PVC-coated fabrics are widely used in tarpaulin applications due to their durability and flexibility. This study investigates the tearing behavior of PVC-coated fabric specimens containing central straight and inclined slits, sampled from S8210-type tarpaulins used in freight wagons. Uniaxial tensile tests were performed on both intact and slit specimens. Horizontal slits having lengths of 5, 10, and 20 mm, as well as inclined slits of 20 mm at 30°, 45°, and 60° angles, were analyzed. The results reveal two distinct fracture modes: progressive and abrupt failure. Inclined slits exhibited a progressive, Z-shaped crack propagation pattern while the horizontal slits lead to a more sudden failure. Key fracture parameters, including maximum fracture force, fracture energy, and the influence of slit geometry on fracture strength, were determined. A critical opening length beyond which the tarpaulin's repair is recommended was also identified. The paper translates the theoretical concepts of breaking energy in a directly measurable parameter (aech). This can be investigated directly on site by the maintenance personnel and based on the reading and proposed tables for this material, can make safety decisions.
This paper proposes an optimized control strategies for a dynamics 6-link robot manipulator using Model Predictive Control (MPC) and traditional Computed Torque Control (CTC). Two optimization methods are used for tuning of MPC controller, namely MPCbased Dandelion Optimization (DO) and MPCbased Genetic Algorithm (GA). The strategies are assessed in terms of performance indices based timedomain, settling time, rise time, overshoot percentage, and steady-state error (SSE). Simulation results depicted that traditional CTC suffers from higher control effort and slower transient response, mainly for links with high order. In contrast, MPC-based control strategy significantly improve dynamic performance and tracking accuracy. Among the tested approaches, MPCbased DO achieves minimal overshoot, smoother torque profiles, fastest convergence, and the lowest SSE across all six robotics links. The attained MATLAB/SIMULINK results confirm and prove that optimized MPC controller, especially MPCbased DO, offers an efficient and robust control solution for high DOF robotic manipulators.
Dynamic flexible job shop scheduling under machine tool breakdowns represents a complex and highly constrained combinatorial optimization problem in modern manufacturing systems. This research paper proposes an integrated optimization framework that simultaneously determines operation sequencing, machine tool assignment, tool selection, and tool orientation with the objective function of minimizing makespan. A unified multi-string solution representation is developed to simultaneously model all decision layers. Three biologically inspired metaheuristic algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Hippopotamus Optimization (HO), are implemented using the proposed encoding scheme. A rescheduling strategy is introduced to preserve completed operations while rescheduling the affected operations after machine tool failures. Experimental verification demonstrates that the integrated framework effectively handles dynamic disturbances and significantly improves scheduling performance. Comparative analysis shows that the hippopotamus optimization algorithm achieves superior convergence behavior and better objective function values than the other approaches. The proposed method provides a robust framework for resilient scheduling under multiple resource constraints.
This study explores the use of soybean oil-based nanolubricants that incorporate hexagonal boron nitride (h-BN), multi-walled carbon nanotubes (MWCNTs), and their hybrid at a concentration of 0.15 wt% for minimum quantity lubrication (MQL) in the computer numerical control (CNC) milling of AISI 1045 steel. Nano-cutting fluids were prepared via a two-step process, involving stirring followed by ultrasonication, and were characterized for density, viscosity, thermal conductivity, and rheology. Machining responses comprised tool wear length, cutting temperature, surface roughness, chip morphology, and chip colors. All samples exhibited Newtonian behavior at 40 °C and 100 °C. The hybrid h-BN/MWCNT formulation delivered the best overall performance, achieving the lowest tool wear length (~0.067 mm), reduced cutting temperatures, and the smoothest surfaces relative to dry cutting and single-additive fluids. The improvements are attributed to a synergistic mechanism in which h-BN platelets form a boundary-lubrication tribofilm, while MWCNT act as nano-bearings and a thermally conductive network that stabilizes the cutting zone. Simple sedimentation observations indicated higher dispersion stability for MWCNT-containing nano-cutting fluids than for h-BN alone. These results suggest that hybrid nano-additives in plant-oil-based cutting fluids may offer practical benefits for MQL-assisted machining or, particularly by helping reduce tool wear, improve thermal management, and support more sustainable machining.
This paper presents a comprehensive framework for predicting the power requirements of high-speed craft, including semi-displacement, semi-planing, and planing hull forms, through the use of statistically-based mathematical models. These models belong to the class of reduced-order models, which streamline the inherently complex processes of modeling hull resistance, dynamic trim, and propeller efficiency, including the interactions of multiple design parameters. Model development has relied on various regression techniques and, more recently, artificial neural networks (ANN). These mathematical models often offer advantages over the CFD techniques that nowadays dominate professional journals and therefore deserve careful attention. This review presents the methodology in a structured way to facilitate its integration into power prediction and design-optimization workflows and adopts a user-focused perspective.
