Introduction to Stereology and Quantitative Metallography - Volume Fractions and Spacings

Introduction to Stereology and Quantitative Metallography - Volume Fractions and Spacings

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Introduction to Stereology and Quantitative Metallography - Measurement of Size and Shape

Introduction to Stereology and Quantitative Metallography - Measurement of Size and Shape

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Introduction to Stereology and Quantitative Metallography - Measurement of Grain Size

Introduction to Stereology and Quantitative Metallography - Measurement of Grain Size

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Introduction to Stereology and Quantitative Metallography - ASTM E 1245

ASTM E 1245 - A Stereological Procedure to Characterize Discrete Second-Phase Particles

Uses field and feature-specific measurements. While the measurements employ stereological parameters, they may be made on only one plane, for example, the longitudinal. If the threedimensional values are desired, then additional test planes must be assessed.

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Introduction to Stereological Principles

Although the fundamental relationships for stereology, the foundation of quantitative metallography, have been known for some time, implementation of these concepts has been restricted when performed manually due to the tremendous effort required. Further, while humans are quite good with pattern recognition, as in the identification of complex structures, they are less satisfactory for repetitive counting.

Many years ago, George Moore (1) and members of ASTM Committee E-4 on Metallography conducted a simple counting experiment. About 400 people were asked to count the number of times the letter "e" appeared in a paragraph without striking out the letters as they counted. The correct answer was obtained by only 3.8% of the people. Results were not Gaussian, however, as only 4.3% had higher values while 92% had lower values, some much lower. The standard deviation was 12.28. This experiment revealed a basic problem with manual ratings. In this case the subject was one very familiar to the test subjects, yet only 3.8% obtained the correct count. What degree of counting accuracy can be expected when the subject is less familiar, such as microstructural features? Image analyzers, on the other hand, are quite good at counting but not as competent at recognizing features of interest. Fortunately, there has been tremendous progress in the development of powerful, user-friendly image analyzers over the past two decades.

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Introduction to Quantitative Metallography

For most of its history, metallographic observations have been largely qualitative in nature. The structure might be described as being relatively coarse or fine, or layered, or uniform. Particles might be labeled as globular or spheroidal, lamellar, acicular, or blocky. Microstructures were single-phase or duplex, and so forth.

Forty some years ago when I entered industry, chart ratings and visual examinations were the main approach toward quantitation. I can well remember the mill metallographers looking at spheroidized carbide tool steel structures and stating that it was, for example, 90% spheroidized (many raters would never say 100%, just as some teachers would never grade an essay at 100%!) or that it was 60% spheroidized and 40% lamellar tending to spheroidize. Or, without looking at the chart (a seasoned rater never did!), they would pronounce that the grain size was, for example, 100% 6 to 8 or perhaps 70% 8 and 30% 3 to 5 if it was duplex in appearance.

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Image Analysis Measurements of Duplex Grain Structures

Several types of duplex grain size distributions in five different alloys were evaluated using image analysis. Most of the grain structures contained annealing twins. Those with straight interfaces could be recognized and deleted from the image, leaving only grain boundaries. One specimen exhibited curved twin boundaries, caused by deformation, and they could not be discriminated by the system as currently programmed. Grain areas were measured and grouped according to their relationship to the ASTM grain size scale. An area-weighted histogram was shown to be excellent for revealing the nature of the distribution, while a numerical-frequency histogram was insensitive. The intersection of these two curves separated only one of the four bimodal distributions. A deconvolution approach, using the area-weighted curve only, should be evaluated. An arithmetic grain area classification approach using 25 classes based on the data range, to split the two grain area populations based upon the intersection of the number percent and area percent curves, worked well for two of the four specimens. Image analysis detection of grains results in a small portion of the image (about 6-12%) assigned to the grain boundaries. In manual measurement methods, the area occupied by the grain boundaries is not considered, and it does not influence measurements. Thus, compared to manual methods, image analysis undersizes grains slightly producing a relatively small positive bias in the grain size number, which could be ignored, but can be eliminated or reduced.

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resourcesThe articles and presentations that can be down-loaded from this web site are based upon work done by GFV while employed at Bethlehem Steel (1967-1983), Carpenter Technology (1983-1996), Buehler Ltd. (1996-2009) and Struers (2009-Present) and from the authors consulting work for companies such as, Latrobe Steel, Scot Forge, etc., and from his litigation work. GFV's bylined articles appearing in various issues of the ASM Handbook series have been listed here courtesy of ASM International, Materials Park, Ohio.

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