An important step towards improving performance while reducing weight and maintenance needs is the integration of composite materials into mechanical and aerospace engineering. This subject explores the many aspects of composite application, from basic material characterization to state-of-the-art advances in manufacturing and design processes. The major goal is to present the most recent developments in composite science and technology while highlighting their critical significance in the industrial sector—most notably in the wind energy, automotive, aerospace, and marine domains. The foundation of this investigation is material characterization, which offers insights into the mechanical, chemical, and physical characteristics that determine composite performance. The papers in this collection discuss the difficulties of gaining an in-depth understanding of composites, which is necessary to maximize their overall performance and design. The collection of articles within this topic addresses the challenges of achieving a profound understanding of composites, which is essential for optimizing design and overall functionality. This includes the application of complicated material modeling together with cutting-edge simulation tools that integrate multiscale methods and multiphysics, the creation of novel characterization techniques, and the integration of nanotechnology and additive manufacturing. This topic offers a detailed overview of the current state and future directions of composite research, covering experimental studies, theoretical evaluations, and numerical simulations. This subject provides a platform for interdisciplinary cooperation and creativity in everything from the processing and testing of innovative composite structures to the inspection and repair procedures. In order to support the development of more effective, durable, and sustainable materials for the mechanical and aerospace engineering industries, we seek to promote a greater understanding of composites.

Purpose: The present study deals with the numerical modeling of the low-velocity impact damage of laminated composites which have increasingly important applications in aerospace primary structures. Such damage, generated by various sources during ground handling, substantially reduces the mechanical residual performance and the safe-service life. The purpose of this paper is to present and validate a computationally efficient approach in order to explore the effect of critical parameters on the impact damage characteristics. Design/methodology/approach: Numerical modeling is considered as one of the most efficient tool as compared to the expensive and time-consuming experimental testing. In this paper, a finite element model based on explicit dynamics formulations is adopted. Hashin criterion is applied to predict the intralaminar damage initiation and evolution. The numerical analysis is performed using the ABAQUS® programme. Findings: The employed modeling approach is validated using corresponding numerical data found in the literature and the presented results show a reasonable correlation to the available literature data. It is demonstrated that the current model can be used to capture the force-time response as well as damage parameter maps showing the intralaminar damage evolution for different impact cases with respect to the physical boundary conditions and a range of impact energies. Originality/value: Low-velocity impact damage of laminated composites is still not well understood due to the complexity and non-linearity of the damage zone. The presented model is used to predict the force-time response which is considered as one of the most important parameters influencing the structural integrity. Furthermore, it is used for capturing the damage shape evolution, exhibiting a high degree of capability as a damage assessment computational tool.

This study explores the shape-morphing behavior of 4D-printed structures made from Polylactic Acid (PLA), a prominent bio-sourced shape-memory polymer. Focusing on the response of these structures to thermal stimuli, this research investigates how various printing parameters influence their morphing capabilities. The experimental approach integrates design and slicing, printing using fused deposition modeling (FDM), and a post-printing activation phase in a controlled laboratory environment. This process aims to replicate the external stimuli that induce shape morphing, highlighting the dynamic potential of 4D printing. Utilizing Taguchi’s Design of Experiments (DoE), this study examines the effects of printing speed, layer height, layer width, nozzle temperature, bed temperature, and activation temperature on the morphing behavior. The analysis includes precise measurements of deformation parameters, providing a comprehensive understanding of the morphing process. Regression models demonstrate strong correlations with observed data, suggesting their effectiveness in predicting responses based on control parameters. Additionally, finite element analysis (FEA) modeling successfully predicts the performance of these structures, validating its application as a design tool in 4D printing. This research contributes to the understanding of 4D printing dynamics and offers insights for optimizing printing processes to harness the full potential of shape-morphing materials. It sets a foundation for future research, particularly in exploring the relationship between printing parameters and the functional capabilities of 4D-printed structures.

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.