Prediction of PEKK properties related to crystallization by molecular dynamics simulations with a united-atom model

Possibilities of Influencing the Crystallization Process of Bisphenol A- and Bisphenol F-Based Epoxy Resins Used for Hydrophobic Coatings on Concrete

Crystallization of bisphenol A (DGEBA)- and bisphenol F (DGEBF)-based epoxy resins is a natural property of these oligomers. However, manufacturers of coatings and other systems based on these epoxy resins are making efforts to slow down the crystallization process as much as possible, thereby extending the shelf life and improving the competitiveness of their products. This paper focuses on the kinetics of the crystallization process of epoxy resins and the effect of the presence of a certain degree of crystallinity on selected parameters of epoxy-based materials. Furthermore, an analysis of the impact of a certain degree of crystallinity of the epoxy base on the resulting coating parameters was carried out. The highest value of crystallinity (17%) was achieved in the sample containing the highest proportion of DGEBF in the crystallization phase "c", and the enthalpy of melting (Ht) of the crystalline DGEBF sample was 6.3 J/g. Mechanical parameters as well as chemical and thermal resistance of hydrophobic epoxy systems were investigated. The best abrasion resistance (1.5 cm3/50 cm2) was achieved with the blend containing only amorphous DGEBA. The adhesion of the epoxy samples on concrete was greater than 6.5 MPa. The chemical resistance tests performed showed that, in general, the chemical resistance of epoxy systems decreases with increasing crystallinity content. The tighter arrangement of molecules in the crystalline regions of the epoxy matrix results in an increase in density, strength and hardness. This study presents a comprehensive examination of the crystallization of DGEBA and DGEBF, which is, as yet virtually unavailable. It also contributes to knowledge by outlining the possibility of speeding up or slowing down the crystallization process of epoxy resins, including the principle of selecting nucleating agents.

Molecular Dynamics Simulation of Poly(Ether Ether Ketone) (PEEK) Polymer to Analyze Intermolecular Ordering by Low Wavenumber Raman Spectroscopy and X-ray Diffraction

Poly(ether ether ketone) (PEEK) is an important engineering plastic and evaluation of its local crystallinity in composites is critical for producing strong and reliable mechanical parts. Low wavenumber Raman spectroscopy and X-ray diffraction are promising techniques for the analysis of crystal ordering but a detailed understanding of the spectra has not been established. Here, we use molecular dynamics combined with a newly developed approximation to simulate local vibrational features to understand the effect of intermolecular ordering in the Raman spectra. We found that intermolecular ordering does affect the low wavenumber Raman spectra and the X-ray diffraction as observed in the experiment. Raman spectroscopy of intermolecular vibration modes is a promising technique to evaluate the local crystallinity of PEEK and other engineering plastics, and the present technique offers an estimation without requiring heavy computational resources.

Elucidating the Interfacial Bonding Behavior of Over-Molded Hybrid Fiber Reinforced Polymer Composites: Experiment and Multiscale Numerical Simulation

This paper implements molecular dynamics (MD) simulation using reactive force field (ReaxFF) to evaluate the atomistic origin of the interfacial behavior in the overmolded hybrid unidirectional continuous carbon fiber low-melt PAEK (CFR-LMPAEK)-short carbon fiber reinforced PEEK (SFR-PEEK) polymer composites. From the MD simulation, it was observed that the interfacial properties improve with increasing maximum processing temperature and injection pressure although such an improving trajectory gets saturated beyond specific limits. The interfacial strength and fracture response of the hybrid polymer system at the interface are also evaluated. The mechanical responses obtained from MD simulation are used as adhesive properties in the macroscale finite element analysis (FEA)-based single lap joint (SLJ) model where the interfacial behavior between the adherends (CFR-LMPAEK and SFR-PEEK) is implemented using cohesive zone model (CZM). The simulated FE results show a good correlation with the SLJ experimental data. Thus, by linking the interfacial properties at the molecular scale to the performance of the interfacial bond at the macroscale, the comprehensive approach presented here opens up various efficient avenues toward atomistically engineered performance tuning in hybrid overmolded fiber-reinforced polymer composites to meet desired large-scale performance needs.

Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning

The use of composite materials is increasing in industry sectors such as renewable energy generation and storage, transport (including automotive, aerospace and agri-machinery) and construction. This is a result of the various advantages of composite materials over their monolithic counterparts, such as high strength-to-weight ratio, corrosion resistance, and superior fatigue performance. However, there is a lack of detailed knowledge in relation to fusion joining techniques for composite materials. In this work, ultrasonic welding is carried out on a carbon fibre/PEKK composite material bonded to carbon fibre/epoxy composite to investigate the influence of weld process parameters on the joint's lap shear strength (LSS), the process repeatability, and the process induced defects. A 3³ parametric study is carried out and a robust machine learning model is developed using a hybrid genetic algorithm-artificial neural network (GA-ANN) trained on the experimental data. Bayesian optimisation is employed to determine the most suitable GA-ANN hyperparameters and the resulting GA-ANN surrogate model is exploited to optimise the welding process, where the process performance metrics are LSS, repeatability and joint visual quality. The prediction for the optimal LSS was subsequently validated through a further set of experiments, which resulted in a prediction error of just 3%.