Lotfi Toubal

Additive manufacturing (AM), particularly in composite materials, has revolutionized the industries, offering design flexibility and enabling the creation of lightweight, high-strength components. In structural applications, joining composite parts often requires holes, which can be generated through various methods. This study investigates the effects of two hole generation techniques: conventional drilling and direct printing on the mechanical performance of additively manufactured Carbon fiber-reinforced polymer (CFRP) composites. Experimental tests, including tensile and three-point bending (3PB), were conducted and instrumented with Digital Image Correlation (DIC) and Acoustic Emission (AE). DIC was used to monitor strain localization, whereas AE was employed as a damage characterization tool to identify and compare the different failure mechanisms throughout the loading process. Finite Element Models (FEM) were developed to validate the experimental findings. Results showed slightly higher tensile stiffness and strength for the drilled-hole specimens. This difference was associated with variations in hole geometry, edge condition and local fiber architecture. Conversely, printed specimens showed higher displacement at failure, attributed to concentric rings around the holes. On the other hand, 3PB tests highlighted brittle failure in drilled specimens and progressive post-peak response with greater deformation before final failure in printed ones. FEM models showed good agreement with experimental data, validating their accuracy in predicting mechanical behavior and damage localization. This study highlights the critical influence of hole generation methods on the mechanical performance of AM composites and underscores the need for further optimization to enhance mechanical performance. Future research should focus on microstructural analysis, environmental testing, and sustainability assessments to advance AM applications in industry.

This study compares the mechanical, dynamic, and damage behavior of transfemoral prosthetic sockets manufactured from carbon-fiber (OPNC) and flax-fiber (OPNF) composites using vacuum molding with C-Orthocryl resin. Standardized specimens were subjected to tensile, three-point bending, and split-disk tests, while impulse excitation and acoustic emission (AE) analyses were employed to investigate dynamic stiffness, damping, and damage evolution. Fracture surfaces were characterized using scanning electron microscopy (SEM). Under tensile loading, OPNC exhibited higher strength and stiffness (79.9 MPa and 11.9 GPa) than OPNF (63.2 MPa and 6.1 GPa). Similarly, OPNC showed greater circumferential strength in split-disk tests (25.45 MPa) compared with OPNF (19.64 MPa). However, OPNF demonstrated higher strain to failure and a more progressive damage evolution, indicating lower brittleness and greater deformability. In bending, both laminates displayed comparable stiffness, with flexural moduli of 4.83 GPa for OPNC and 4.72 GPa for OPNF. Dynamic analysis revealed higher resonance frequency and damping factor for OPNF (786 Hz and 0.044) than for OPNC (730 Hz and 0.035), whereas OPNC retained a slightly higher dynamic modulus (7.78 GPa vs 7.38 GPa). AE monitoring showed gradual damage accumulation in OPNF, dominated by matrix cracking and delamination, while OPNC exhibited abrupt energy release associated with brittle fiber fracture. SEM observations confirmed stronger interfacial adhesion in OPNF and rapid crack propagation in OPNC. Overall, flax-fiber composites provided a balanced combination of mechanical integrity, damage tolerance, vibration damping, and sustainability, highlighting their potential as an alternative to carbon-fiber composites for prosthetic socket applications.

This study investigated the shape memory behavior of 4D printed polylactic acid (PLA) and its natural fiber-based biocomposites reinforced with bamboo and flax. A full factorial design approach was applied to evaluate the influence of specimen thickness (0.5 mm and 1 mm), printing orientation (0° and 90°), and programmed deformation angle (90° and 130°) on recovery efficiency. Specimens (S) were fabricated by fused deposition modeling, thermomechanically programmed at 60 °C, and evaluated for thermal recovery. The results demonstrate that printing orientation was the dominant factor, with 90° alignment consistently enhancing recovery due to improved stress distribution and chain mobility. A negative effect of thickness was observed, particularly for PLA/Flax 79.5%–72.47%. PLA/flax composites showed the best and most stable recovery behavior variations <±2°, while PLA/Bamboo composites exhibited intermediate but reliable performance. Kinetic analysis revealed a three-phase recovery mechanism: rapid initial response 0–3 s (s), progressive stabilization 3–10 s, and thermomechanical equilibrium 10–30 s. Mechanical tests confirmed that fiber reinforcement reduced tensile strength but enhanced dimensional stability. Differential scanning calorimetry and thermogravimetric analysis studies validated the thermal suitability and processing stability of the materials.

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R²) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R² > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring.

Modern trends in using materials for drive applications encourage new research and solutions based on green materials. To expand the use of these materials in specific industrial environments, it is essential to understand their properties, which are determined through basic laboratory tests that simulate the product's real operation. Evaluating the performance of these materials on test specimens and real parts, such as gears, will enable precise optimization for specific applications. This paper presents systematic fatigue and wear characterization of high‐density polyethylene (HDPE) reinforced with 30% birch natural wood fibers for selected gear pair cases. The results showed that using the material in combination with a drive steel gear is more desirable than using the same material in a gear pair. The calculated wear coefficient of the biobased composite is comparable to numerical values of wear coefficients for engineering polymer materials.

This paper offers an experimental approach to use machine learning (ML) models and Acoustic Emission (AE) data for the identification of damage mechanisms and predict the mechanical properties of 3D printed bio-composite. The specimens were produced using a bio-filament made of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers. AE signals were gathered during tensile and flexural tests in order to monitor the progression of damage under mechanical loading. subsequently, using Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Network (ANN), and Decision Tree (DT) models, the stress levels of the specimens under both test conditions were predicted. These algorithms were trained with 80% of the data, in a Python environment. 20% remained, to be used for testing. The models' accuracy was assessed using R-squared (R²) and Mean Squared Error (MSE) metrics. While the other models also demonstrated outstanding prediction capabilities for both tensile and flexural stresses, the RFR model outperformed the others. In addition, 5-fold cross-validation yielded results consistent with the hold-out test, further validating the models' accuracy. This research demonstrates how well these machine learning algorithms analyze AE data for material property evaluation, paving the way for data-driven approaches in material testing and health monitoring.

The use of thick composites in civil engineering and aeronautics fields is becoming increasingly common in primary or secondary structures, and natural fiber composites present a valuable alternative in terms of mechanical performance, cost and environmental impact. Investigating the mechanical characteristics and damage mechanisms that occur in thick flax/epoxy laminates is the primary objective of this research. Firstly, multi-instrumented bending tests and interlaminar shear tests were carried-out to assess the mechanical properties and failure characteristics of the composite. This multi-instrumentation concerns, the Acoustic Emission and the Digital Image Correlation techniques. In addition, postmortem observations utilizing Computed Tomography (CT) Scan and scanning electron microscopy (SEM) were carried out. Finite Elements and analytical models were proposed to evaluate the mechanical response. At the macroscale, results demonstrate a good correlation in the elastic zone between the numerical, analytical and the experimental. However, at the mesoscale the correlation is less evident especially close to the final failure of the laminate. Based on SEM and X-ray tomography images, the differences recorded are related mainly to the plies thickness variations and the waviness phenomenon induced by the manufacturing process.