Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Mechanical

The polymer matrix composites attract many industrial applications due to its light weight, less cost and easy for manufacturing. In this paper, an attempt is made to prepare and study of the tensile strength of hybrid (two natural) fibers reinforced hybrid (Natural + Synthetic) polymer matrix composites. The samples were prepared with hybrid reinforcement consists of two different fibers such as jute and Gongura and hybrid polymer consists of polyester and cashew nut shell resins. The hybrid composites tensile strength is evaluated to study the influence of various fiber parameters on mechanical strength. The parameters considered here are the duration of fiber treatment, the concentration of alkali in fiber treatment and nature of fiber content in the composites.

School of Mechanical Engineering

Chennai Campus

VIT Unitversity

Fulltext

IOP Conference Series: Materials Science and Engineering

Evaluation of tensile strength of hybrid fiber (jute/gongura) reinforced hybrid polymer matrix composites

World University root node

Journal of Nanomaterials

Nanomaterials for Medical and Dental Applications

Tyre pressure monitoring systems are automotive systems which monitor tyre pressure in real-time. Most systems depend on barometric pressure sensors to measure tyre pressure and transmitters to send data toward the receiver. These sensors have to be placed between the tyre and the rim which exposes them to harsh mechanical conditions; extreme temperature and mechanical shocks. This paper proposes a new indirect method to monitor the tyre pressure by harvesting the vertical vibrations from the wheel hub of a running vehicle. The problem will be treated as a fault diagnosis problem. The vibration signals would then be represented as histogram features. The acquired histogram features would be classified using support vector machine algorithms to acquire the best classification accuracy there by determining the best algorithm. © Research India Publications.

International Journal of Applied Engineering Research

Harvesting vertical vibration of automotive tyre to monitor tyre pressure using applied machine learning technique

Prediction of surface roughness is always considered important in the manufacturing field. A product may require a particular roughness that may be specified by the designer for various reasons, either functional requirement or aesthetic appeal. While modern manufacturing systems and machines have always contributed towards better control of surface quality, better computational facilities and the availability of newer algorithms attract researchers to understand the prediction of quality in a better manner. In this paper, prediction of surface roughness by multiple regression analysis is presented. The predictors are cutting parameters, tool wear and the statistical parameters extracted from the vibration signals of a turning centre. The contribution of various statistical parameters in prediction of surface roughness is studied. A Machine learning approach using feature reduction using principle component analysis is attempted to achieve higher predictability and low computational effort. © 2015 The Authors. Published by Elsevier B.V.

Procedia Computer Science

Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned. © 2015, Prognostics and Health Management Society. All Rights Reserved.

International Journal of Prognostics and Health Management

Health monitoring of a hydraulic brake system using nested dichotomy classifier – A machine learning approach

International Journal of Fuzzy Computation and Modelling

Fuzzy classifier with automatic rule generation for fault diagnosis of hydraulic brake system using statistical features

An autonomous robot completely rely on the sensor system for interacting environment, it is critical task to navigate the robot. The limitations of the sensors are due to anatomy of the robot and errors in the measurement of the sensor system. These two makes the navigation more challenging. Kalman filter can be effectively use to eliminate errors and noises in the measurement system which make the navigation more accurate. The article provide the information on kalman filter and its application in odometer based navigation and vehicle positioning. © Research India Publications.

Understanding kalman filter for odometer based vehicle positioning application

Optical radiators and power meters are used to test the optical receivers in industrial bulk production. By the use of optical radiator, the variable data transmission is done as the form optical source. The radiator varies the intensity of the optical source by varying the diameter of bushes by mechanical methods. At the receiver, the power meter is used to calculate the amount of power received. Optical receivers are used to acquire the data (in the form of signals) for long-range medium. Testing of the receivers is by combining the power meter and radiator. Optical receivers are tested with different tests like zero drift, sensitivity, and transfer coefficient. Sensitivity tests are of two types, depending on the supply voltage of ±11V and ±15.5V. Zero drift is calculated to check whether any pin other than the output pin is acquiring any of the signals. Sensitivity defines the receiver response with the source. © Research India Publications.

Design and development of optical radiator and power meter for testing receivers

Electrochemical machining (ECM), is an advanced manufacturing technique capable of machining hard composite materials with good tolerance. However, the huge investment and operating costs make the ECM process uneconomical. This work is hence intended to develop cost-tolerance relationship for ECM process for 6061Al/10%wt Al2O3/5%wt SiC composites. The coefficient of determination (R2) values justifies the adequacy of developed cost and tolerance models. Analysis of variance (ANOVA) test is performed to understand the effect of ECM process parameters on cost and tolerance. The process parameters such as current, voltage and feed rate significantly affect the operating costs while the tolerance is affected by all the parameters.

Engineering Review

Cost-tolerance prediction models for electrochemical machining of metal matrix composites

Wire electrical discharge turning (WEDT) process is one of the emerging non-traditional machining processes for manufacture of micro- and axi-symmetric components. In WEDT process, material is removed by successive sparks that form craters. The material removal by crater formation is associated with energy supplied in the gap referred as discharge energy. This energy must be controlled for effective machining. In this paper, a model is proposed for predicting the crater diameter based on anode erosion. Finite element method (FEM) is used to simulate the crater for different plasma flushing efficiency. Effect of discharge energy developed in the gap, physio-thermal properties of the material are considered for modeling. The erosion energy required to form a crater is also evaluated using anode erosion model. The proposed models are validated by conducting WEDT experiments on high-tensile steel [AISI 4340]. The crater morphology is investigated by using images obtained from scanning electron microscope (SEM) and energy-dispersive X-ray analysis. The crater diameter predicted by anode erosion and FEM models are compared with diameter obtained from SEM micrograph. The results obtained from the proposed models are well in agreement with the experimental results. The anode erosion model predicts the crater diameter and erosion energy with an average absolute error of 5.65 and 17.86 %, respectively. By estimating the energy required to erode a material and by setting appropriate process settings, the discharge energy can be effectively utilized for material removal. © 2014, Springer-Verlag London.