Fast-curing epoxy resins enable substantial reduction of cycle times during production of thermoset polymer matrix composites. Due to the snap-cure behaviour, both characterisation and processing of these resins are associated with high complexity which motivates the development of a high-fidelity framework for the prediction of the process-dependent behaviour ranging from experiment to model validation. In order to determine influence of time, temperature, and degree of cure, a multitude of rheometer and dynamic mechanical analysis experiments are conducted and evaluated. Building on the experimental results, a material model based on a generalised Maxwell model is developed. It is calibrated on the results obtained in the tests and shown to describe the material’s behaviour with high accuracy under all investigated conditions. The model’s predictive capabilities are further tested by applying it to a dynamic mechanical analysis, exposing the model to previously unknown loading and temperature conditions. It is demonstrated that the model is capable of predicting such changing boundary conditions with high accuracy.

The 3D shear deformation and failure behaviour of a glass fibre reinforced polypropylene in a shear strain rate range of $\dot{\gamma }=2.2\times {10}^{-4}\mathrm{to}3.4\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\mathrm{s}$}\right.$ is investigated. An Iosipescu testing setup on a servo-hydraulic high speed testing unit is used to experimentally characterise the in-plane and out-of-plane behaviour utilising three specimen configurations (12-, 13- and 31-direction). The experimental procedure as well as the testing results are presented and discussed. The measured shear stress–shear strain relations indicate a highly nonlinear behaviour and a distinct rate dependency. Two methods are investigated to derive according material characteristics: a classical engineering approach based on moduli and strengths and a data driven approach based on the curve progression. In all cases a Johnson–Cook based formulation is used to describe rate dependency. The analysis methodologies as well as the derived model parameters are described and discussed in detail. It is shown that a phenomenologically enhanced regression can be used to obtain material characteristics for a generalising constitutive model based on the data driven approach.

Process-induced surface waviness effects represent a major concern for series production of high-quality lightweight structures based on fiber reinforced plastics (FRP). This paper suggests a method for the numerical prediction of these effects by using the example of processing glass fiber reinforced plastics (GFRP) in a resin transfer molding (RTM) process. The influence of reaction kinetics, chemical shrinkage and cure-dependent viscoelastic properties of the resin are taken into account. Furthermore, the dependence of surface quality on curing cycle, consolidation pressure, textile architecture and thickness of neat resin layer (NRL) at the part surface are investigated. The work is based on published material data and a visco-thermo-elastic simulation approach which has been previously presented and validated. All numerical results are compared to the surfaces of FRP plates that were manufactured with the corresponding parameter variations. Based on a literature survey, different surface waviness values have been identified for comparison of experimental and numerical results. Satisfactory agreement between experiments and simulations is found. Furthermore, it is shown that the analyzed NRL thickness has no relevant influence on the surface waviness while the curing temperature significantly affects the surface waviness. The role of relaxation-induced change of the surface waviness is highlighted by performing long-term measurements and corresponding time-dependent simulations. It is concluded that relaxation plays a decisive role in the appropriate selection of the subsequent surface finishing process. The suggested simulation approach provides a basis for optimization strategies to improve surface quality and reduce post-processing effort.

Modern impact monitoring systems combine multiple inputs with machine learning (ML) models for impact detection, localization, and event assessment. Their accuracy relies on large, event-representative datasets, used for algorithmic development and ML model training. High-fidelity numerical models can provide augmented datasets by overcoming the cost and time limitations of experimental methods. This research presents an end-to-end numerical methodology for impact detection based on simulation (training) and experimental (testing) data. Initially, a finite element model (FEM) of our experimental setup utilizing piezoelectric transducer (PZT) sensors mounted on a thermoplastic plate is created. From the experimental impact signals, a few consistent cases are identified for feature extraction. A design of experiments explores the range of each parameter, and through surrogate optimization, the material and piezoelectric properties of the setup are determined. Subsequently, a virtual dataset, involving multiple impact cases, is created to train the ML models performing impact detection. Testing with experimental data shows results consistent with literature studies that used only experimental data for both training and testing. This work provides a systematic methodology for representative dataset generation and impact monitoring through ML, while addressing accurate FEM parameter identification from a few experimental tries.