A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters

Gerritzen, Johannes; Hornig, Andreas; Gröger, B.; Gude, Μaik

doi:10.3390/jcs6100318

Cited by 3 publications

(2 citation statements)

References 41 publications

(53 reference statements)

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“…As described in many studies [1,2,50] and also proven by our own work on this material [8,28,52], an increase in stiffness can be observed with increasing load rate. Therefore, the direction-dependent elastic moduli are dependent on the strain rate ε.…”

Section: Adjunctive Damage Modelsupporting

confidence: 75%

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Adjunctive Damage Model to Describe the Interaction of Different Defect Types in Textile Composites on the Strain-Rate-Dependent Material Behaviour

Protz,

Koch,

Gude

2023

J. Compos. Sci.

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Textile composites are predestined for crash-loaded lightweight structures due to their adjustable energy absorption capacity, but they can exhibit different types of defects that occur during production (voids) and in operation (fatigue). The influence of such defects, especially the interaction of several defect types on the strain-rate-dependent material behaviour, is still insufficiently researched and can represent a safety risk. Therefore, this paper presents a phenomenological model that can be used to mathematically describe the strain-rate-dependent stress-strain behaviour of nominally defect-free and defect-affected textile composites. An adjunctive damage model in the sense of continuum damage mechanics is introduced, which also considers the interaction of both defect types for the first time. For the model validation, extensive experimental tests on glass fibre non-crimp fabrics reinforced epoxy (GF-NCF/EP) composites are performed. The focus is put on the influence of voids and fatigue-related pre-damage under subsequent tensile loading at strain rates up to 40 s−1. The theoretical studies show a good coincidence with the experimental results. The novel model provides a method for the efficient generation of material maps for numerical highly dynamic crash and impact analyses for defect-free and defective textile composites. As a result, a flexible and practice-oriented model approach is available, which makes a significant contribution to an improved understanding of materials and enables a future defect-tolerant design of textile composites.

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Section: Adjunctive Damage Modelsupporting

confidence: 75%

“…Due to their adjustable energy absorption capacity, non-crimp fabric composites (NCFcomposites) are predestined for lightweight structures that are exposed to high deformation rates during their use [1,2]. NCF-composites can be compromised by defects that arise during manufacture, like voids [3,4] and fibre misalignments [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Adjunctive Damage Model to Describe the Interaction of Different Defect Types in Textile Composites on the Strain-Rate-Dependent Material Behaviour

Protz,

Koch,

Gude

2023

J. Compos. Sci.

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A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

Gerritzen,

Hornig,

Winkler

et al. 2024

Computational Materials Science

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Effect of Microstructure on the Machinability of Natural Fiber Reinforced Plastic Composites: A Novel Explainable Machine Learning (XML) Approach

Ma,

Zhong,

Wang

et al. 2023

Journal of Manufacturing Science and Engineering

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Natural fiber-reinforced plastic (NFRP) composites are ecofriendly and biodegradable materials that offer tremendous ecological advantages while preserving unique structures and properties. Studies on using these natural fibers as alternatives to conventional synthetic fibers in fiber-reinforced materials have opened up possibilities for industrial applications, especially for sustainable manufacturing. However, critical issues reside in the machinability of such materials because of their multiscale structure and the randomness of the reinforcing elements distributed within the matrix basis. This paper reports a comprehensive investigation of the effect of microstructure heterogeneity on the resultant behaviors of cutting forces for NFRP machining. A convolutional neural network (CNN) links the microstructural reinforcing fibers and their impacts on changing the cutting forces (with an estimated R-squared value over 90%). Next, a model-agnostic explainable machine learning approach is implemented to decipher this CNN black-box model by discovering the underlying mechanisms of relating the reinforcing elements/fibers’ microstructures. The presented xml approach extracts physical descriptors from the in-process monitoring microscopic images and finds the causality of the fibrous structures’ heterogeneity to the resultant machining forces. The results suggest that, for the heterogeneous fibers, the tightly and evenly bounded fiber elements (i.e., with lower aspect ratio, lower eccentricity, and higher compactness) strengthen the material and thereafter play a significant role in increasing the cutting forces during NFRP machining. Therefore, the presented framework of the explainable machine learning approach opens an opportunity to discover the causality of material microstructures on the resultant process dynamics and accurately predict the cutting behaviors during material removal processes.

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A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters

Cited by 3 publications

References 41 publications

Adjunctive Damage Model to Describe the Interaction of Different Defect Types in Textile Composites on the Strain-Rate-Dependent Material Behaviour

Adjunctive Damage Model to Describe the Interaction of Different Defect Types in Textile Composites on the Strain-Rate-Dependent Material Behaviour

A methodology for direct parameter identification for experimental results using machine learning — Real world application to the highly non-linear deformation behavior of FRP

Effect of Microstructure on the Machinability of Natural Fiber Reinforced Plastic Composites: A Novel Explainable Machine Learning (XML) Approach

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