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Recrystallisation Characteristics of a Cu Bearing HSLA Steel Assessed Through High Temperature Compressive Deformation
Get PDFDynamic (DRX) operative duringdeformation and static recrystallization (SRX) operative after deformation are considered responsible for the changes in microstructure and texture of deformed materials. Especially in the case of advanced steels that are required in the form of plates of various thicknesses, hot rolling is the main manufacturing process during which the steel undergoes DRX under the rolls and SRX between rolling passes/strands. Knowledge on DRX and SRX characteristics of such steels is crucial for optimisation of hot rolling parameters,achieving the desired microstructure and consequently the targeted mechanical properties.In this study, certain key aspects of both dynamic (DRX) and static recrystallisation (SRX) behaviour of aCu-bearing HSLA steel, which was developed at DMRL, have been explored through high temperature deformation studies using Gleeble thermo-mechanical simulator. Through uniaxial compression testing in the austenitic regime, domains of continuous and discontinuous DRX prevalent in the steel were identified and critical parameters for initiation of dynamic recrystallisation viz., critical strain (ec), critical stress (sc), peak stress (sp) and peak strain (ep) were determined as a function of temperature and strain rate. SRX characteristics were assessed through uniaxial double hit compression tests with fixed strain rate and strain per hit but at different temperatures and with different imposed intermediate static recrystallisation (ISRT) times. From the fractional softening data, parameters such as time for 50% recrystallisation, t0.5, temperature for 50% recrystallisation, T0.5, and activation energy, QSRX have been estimated.Although the steel exhibited good plastic deformation characteristics, the results suggest that the role of copper in retarding recrystallisation is significant
Design Analysis of Mid IR DIAL System for Detection of Hazardous Chemical Species
Get PDFNowadays, both military and public agencies are concerned with the remote detection of toxic gases and chemical warfare agents in the atmosphere. A promising method for the remote detection of such harmful chemicals in the atmosphere is Differential Absorption Lidar (DIAL). In the current paper, system design analysis has been carried out to build a DIAL system for the detection of toxic chemical warfare agents, chemical warfare simulants, explosive precursors, and pollutants. The proposed DIAL system comprises an Optical Parametric Oscillator (OPO) based tuneable laser, a 203 mm diameter Cassegrain telescope, a TE-cooled MCT detector, suitable data acquisition hardware, etc. The DIAL output parameters like return signals, SNR, and minimum measurable concentrations have been simulated under different weather conditions such as clear sky, moderately hazy sky, and hazy atmospheric conditions for given system input parameters (pulse energy, detectivity, bandwidth, DAQ resolution, etc.). We have considered chemicals such as Sarin, Thiodiglycol (TDG), acetone, and methane to be detected using the system. Analysis has been carried out for these chemicals present at different locations with varying concentrations. Our analysis reveals that the DIAL system with a laser transmitter of 5 mJ energy and 203 mm receiver telescope is capable of detecting a few ppm concentrations of toxic chemicals present anywhere between the ranges from a few tens of meters to 2 km with topographic target present. The sensitivity of the system in terms of minimum detectable concentrations for the considered chemicals is also estimated for different atmospheric conditions. It is seen that the minimum detectable concentration of TDG is 3.2 ppm in clear weather conditions which increases to 9.2 ppm under a hazy atmosphere. A similar analysis has been carried out for other toxic chemicals and has been discussed in the paper
Design of a Scan Chain for Side Channel Attacks on AES Cryptosystem for Improved Security
Get PDFScan chain-based attacks are side-channel attacks focusing on one of the most significant features of hardware test circuitry. A technique called Design for Testability (DfT) involves integrating certain testability components into a hardware design. However, this creates a side channel for cryptanalysis, providing crypto devices vulnerable to scan-based attacks. Advanced Encryption Standard (AES) has been proven as the most powerful and secure symmetric encryption algorithm announced by USA Government and it outperforms all other existing cryptographic algorithms. Furthermore, the on-chip implementation of private key algorithms like AES has faced scan-based side-channel attacks. With the aim of protecting the data for secure communication, a new hybrid pipelined AES algorithm with enhanced security features is implemented. This paper proposes testing an AES core with unpredictable response compaction and bit level-masking throughout the scan chain process. A bit-level scan flipflop focused on masking as a scan protection solution for secure testing. The experimental results show that the best security is provided by the randomized addition of masked scan flipflop through the scan chain and also provides minimal design difficulty and power expansion overhead with some negligible delay measures. Thus, the proposed technique outperforms the state-of-the-art LUT-based S-box and the composite sub-byte transformation model regarding throughput rate 2 times and 15 times respectively. And security measured in the avalanche effect for the sub-pipelined model has been increased up to 95 per cent with reduced computational complexity. Also, the proposed sub-pipelined S-box utilizing a composite field arithmetic scheme achieves 7 per cent area effectiveness and 2.5 times the hardware complexity compared to the LUT-based model
