Optical Inspection Of Manufactured Glass Using Adaptive Fourier Filtering

1988 ◽  
Vol 27 (5) ◽  
Author(s):  
R. Cormack ◽  
K. M. Johnson ◽  
Lin Zhang ◽  
W. T. Cathey
Keyword(s):  
Optical Inspection ◽  
Fourier Filtering

scholarly journals Optical inspection of appearance faults for auto mirrors using Fourier filtering and convex hull arithmetic

2021 ◽  
Vol 19 (4) ◽  
pp. 279-293
Author(s):  
Yuan-Shyi Peter Chiu ◽  
Hong-Dar Lin ◽  
Hsu-Hung Cheng
Keyword(s):  
Fault Detection ◽  
Convex Hull ◽  
Mirror Reflection ◽  
Classification Rate ◽  
Foreign Objects ◽  
Optical Inspection ◽  
Fourier Filtering ◽  
Almost All ◽  
Crucial Part ◽  
High Pass

Auto mirrors are indispensable essential in reflection of objects behind the car and act a crucial part in driving security.  In manufacturing stages of auto mirrors, certain tasks operated unusually will cause producing scratches, chips, pinholes, bubbles, damaged edges, the general surface and profile faults on auto mirrors.  Those appearance faults sometimes will severely have an impact on standard of the mirror reflection and grow the driving hazard.  At traditional examination of auto mirrors in manufacturing process, almost all works are performed by human examiners.  Manual examination is simple to be disturbed by foreign objects reflected on the mirror surfaces and arouse causing mistaken determinations of fault examination.  Thence, this study works toward investigating the automatic appearance fault detection of auto mirrors.  We propose a fault enhancement technique based on Fourier high-pass filtering and the convex hull arithmetic to inspect appearance faults on auto mirrors.  This approach only utilizes their own information of testing images to judge whether there are any irregular appearance changes without the need of standard patterns for matching.  Experimental outcomes illustrate that the appearance fault detection rate reaches to 95.13%, and the false alarm rate decreases to 1.88%, and the correct classification rate attains to 98.11%. 

Glass Knife Geometry as Seen by the SEM

1971 ◽  
Vol 29 ◽  
pp. 454-455
Author(s):  
J. Temple Black
Keyword(s):  
Low Cost ◽  
Amorphous Material ◽  
Stress Concentrations ◽  
Basic Tool ◽  
Tool Materials ◽  
Optical Inspection ◽  
Surface Flaws ◽  
Slip Planes ◽  
Glass Knife ◽  
Diamond Knife

In ultramicrotomy, the two basic tool materials are glass and diamond. Glass because of its low cost and ease of manufacture of the knife itself is still widely used despite the superiority of diamond knives in many applications. Both kinds of knives produce plastic deformation in the microtomed section due to the nature of the cutting process and microscopic chips in the edge of the knife. Because glass has no well defined slip planes in its structure (it's an amorphous material), it is very strong and essentially never fails in compression. However, surface flaws produce stress concentrations which reduce the strength of glass to 10,000 to 20,000 psi from its theoretical or flaw free values of 1 to 2 million psi. While the microchips in the edge of the glass or diamond knife are generally too small to be observed in the SEM, the second common type of defect can be identified. This is the striations (also termed the check marks or feathers) which are always present over the entire edge of a glass knife regardless of whether or not they are visable under optical inspection. These steps in the cutting edge can be observed in the SEM by proper preparation of carefully broken knives and orientation of the knife, with respect to the scanning beam.

Application of cross correlation to HREM images of surfaces

1987 ◽  
Vol 45 ◽  
pp. 754-755
Author(s):  
D. E. Luzzi ◽  
L. D. Marks ◽  
M. I. Buckett
Keyword(s):  
Cross Correlation ◽  
A Priori ◽  
Matrix Material ◽  
Correlation Method ◽  
Accurate Knowledge ◽  
Complex Image ◽  
Fourier Filtering ◽  
The Matrix ◽  
Low Pass ◽  
Data Reduction Technique

As the HREM becomes increasingly used for the study of dynamic localized phenomena, the development of techniques to recover the desired information from a real image is important. Often, the important features are not strongly scattering in comparison to the matrix material in addition to being masked by statistical and amorphous noise. The desired information will usually involve the accurate knowledge of the position and intensity of the contrast. In order to decipher the desired information from a complex image, cross-correlation (xcf) techniques can be utilized. Unlike other image processing methods which rely on data massaging (e.g. high/low pass filtering or Fourier filtering), the cross-correlation method is a rigorous data reduction technique with no a priori assumptions.We have examined basic cross-correlation procedures using images of discrete gaussian peaks and have developed an iterative procedure to greatly enhance the capabilities of these techniques when the contrast from the peaks overlap.

