Automated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite manufacturing. The interlaminar strength of AFP-manufactured composites depends on the in-situ thermal history during manufacturing. The thermal history is controlled by the choice of processing conditions and improper interfacial temperatures may result in insufficient bonding. Being able to better predict such maintenance issues in real-time is an important focus of smart manufacturing and Industry 4.0 to improve manufacturing operations. The data analysis of real-time temperature measurements during AFP composites manufacturing requires the temperature profiles from Finite Element Analysis (FEA) based simulations of the AFP process to better predict the quality of layup. However, the FEA simulations of the AFP process are computationally expensive. This study focuses on developing a digital tool enabling real-time process monitoring and predictive maintenance of the AFP process. The digital tool constitutes a machine learning-based surrogate model based on results from Finite Element Analysis (FEA) simulations of the AFP process to predict the in-situ thermal profile during AFP manufacturing. Multivariate Linear Regression, Multivariate Polynomial Regression, Support Vector Machine, Random Forest and Artificial Neural Network (ANN)-based models are compared to conclude that ANN based surrogate model performs best by predicting the important parameters of thermal profiles with a mean absolute percentage error of 1.56% on additional test data and reducing the time by four orders of magnitude as compared to FEA simulations. The predicted thermal profile can be compared with the real-time in-situ temperatures during manufacturing to predict the quality of the layup. A GUI application is developed to provide predicted thermal profiles data for analysis in conjunction with real-time temperatures during manufacturing enabling monitoring and predictive maintenance of the AFP process and paving way for the development of a digital twin of the AFP composites manufacturing process.

Mechanical properties of fibre reinforced composites are primarily dependent on those of fibres. Fibre properties are used for estimating the damage and strength behaviour of composite materials and structures. Tensile strength of fibres is commonly determined by single fibre tensile tests, which is challenging and is prone to measurement errors. In this study, different possible sources of errors due to experimental limitations in the fibre testing process were identified. Their effect on fibre tensile strength was analytically modelled. This model was used to evaluate the uncertainty in experimentally determined fibre strength. A sensitivity analysis was conducted to rank the relative significance of input quantities on the calculated fibre strength. Since composite models require fibre properties determined at very small gauge lengths, the results of the sensitivity analysis were extrapolated to determine critical parameters for tests done at those small gauge lengths of a few millimetres. It was shown that, for sufficiently long fibres, their strength depends mainly on the diameter and failure force; however, for shorter gauge lengths, the effects of misalignment become very significant. The knowledge of uncertainty would be useful in estimating the reliability of the predictions made by composite strength models on the damage and failure behaviour of composite materials and structures. Minimising the influence of critical parameters on fibre strength would help in designing improved single fibre testing systems capable of determining fibre strength more accurately.

Knowledge of fibre strength is crucial for understanding the failure behaviour of fibre-reinforced composite materials and structures. Measuring the properties of technical fibres has been known to be very challenging, and the different challenges associated with single fibre characterisation are illustrated in this article. An improved and automated experimental methodology for tensile testing of single fibres is described. This process has been used to generate fibre strength data for T700 carbon fibres at three different gauge lengths of 4, 20 and 30 mm. The variability in strength and modulus of short fibres was found to be much larger than that of longer fibres. Statistical analysis of this large data set has also highlighted the limitations of the standard Weibull distribution for representing fibre strength behaviour. The need for a better statistical representation of the fibre strength data in order to provide a more accurate description of the fibre strength behaviour has been emphasized.

Many numerical models of the Automated Fibre Placement (AFP) manufacturing process have been developed to assist in the design process of composite components and structures. Although the tape placement process, in general is a 3-D transient process, limited models considering the 3-D nature of the process exist. This paper describes the development of a 3-D Finite Element (FE) model incorporating the effect of non-concentrated input heat flux distribution on adjacent paths. The model can predict an accurate thermal history of a thermoplastic composite laminate for a complete AFP deposition process. Composite laminates having plies made up of both a single tow and multiple tows have been simulated to study their thermal behaviour during the manufacturing process. Simulations have been conducted both with and without considering the input heat effect on adjacent tows for comparison. Temperatures were measured by embedding two different configurations of optical fibre Bragg grating (FBGs) sensors within the laminate during the lay-up process. This served a dual purpose of validating the model as well as the experimental methodology.