Fault Tolerant Brushless DC Motor Drive for Aerospace Applications
Get PDFThis article brings out a Fault-tolerant BLDC motor drive for aerospace applications using the redundancy concept. In a way, it brings out a fault-tolerant strategy that can be used to continue the regular operation of a BLDC motor drive even after the occurrence of faults. As BLDC motors are used in critical and dangerous control areas like military services and space vehicles, a fault-tolerant drive is essential to maintain drive operation and provide desirable output. This article compares fault simulation results in the software model of a BLDC motor drive to those of fault simulation results in hardware for three main types of faults. Fault simulation is carried out for three types of faults, viz. inverter device open circuit fault, motor winding open circuit fault, and rotor position sensor (hall sensor) open-circuited fault. Fault tolerance is ensured by introducing a redundant drive (drive-2), which operates the complete drive at the advent of any of the faults mentioned above in the main (healthy) drive-1. A fault-tolerant (redundant) hardware comprising dual stator BLDC motor and redundant controllers is realized and operationalized. Fault simulation is carried out in this hardware, and these results are validated with the results of fault simulation in the MATLAB SIMULINK model. Software and hardware results are comparable and form a basis for developing fault-tolerant electro-mechanical actuation systems for high-reliability, high-cost applications, mainly aerospace
A Multistage High Capacity Reversible Data Hiding Technique Without Overhead Communication
Get PDFReversible Data Hiding(RDH) has been extensively investigated, recently, due to its numerous applications in the field of defence, medical, law enforcement and image authentication. However, most of RDH techniques suffer from low secret data hiding capacity and communication overhead. For this, multistage high-capacity reversible data hiding technique without overhead is proposed in this manuscript. Proposed reversible data hiding approach exploits histogram peaks for embedding the secret data along with overhead bits both in plain and encrypted domain. First, marked image is obtained by embedding secret data in the plain domain which is further processed using affine cipher maintaining correlation among the pixels. In second stage, overhead bits are embedded in the encrypted marked image. High embedding capacity is achieved through exploiting histogram peak for embedding multiple bits of secret data. Proposed approach is experimentally validated on different datasets and results are compared with the state-of-the-art techniques over different images
Formal Modelling and Verification of the Clock Synchronization Algorithm of FlexRay
Get PDFThe hundreds of electronic control devices used in an automotive system can effectively communicate with one another, thanks to an in-vehicle network (IVN) like FlexRay. Even though every node in the network will be running on its local clock, a global notion of time is essential. The clock synchronisation algorithm accomplishes this global time between the nodes in FlexRay. In this era of self-driving cars, the vehicle’s safety is paramount. For the vehicle to operate safely and smoothly, timely communication of information is critical, and the clock synchronisation algorithm plays a vital role in this. It is essential to formally test the clock synchronisation algorithm’s correctness. This paper attempts to model and verify the clock synchronisation algorithm of FlexRay using formal methods, which in turn enhance the reliability of safety-critical automotive systems. The clock synchronisation is modelled as a network of six timed automata in the UPPAAL model checker. Three system models were developed, a model for an ideal clock, another for a drifting clock, and a third model considering propagation delay. The precision of the clocks is verified to be within the prescribed limits. Simulation studies are also conducted on the model to ensure that the clock’s drift is always within the precision
Simulation of Cognitive Electronic Warfare System With Sine and Square Waves
Get PDFToday’s Electronic Warfare (EW) receivers need advanced technology to achieve real-time surveillance operations. Dynamic and intelligent systems are required for UAVs and other airborne applications. The airborne Electronic Warfare systems must be knowledge-based systems, learning from the threat scenario with highly integrated capabilities to detect, react, and adapt to radar threats in real-time. Artificial intelligence is a machine-dependent process, by adapting certain rules and logic supported by human intelligence, AI can be used for cognitive processing. Cognitive signal processing is required for making the system autonomous and dynamic in nature. Military action on radar signatures requires a set of commands to be executed dynamically with the help of the proposed EW system. It is proposed to design and develop a cognitive EW architecture and simulation of machine learning that combines neural network architecture with the help of sine and square waves as input. This paper presents the Cognitive signal processing for EW systems with Neural Network, Recurrent Neural Network (RNN), Machine learning (ML), and Deep learning (DL) techniques with their simulation with sine and square waves
