IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

The threat posed by coolants to environment and worker health, escalating manufacturing costs due to coolant usage and stringent regulations enforced by the government for coolant waste disposal trigger research in unconventional coolant delivery techniques. Minimum Quantity Lubrication (MQL) is a technique delivering very small amount of coolant in the form of spray droplets that easily penetrate the tool-work piece interface for improving the cutting action, augmenting tool life and work piece quality. The droplet size which plays an import role in removing the heat and provide a lubrication action in the contact zone is controlled by the spray parameters. In this paper, an analytical model has been developed to determine the Sauter Mean Diameter (SMD) of coolant spray in MQL applied to machining processes, as a function of the spray parameters such as air pressure, viscosity of air, coolant flow rate, surface tension and density of coolant oil, based on the first principles and the conservation methods. The model developed has been validated with the experimental results and it has correlated well with reasonable accuracy. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

Department of Mechanical Engineering

GENERAL

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Procedia CIRP

Analytical model to predict sauter mean diameter in air assisted atomizers for MQL in machining application

In a machine tool, the cutting tool is mainly responsible for producing a component with good surface quality. With the time the cutting tool wear out and affects the surface quality. Hence it is very important to monitor the condition of the cutting tool to avoid the production of substandard parts. In this work the face milling cutter is made to interact with hardened steel components to manufacture the required surfaces with a specified amount of stock removal. The cutting conditions are selected and machining is done till the tool reaches its critical flank wear value. The captured surface images are analyzed using the statistical and spectral texture analysis methods. The flank wear of the cutting insert is measured at frequent intervals. The evaluated texture features are correlated with the flank wear using the multivariate correlation methods. The significant features are selected based on the correlation value and its mutual correlation value with other features. The selected texture features are plotted against machining time or the number of components. The developed regression model based on the selected parameters and the time is used to predict the flank wear. © 2014 IEEE.

2014 International Conference on Prognostics and Health Management, PHM 2014

Prognostics model for tool life prediction in milling using texture features of surface image data

Assessing the grinding wheel surface condition during dressing is very important in order to decide about the number of dressing passes required to retain the cutting ability of the grinding wheel and also to reduce the wastage of the grinding wheel material. The dressing process removes the loaded particles and brings out the new grains in order to retain the cutting ability of the grinding wheel. The selection of correct dressing parameters and the condition of the dresser are very important to carryout proper dressing. In this work, an attempt has been made to arrive out the number of dressing passes required to dress the grinding wheel based on the texture features of the images of the grinding wheel. The single point diamond dressing was carried out with selected dressing variables. After each pass the images of the grinding wheel was captured in the same location by properly positioning the grinding wheel. Then the images were analyzed and the evaluated texture parameters were used to indicate the condition of the grinding wheel. © 2014 IEEE.

Assessment of grinding wheel conditioning process using machine vision

This paper deals with the application of the ultrasonic reflection method of non-destructive evaluation to monitor the condition of a grinding wheel surface. As the grinding process continues, the grinding wheel wear and loading changes the texture of the grinding wheel surface which changes the reflective characteristics of the grinding wheel surface. Ultrasonic signals are made to impinge on the surface of a grinding wheel and the amplitude of the reflected waves is evaluated to reveal the changes in the surface condition of the grinding wheel. A variation of the intensity of reflected waves with the increase in wheel wear helps us determine the redressing time for the grinding wheel. The intensity of the echo amplitude clearly indicates the changes in the grinding wheel condition. This indicates the suitability of this method for in process monitoring of the grinding wheel surface condition to evaluate the dressing time in order to avoid the production of defective products. © 2011 Inderscience Enterprises Ltd.

International Journal of Abrasive Technology

A preliminary investigation on the application of air-coupled ultrasound to evaluate surface condition of the grinding wheel

The surface quality of the component produced mainly depends on the condition of the grinding wheel. In this paper, the working surface condition of the grinding wheel is assessed by the texture analysis methods. The grinding wheel images are captured at different time intervals using a CCD camera. Then, the captured images are pre-processed using histogram equalisation to reduce the effect of non-uniform illumination. After pre-processing, Fourier power spectrum based texture parameters are evaluated and used for discriminating the grinding wheel condition. The evaluated parameters identify the condition of the grinding wheel efficiently, which in turn provide an opportunity for online condition monitoring of grinding wheel using machine vision. Copyright © 2010 Inderscience Enterprises Ltd.

International Journal of Manufacturing Technology and Management

Fourier Transform based texture measures for grinding wheel condition monitoring using machine vision

The surface quality of the components produced in a grinding process mainly depends on the grinding wheel topography. To maintain the quality of the component, the condition of the grinding wheel should be monitored and the periodical dressing has to be done to retain the shape and the sharpness of the cutting edges. In the present paper, the machine-vision-based texture analysis methods are introduced to discriminate the surface condition of the grinding wheel. The grinding wheel images are acquired using a charge coupled device (CCD) camera at different intervals of time during the process and the texture of the grinding wheel image is analysed using appropriate statistical and fractal methods. The variation of the parameters with the condition of the grinding wheel surface is analysed and presented. The evaluated parameters are clearly discriminating the texture of the grinding wheel at every stage. The methods presented in this paper can be used to assess the grinding wheel condition effectively. © IMechE 2007.

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Texture analysis for grinding wheel wear assessment using machine vision

This paper describes a non-contact technique to assess the differences in surface characteristics of the ground components. The computer vision based system is used to analyze the pattern of scattered light from the surface to asses the surface roughness of the component. The ground specimens were manufactured using varying machining parameters. The images of the specimens are captured using a CCD camera. The image parameters based on the wavelet transform are evaluated. Then, the evaluated parameters along with the cutting parameters were used to train the artificial neural network to predict the surface roughness parameters Ra, which is measured using the stylus instrument. The comparison of stylus Ra and that predicted using ANN are presented and analyzed in this paper. Copyright© (2006) by the International Measurement Federation (IMEKO).

18th IMEKO World Congress 2006: Metrology for a Sustainable Development

Vision based surface roughness evaluation of ground components using wavelet transform and neural network

Advanced high strength steels have excellent strength-to-weight ratio and are widely used for automotive and structural applications. Grinding is commonly used as a final machining process for these materials to achieve the required dimensional tolerance and surface quality. In this work, the grindability of as-received microstructure of ferrite-pearlite and in-house developed ferrite-bainite-martensite steels using polycrystalline white fused alumina and microcrystalline sol-gel alumina grinding wheels under flood and minimum quantity lubrication conditions is studied. Grindability of these steels was evaluated based on the wheel wear, grinding forces, force ratio, specific cutting energy, surface roughness (Ra), and surface morphology. The sol-gel alumina wheel was observed with higher wear flat area and forces under all the grinding conditions as compared with white fused alumina wheel. The white fused alumina wheel exhibited higher force ratio values for all the grinding conditions than the sol-gel alumina wheel. The energy required per unit volume of material removal is lower for the white fused alumina wheel in both the steels under flood and minimum quantity lubrication conditions. This is due to the retention of sharpness of abrasive grains by self-sharpening for white fused alumina wheel than the sol-gel alumina. The presence of fractured, melted, and hollow chips on the ferrite-pearlite and ferrite-bainite-martensite steels under both the grinding condition confirms the change in material removal mechanism with the presence of higher wear flat area of the sol-gel alumina wheel. Based on the obtained results, the white fused alumina wheel was found to be a suitable grinding wheel for grinding of both the steels under all grinding conditions compared to the sol-gel alumina wheel.