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Para-aramid fabrics see service in a great variety of applications, such as heavy weight lifting applications, penetration protective multilayer panels, etc. It is, therefore, increasingly important to understand the strain rate range at which the fabric has optimum mechanical properties. Although this is a field that has not been studied before, it is of great significance since it allows for the determination of the fabric’s layer location within the multilayered structure which offers maximum overall performance. The paper aims to discuss this issue.

Rectangular strips of PARAX 300 S8 woven para-aramid fabric underwent uniaxial tensile tests at various extension rates. The angle between two fibers at the center of each specimen was measured after the fabrics were elongated at different tensile extensions. This recovery angle was determined by visual analysis of the test video recordings after specimen unloading. Based on this, the recovery of the weaving form after unloading was also estimated for each tensile extension. A recovery degree based deformation characterization of the sections of a typical load/extension curve has been introduced.

The fabric does not appear to be strain rate sensitive for a strain rate range of 0.03 s-1 to 0.53 s-1, and its load/extension characteristics are generally not affected by the extension rate. However, break load and maximum elongation values appear reduced at actuator velocity of 2,400 mm/min and enhanced at 3,600 mm/min. Finally, the effect of extension rate on the different deformation zones of the material is reported and discussed.

The current research work offers a novel approach for the investigation of non-standard response of woven para-aramid fabrics when subjected to tensile loading under various strain rates. Additionally, a new approach is introduced to explain in detail the deformation zones based on the recovery degree of the fiber orientation angle after unloading.

Undertakes an analysis of electromagnetic forming process. Despite the fact that it is an old process, it is able to treat current problems of advanced manufacturing technology. Primary emphasis is placed on presentation of the physical phenomena, which govern the process, as well as their numerical representation by means of simplified electrical equivalent circuits and fully coupled fields approach of the electromagnetic‐mechanical‐electric phenomena involved. Compares the numerical results with measurements. Finally, draws conclusions and perspectives for future work.

An electromagnet that can produce strong pulsed magnetic fields at kHz frequencies is potentially very favourable to exert a Lorentz force on the metallic workpiece. One of the applications of the pulsed magnetic field is the electromagnetic forming where the design of robust electromagnet is critical. The purpose of this paper is to design a robust electromagnet (coil) for high velocity electromagnetic tube forming operation.

First of all, an analytical model is developed to design the electromagnet and predict the aluminium tube velocity under the action of the estimated pulsed magnetic field. Next, the finite element-based numerical model is used to test the robustness of the designed coil and validate the analytical model. The coil is fabricated and implemented for free forming of aluminium tube. Experimental results of tube displacement are further compared with numerical and analytical model results.

The experimental tube displacement results are showing a good match with analytical and numerical results. The designed electromagnet has generated a peak magnetic field around 14 T at 20 µs rise time and deformed the aluminium tube with a peak velocity of 160 m/s. Robustness of the electromagnet under the action of forming stress is insured by numerical stress analysis and experiments.

Though the designed model in this work is for the 2.4 mm aluminium tube forming, it can also be used for different tube materials, tube dimensions and other electromagnetic forming applications with some modifications.

The research results provide powerful theoretical, numerical simulation and experimental support for the robust electromagnet design.

The purpose of this study is the investigation of the friction performance of 3D-printed polylactic acid (PLA) at different infill densities.

PLA samples were printed with fused filament fabrication (FFF). Friction performance test of PLA samples were performed under 18 N load at 20 min, 40 min and 60 min using a pin-on-disc tester. Diameter deviation, hardness of 3D-printed PLA, weight variation, coefficient of friction, temperature and wear images were chosen as performance criteria.

The hardness values of the samples with 30%, 50% and 70% infill density were determined as 93.9, 99.93 and 102.67 Shore D, respectively. The friction of coefficient values obtained in these samples at 20 min, 40 min and 60 min were measured as 0.5737, 0.4454 and 0.3824, respectively. The least deformation occurred in the sample with 50% occupancy rate and during the test period of 20 min.

The aim of this study was to determine the best friction performance of 3D-printed biodegradable and biocompatible PLA with different infill densities.

