Department of Mechanical Engineering

We report significantly enhanced sensitivity of AlGaN/GaN-based high electron mobility transistor (HEMT) sensor by the targeted synthesis of IT and 2H coexisting phase MoS2 and applying the gate bias voltage. The HEMT structures on Si (111) substrates were used for the detection of Hg2+ ions. The optimum sensitive regime in terms of V GS and V DS of the sensor was investigated by keeping the drain source voltage V DS constant at 2 V and by only varying the gate bias voltage V GS from 0 to 3 V. The strongest sensing response obtained from the device was around 0.547 mA ppb-1 at V GS = 3 V, which is 63.7% higher in comparison to the response achieved at 0 V which shows a sensing response of around 0.334 mA ppb-1. The current response depicts that the fabricated device is very sensitive and selective towards Hg2+ ions. Moreover, the detection limit of our sensor at 3 V was calculated around 6.21 ppt, which attributes to the strong field created between the gate electrode and the HEMT channel due to the presence of 1T metallic phase in synthesized MoS2, indicating that the lower detection limits are achievable in adequate strong fields.

The present work primarily deals with the study of the influence of internal moving pulsatile fluid on the dynamic stability of cantilever based single-walled carbon nanotubes considering the modified couple stress elasticity theory and the Timoshenko beam model. The axial fluid speed is characterized as simple harmonic variation about a constant mean speed. The second order method of multiple scales is employed in order to determine the closed-form expressions for the instability regions subjected to parametric resonance condition. The influences of the length scale parameter, Poisson's ratio, aspect ratio of SWCNTs, and the amplitude of static and dynamic components of the flow velocity, on the parametric instability regions are investigated. These results obtained from perturbation analysis are verified by solving the temporal equation using numerical technique.

This paper presents an adaptive phase field model for simulating fracture due to coupled interactions (such as thermal quenching and hot cracking in additive manufacturing). The proposed model is implemented in an open-source finite element framework, FEniCS. The model considers spatial variations of the fracture toughness and differential coefficients of thermal shrinkage. Several paradigmatic case studies are addressed to demonstrate the potential of the proposed modeling framework. Specifically, we (a) benchmark our crack predictions for mechanical and thermal boundary condition interactions with the results from alternative numerical methods, (b) accurately reproduce experimentally observed complex crack trajectories due to thermal quenching and hot cracking in additive manufacturing, and (c) demonstrate the ease of extending of the proposed framework to thermal cracking problems in three dimensions. The current implementation provides the basic for an efficient framework for fracture problems due to multi-physical interactions for practical engineers with less programming expertise.

We investigate the effect of frequency mismatch in two indirectly coupled Rössler oscillators and Hindmarsh-Rose neuron model systems. While identical systems show in-phase or out-of-phase synchronization states when coupled through a dynamic environment, mismatch in the internal frequencies of the systems drives them to a fixed point state, i.e., amplitude death. There is a region in the parameter space of the frequency mismatch and coupling strength where system shows amplitude death. The numerical results of Rössler system are also experimentally verified using piece-wise Rössler circuits. © 2012 Elsevier B.V. All rights reserved.

F1exible micro and nano sensors are a highly attractive option for the monitoring of physiological parameters like temperature, respiration rate, heartbeat, etc. in the healthcare field. Smart textiles and wearables are one of the most suitable ways in which vital parameters of a person can be monitored continuously without severely affecting the comfort and health of the person. In this paper, a textile based flexible sensor has been developed to create a thin wrist band which can be used to monitor the temperature of the person wearing it so as to detect alarming changes in temperature like in the case of fever or hypothermia. The constituent materials and the weaving pattern have been selected so as to facilitate temperature sensing with high precision. Through a series of experiments in a controlled environment, the optimal number of strands, optimal weaving pattern and the optimal length of the fabric are found by varying the aforementioned parameters and calculating the temperature coefficient of resistance (TCR). The readings obtained from the sensor do not fluctuate vividly with the passage of time, which indicates that the readings are stable. From the change in resistance the corresponding change in temperature (from the body temperature for which the fabric is calibrated for) is calculated on the basis of TCR.

The advancement of clean and sustainable energy technologies is necessary to fulfill the world's growing energy demand. Food preservation must consider the usage of PV-assisted solar dryers to eliminate the traditional energy-intensive drying systems. However, there is a significant technology gap in the drying system to effectively utilize solar energy. Some modifications can be done through the absorber surface by adding obstacles for the selection of an effective design that increases the heat transmission to the fluid flowing inside the solar collector and dryer. The primary objective of this research was to improve the PV-assisted solar dryer performance by modifying the absorber through the incorporation of square obstacles equipped with threaded pin fins. The investigations were carried out in plain absorber (Model-I) and modified absorber (Model-II) to compare the performance at 0.0024, 0.0072, and 0.012 kg/s airflow rates. The reduction in moisture content of green papaya and green banana was 71.42 % and 49.27 % more using Model-II instead of drying outside the dryer. The maximum drying efficiency was 29.55 % and 29.80 % in Model-II for crops A and B, respectively at 0.0072 kg/s. The thermal energy and thermal exergy efficiency were 33.43 % and 27.17 % higher, respectively in Model-II than compared to Model-I. The lower payback of 1.51 yr and higher CO2 mitigation of 46.17 tons were attained. The quality analysis findings recommended that the proposed design can be applied in numerous food drying applications and is a promising solution for further advancing absorber design for solar collectors and dryers.

