# AGMA 923-B05 PDF

(This Information Sheet is NOT an AGMA Standard). American Gear Metallurgical Specifications for Steel Gearing Manufacturers AGMA B05 CAUTION. (This Information Sheet is NOT an AGMA Standard). American Gear Metallurgical Specifications for Steel Gearing Manufacturers AGMA B05 Association. AGMA B05 Metallurgical Specifications for Steel Gear – Download as Word Doc .doc /.docx), PDF File .pdf) or read online. AGMA B05 Metallurgical.

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These limits allow gear designers and producers to select material suppliers that will meet the minimum expectations for material fatigue performance, but do not provide the data needed by designers to meet ever-increasing demands for high power agam gearing applications faced today.

Modern electric arc furnace EAF and vacuum refining VR steelmaking technologies have enabled steelmakers to improve steel oxide inclusion cleanness to levels that rival vacuum arc re-melted Agmaa steels at a fraction of the cost. The ability 932-b05 fully characterize the geometric and chemical characteristics of micro and macro oxide inclusion populations using automated image analysis SEM allows the steelmaker to aggma how practices employed in the melting and teaming pouring and solidification of the steel affect oxide cleanness.

The presence of hard oxide inclusions can result in fatigue failures. The oxide inclusion content limits for Ultrapremium steels are on par with typical values for oxide inclusions in vacuum arc re-melted VAR steels, but at a much lower cost. To produce and certify steel to Ultrapremium air-melted quality, TimkenSteel employs advanced vacuum refining and teaming practices and measures the steel cleanness with SEM image analysis and statistical evaluation.

TimkenSteel can produce any of its grades to this new steel micro-cleanness standard. At this time, the Ultrapremium practice and certification limits with steels are produced using bottom-poured ingots.

The Ultrapremium practice for its strand cast process path is under development. Historically, inclusions are measured against ASTM E45 [2] or similar micro cleanness specifications using light optical microscopy LOM and six samples to represent a heat. The AGMA information sheet on steels for gears requires that oxide inclusion 92-b05 meet certain limits in order to meet grade 2 or 3 gear 923-bb05 quality requirements.

In the rating method called out in ASTM E45 Method A, an operator uses a light optical microscope to scan the polished surface of a specimen at times magnification looking for the worst field for each of four inclusion types, with thin and heavy categories for each type. The field size used for rating is 0. Once the worst 0. This process is repeated for each stringer 923-n05 type A, B, C. For D type globular oxides, the worst 0.

## AGMA 923-B05

This method of steel cleanness rating has been used for decades in order to assure the level of steel cleanness is achieved per a specified limit. However, this method does not provide inclusion metrics that are relevant to gear design, and it cannot provide the statistically robust data needed to predict gear performance.

Steels rated with this method and meeting the AGMA grade 3 requirements can have very different inclusion populations when examined more closely. Z-contrast facilitates the automated identification of oxide particles in a steel matrix as a result of the significant difference between the atomic number of iron, with an atomic number of 26, and oxygen, with an atomic number of 8. Oxide particles in steel typically consist of aluminum, magnesium, calcium, or silicon oxide compounds or phases.

In each case, the Z-contrast of these particles against the steel matrix makes them readily detectable. Figure 2 shows an example of a Z-contrast SEM image of inclusions in steel. When high-energy electrons from the electron beam strike the sample, some of the inner-shell electrons contained in the elements of the sample may be excited to a higher-energy shell, leaving an electron hole in the inner shell. An outer-shell, high-energy electron then fills the hole, and the difference in energy is released as a characteristic X-ray.

Because each element has a unique atomic structure, each element has a unique set of peaks on its X-ray emission spectrum. Figure 1 shows an example of EDS analysis of a macro oxide stringer inclusion.

The ability to characterize the chemistry of inclusion populations is critical to developing a strong understanding of how steelmaking practices affect the generation of inclusions. This capability then facilitates the systematic study and optimization of steelmaking practices to minimize oxide inclusion population density. Characterization of the inclusion population for a heat of steel requires tens of hours of SEM run time, but only tens of minutes of sample preparation and operator time.

Forty-eight samples from six locations in the heat are collected from front, middle, and back portions of the heat and top and bottom positions of the ingot. These 48 metallurgical samples are prepared on the longitudinal plane and polished. The operator loads a carousel of 12 samples into the SEM and starts the automated analysis process. No further operator interaction is needed until the analysis is completed and the system is ready for the next carousel of samples.

