Manufacturing process and mechanical properties characterization for steel skin – Carbon fiber reinforced polymer core laminate structures

Quagliato, Luca; Jang, Changsoon; Kim, Naksoo

doi:10.1016/j.compstruct.2018.10.078

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…It has demonstrated superior performance in impact resistance, energy absorption, vibration reduction, heat dissipation, and sound insulation [ 6 , 7 , 8 , 9 ]. The results have shown that steel/carbon fiber composite materials have a higher load-bearing capacity and better energy absorption performance than single-material structures, making them highly applicable in the automotive and rail transportation industries [ 10 ]. This combination offers structural strength and lightweight design advantages, providing innovative solutions for various engineering applications [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates

Hu,

Wei,

Wang

et al. 2024

Polymers

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This study unveils a machine learning (ML)-assisted framework designed to optimize the stacking sequence and orientation of carbon fiber-reinforced polymer (CFRP)/metal composite laminates, aiming to enhance their mechanical properties under quasi-static loading conditions. This work pioneers the expansion of initial datasets for ML analysis in the field by uniquely integrating the experimental results with finite element simulations. Nine ML models, including XGBoost and gradient boosting, were assessed for their precision in predicting tensile and bending strengths. The findings reveal that the XGBoost and gradient boosting models excel in tensile strength prediction due to their low error rates and high interpretability. In contrast, the decision trees, K-nearest neighbors (KNN), and random forest models show the highest accuracy in bending strength predictions. Tree-based models demonstrated exceptional performance across various metrics, notably for CFRP/DP590 laminates. Additionally, this study investigates the impact of layup sequences on mechanical properties, employing an innovative combination of ML, numerical, and experimental approaches. The novelty of this study lies in the first-time application of these ML models to the performance optimization of CFRP/metal composites and in providing a novel perspective through the comprehensive integration of experimental, numerical, and ML methods for composite material design and performance prediction.

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Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates

Hu,

Wei,

Wang

et al. 2024

Polymers

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“…In recent years, the injection molding process has been growing in both importance and utilization thanks to the development of innovative polymers and reinforcements [1,2]. This is especially the case in the automotive industry, where lightweight design [3] and recyclability [4] are becoming ever-growing focus points for research and development. Although injection molding is considered a fairly well-established manufacturing process, several independent parameters must be properly set to achieve a high quality of the molded parts, especially in terms of minimizing warpage, shrinkage, and weld-lines on the critical region of the component [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Gate design algorithm to maximize the fiber orientation effectiveness in thermoplastic injection-molded components

Perin

2023

Materials Research Proceedings

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Abstract. This research presents an automatic algorithm, implemented in a Visual Basic Architecture (VBA), for the optimization of the gate location in thermoplastic injection molding of short fibers reinforced composite materials. The algorithm receives, as input, the geometry of the component and, according to the user’s choice, defines the injection points grid, and relevant versors, on a pre-constructed mesh, automatically runs the finite volume method (FVM) simulation and exports the fiber orientation tensor (FOT) on each node of the mesh. The nodal coordinate of the part and the relevant FOT are then used as the training dataset for a Gradient Boosting (GB) algorithm for the full correlation between injection gate locations and the resulting fiber orientation distribution (FOD), allowing to define the injection gate configuration better suited to maximize the effectiveness of the reinforcement fibers. By coupling the trained GB algorithm with a finite element method (FEM) simulation it was confirmed that the developed algorithm can predict the influence of the gate location on the FOD and the resulting mechanical performances, improving the stiffness between 3.8% and 32.6%, on simple and complex geometries alike.

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“…Therefore, it is important to predict the adhesive behavior at the interface between the steel and the CFRP. Quagliato et al evaluated the bonding strength of dissimilar materials in the forming process of sandwich panels consisting of steel and CFRP [ 8 ]. Kim et al proposed a technique to measure the energy release rate at the adhesive interface of dissimilar materials and investigated the causes of changes in the energy release rate [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Delamination at the Steel and CFRP Interface of Hybrid Composite Part

et al. 2021

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The purpose of this study was to predict the adhesive behavior of steel and carbon-fiber-reinforced plastic (CFRP) hybrid parts based on the cohesive zone model (CZM). In this study, the steel sheet and CFRP were joined by epoxy resin in the CFRP prepreg during the curing process, which could generate delamination at their interface because of the springback of steel or the thermal contraction of the CFRP. First, double cantilever beam (DCB) and end-notched flexure (ENF) tests were performed to obtain various adhesion properties such as the critical energy release rate of mode I, mode II (GI, GII), and critical stress (σmax). A finite element (FE) simulation was performed to predict delamination using CZM, which was also used to describe the interfacial behavior between the steel sheet and the CFRP. Finally, a U-shape drawing test was performed for the steel/CFRP hybrid parts, and these results were compared with analytical results.

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Manufacturing process and mechanical properties characterization for steel skin – Carbon fiber reinforced polymer core laminate structures

Cited by 10 publications

References 23 publications

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates

Experimental and Numerical Investigation Integrated with Machine Learning (ML) for the Prediction Strategy of DP590/CFRP Composite Laminates

Gate design algorithm to maximize the fiber orientation effectiveness in thermoplastic injection-molded components

Prediction of the Delamination at the Steel and CFRP Interface of Hybrid Composite Part

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