Hybrid Deep Neural Network for Data Driven Missile Guidance with Maneuvering Target
Get PDFMissile guidance, owing to highly complex and non-linear relative movement between the missile and its target, is a challenging problem. This is further aggravated in case of a maneuvering target which changes its own flight path while attempting to escape the incoming missile. In this study, to achieve computationally superior and accurate missile guidance, a deep learning is employed to propose a self-tuning technique for a fractional-order proportional integral derivative (FOPID) controller of a radar-guided missile chasing an intelligently maneuvering target. A multi-layer two-dimensional architecture is proposed for a deep neural network that combines the prediction feature of recurrent neural networks and estimation feature of feed-forward artificial neural networks. The proposed deep learning based missile guidance scheme is non-intrusive, data-based, and model-free wherein the parameters are optimized on-the-run while predicting the target’s maneuvering tactics to correct for processing time and loop delays of the system. Using deep learning for online optimization with minimal computational burden is the core feature of the proposed technique. Dual-core parallel simulations of missile-target dynamics and the control system were performed to demonstrate superiority of the proposed scheme in feasibility, adaptability, and the ability to effectively minimize the miss-distance in comparison with traditional and neural offline-tuned PID and FOPID based techniques. Compared to state-of-the-art offline-tuned neural control, the miss-distance was reduced by 68.42% for randomly maneuvering targets. Furthermore, a minimum miss-distance of 0.97 m was achieved for intelligently maneuvering targets for which the state-of-the-art method failed to hit the target. Overall, the proposed technique offers a novel approach for addressing the challenges of missile guidance in a computationally efficient and effective manner
A Comparative Investigation of Random Forest Regression and Artificial Neural Networks for Predicting Crack Growth Life of a Fighter Aircraft Wing Joint Under Spectrum Loading
Get PDFEstimating fatigue life is challenging due to the input parameters’ statistical natures, such as the manufacturing process, scatter of service loads, microstructure, etc. Regarding fatigue life calculation in the aerospace industry, the importance of an accurate estimation becomes more critical due to strict safety, certification, service costs, and competitiveness regulations. The ability of soft computing methods to reveal complex relationships between multiple parameters and their computational speed could help predict fatigue life, especially in the service. This study compares random forest regression and artificial neural network methods to estimate the crack growth life of a fighter jet aircraft wing joint in terms of their computational time and accuracy. In addition, permutation feature importance and hyper parameter optimisation studies are conducted to extract essential features, investigate their effects on estimation performance, and fine-tune model parameters. The analysed joint is made of 7050-T7451 aluminium, widely used as a structural element in the aerospace industry. Since a hole is one of the major sources of stress concentration, and there may be many holes involved in any engineering structure, it is reasonable to assume that fatigue cracks may initiate at some of these holes during the service life of engineering structures. The crack type considered is a thru-crack around a hole, which is more severe than a corner crack. Load spectra are derived using the Fighter Aircraft Loading Standard for Fatigue (FALSTAFF) to calculate crack growth life. Considering particular service load conditions, ninety different spectra are developed, and the crack growth life of the joint is calculated based on linear elastic fracture mechanics correspondingly. Also, to simulate the worst-case scenario, friction between members and the retardation effect of load spectra are not considered when calculating crack growth life. Python’s Tensor Flow and Scikit-learn libraries are utilised to build machine learning models.....
Evolvable Hardware Based Optimal Position Control of Quadcopter
Get PDFTrading off performance metrics in control design for position tracking is unavoidable. This has severe consequences in mission-critical systems such as quadcopter applications. The controller area and propulsion energy are conflicting design parameters, whereas the reliability and tracking speed are related metrics to be optimized. In this research, a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Programmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed, propulsion energy, and reliability optimization under similar error performance. To improve the computation power and controller area, the dynamic partial reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE), where a ranking-based approach brings into action either proportional derivative, sliding mode, or model predictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed, reliability, controller area, and propulsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions. Concerning the implementation results of the controller with the switching-based controller, there is 6 % computation power and 30 % resource savings due to DPR