Variation in dynamic contact resistance with the change in welding process parameters such as weld current, weld time and electrode force were taken into account for establishing the range of adequate nugget formation parameters. Influence of the welding process parameters on the shear – tensile strength, nugget diameter and the observed failure mode was analysed. The adequate resistance spot welding process parameters for galvannealed high strength interstitial free steel sheets of 1.6 mm thickness were estimated as 8 kA weld current, 250 ms weld time and 3.5 kN electrode force. Increase in the mean dynamic contact resistance led to a significant reduction in nugget diameter. A critical nugget diameter distinguishing between the IF and PF mode was experimentally determined by failure mode analysis. Different numerical models for estimation of critical nugget diameter were evaluated.

The dissimilar joint between aluminium alloy 6063 and aluminium has been made using the friction stir welding process. Three input parameters: Tool rotational speed, feed rate and dwell time have been varied and their effect of the mechanical properties has been studied. Obtained weld joints have been characterized for tensile properties, hardness, and corrosion behavior. Effort has been made to study the in process force acting on the joint and establishing its dependence on the welding parameter. The use of full factorial design methodology brings out the contribution of input parameters and their effect on the output mechanical properties.

Friction stir welding (FSW) is a non-traditional welding procedure for joining identical and divergent substances that are comparatively hard to weld using fusion techniques. The FSW is more competent and eco-responsive when correlated with the traditional processes. The tensile characteristics of thermally susceptible alloys are minimized as a result of heat iterations. The roughening of the consolidated deposits in friction stir welding induces joint moderation. For non-heat-treatable alloys, intergranular and stress corrosion cracking are developed in grain boundaries during the FSW. Because of this, the overall performance and mechanical properties were reduced. Underwater friction stir welding (UWFSW) is preferred to prevail over the problems. UWFSW is appropriate for both heat-treatable and non-heat-treatable alloys that are receptive to thermal activity throughout the procedure. This research examines the effects of response surface methodology (RSM)-based grey relational methods on the optimization of UWFSW procedural characteristics such as the straight hexagonal tool profile (SH), tool rotational speed (TRS) of 1200 rpm, tool transverse velocity (TTV) of 20 mm/min, and water head (WH) of 10 mm in military-grade AA5083 alloy. The mechanical properties of the AA5083 UWFSW joint, such as its average ultimate tensile strength (UTS) and hardness, have been greatly improved. Grey Relation Analysis was used to determine welding specifics such as UTS and hardness. The ANOVA was used to assess the formulation's significance. The micro-structural behaviour and grain structure of different weld zones were investigated with the help of the scanning electron microscope (SEM). The image processing technique can be used to quantitatively assess the UWFSW process using the non-destructive testing (NDT) ultrasonic B-scan image and radiographic image. Confirmation tests at the optimal parameter level improved the performance of each response.

Nickel-Based Superalloys (NBSAs) are widely used for components subjected to high-temperature applications due to their excellent mechanical strength, toughness, and corrosion resistance. Despite favorable properties, NBSAs work-harden during machining, resulting in acute temperature rise at the cutting edge, severe plastic deformation, and rapid tool wear. The lower thermal conductivity, intense friction at the chip-tool interface, chemical affinity with tool material, and temperature gradients typically lead to abrupt crater formation or cutting-edge chipping in addition to rapid flank wear. Three distinct phenomena characterize tool wear during end milling of NBSAs; rapid flank wear, abrupt crater formation, and cutting-edge chipping. The continued use of worn or damaged cutting tools leads to poor surface finish and, eventually, catastrophic failures, resulting in significant machine downtime. As each tool wear condition has a unique mitigation strategy, timely identification and classification are imperative to implement solutions that minimize wear and guide tool replacement. In recent years, the augmentation of vision-based systems with pre-trained Convolutional Neural Networks (CNNs) has shown great promise in failure identification and classification tasks. The present work develops an image-based classification model using a pre-trained CNN, Efficient-Net-b3, for identifying three tool wear conditions during end milling of Inconel 718 (IN718). The network training uses labeled image datasets that capture various tool wear characteristics generated using end-milling experiments. The extensive training dataset requirement of the CNN was met using image augmentation techniques by varying the brightness, contrast, and orientation of the captured images. The prediction abilities of the algorithm were corroborated by validating the model on a validation dataset and further testing on new unseen datasets. It has been shown that Efficient-Net-b3 demonstrates robust prediction accuracy for all three tool wear conditions. The proposed classification model can be further employed for developing an on-machine vision-based tool wear classification system.