KCG College of Technology

About: KCG College of Technology is a(n) based out in . It is known for research contribution in the topic(s): Adsorption & Diesel fuel. The organization has 427 authors who have published 381 publication(s) receiving 2193 citation(s).

Crack detection using image processing: A critical review and analysis

Abstract: Cracks on the concrete surface are one of the earliest indications of degradation of the structure which is critical for the maintenance as well the continuous exposure will lead to the severe damage to the environment. Manual inspection is the acclaimed method for the crack inspection. In the manual inspection, the sketch of the crack is prepared manually, and the conditions of the irregularities are noted. Since the manual approach completely depends on the specialist’s knowledge and experience, it lacks objectivity in the quantitative analysis. So, automatic image-based crack detection is proposed as a replacement. Literature presents different techniques to automatically identify the crack and its depth using image processing techniques. In this research, a detailed survey is conducted to identify the research challenges and the achievements till in this field. Accordingly, 50 research papers are taken related to crack detection, and those research papers are reviewed. Based on the review, analysis is provided based on the image processing techniques, objectives, accuracy level, error level, and the image data sets. Finally, we present the various research issues which can be useful for the researchers to accomplish further research on the crack detection.

Studies on dual fuel operation of rubber seed oil and its bio-diesel with hydrogen as the inducted fuel

Abstract: The main problems with the use of neat vegetable oils in diesel engines are higher smoke levels and lower thermal efficiency as compared to diesel. The problem can be tackled by inducting a gaseous fuel in the intake manifold along with air. In this investigation, hydrogen is used as the inducted fuel and rubber seed oil (RSO), rubber seed oil methyl ester (RSOME) and diesel are used as main fuels in a dual fuel engine. A single cylinder diesel engine with rated output of 4.4 kW at 1500 rpm was converted to operate in the dual fuel mode. Dual fuel operation of varying hydrogen quantity with RSO and RSOME results in higher brake thermal efficiency and significant reduction in smoke levels at high outputs. The maximum brake thermal efficiency is 28.12%, 29.26% and 31.62% with RSO, RSOME and diesel at hydrogen energy share of 8.39%, 8.73% and 10.1%, respectively. Smoke is reduced from 5.5 to 3.5 BSU with RSOME and for RSO it is from 6.1 to 3.8 BSU at the maximum efficiency point. The peak pressure and maximum rate of pressure rise increase with hydrogen induction. Heat release rate indicates an increase in the combustion rate with hydrogen induction. On the whole it is concluded that hydrogen can be inducted along with air in order to reduce smoke levels and improve thermal efficiency of RSO and its bio-diesel fuelled diesel engines.

Measurement and optimization of surface roughness and tool wear via grey relational analysis, TOPSIS and RSA techniques

Abstract: Magnesium alloy (Mg alloy) is one among the lightest materials and which has wide applications in the production of aircraft engines, airframes, helicopter components, light trucks, automotive parts and computers parts for its attractive properties. In this paper, a study to analyze the turning properties of magnesium alloy AZ91D in dry condition with polycrystalline diamond (PCD) cutting inserts is presented. Firstly, to investigate turning of magnesium alloy using grey relational analysis and TOPSIS of optimum cutting parameter values. Secondly, to determine using response surface analysis of mathematical model depending on cutting parameters of surface roughness and tool flank wear in turning. The adequacy of the developed mathematical model is proved by ANOVA. The findings from the investigation showed that feed rate and cutting speed are the dominant factors for surface roughness and tool flank wear respectively.

An empirical study on prediction of heart disease using classification data mining techniques

Abstract: In this research paper, the use of pattern recognition and data mining techniques into risk prediction models in the clinical domain of cardiovascular medicine is proposed. The data is to be modelled and classified by using classification data mining technique. Some of the limitations of the conventional medical scoring systems are that there is a presence of intrinsic linear combinations of variables in the input set and hence they are not adept at modelling nonlinear complex interactions in medical domains. This limitation is handled in this research by use of classification models which can implicitly detect complex nonlinear relationships between dependent and independent variables as well as the ability to detect all possible interactions between predictor variables.

Measurement and optimization of performance characteristics in turning of Mg alloy under dry and MQL conditions

Abstract: Recent research in the field of magnesium has brought out the enormous potential in terms of their applications and machining process. Lightness and high strength-to-weight ratio of such materials have led to an increased interest in their use in automobile and aerospace industries. In this paper investigation on turning of magnesium alloy using uncoated tungsten carbide cutting insert in dry and minimum quantity lubrication (MQL) cutting conditions have been presented. This work includes input parameters like cutting conditions, cutting speed, feed and depth of cut as cutting factors and the responses of tool flank wear, surface roughness, cutting force and cutting temperature have been optimized by grey relational analysis. The optimization has been performed firstly as mono-objective optimization by using Taguchi method; secondly as multi-objective optimization through Taguchi based grey relational analysis (GRA). The mono-objective optimization results gave the combination of 40 m/min cutting speed, 0.10 mm/rev feed rate and 0.5 depth of cut when run under MQL for optimizing the tool wear and cutting temperature and also the same combination optimized cutting force when operated under dry condition. The optimal condition for surface roughness was at 140 m/min cutting speed, 0.10 mm/rev feed rate, 0.5 depth of cut at MQL. GRA technique provided optimum condition i.e. 90 m/min cutting speed, 0.1 mm/rev feed rate and 0.5 mm depth of cut at MQL that minimized the output responses.