Machinability study of Aluminium 6082 reinforced with boron carbide and titanium by stir casting method using Abrasive water jet machining process

Aluminium Metal Matrix Composite (AMMC) has broad uses in the medical, aerospace, and automobile industries, which have long sought lightweight materials with superior designs and improved properties to improve performance. This analysis has aimed to prepare an AMMC to investigate its machining and mechanical properties. The AMMC is produced using a stir casting process by reinforcing boron carbide and titanium with aluminium 6082. The material's mechanical properties are studied by using wear test, hardness test, and corrosion test. The wear rate increases when the load increases by varying the load and time with speed as a constant. It is found that the hardness of a material is increased due to titanium and boron carbide as the reinforcement particle in the fabricated AMMC. Using the pitting corrosion technique, the corrosion occurs on the AMMC under the estimated time at room temperature. In order to illustrate the machining characteristics of the aluminium metal matrix composite, an abrasive water jet machining process has been used. The experiments use L9 orthogonal Array using Taguchi's method and ANOVA analysis. The input parameters considered are Traverse rate, Stand-off distance, and Nozzle diameter. To find the optimum value of circularity, cylindricity, and surface roughness by varied input parameters. The respective graphs are also plotted. Scanning electron microscopic analysis was performed on the wear-tested specimen and machining surface of the material to determine the distribution of reinforced material and investigate the material's fracture mechanism. It is found that wear tracks, voids, delamination, micro pits, embedded garnet abrasive particles are located on the machined surface of the AMMC.

Influence of tool rotational speed on the evolution of microstructure and mechanical properties of precipitation-hardened Aluminium 6061 butt joint during friction stir welding

The present investigation intends to interpret the effect of tool rotational speed on the mechanical properties and microstructural evolution in Aluminium 6061-T6 alloy during friction stir welding. A higher value of tool rotation produces more hardness at the nugget zone, which is attributed to the higher intensity of reprecipitation at higher rpm, revealed by transmission electron microscopy. The nugget zone is revealed as a nearly precipitate-free region, while the thermo-mechanically affected zone contains coarse precipitates, deformed and dynamically recovered grains with a few recrystallized grains. Significant reduction in grain size in the stirred zone is also a key finding. The observations depict the dependence of microstructure, and thus mechanical behaviour on tool rotational speed. A specific combination of process parameters has been determined from experiments, which corresponds to the maximum joint efficiency.

Simultaneous tailoring of pulsed thermography experimental and processing parameters for enhanced defect detection and sizing in adhesive bonds in carbon fibre composites

Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8 % on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1- 2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.

Development of a bipolar electrostatic chuck with a compliant beam-array assembly having four 3D-printed layers for large film handling

This study proposes and verifies bipolar electrostatic grippers stacking 3D-printed-layered modules consisting of arrays of elastically deformable bipolar beams. The influence of the mechanical compliance of grippers on the attractive force that it generates is clarified by comparing two types of modules having either high or low mechanical compliances. Experiments measured the attractive force of the gripper and demonstrated the pick-and-place performance of a thin film. The results show that mechanical compliance plays an important role in mitigating the attractive force decrease in stacking modules. The grippers’ ability for thin film handling is demonstrated by observing pick-and-place behaviours of the proposed bipolar electrostatic grippers.

Understanding the dielectric properties and electromagnetic shielding efficiency of zirconia filled epoxy-MWCNT composites

In the present study, hybrid composites are prepared by reinforcing various concentrations of high permittivity zirconia nanofiller into epoxy/CNT compositions to test their usability in EMI shielding applications in the X and Ku bands. ZrO2 nanofiller is added in different proportions to improve absorbance shielding while maintaining the composite conductivity uniform by adding constant CNT concentration to restrict the reflectance shielding. The microscopic studies have revealed an efficient dispersion of ZrO2 nanoparticles in the CNT networks and provided a smoother surface. The presence of zirconia nanofillers increased the dielectric properties, viz. the dielectric constant (194 at 0.1 Hz) and loss tangent (1.57 at 0.1 Hz), respectively, whereas the conductivity was found to be invariantly constant. The increased permittivity of composites enhanced the shielding by absorption, while the shielding by reflection is least influenced by the addition of zirconia nanofiller. The addition of zirconia nanofillers increased the permittivity and tan delta, allowing charges to accumulate at the interfacial areas for incoming EM radiations, resulting in increased absorbance shielding. Limiting the CNT concentration in all composites to the same level resulted in the formation of conductive networks, thus resulting in uniform reflectance shielding for all the hybrid composites in the present study. The dynamic mechanical analysis showed the improvement in the storage modulus and activation energy due to the enhanced interfacial adhesion and cross-linked polymer density.

