In this paper, we put forward a classifier to automatically identify the most suitablemechanical behaviour law among two candidates, enabling optimal modelling ofexperimental data from a uniaxial tensile test, represented as elongation–specific stresscurves. An ArtificialNeuralNetwork (ANN) is employed to performthis classificationtask and assist the modeler, even when the resulting curves from different models arevery close. This paper compares two different methods that enable supervised learningof the neural network from a training dataset labelled with specific stress values as afunction of elongation, without requiring any external data. The learning process isthen validated by testing the network’s ability to correctly identify the most suitablemodel from experimental data it has never encountered before. This approach couldpotentially pave the way for adaptations to more complex tasks in a multidimensionalcontext or to feature recognition in imaging, in the frame of various fields of materialsmechanics.
The use of cutting fluids (CF) is regarded as one of the most efficient supplementarymethods to lower production expenses and improve the quality of machined surfaces.In this study, the influence of CF’s flow rate on machining performance of AISI1045 was comparatively investigated in turning operations. The experimental designincluded different cutting velocities, feed rates, cutting depths and three flowing rates(2.5, 4, 6 l/min) of CF. 5% concentration of potassium dichromate (K2Cr2O7) in waterwas used as a CF. The evaluation of machining performance was studied based onthermos-electromotive force (Thermo-EMF), chip shrinkage and formation, and toolwear characterization. The results indicated that high rates of fluid flow into cuttingzone yielded better results in terms of reducing the thermo-EMF, minimizing toolwear and BUE, and improving chip morphology. It was then found that increasing thefluid flow rate during cutting leads to early fracture of the chip and the segmented chipformation. In addition, with an increase in the CF flow rate from 2.5 l/min to 6 l/min,the thermo-EMF generated during turning of AISI 1045 with titanium alloyed carbidetools decreased to 13%, and flange wear to 160%.
The flutter of a shape memory alloy (SMA) fiber-reinforced panel in hypersonicflow is investigated. A nonlinear aerothermoelastic governing equation is formulatedby coupling Von Kármán’s large deflection plate theory with first-order piston theoryaerodynamics. Discretization via the Galerkin method enables the derivation ofanalytical solutions for the critical dynamic pressure and flutter frequency, using theRouth-Hurwitz criterion and Hopf bifurcation theory. Numerical integration confirmsthe predicted stability loss via a supercritical Hopf bifurcation, leading to limit cycleoscillations. The results demonstrate thatSMAfibers raise the flutter boundary throughrecovery stress and a high elastic modulus, which increase the equivalent structuralstiffness –an effect that intensifies with SMA volume fraction. Conversely, aerodynamicheating induces thermal softening, creating a destabilizing thermomechanicalfeedback loop that reduces the stability margin. A nonlinear saturation effect is observed:beyond a critical fiber content, the marginal stability benefit diminishes. Thissuggests that strategically placing localized, high-concentration SMA fibers providesa mass-efficient strategy for significant stability enhancement.
Solar energy is a source of renewable energy and is considered a clean, convenient,sustainable, safe, abundant, and economical source. Solar Air Heaters (SAHs) are oneof the applications of solar energy. SAH has a wide variety of applications but has lowthermal efficiency. This low thermal efficiency is due to the low thermal capacity ofthe air, and the poor process of heat transfer between the absorbing plate channel andair.With the aim to improve thermal efficiencies, the novel dimple/protrusion surfaceshave been incorporated onto the 307 × 212 mm aluminum absorbing plate. A 3 × 3array of 5 mm impinging jets is used to further decrease the temperature of the plate.A set of five different plate configurations, i.e., smooth, dimpled, dimpled-protruded,protruded-dimpled, and protruded configurations are simulated by using the experimentallyvalidated Computational Fluid Dynamics (CFD) model. The values of heattransfer enhancement, pressure drop, and Thermal Performance Factor were evaluatedfrom the CFD study. The protruded plate is selected as the best plate configuration,based on the Thermal Performance Factor. An overall improvement of 33.5% is observedin the current research work, as compared to the smooth configuration. Hencepromising is the augmentation.
Flexible manufacturing systems (FMS) are widely used in modern mechanicalengineering and instrumentation due to their adaptability and high productivity. However,their effective operation is strongly dependent on accurate management of cuttingtool (CT) resources, which are subject to wear during machining processes. This studyaims to develop and justify a methodology for evaluating CT resource consumptionwith higher precision to support decision-making in FMS operations. Machining operationsare classified into positional and contour types based on their tool engagementcharacteristics. For each type, appropriate quantitative wear criteria are proposed: thenumber of discrete tool actions for positional machining and the processed volume orpath length for contour machining. A Monte Carlo-based numerical procedure is developedto estimate the actual volume of material removed during contour operations,accounting for overlapping tool trajectories and idle movements. The results confirmthat the proposed approach significantly improves the accuracy of tool workloadestimation compared to traditional time-based methods. The methodology enables more reliable forecasting of CT wear, supports predictive maintenance strategies, andimproves overall system efficiency. The proposed algorithm can be integrated intodigital manufacturing workflows, contributing to the implementation of intelligenttool management in line with Industry 4.0 objectives.
