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(Volume: 3, Issue: 4)
Vibration/Noise Elimination Mandated in Switched Reluctance Motors-Based EVs
Comfort and efficient driving, along with reduced costs being spent on fuel and maintenance, have caused the sale of Electric Vehicles (EV) to experience an upward trend. The current market is flooded up with EVs designed with various motors like, Permanent Magnet Synchronous Motor (PMSM), Induction Motor (IM) and Switched Reluctance Motor (SRM), each of which has its own pros and cons, in terms of power efficiency, size, cost and driving comfort. For instance, an EV with PMSM is opted for its high-power density operation, while an EV with IM will be a choice for the people preferring cost over power efficiency. Presently, EVs with PMSM and IM are more common than the EVs operated with SRMs. The reasons, which might be cited for the reduced usage of SRMs in EVs, are: (i) The high non-linearity, which impose the need for sophisticated control algorithms and (ii) The vibration as well as the noise produced because of the high torque ripple in SRMs. However, the SRMs are better known to impart high torque density at higher speeds and higher efficiency at specific operating ranges with a simple and robust design. So, in an electric vehicle, the SRMs can be a better alternative to PMSM that needs expensive permanent rare-earth magnets for their operation or an IM that prioritizes cost over power, if and only if the vibration, noise and the non-linearity are reduced or eliminated. Vibration and noise have a greater impact on the driving performance indeed!!! Various researchers have dealt with the cancellation of vibration and noise in SRMs. Vijayalakshmi Karunakaran and Srinivas Kandadai Nagaratnam of SRM Institute of Science and Technology, Ramapuram Campus, Chennai have focussed on eliminating the vibration and noise in SRM for application in EVs. They have performed an electromagnetic analysis of a 6/4 pole, 1.1 kW SRM, followed by a vibration and thermal analysis to identify the modal frequencies, which are responsible for vibration and noise in the SRMs. In their article being published in the Journal of Electrical Engineering & Technology, Vol. 18 of Springer, the researchers have stated that if these identified modal frequencies are skipped, an SRM can have reduced vibration/ noise and impart better driving experience in EVs. To perform the analysis, the authors have also identified a silicon steel material for the SRM stator, which exhibited less torque ripple as well as iron loss and provided better efficiency. Though SRMs can lead to the development of simple, cost-efficient and high torque EVs, research on various EV modules, either in their structural design or power management units, are still mandated to balance the efficiency and power output intended by the vehicle for a specific use.
Image courtesy: www.freepik.com
Image courtesy: www.freepik.com
Identify Plant Diseases with Artificial Intelligence
Technology has influenced various agricultural practices at several stages, starting from the ploughing of lands to the reaping or selling of crops, and the only aim is to increase the crop yield. Higher crop yields are utmost essential to meet the food demands of the exponentially-growing global population, so as to ensure food security at all times. Not only that, the crop yield has a magnificent role in improving the farmer’s livelihood and contribute majorly towards a country’s economy. However, it is not going to be a bed of roses for the farmers to improve the crop yield because of the changing climates, the costs associated with the agricultural practices and the buying of relative machineries or equipment and finally, the occurrence of plant diseases!!! In fact, the plant diseases are very perilous because they can affect the medicinal or the nutritional value of crops and gradually sweep away the entire vegetation in a region, causing severe food shortage and starvation in and around that region. So, early diagnosis of plant diseases becomes vital to plan, manage and control the pests using chemical or biological means and to reduce the ailments in plants. However, as the manual diagnosis of plant diseases might often involve tedious tasks by the farmers with uncontrollable costs being encountered, numerous researchers have rendered hands to ease up the process with machine learning and artificial intelligence-based techniques. One such technique is being provided by Satishkumar D and his team of five researchers from New Horizon College of Engineering Bangalore, India. In their article in the 4th International Conference on Innovative Trends in Information Technology (ICITIIT), held at Kottayam, the researchers have attempted to classify seven species of plants as healthier or not based on the spots in the leaves and using Convolutional Neural Networks (CNN). The researchers state that their approach can diagnose any plant disease variety with less computational complexity, but the data availability on a particular disease greatly affects the gained accuracy. Hence, the upcoming researchers can also focus their research on using or proposing highly-advanced artificial intelligence paradigms, which can even identify the plant diseases at times of data insufficiency.
Electrochemical Micromachining for Magnesium Alloys
Good electrical conductivity, low weight density, improved thermal resistance and better damping characteristics of magnesium (Mg) alloys have made it to be the choice for manufacturing devices or associated components in aerospace, medical and electronic industries. Usually, the component fabrication in different shapes, even at micro or nano scales, can be achieved with the micromachining of materials by mechanical, thermal, laser, chemical or electrochemical means. However, not all these micromachining approaches hold well for machining magnesium alloys because of their poor corrosion as well as wear resistance properties. Electrochemical Micromachining (ECM) seems to be better in removing the unwanted, intricate regions in a Magnesium alloy and to shape it with minimal heat or stress and increased accuracy, when comparing with its other micromachining counterparts. With a voltage being applied between the tool (cathode) and the work piece (anode), which is being immersed in an electrolyte solution, the more-complicated precision machining of Mg alloys at micro or nano levels can also be achieved with ease. However, the ECM also imposes challenges in the selection of electrolyte for managing corrosion and process stability, in addition to controllably machining the Mg alloy. N. Sivashankar, R. Thanigaivelan and K.G. Saravanan, three researchers from Tamil Nadu, India, have worked on these challenges, while trying to micromachine the magnesium AZ31 alloy. The researchers have used sodium nitrate as the suitable electrolyte for performing ECM and they have compared the machining performance obtained in two cases, one is electrolyte flooding and the other is Minimum Quantity Electrolyte (MQE). The latter MQE approach involved the electrolyte to be isolated and applied in drops using a micro flow control valve, rather than flooding the electrolyte that induced galvanic corrosion with larger surface defects in the AZ31 alloy. In their article in Bulletin of the Chemical Society of Ethiopia, vol. 37(5), the researchers have also optimized the ECM experimental parameters like, the electrolyte supply type, the machining voltage, the electrolyte concentration and the duty cycle using Taguchi, TOPSIS and Artificial Neural Network to obtain an AZ31 alloy with improved Material Removal Rates and reduced Over Cuts. However, the upcoming researchers still have a space to explore the micromachining approaches for magnesium or similar other alloys with different electrolytes, tool, experimental constraints and machine learning approaches for use in applicable sectors.