Two structural states of the Z-band in cardiac and skeletal muscle

1989 ◽  
Vol 47 ◽  
pp. 1022-1023
Author(s):  
J.P. Schroeter ◽  
M.A. Goldstein ◽  
J.P. Bretaudiere ◽  
L.H. Michael ◽  
R.L. Sass
Keyword(s):  
Skeletal Muscle ◽  
Cross Section ◽  
Real Space ◽  
Contractile State ◽  
Contour Maps ◽  
False Color ◽  
Grey Scale ◽  
Fourier Filtering ◽  
Cardiac Muscles ◽  
Enhancement Algorithm

We have recently established the existence of two structural states of the Z band lattice in cross section in cardiac as well as in skeletal muscle. The two structural states are related to the contractile state of the muscle. In skeletal muscle at rest, the Z band is in the small square (ss) lattice form, but tetanized muscle exhibits the basket weave (bw) form. In contrast, unstimu- lated cardiac muscle exhibits the bw form, but cardiac muscles exposed to EGTA show the ss form.We have used two-dimensional computer enhancement techniques on digitized electron micrographs to compare each lattice form as it appears in both cardiac and skeletal muscle. Both real space averaging and fourier filtering methods were used. Enhanced images were displayed as grey-scale projections, as contour maps, and in false color.There is only a slight difference between the lattices produced by the two different enhancement techniques. Thus the information presented in these images is not likely to be an artifact of the enhancement algorithm.

Material analysis techniques using the ion mill

1996 ◽  
Vol 54 ◽  
pp. 1034-1035
Author(s):  
J. P. Benedict ◽  
R. M. Anderson ◽  
S. J. Klepeis
Keyword(s):  
Polished Surface ◽  
Surface Material ◽  
Analysis Techniques ◽  
Material Analysis ◽  
Optical Inspection ◽  
Electron Microscopy Analysis ◽  
Microscopy Analysis ◽  
Argon Ions ◽  
The Impact ◽  
Scanning Electron

Ion mills equipped with flood guns can perform two important functions in material analysis; they can either remove material or deposit material. The ion mill holder shown in Fig. 1 is used to remove material from the polished surface of a sample for further optical inspection or SEM ( Scanning Electron Microscopy ) analysis. The sample is attached to a pohshing stud type SEM mount and placed in the ion mill holder with the polished surface of the sample pointing straight up, as shown in Fig 2. As the holder is rotating in the ion mill, Argon ions from the flood gun are directed down at the top of the sample. The impact of Argon ions against the surface of the sample causes some of the surface material to leave the sample at a material dependent, nonuniform rate. As a result, the polished surface will begin to develop topography during milling as fast sputtering materials leave behind depressions in the polished surface.

Optical inspection of laser drilled cooling holes in jet engine blades

2006 ◽  
Author(s):  
Devdas Shetty ◽  
Tom Eppes ◽  
Nikolay Nazaryan ◽  
Claudio Campana ◽  
Jun Kondo
Keyword(s):  
Jet Engine ◽  
Optical Inspection

Circuit Voltage Probe Based on Time-Integrated Measurements of Optical Emission From Leakage Current

2002 ◽  
Author(s):  
Franco Stellari ◽  
Peilin Song ◽  
James C. Tsang ◽  
Moyra K. McManus ◽  
Mark B. Ketchen
Keyword(s):  
Leakage Current ◽  
Voltage Drop ◽  
Optical Emission ◽  
Channel Length ◽  
Operating Conditions ◽  
Power Circuit ◽  
Hot Carrier ◽  
Optical Inspection ◽  
Low Threshold ◽  
Circuit Power

Abstract Hot-carrier luminescence emission is used to diagnose the cause of excess quiescence current, IDDQ, in a low power circuit implemented in CMOS 7SF technology. We found by optical inspection of the chip that the high IDDQ is related to the low threshold, Vt, device process and in particular to transistors with minimum channel length (0.18 μm). In this paper we will also show that it is possible to gain knowledge regarding the operating conditions of the IC from the analysis of optical emission due to leakage current, aside from simply locating defects and failures. In particular, we will show how it is possible to calculate the voltage drop across the circuit power grid from time-integrated acquisitions of leakage luminescence.

scholarly journals Demystifying data and AI for manufacturing: case studies from a major computer maker

2021 ◽  
Vol 10 ◽  
Author(s):  
Yi-Chun Chen ◽  
Bo-Huei He ◽  
Shih-Sung Lin ◽  
Jonathan Hans Soeseno ◽  
Daniel Stanley Tan ◽  
...  
Keyword(s):  
Training Data ◽  
Smart Manufacturing ◽  
Reliable System ◽  
Process Innovations ◽  
Optical Inspection ◽  
Technical Details ◽  
Automation Technology ◽  
Electronic Parts ◽  
Traditional Approaches ◽  
Inspection Machine

In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.

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