Measurement Techniques for the Inspection of Porosity Flaws on Machined Surfaces

[+] Author and Article Information
D. Steiner1

Engineering Research Center for Reconfigurable Manufacturing Systems (ERC/RMS), University of Michigan, Ann Arbor, MIdvirs@engin.umich.edu

R. Katz

Engineering Research Center for Reconfigurable Manufacturing Systems (ERC/RMS), University of Michigan, Ann Arbor, MIReuven@engin.umich.edu


Corresponding author.

J. Comput. Inf. Sci. Eng 7(1), 85-94 (Jul 03, 2006) (10 pages) doi:10.1115/1.2424244 History: Received November 27, 2005; Revised July 03, 2006

Continuous improvement of product quality is essential to the success of manufacturing enterprises. In-process inspection plays an important role in improving product quality control of mass produced products. The change that has occurred in traditional mass production, i.e., the change from dedicated manufacturing to reconfigurable and flexible manufacturing, has increased the demand for computer integrated manufacturing (CIM). Automated inspection serves as a key component of CIM, for example, detecting and measuring surface flaws. Today, inspection of surface flaws is mainly performed on sheet metal, paper, glass, rags, etc. These objects are flat in nature and do not include 3D features or edges of features. However, structural parts which are produced using casting process are complicated and are composed of a large number of features. When these parts are machined (flat milled) porosity defects arise in addition to surface flaws such as scratches and texture defects. Porosity defects arise on the surface when the cutting tool cuts through an air bubble (pore void) created during casting. In many applications, inspection and accurate measurement of porosity flaws, within the capabilities of the measuring device, is essential and crucial to determine the ability of the product to function well. In this paper we present a technique for measuring the correct size of a pore using a low value threshold, to avoid false detection, miss detection and underestimation of pore size. Also we present two algorithms for detecting edge-connected pores which are pores on the edge of features. Detection is done without the use of a feature data base. One of the edge-connected pore detection algorithms uses basic morphological operations for determining the locations of the pores, while the other algorithm uses curve analysis for the same purpose. The feasibility of the proposed porosity measurement technique and edge-connected pore detection algorithms is demonstrated and validated on the joint face of an engine cylinder head.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 12

Edge-connected pore: (a) Image or edge-connected pore and (b) curve through the edge-connected pore

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Figure 2

Pore as a 2.5D surface (a) top view over a pore and it’s surrounding represented as a 2.5D surface, (b) zooming of a pore with surface level cut in solid line and threshold cut in dashed line, and (c) sideview of the surface with surface level marked in solid line and threshold level in dashed

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Figure 3

An example of binary image maintained by different threshold values

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Figure 4

New approach for pore dimensional calculation

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Figure 5

Pore image under microscope

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Figure 6

Threshold before and after subtracting the gradient magnitude: (a) Blue curve demonstrate gray level equal to 0.52 on the original image, (b) red curve demonstrate gray level equal to 0.52 on the original image after the gradient magnitude was subtracted, (c) distribution of the gradient inside the pore, and (d) demonstrating (a) and (b) while the yellow curve demonstrates gray level 0.67 on the original image

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Figure 7

Comparison between radiuses of circumscribing circles around pores using gradient method when the threshold equals to 0.4 (blue) and between different threshold values (green). (a) Threshold equals 0.4, (b) threshold equals 0.5, (c) threshold equals 0.6, (d) Threshold equals 0.7, (e) threshold equals 0.8, and (f) table that reviews eight pores on the surface. The table refers to the pores as numbered in (a).

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Figure 8

The joint face of an engine cylinder head

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Figure 9

Two approaches for edge-connected pore detection

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Figure 10

Features image: (a) Binary features image with pores, (b) binary features without pores, (c) contours of the features and large pores, and (d) injected edge connected pores

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Figure 11

Edge-connected pores using morphological operation close: (a) Filled pores after close operation and (b) pores after subtraction

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Figure 13

Light bumps: (a) Pores bump light and (b) reflected light from features and pores

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Figure 14

Tilted vision system

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Figure 15

Top view of the proposed system setup for pore inspection and depth validation: (a) Both cameras look on the same line on the surface and (b) the tilted vision system is translated on the x axis

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Figure 16

Edge-connected-pore detection using curve analysis: (a) Edge-connected pores detected in red and (b) feature curves

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Figure 17

Top view of the experimental system in its tilted setup

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Figure 1

Pores dimensional analysis using binarization.

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Figure 18

Validation of pores using side light: (a)–(b) Subimage taken by the perpendicular setup, (d)–(f) The same area as in (a)–(c) using the tilted setup

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Figure 19

The side light effects: (a) Pore reflected light and (b) pore reflection using microscope



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