General practices to enhance bast fiber composite properties for state of art applications – a review

Composite materials are revolutionizing to realize the demanding needs of aeronautical, automobile, construction, chemical, and biomedical applications. The natural fiber composite is chosen as one of the best choices among composites due to its sustainable goods like eco-friendly nature, better properties and Greenhouse gas (GHG) balance. Furthermore, the bast fiber composites are identified as promising industrial composites based on the availability, strength-to-weight ratio, manufacturing ease, and economics for commercialization. However, product quality and production volume significantly influence commercial adoption of the bast fiber composites. Especially the product quality primarily suffer due to climatic conditions, damage while harvesting, extraction method, retting issues, and extraction location. Consequently, this review aims to provide an overview of the bast fibers & their composites, properties enhancement techniques, overall mechanical behaviours and thermal stability with suitable applications.

Biomechanics of skin grafts: effect of pattern size, spacing and orientation

Skin graft expansion is the key to the treatment of severe burn injuries requiring skin transplantation. While high expansions have been claimed by a majority of graft manufacturers, the realistic expansions reported to date with skin grafts are much lower. To clarify this discrepancy, we attempted to understand the biomechanics of skin grafts through the study of common graft pattern sizes, spacing, and orientation, and their influence on mesh expansion and induced stress. A novel skin simulant material and additive manufacturing were employed to develop the skin graft models. Tensile testing experiments were conducted to study expansion and overall stresses, and a finite element model (FEM) was used to characterize the local trends. At low strains (i.e., <1), the mesh expansion ratio was reported to be below 1, which increased up to 1.93 at a high strain of 2. The pattern size and spacing were not observed to affect the expansion much (i.e., <10%). With a change in orientation, the expansion decreased across all graft models and strains. High localized induced stresses were reported for high strains, which varied with graft orientation. The novel observations highlight the achievable expansions without overstressing, with standard slit patterning in skin grafts. These findings will not only help achieve better mesh expansion outcomes in burn surgeries but also guide the development of novel graft patterns for enhanced expansion in the future.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

Fault Tolerance and Noise Immunity in Freespace Diffractive Optical Neural Networks

Free-space diffractive optical networks are a class of trainable optical media that are currently being explored as a novel hardware platform for neural engines. The training phase of such systems is usually performed in a computer and the learned weights are then transferred onto optical hardware ("ex-situ training"). Although this process of weight transfer has many practical advantages, it is often accompanied by performance degrading faults in the fabricated hardware. Being analog systems, these engines are also subject to performance degradation due to noises in the inputs and during optoelectronic conversion. Considering diffractive optical networks (DON) trained for image classification tasks on standard datasets, we numerically study the performance degradation arising out of weight faults and injected noises and methods to ameliorate these effects. Training regimens based on intentional fault and noise injection during the training phase are only found marginally successful at imparting fault tolerance or noise immunity. We propose an alternative training regimen using gradient based regularization terms in the training objective that are found to impart some degree of fault tolerance and noise immunity in comparison to injection based training regimen.

A comparative study of different emitter diffusion profiles on the performance of Si solar cells

We have investigated the effect of emitter design key parameters such as depth factor and the peak concentration for different types of emitter diffusion profiles (uniform, exponential, Gaussian, and Erfc) on the performance of silicon (Si) solar cells. The value of the depth factor is optimized as 0.1 µm for all these emitter diffusion profiles. Afterward, the peak concentration value is optimized for all the diffusion profiles. A close examination of relative diffusion lengths, conductivities, recombination rates, internal and external quantum efficiencies for these diffusion profiles revealed that among all the considered emitter diffusion profiles, the Erfc profile exhibits the maximum efficiency of 23.53% with an optimized peak concentration of 2×1020 cm-3 for emitter and 1×1019 cm-3 for the back surface filed doping. PC1D was used for all the simulations.