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Combine the Benefits of Radar and Lidar to Achieve Improved Autonomous Driving
Autonomous driving vehicle is a new technological boon in the automobile industry, which is capable of combating the human errors in driving like, rash driving, distracted driving, over speed and fatigue driving, thus preventing fatal accidents from happening. But, how can a vehicle be operated without a driver? It is by sensing and making decisions on the drivable path using the information about the vehicle’s location and its surroundings. The cameras and the various sensors connected with the vehicle as well as the Global Positioning System (GPS) does provide the data needed for obstacle detection and to make driving decisions using software processes. The sensors actually have a mightier role in autonomous driving. Though cameras enact the human vision, additional sensors like, radars and lidars are necessary for non-obstructive driving, at times of poor lighting or weather conditions. One mig ht think “Why both a radar and a lidar are essential, while both of them can provide the distance measurement from the surrounding obstacle?”. The simple answer can be that the redundant data from the camera and the sensors ensure extreme safety, while driving without a human command. There are also differences between the radar and lidar technology for automotive applications!!! Igal Bilik from the School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel have compared and presented an analysis on both the technologies in IEEE Intelligent Transportation Systems Magazine, vol. 15(1). The researcher has considered thirty different parameters to make the comparisons between radar and lidar, which make distance measurements using radio waves and laser pulses, respectively. As per the researcher’s findings, the radar technology imposes low-installation cost with long-range obstacle detection, even in poor lighting and harsh weather conditions. On the contrary, the lidar technology was found to provide precise 3-D mapping of the surrounding with high angular resolution as well as spatiotemporal consistency. The researcher also paves way for future research in two ways: (i) Finding a radar with the angular resolution of lidar, without compromising the radar benefits and (ii) Finding a low-cost lidar that can provide long-range obstacle detection as that of radar, along with the precision 3-D mapping. However, in-depth research is also required on various software processes, data acquisition and component design to make autonomous driving a reliable, efficient, cost-aware and secure means of transport for the public or an individual of any age, being physically-challenged or working in hazardous environments like, mining. Image courtesy: www.freepik.com
Enhance 3D Printing with Poly(lactic acid) Blends
Have you designed an object digitally, most preferably by means of Computer-Aided Design (CAD)? If so, you can now create it out as a three-dimensional structure using additive manufacturing and check its usability without much delay. Commonly called as 3D printing, the additive manufacturing uses plastics, ceramics, metals or even biological materials to print an object in a layered fashion, until the entire 3D structure in the digital design is formed. Fused Deposition Modeling (FDM), Electron Beam Melting (EBM), Selective Laser Sintering (SLS) and Stereolithography (SLA) are a few 3D printing technologies. However, FDM is highly demanded by the hobbyists and manufacturers, who need an easy and affordable means for low-volume production of objects with less intricate details. This is because the FDM, also known as Fused Filament Fabrication (FFF), melts only a thermoplastic filament and solidifies it to create the 3D structure, without needing complicated tooling or machining procedures. Nowadays, FDM increasingly uses Poly(lactic acid) (PLA) blends, which are made primarily from corn starch or sugar and suitable additives to strengthen its properties. The biodegradability, non-toxic emissions, wider availability and the durable as well as flexible nature of their stable prints have made the PLA blends to be extensively used in FDM. However, the homogeneous PLA blends are also subjected to microscopic or macroscopic phase separation or gradual migration over time. Hence, finding novel PLA blends with improved strength and ductility has turned out to be an active area of research in the field of additive manufacturing. Premkumar Kothavade, Prashant Yadav, Animesh Gopal, Harshawardhan Pol, Abdullah Kafi, Stuart Bateman and Kadhiravan Shanmuganathan have synthesized a triblock copolymer made of PLA and Poly(Ethylene Glycol) (PEG), termed as PLA−PEG−PLA, to create a PLA blend that can enhance the crystallization kinetics as well as the mechanical properties of PLA used in 3D printing. In their article in ACS Applied Polymer Materials, vol. 6(10), the authors have blended the triblock copolymer in various proportions to PLA, achieving a 45-fold increase in elongation at break and a 23-fold enhancement in toughness than neat PLA, at a blending weight proportion between 10% and 20%. However, additive manufacturing finds extensive applications in healthcare, aerospace or automotive industries, prototyping or manufacturing consumer goods and similar other applications in future. Hence, finding eco-friendly polymer blends with improved mechanical properties is on what the future researchers in civil, mechanical, structural and polymer engineering might probably work upon. Image courtesy: www.freepik.com
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