An Improved Technique to Determine Coolant Flow Patterns for In- process Measurement

In order to determine system parameters for in-process optical measurement for workpiece surface profiles during machining, an experimental system was established to validate theoretical models. It was found that the coolant flow patterns were one of the critical factors, which affect the effectiveness of the measurement. To obtain the flow patterns, an image processing method had been proposed. However, in this method, a fluid mechanic model would be required. To avoid this prerequisite, a new enhanced technique that utilizes image histogram processing, flow pattern detection based on the analysis of the pixel intensities, and curve fitting of the pattern edges is presented. A number of digital images were processed using the new method. Results are shown to demonstrate the effectiveness of the approach. Introduction It is known that, in machining processes, the interactions between the workpiece and the cutting tool can lead to thermal deformations in the workpiece, and as such, the accuracy of the machining process can be seriously affected. In this case, coolants can be used to reduce the effect due to the heat. However, the opaque optical property of the coolants can be a problem for the in-process measurement of surface profiles using optical instruments. To solve the problem, a transparent fluid beam can be used to produce an optically clean zone on the workpiece surface to realize the optical measurement. In order to determine the system parameters, the coolant flow patterns have to be investigated using a digital camera system to acquire images for the coolant flows [1-2]. Digital image processing has become a useful tool to study flow patterns of fluids [3-7]. There are a number of techniques, such as the Hough transform technique [8], wavelet transform technique [9], and boundary detection scheme based on edge flow [10]. In our previous study, a technique which used prior knowledge of the coolant flow was proposed. A model of coolant flow patterns was assumed in the technique [11]. To avoid this pre-requisite, a new algorithm is proposed to provide an improvement to process images of the flow patterns to find equations of the edge curves of the patterns. The investigation is important, because the size of the area enclosed in the edge curves is critical to the in-process optical measurement. If the area is too small, the permitted measurement range of a laser sensor cannot be fully utilized. In some cases, the measurement cannot be achieved at all. Hence, the system parameters should be optimized so that a suitable size of area is ensured for the in-process measurement. To do this, an experimental approach has been used and the proposed technique is to be used to process the experimental data. In the new approach, coolant images are initially analyzed using image histogram processing such that certain regions of the background can be found. The flow pattern detection is given by analyzing pixel intensities. Finally, the equations of the edge curves of the patterns are obtained by curve fitting of the points on the detected edges. A number of experimental data sets were processed using the proposed approach. One of the examples is shown to demonstrate the effectiveness of the proposed technique. Key Engineering Materials Vols. 238-239 (2003) pp 199-204 online at http://www.scientific.net © (2003) Trans Tech Publications, Switzerland Online available since 2003/Apr/15 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of the publisher: Trans Tech Publications Ltd, Switzerland, www.ttp.net. (ID: 130.203.133.34-16/04/08,15:43:57) System for Coolant Image Acquisition In order to perform in-process measurement, an experimental system was established [1]. The system setup includes three sub-systems. The coolant and fluid injection system is to assist the implementation of the in-process optical measurement. An optical clean zone is constructed by a transparent fluid beam which is ejected from the beam nozzle to a rotating disc. The workpiece is located in that region such that the measurement can be achieved. The surface profile measurement system is to measure the workpiece profile by a triangulation sensor. The resolution, working range, accuracy, and maximum sampling rate of the sensor are 0.125μm, 300μm, 1.0μm, and 1000 samples/s respectively. Also, the flow visualization system is to record and process the images of the flow patterns of the fluid beam and the coolant. It includes a charge-coupled device or CCD camera, a video inspection microscope, a fiber optic illuminator and a computer with an image capture card. The coolant image acquisition system is shown in Fig. 1. Fig. 1 Experimental system for in-process measurement testing Enhanced Coolant Image Processing The original image obtained from experiment is an RGB image. It is converted to be a gray scale intensity image I(x, y) which covers 0 to 255 gray levels. The advantages of processing the gray scale images rather than the color images are because the former can enhance the contrast between the coolant flow patterns and the background and the file size can be reduced. Also, the histogram of the image is obtained based on the following equation. The image has L gray levels, and n is the number of pixels having a particular grey level intensity k. The plot of the number of pixels at a certain gray level value is the image histogram, which represents the function F of the gray level. Therefore, ( , ) ( ( , )) |I x y k F I x y n = = , (1) and n is also noted as nk. Based on the properties of the original images obtained from the experiment, it was found that all the pixels for the peak of the histogram are the background and all the pixels for the lowest point are the flow pattern. The intensities of a pixel, the background, and the flow pattern are defined as follows. Therefore, the intensity of a pixel is I(x, y) = F (nk). (2) The intensity of the background is 200 Advances in Abrasive Technology V