In the literature, several studies can be found on the mechanical characteristics of 3D-printed parts produced with PLA. However, investigations on the wear characterisation of these parts are very limited. In this regard, the friction coefficient results obtained from different infill density of 3D-printed PLA used in this study will significantly contribute to the literature.

The aim of this paper is to better understand the generation and transmission mechanism of the electromagnetic acoustic transducer (EMAT).

A semi-analytical method was used to calculate the Lorentz force. Both the hypothetical magnetic field mirror method and the diffusion equation were adopted to solve the eddy current distribution by variables separation method in time domain. A three-dimension magnetostatic finite element model was used to calculate the static magnetic field and the relative permeability. And an experimental platform with a piezoelectric probe to generate and an EMAT to receive, the ultrasonic wave was set up to verify the distribution of the Lorentz force.

The Lorentz force at different time and in different positions of the steel plate can be easily calculated. The experimental results show a good agreement with the analytical results.

The accurate prediction of the Lorentz force provides an insight into the physical phenomenon of EMAT and a powerful tool to design optimum EMAT.

This paper aims to study the breakdown, oscillation and vanishing of the discharge channel and its influence on crater formation with simulation and experimental methods. The experiment results verified the effect of the oscillating characteristics of the discharge channel on the shape of the crater.

A mathematical model that considers the magnetohydrodynamics (MHD) and the discharge channel oscillation was established. The micro process of discharging based on magnetic-fluid coupling during electrical discharge machining (EDM) was simulated. The breakdown, oscillation and vanishing stage of the discharge channel were analyzed, and the crater after machining was obtained. Finally, a single-pulse discharge experiment during EDM was conducted to verify the simulation model.

During the breakdown of the discharge channel, the electrons move towards the center of the discharge channel. The electrons at the end diverge due to the action of water resistance, making the discharge channel appear wide at both ends and narrow in the middle, showing the pinch effect. Due to the mutual attraction of electrons and positive ions in the channel, the transverse oscillation of the discharge channel is shown on the micro level. Therefore, the position of the discharge point on the workpiece changes. The longitudinal oscillation in the discharge channel causes the molten pool on the workpiece to be ejected due to the changing pressure. The experimental results show that the shape of the crater is similar to that in the simulation, which verifies the correctness of the simulation results and also proves that the crater generated by the single pulse discharge is essentially the result of the interaction between transverse wave and longitudinal wave.

In this paper, the simulation of the discharge breakdown process in EDM was carried out, and a new mathematical model that considers the MHD and the discharge channel oscillation was established. Based on the MHD module, the discharge breakdown, oscillation and vanishing stages were simulated, and the velocity field and pressure field in the discharge area were obtained.

This paper aims to present an evaluation method for energy-absorption characteristics of thin-walled composite structures with random uncertain parameters.

The mechanical properties of T700/3234 are obtained by material performance tests and energy-absorption results are obtained by quasi-static crushing tests of thin-walled composite circular tubes. The indicators of triggering specific load (TSL) and specific energy absorption (SEA) are introduced and calculated to determine the energy-absorption characteristics and validate the probability finite element analysis model. The uncertainty in the parameters contain the machining tolerance for the thickness and inner diameter of composite circular tubes and are associated with the composite material system. The Plackett–Burman method is used to choose the measurement parameters. Then, the response surface method is used to build a second-order function of random uncertain parameters versus TSL/SEA, and the Monte Carlo method is finally used to obtain the probabilities of TSL and SEA.

The finite element models can accurately simulate the initial peak load, load-displacement curve and SEA value. The random uncertain parameter method can be used to evaluate the energy-absorption characteristics of thin-walled composite circular tubes.

The presented evaluation method for energy-absorption characteristics of thin-walled composite structures is an approach that considers uncertain parameters to increase the simulation accuracy and decrease the computational burden.

This methodology considers uncertain parameters in evaluating the energy-absorption characteristics of thin-walled composite structures, and this methodology can be applied to other thin-walled composite structures.

This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.

In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).

The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.

The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.

Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies.

Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated.

The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures.

While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc.

This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined.

The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.