Applied Mechanics

Non-crimp fabric (NCF) composites are increasingly adopted for structural components due to their high mechanical performance and processability. Like other fibre-reinforced plastics, NCFs remain vulnerable to in-service damage from tool drops or unintended collisions, which can substantially reduce load-bearing capacity. This study aimed to develop a validated numerical model capable of simulating damage initiation and post-impact behaviour through an integrated experimental–numerical approach. The mechanical properties of a representative unidirectional NCF composite were first experimentally established. Then, tubular NCF subcomponents were fabricated and tested under a two-phase loading protocol. In the first phase, damage was introduced using quasi-static indentation or controlled low-velocity impact. In the second phase, the residual load-bearing capacity of the damaged subcomponents was assessed under four-point bending. To support the research objective, a finite element model was developed in LS-DYNA to simulate both phases, using the MAT_ENHANCED_COMPOSITE_DAMAGE (MAT54) material formulation. Non-measurable input parameters, including stress limit factors and erosion strain thresholds, were calibrated via parameter estimation, sensitivity analysis, and iterative refinement. The final model showed close agreement with experiments in predicted damage location, deformation mode, and residual strength. X-ray computed tomography was used to validate delamination predictions. The findings support the development of reliable and cost-effective numerical tools for damage assessment in advanced composite structures.

The dynamic behavior of the DS306 detacher, a critical component in industrial fiber processing lines, plays a decisive role in maintenance performance and overall operational reliability. This study introduces a strengthened preventive maintenance strategy that leverages vibration analysis and dynamic modeling with a strong emphasis on early fault anticipation. A detailed numerical finite element model of the detacher was developed to determine its natural frequencies, critical modes, and dynamic response under real operating conditions. Experimental vibration measurements were conducted to validate the numerical model and identify characteristic frequencies associated with imbalance and wear. The results show that the proposed predictive framework not only reproduces the machine’s dynamic behavior with high accuracy but also anticipates mechanical degradation trends well before the occurrence of critical failures. This early-warning capability allows maintenance teams to plan interventions proactively, significantly reducing unexpected downtime, avoiding cascading damage, and improving long-term equipment availability. Overall, the study provides a robust and practical methodology for dynamic diagnosis, fault prediction, and optimized preventive maintenance in industrial rotating machinery.

The Discrete Element Method is widely used in applied mechanics, particularly in situations where material continuity breaks down (fracturing, crushing, friction, granular flow) and classical rheological models fail (phase transition between solid and granular). In this study, the Discrete Element Method was employed to simulate stick–slip cycles, i.e., numerical earthquakes. At 2000 selected, regularly spaced time checkpoints, parameters describing the average state of all particles forming the numerical fault were recorded. These parameters were related to the average velocity of the particles and were treated as the numerical equivalent of (pseudo) Acoustic Emission. The collected datasets were used to train the Random Forest and Deep Learning models that successfully predicted the time to failure. SHapley Additive exPlanations (SHAP) was used to quantify the contribution of individual physical parameters of the particles to the prediction results. The main novelty of this study was the prediction of time to failure for entire event sequences. Using only instantaneous particle velocity statistics and without using information about the history of previous events, coefficients of determination in the range R² = 0.81–0.96 were obtained.