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K. L. Senthil Kumar

Bio: K. L. Senthil Kumar is an academic researcher from Bannari Amman Institute of Technology, Sathy. The author has contributed to research in topics: Machining & Electrical efficiency. The author has an hindex of 5, co-authored 20 publications receiving 105 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, an attempt is made to machine the A356/SiC p composite work material using Electro Chemical Machining process and the results showed that all the four selected factors are significant and from the S/N graph the optimum level of each factor is chosen.
Abstract: Aluminium metal matrix composites (AMMCs) are now gaining their usage in aerospace and automotive industries. Because of their inherent nature, difficult to machine, they find very little applications in other sectors. Even non traditional processes like Laser Jet Machining and Electro Discharge Machining result in significant sub surface damage to the work. In this paper, an attempt is made to machine the A356/SiC p composite work material using Electro Chemical Machining process. Silicon carbide with an average particle size of 40 microns is tried in three different proportions, namely 5%, 10% and 15% by weight. Taguchi’s L 27 orthogonal array is chosen to design the experiments and 54 trials are conducted to study the effect of various parameters like applied voltage, electrolyte concentration, feed rate and percentage reinforcement on maximizing the material removal rate. ANOVA results have shown that all the four selected factors are significant and from the S/N graph the optimum level of each factor is chosen. A mathematical model is also developed using the regression method. Confirmation experiment is conducted and found that the data obtained have close match with the data predicted using the model.

39 citations

Journal ArticleDOI
TL;DR: In this paper, a sump-up of the latest developments taken place in aluminum-based composite and other particulate reinforcement effects has been given, where the authors have focused on AA6061 and AA7075 alloy due to commercial easy available and it is widely used for structural purpose in manufacturing sectors.

37 citations

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TL;DR: In this article, the use of PCM content for phase change is now one of the most important methods for minimizing and controlling the temperature of PV panels and increasing their electrical performance.

16 citations

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TL;DR: In this article, the effect of PCM material on the thermal behavior and electrical performance of a photovoltaic panel using COPPER (Cu), SILICON CARBIDE (Sic), PARAFFIN WAX (Petroleum wax), and SILICA (Salinamide) was investigated.

12 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the application of conducting polymers, polymer composites and nanocomposites for corrosion protection of different industrial metal substrates are explored based on reported experimental data and their mechanism of inhibition explained.

154 citations

Journal ArticleDOI
TL;DR: A review of the different manufacturing processes and the various reinforcing elements used throughout the preparation of polymeric matrix composites is presented in this article, where the reviewed material systems forming polyester nano composites are analysed separately and jointly, showing their scientific developments.

129 citations

Journal ArticleDOI
TL;DR: In this paper, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments, the highest performance was obtained by 4-10-1 network structure with LM learning algorithm.
Abstract: Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R2 and lower RMSE and MEP.

94 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed.
Abstract: Sensors play a crucial role in factory automation in making the system intellectual. Different types of sensors are available as per the suitability and applications; some of them are produced in mass and available in the market at affordable costs. The standard sensor types available are position sensors, pressure sensors, flow sensors, temperature sensors, and force sensors. They are used in many sectors, such as motorsport, medical, industry, aerospace, agriculture, and daily life. The objective of Industry 4.0 is to increase efficiency through automation. Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed. Smart factories will also enhance sustainability by tracking real-time output, and automated control systems will minimise potential factory maintenance costs. It can also be seen that digitalisation can improve production mobility, which gives advanced manufacturing firms a competitive advantage. This paper discusses sensors and their various types, along with significant capabilities for manufacturing. The step-by-step working Blocks and Quality Services of Sensors during implementation in Industry 4.0 are elaborated diagrammatically. Finally, we identified thirteen significant applications of sensors for Industry 4.0. Industry 4.0 provides an excellent opportunity for the development of the sensor market across the globe. In Industry 4.0, sensors will enjoy higher acceptance rates and benefit from a fully enabled connecting and data exchange and logistics integration. In the coming years, sensor installations may grow in process management, automated production lines, and digital supply chains.

78 citations

Journal ArticleDOI
TL;DR: A comprehensive literature review of the conventional machining processes along with optimization methods used in metal matrix composites (MMCs) such as turning, milling, drilling, and grinding machining is presented in this article.
Abstract: This paper offers a comprehensive literature review of the conventional machining processes along with optimization methods used in metal matrix composites (MMCs), such as turning, milling, drilling, and grinding machining processes. The tool wear mechanism and machinability of MMCs along with surface quality are discussed in the number of different manufacturing processes and examined thoroughly. Additionally, the manufacturing of MMC products through nonconventional machining processes such as electrical discharge machining (EDM), wire electrical discharge machining (WEDM), laser machining, electrochemical machining, ultra- sonic machining (USM), and high-speed machining are investigated and considered, in connection with MMC processing are discussed, as alternatives to the aforementioned processes. Moreover, this review focuses on the modeling of the machining process, finite element modeling, and simulation and optimization of soft computing methods in MMCs. The study will emphasize on the most generally used methods, namely, response surface methodology, artificial neural network, Taguchi method, and fuzzy logic as soft computing optimization methods. Finally, the comprehensive open issues and conclusions have drawn on the machining and optimization of particle-reinforced MMCs.

76 citations