Tension-Compression Fatigue Induced Stress Concentrations in Woven Composite Laminate

Tension-compression (T-C) fatigue response is one of the important design criteria for carbon-fibre-reinforced polymer (CFRP) material, as well as stress concentration. Hence, the objective of the current study is to investigate and quantify the stress concentration in CFRP dog-bone specimens due to T-C quasi-static and fatigue loadings (with anti-buckling fixtures). Dog-bone specimens with a [(0/90),(45/−45)4]s layup were fabricated using woven CFRP prepregs and their low-cycle fatigue behaviour was studied at two stress ratios (−0.1 & −0.5) and two frequencies (3 Hz & 5 Hz). During testing, strain gauges were mounted at the centre and edge regions of the dog-bone specimens to obtain accurate, real-time strain measurements. The corresponding stresses were calculated using Young’s moduli. The stress concentration at the specimen edges, due to quasi-static tension, was significant compared to quasi-static compression loads. Furthermore, the stress concentration increased with the quasi-static loading within the elastic limit. Similarly, the stress concentration at the specimen edges, due to tensile fatigue loads, was more significant and consistent than due to compressive fatigue loads. Finally, the effects of the stress ratio and loading frequency on the stress concentration were noted to be negligible.

Analysis of the Failure Process of Elements Subjected to Monotonic and Cyclic Loading Using the Wierzbicki–Bai Model

This article presents the results of a simulation in which smooth cylindrical and ring-notched samples were subjected to monotonic and fatigue loads in an ultra-short-life range, made of Inconel 718 super alloy. The samples displayed different behaviors as a result of different geometries that introduced varying levels of stress triaxiality and loading methods. The simulations used the Wierzbicki–Bai model, which took into account the influence of stress tensors and stress-deviator invariants on the behavior of the material. The difference in the behaviors of the smoothed and notched specimens subjected to tensile and fatigue loads were identified and described. The numerical results were qualitatively supported by the results of the experiments presented in the literature.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.