Multi-Objective Patch Optimization with Integrated Kinematic Draping Simulation for Continuous–Discontinuous Fiber-Reinforced Composite Structures

Fengler, Benedikt; Kärger, Luise; Henning, Frank; Hrymak, Andrew N.

doi:10.3390/jcs2020022

Cited by 28 publications

(17 citation statements)

References 34 publications

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“…To optimise the costs resulting from the tolerances of the laminate design parameters, the methods of tolerance optimisation can be used and adapted to the needs of composite parts. Generally the most common types of tolerance optimisation objectives are distinguished to least cost, best quality and multi-objective optimisation [32], which is as well widely used in composite design [39,40]. Differences of tolerance optimisation of composite structures to common tolerance optimisation are the lack of data for tolerance costs and the difference in the scrutinised design parameters.…”

Section: Manufacturing Tolerance Optimisation For Composite Laminate Parametersmentioning

confidence: 99%

Tolerance management during the design of composite structures considering variations in design parameters

Franz¹,

Schleich²,

Wartzack³

2021

Int J Adv Manuf Technol

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Composite structures play an important role in realising resource-efficient products. Their high lightweight potential and improved manufacturing technologies lead to an increased use in high-volume products. However, especially during the design and development of high-volume products, the consideration of uncertainties is essential to guarantee the final product quality. In this context, the use of modern lightweight materials, such as fibre reinforced plastics (FRP), leads to new challenges. This is due to their high number of design parameters, which are subject to deviations from their nominal values. Deviating parameters, e.g. ply angles and thicknesses, influence the manufacturing process as well as the structural behaviour of a composite part. To consider the deviating design parameters during the design process, a new tolerance optimisation approach is presented, defining tolerance values for laminate design parameters, while ensuring the functionality of the composite structure. To reduce the computational effort, metamodels are used during this optimisation to replace finite element simulations. The proposed approach is applied to a use case with different key functions to show its applicability and benefits.

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Section: Manufacturing Tolerance Optimisation For Composite Laminate Parametersmentioning

confidence: 99%

Tolerance management during the design of composite structures considering variations in design parameters

Franz¹,

Schleich²,

Wartzack³

2021

Int J Adv Manuf Technol

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“…Hybrid continuous-discontinuous composites constitute a new class of materials for structural parts locally bearing increased loads. [1][2][3] In this work, a discontinuous glass fiber-reinforced thermoset is considered. The glass fibers exhibit a length of nearly 25 mm and are embedded into an unsaturated polyester-polyurethane hybrid (UPPH) resin.…”

Section: Introductionmentioning

confidence: 99%

Mean-field homogenization of thermoelastic material properties of a long fiber-reinforced thermoset and experimental investigation

Kehrer

Wood

Böhlke

2020

Journal of Composite Materials

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Fiber-reinforced polymers contribute significantly to weight-reducing components for various industrial applications. A discontinuous glass fiber-reinforced thermoset resin is considered which is produced by the sheet molding compound (SMC) process. Related to the production process, the samples considered in this work exhibit an anisotropic fiber orientation distribution which highly affects the thermomechanical properties. The thermoviscoelastic material behavior of three selected samples is characterized by means of dynamic mechanical analysis. These tests show the temperature-dependent elastic modulus and the glass transition of the composite. Measurements of the thermal expansion of the SMC composite provide data on the coefficient of thermal expansion (CTE). These experimental investigations provide data for the thermoelastic material modeling. Aiming at the prediction of the effective thermal and mechanical properties, a Hashin–Shtrikman-based homogenization method is presented. Based on an eigenstrain formulation, the effective Young’s modulus and CTE are computed in two steps. Moreover, the mean-field method is given in dependence of a variable reference stiffness allowing to tailor the approach to the material system. The influence of this variable reference stiffness on the effective quantities as well as the predicted behavior is analyzed with respect to the experiments. The presented numerical results are in good agreement with the experimental data.

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“…For both, optimisation approaches have been studied in the past. For example, [1] and [2] address ply stack optimisation and patch optimisation for a fix geometry, while [3] focuses on local geometry adjustment for improved structural performance. Typically, current structural optimisation approaches assume perfect manufacturability and do not reflect potential defects.…”

Section: Introductionmentioning

confidence: 99%

An approach for rapid prediction of textile draping results for variable composite component geometries using deep neural networks

Zimmerling

Trippe

Fengler

et al. 2019

AIP Conference Proceedings

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Continuous fibre reinforced plastics (CoFRPs) offer remarkable mechanical properties at low density and have thus drawn increasing attention in weight-sensitive industries over the last decades. Contrasting metals, manufacturing of CoFRPs consists of multiple steps, often comprising a forming process of a textile (draping). However, managing the inherently complex, anisotropic and non-linear material behaviour during textile forming and avoiding forming defects is a great challenge in serial production. To assess formability prior to manufacture, virtual process simulations can be applied. For optimum part quality, component design and applied process parameters must complement each other, which in turn requires a high number of optimisation iterations and quickly exceeds reasonable computation times. Considerable effort has been made with respect to obtaining optimum process parameters, however considering geometry adaptions to achieve manufacturability is rarely addressed. Deep Learning techniques using convolutional neural networks (CNN) are capable of learning complex system dynamics from supplied samples. In the work presented here, CNNs are used to rapidly predict textile forming results of variable component geometries. A large database of highly variant geometries and corresponding draping examples is generated, on which the CNNs are trained. The paper shows, that CNNs are capable of reproducing the underlying forming dynamics and that they generalise well to unknown test geometries. Contrasting traditional metamodel approaches, the presented method estimates not just a scalar part quality attribute, but predicts the complete shear strain field, which facilitates engineering interpretation. The method is demonstrated on different geometries ranging from simple shapes to complex geometries. Being computational inexpensive, CNNs give immediate feedback for real-time geometry iterations during component design. Thus, CNNs are considered a promising and time-efficient tool to reflect manufacturability during part and process design.

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Multi-Objective Patch Optimization with Integrated Kinematic Draping Simulation for Continuous–Discontinuous Fiber-Reinforced Composite Structures

Cited by 28 publications

References 34 publications

Tolerance management during the design of composite structures considering variations in design parameters

Tolerance management during the design of composite structures considering variations in design parameters

Mean-field homogenization of thermoelastic material properties of a long fiber-reinforced thermoset and experimental investigation

An approach for rapid prediction of textile draping results for variable composite component geometries using deep neural networks

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