Dzadz

Pszczółkowski²

2020

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“…The features are proposed for the exemplar SM models in AM, which is fused filament fabrication. To capture the effect of the geometry or deposition path, different approaches have been explored including

grid-connection of a 2D layer [ 9 ], 2D images [ 11 , 13 ], the shortest distances from the free surfaces [ 12 ], trajectory descriptors [ 16 ], and process parameters [ 13 ]. The choice of features related to the geometry is dependent on the AM process under consideration.…”

Section: Feature Engineering For the Sm Of Consolidation Degreementioning

confidence: 99%

“…In recent years, there have been multiple efforts focusing on creating surrogate models to cope with the computational cost of the HFMs for AM [ 5 , 9 , 10 , 11 , 12 , 13 , 14 ]. In general, the data acquired from the high fidelity simulations or experiments are used for training the surrogate models (see Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

“…We sort the models listed in Table 1 based on the size of data used for the training. The size can be as small as 60 data points when the output is a scalar (i.e., the pull-up stress in [ 9 ]) or as high as 250,000 for the time-series temperature prediction [ 12 ]. Three to four orders of magnitude difference in terms of size can be attributed to the high dimensionality of the output of SM.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to the above-mentioned models handling geometry or deposition pattern and/or predict time-series output. Here, we compare two representative models [ 9 , 10 ]. For example, Tapia et al [ 10 ] created a Gaussian process-based surrogate model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing

Roy

Wodo

2021

Materials

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Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model).

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Artificial Intelligence-Based Engineering Applications a Comprehensive Review of Application Areas, Impacts and Challenges

Aksoy,

Salman,

Ekrem

et al. 2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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Data-driven simulation for fast prediction of pull-up process in bottom-up stereo-lithography

Cited by 25 publications

References 35 publications

Analysis of the influence of UV light exposure time on hardness and density properties of SLA models

Analysis of the influence of UV light exposure time on hardness and density properties of SLA models

Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing

Artificial Intelligence-Based Engineering Applications a Comprehensive Review of Application Areas, Impacts and Challenges

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