A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites

The investigation of the mechanical properties of additively manufactured (AM) composite has been the focus of several research over the past decades. However, testing constraints of time and cost have encouraged the exploration of more pragmatic methods such as machine learning (ML) for predicting these characteristics. This study builds on experimental investigations of the flexural, tensile, compressive, porosity, and hardness properties of 3D printed carbon fibre-reinforced polyamide (CF-PA) and carbon fibre-reinforced acrylonitrile butadiene styrene (CF-ABS) composites, proposing the application of ML for predicting these mechanical properties. A comprehensive comparative analysis of various machine learning approaches was executed, with a resultant accuracy ranging between 80 and 99%. The results unveiled the superior predictive performance of ensemble tree learners and the K-NN regressor algorithms when temperature and porosity are selected (based on correlation analysis) as predictors for material hardness and strength in tension, compression, and flexion. In particular, the model built on the extra-tree regressor algorithm demonstrated a remarkably robust fit, with R-squared evaluation scores of 0.9993 and 0.9996 for CF-PA and CF-ABS, respectively. This work develops a ML model that relates porosity to the other mechanical properties of AM composites and the prediction models’ exceptional accuracy, along with their precise alignment with experimental data, provide invaluable insights for the autonomous control and data-driven optimization of the structures

Damage assessment of glass-fibre-reinforced plastic structures under quasi-static indentation with acoustic emission

The use of fibre-reinforced plastics (FRPs) in various industrial applications continues to increase thanks to their good strength-to-weight ratio and impact resistance, as well as the high strength that provides engineers with advanced options for the design of modern structures subjected to a variety of out-of-plane impacts. An assessment of the damage morphology under such conditions using non-destructive techniques could provide useful data for material design and optimisation. This study investigated the damage mechanism and energy-absorption characteristics of E-glass laminates and sandwich structures with GFRP face sheets with PVC cores under quasi-static indentation with conical, square, and hemispherical indenters. An acoustic emission (AE) technique, coupled with a k-means++ pattern-recognition algorithm, was employed to identify the dominant microscopic and macroscopic damage mechanisms. Additionally, a post-mortem damage assessment was performed with X-ray micro computed tomography and scanning electron microscopy to validate the identified clusters. It was found that the specific energy absorption after impact with the square and hemispherical indenters of the GFRP sandwich and the plain laminate differed significantly, by 19.29% and 43.33%, respectively, while a minimal difference of 3.5% was recorded for the conical indenter. Additionally, the results obtained with the clustering technique applied to the acoustic emission signals detected the main damaged modes, such as matrix cracking, fibre/matrix debonding, delamination, the debonding of face sheets/core, and core failure. The results therefore could provide a methodology for the optimisation and prediction of damage for the health monitoring of composites.</p

Damage assessment of glass-fibre-reinforced plastic structures under quasi-static indentation with acoustic emission

The use of fibre-reinforced plastics (FRPs) in various industrial applications continues to increase thanks to their good strength-to-weight ratio and impact resistance, as well as the high strength that provides engineers with advanced options for the design of modern structures subjected to a variety of out-of-plane impacts. An assessment of the damage morphology under such conditions using non-destructive techniques could provide useful data for material design and optimisation. This study investigated the damage mechanism and energy-absorption characteristics of E-glass laminates and sandwich structures with GFRP face sheets with PVC cores under quasi-static indentation with conical, square, and hemispherical indenters. An acoustic emission (AE) technique, coupled with a k-means++ pattern-recognition algorithm, was employed to identify the dominant microscopic and macroscopic damage mechanisms. Additionally, a post-mortem damage assessment was performed with X-ray micro computed tomography and scanning electron microscopy to validate the identified clusters. It was found that the specific energy absorption after impact with the square and hemispherical indenters of the GFRP sandwich and the plain laminate differed significantly, by 19.29% and 43.33%, respectively, while a minimal difference of 3.5% was recorded for the conical indenter. Additionally, the results obtained with the clustering technique applied to the acoustic emission signals detected the main damaged modes, such as matrix cracking, fibre/matrix debonding, delamination, the debonding of face sheets/core, and core failure. The results therefore could provide a methodology for the optimisation and prediction of damage for the health monitoring of composites.</p

A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites

The investigation of the mechanical properties of additively manufactured (AM) composite has been the focus of several research over the past decades. However, testing constraints of time and cost have encouraged the exploration of more pragmatic methods such as machine learning (ML) for predicting these characteristics. This study builds on experimental investigations of the flexural, tensile, compressive, porosity, and hardness properties of 3D printed carbon fibre-reinforced polyamide (CF-PA) and carbon fibre-reinforced acrylonitrile butadiene styrene (CF-ABS) composites, proposing the application of ML for predicting these mechanical properties. A comprehensive comparative analysis of various machine learning approaches was executed, with a resultant accuracy ranging between 80 and 99%. The results unveiled the superior predictive performance of ensemble tree learners and the K-NN regressor algorithms when temperature and porosity are selected (based on correlation analysis) as predictors for material hardness and strength in tension, compression, and flexion. In particular, the model built on the extra-tree regressor algorithm demonstrated a remarkably robust fit, with R-squared evaluation scores of 0.9993 and 0.9996 for CF-PA and CF-ABS, respectively. This work develops a ML model that relates porosity to the other mechanical properties of AM composites and the prediction models’ exceptional accuracy, along with their precise alignment with experimental data, provide invaluable insights for the autonomous control and data-driven optimization of the structures.</p

A machine learning-enabled prediction of damage properties for fiber-reinforced polymer composites under out-of-plane loading

For understanding the damage morphology in fiber-reinforced plastics under out-of-plane loading, engineers/ and designers mostly rely on experimental investigations and numerical models, which could be costly in terms of resources and computational requirements. Thus, pragmatic predictive approaches that can offer ready-to-use tools for material optimization have gained traction in recent years. This paper proposes a machine learning model, developed with the use of previous experiments, to predict the damage behaviour of E-glass fiber-reinforced plastics in sandwich structures and laminates. The focus is on quasi-static indentation with different indenter geometries, using acoustic signals for damage monitoring. A total of 10 optimal-performing regression algorithms were employed to develop the prediction models. The developed models based on total energy gave acceptable results for all specimens (R-squared value: 0.9932 – 0.9999), while prediction models using force produced less accurate results (R-squared value: 0.8693) for a sandwich structure subjected to a conical indenter. Results showed that energy absorption of composites provided the most reliable data for the development of predictive models. The load profiles could also be used when the contact area is limited (e.g., for hemispherical and conical indenters). The best-performing model for higher surface areas of the indenter increased was k-NN, thanks to its ability to capture complex and nonlinear relationships between the input features and the target variable. Overall, the developed model could provide more autonomy and control for the material optimisation of composite structures for industrial applications with reduced time and cost constraints. </p