Curing-dependent thermo-viscoelastic and shrinkage behaviour of photopolymers

Sekmen, Kubra; Rehbein, Thomas; Johlitz, Michael; Lion, Alexander; Constantinescu, Andreï

doi:10.1016/j.mechmat.2023.104566

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…[3] Because the functionalized glass used in single-layer training and the FEP used in multilayer printing have low oxygen permeability, similar reaction kinetics in the adjacent resin are expected. Multilayer parts also provide an opportunity for interactions between layers, such as monotonic heat build-up from the exothermic reactions, [40,41] swelling and shrinkage-induced deformation, [42] oligomer trapping, and arbitrary z-boundary conditions depending on previous layer print geometry. Thus, the single-layer prediction can only provide qualitative insight into the final multilayer part geometry.…”

Section: Predictions Of Single-layer Printingmentioning

confidence: 99%

A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

Killgore

Kolibaba

Caplins

et al. 2023

Small

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Data‐driven U‐net machine learning (ML) models, including the pix2pix conditional generative adversarial network (cGAN), are shown to predict 3D printed voxel geometry in digital light processing (DLP) additive manufacturing. A confocal microscopy‐based workflow allows for the high‐throughput acquisition of data on thousands of voxel interactions arising from randomly gray‐scaled digital photomasks. Validation between prints and predictions shows accurate predictions with sub‐pixel scale resolution. The trained cGAN performs virtual DLP experiments such as feature size‐dependent cure depth, anti‐aliasing, and sub‐pixel geometry control. The pix2pix model is also applicable to larger masks than it is trained on. To this end, the model can qualitatively inform layer‐scale and voxel‐scale print failures in real 3D‐printed parts. Overall, machine learning models and the data‐driven methodology, exemplified by U‐nets and cGANs, show considerable promise for predicting and correcting photomasks to achieve increased precision in DLP additive manufacturing.

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Section: Predictions Of Single-layer Printingmentioning

confidence: 99%

A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

Killgore

Kolibaba

Caplins

et al. 2023

Small

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A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

Jia,

Zhu,

Yan

et al. 2024

Polymer Composites

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The occurrence of process‐induced deformations of composites laminates is challenging for assembly accuracy and may lead to a service life reduction of parts. However, it can be obviously mitigated through different optimization strategies on the basis of the accurate curing process simulation. In this study, a stacking sequence optimization strategy is proposed and applied to multi‐layer thick asymmetric laminates. The shapes of deformed laminate plates are experimentally investigated in virtue of the three‐dimensional coordinate measuring machine. The thermo‐chemical–mechanical behaviors of plates are first verified through the comparisons of model predicted and experimental process‐induced deformations. Then the nonlinear control formula achieved through the regression model is proposed for the direct relationship between stacking sequences and process‐induced deformations. Finally, the required solutions are generated by solving the control formula. With the comparisons between the average deformations before and after optimizations, it is found that the magnitudes of deformations are significantly reduced, especially when the unoptimizated deformations are large.Highlights The thermo‐chemical–mechanical behavior can be described in the curing process simulation model. The deformed shapes of multi‐layer thick asymmetric laminate plates are measured by a three‐dimensional coordinate measuring machine. The stacking sequence optimization strategy is implemented by introducing a nonlinear control formula.

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Development of Novel Frontsheets With Protective Coatings to Increase the Durability and Reliability of Glass‐Free Lightweight PV Modules

Voronko,

Eder,

Reiser

et al. 2024

Progress in Photovoltaics

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In this work, novel PET‐based frontsheet materials with UV‐cured coatings were developed and investigated. UV‐curing urethane acrylates were selected as innovative, fluorine‐free coating systems. Polyurethane coatings exhibit excellent UV resistance, chemical and moisture resistance and, thus, high durability in outdoor applications. A homogeneous application without coating defects such as bubbles, voids or detachments was achieved. Material tests with cross‐cut tests showed no separation from the PET substrate. The water vapor transmission rates and the physical (optical and thermal) and chemical properties of the novel polymeric frontsheets were measured and compared with uncoated reference systems and products already on the market. The aging‐related changes after irradiation and humid heat storage were investigated and described in detail. Based on this comprehensive study, the newly developed frontsheets can be considered a suitable alternative to polymeric frontsheets with fluorine‐containing top layers.

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Curing-dependent thermo-viscoelastic and shrinkage behaviour of photopolymers

Cited by 8 publications

References 35 publications

A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

A Data‐Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U‐Nets and Generative Adversarial Networks

A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

Development of Novel Frontsheets With Protective Coatings to Increase the Durability and Reliability of Glass‐Free Lightweight PV Modules

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