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.

N. Arunachalam

Department of Mechanical Engineering

B. Ramamoorthy

CCD CAMERAS

Fractals

GRINDING WHEELS

Surface topography

Wear of materials

Texture analysis

WHEEL WEAR

Computer vision

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

Texture analysis for grinding wheel wear assessment using machine vision

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

The current trend of Industry 4.0 development requires an integration of the process sensing technologies with the cloud computing network to design the cyber-physical systems which can seamlessly transfer data between the connected devices. This will pave a way for real-time process monitoring as well as the control of the process by the use of decision-making algorithms. The current paper explores the opportunities with this regard in the area of tool wear prediction in end milling of Ti-6Al-4V alloy at various operating conditions. A series of slot milling passes were made at various parameter combinations of feed, speed, and depth of cut until the flank wear on tool crosses the failure criterion. The cutting force data acquired in the process with the dynamometer and the texture features from the image of the milled surface are used to build a model for predicting the flank wear through the Kalman filter approach. The fusion model built using the Kalman filter methodology achieves a good accuracy in predicting the flank wear on the tool. The model is highly accurate in predicting the wear as the tool approaches the failure threshold. Thus the model can enable the decision control module to trigger a tool change signal and improve the overall productivity of the process. © 2018 The Author(s).

Fulltext

Procedia Manufacturing

Tool wear prediction in end milling of Ti-6Al-4V through Kalman filter based fusion of texture features and cutting forces

Grinding is an expensive and complex machining process, characterized by cutting grits undergoing non-uniform wear. The worn out grits influence the surface finish of the part, necessitating timely dressing. Conventionally, the dressing interval is decided either based on the wheel life end criteria viz. visual identification of the workpiece burn mark, chatter occurrence and deterioration in the part finish or on the number of parts produced. Improper dressing interval increases auxiliary machining time and grinding wheel wastage. Prevailing demands towards next generation smart manufacturing include product and process related benefits such as low operational cost, better customer service support, operation optimization and control. In the present work, we propose a low cost, process non-intrusive sensor technology with IoT enabled operational intelligence platform to estimate the redress life of grinding wheel based on wheel condition. Traverse grinding tests were carried out in a CNC surface grinding machine installed with Al2O3 wheel against D2 tool steel under wet condition. During experimentation, the spindle motor current, grinding forces and grinding wheel surface images were acquired using the Hall-effect sensor, dynamometer, and CCD camera respectively. Data acquisition, network connectivity, and cloud communication were empowered by serial output. Statistical time, frequency and wavelet domain features signifying the wheel life characteristic were extracted. To show the usefulness of motor current signals, the extracted features thereof were confirmed against grinding forces and wheel surface images. A time series Auto-Regressive Moving Average (ARMA) predictive model was developed to estimate the grinding wheel redress life using the selected root mean square (RMS) feature of a current signal. An android application was also developed for a graphical visualization of dressing time based on the RMS value of the spindle motor current signal. The developed methodology thus, allows operators and machines with sensors to communicate with each other and facilitates real-time traceability, visibility and control over the dressing action to perform automatic dressing before the wheel reaches its end of life. © 2018 The Author(s).

Multi-Sensor Data Analytics for Grinding Wheel Redress Life Estimation- An Approach towards Industry 4.0

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

Texture studies play a paramount role in many image processing applications. In this paper an attempt is made to study the textural features of machined surfaces (grinding, milling and shaping) using the most widely used statistical methods, viz. co-occurrence matrix approach, the amplitude varying rate statistical approach (AVRS) and the run length matrix approach. Textural features derived from these matrices are studied and analysed. A new matrix for the qualitative evaluation of surfaces, namely the gray-level difference-pixel distance matrix, is presented and its usefulness in texture analysis is analysed. The features calculated from these matrices are correlated with surface parameters, such as roughness, and the different features are studied for classification of these surfaces. Copyright © 1996 Pattern Recognition Society. Published by Elsevier Science Ltd.

Pattern Recognition

Statistical methods to compare the texture features of machined surfaces

The International Journal of Advanced Manufacturing Technology

Assessment and visualisation of machine tool wear using computer vision

NDT & E International

A vision system for surface roughness characterization using the gray level co-occurrence matrix

Connectivity oriented fast Hough transform for tool wear monitoring

Machine Vision and Applications

Tool wear monitoring using a fast Hough transform of images of machined surfaces

Journal	Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
ISSN	09544054
Open Access	No