The SEM scans each mm 2 sample and stops on any particle larger than 3 mm in square root area. The chemical content of the inclusions is of particular use to the steelmaker in defining practices to improve steelmaking practices, while the inclusion geometry and distribution information is of particular use to the gear designer.

If the particle is in proximity to other particle s such that they meet the standard criterion for stringers, then the group is categorized and assessed as a stringer inclusion. Individual or isolated globular oxide particles see Figure 2lower-left are recorded as micro inclusions, and their geometry is reported by square root area.

Stringers of continuous or intermittent oxide particles see Figure 2upper-right are recorded as macro inclusions, and geometry is recorded as individual stringer lengths and widths. A wide range of other geometric measures can be selected as needed.

Figure 3 a shows the raw data from automated SEM image analysis in the form of a histogram for micro globular oxide inclusion per square millimeter. Figure 3 b shows the raw data in histogram form for macro stringer oxide inclusions lengths per square millimeter. Each of these histograms compares five different steel producers. In each case, the steel types are nominally equivalent carburizing steel chemistries.

The first is TimkenSteel Ultrapremium air-melted steel. The next two are from domestic special bar quality SBQ steel mills. Each of the first three steels were produced by electric arc furnace and vacuum refining, and each meet AGMA grade 3 and ASTM A [3] bearing steel quality requirements for oxide inclusion cleanness.

Comparing Figure 3 a to Figure 3 b, one notes that the population of stringers tends to run about one order of magnitude less than the micro inclusion concentrations.

While stringers do tend to be larger and therefore more injurious when present in a critically loaded location, it is much more probable that an injurious micro inclusion will be located in a critical location compared to a stringer. These histograms are particularly useful in comparing the inclusion population between the five steel sources and in considering the concentration of inclusions greater than 10 or 20 mm and stringers longer than and mm for each steel source.

As such, these data alone have great utility in identifying steel sources that can meet the cleanness requirements demanded by highly loaded, power-dense transmission systems. Figure 4 compares the sum of micro oxide inclusions greater than 10 and 20 mm square root area, and Figure 5 compares the sum of stringer oxide inclusions greater than and mm in length.

Micro inclusions at the surface of a gear can be directly considered from these data, while doing so for stringers would require that the gear be machined from bar stock such that all stressed surfaces are along the original longitudinal plane to be directly considered.

## Gear Design Relevant Cleanness Metrics

In order to provide a more direct linkage between gear design and steel cleanness effects on gear fatigue performance, some further analytical processes can be performed on these automated SEM image analysis data. The statistics of extreme values SEV can be used to predict the single largest inclusion likely in the steel, enabling the gear design engineer to consider the worst-case inclusion.

Quantitative stereography can be employed to convert the measured area concentration of inclusions to mean-free path between inclusions and volumetric concentration, enabling the gear design engineer to make direct comparisons of stressed volumes and volumetric inclusion concentrations.

These analytical techniques and their resulting outputs are described in the following two sections. Agmma statistics of extreme values technique [4] applied to inclusion populations has been described in detail by Murakami [5], [6] and are summarized here.

With SEV analysis, one can use the population of inclusions measured on a limited, but statistically robust set of samples to predict a worst case or maximum likely inclusion size. The data set is then arranged in rank order from smallest extreme value to the largest. Next, a measure of the accumulated inspected area for each of the rank ordered samples, described as the reduced variate, Y, is calculated at each j aga.

The reduced variate is a log-log measure of the accumulated inspected area over the set of extreme value samples. The reduced variate is calculated as follows:.

A total reference area A tot is selected, which 923-b055 used to provide a limit for performing an extrapolation of the data set in order to predict the SEV value. Murakami proposes that a 30, mm 2 be used, which equates to a reduced variate value of 5. The Y lim value for the extrapolation is calculated based on the return period, T, and the relevant areas as follows:.

As illustrated in Figure 6a linear regression using the maximum likely linear fit is made of the rank-ordered extreme value data, and the value at which this regression intersects the Y lim value is the SEV maximum 923-05 inclusion value. Table 2 shows the maximum likely globular oxide inclusions for each of the steels reviewed previously. It is important to point out that the SEV value is useful in considering what the largest likely inclusion in the steel is, but it does not provide information about the number density of inclusions that exceed a critical value.

As a result, the SEV value is best used in conjunction with other metrics described in the following section. Quantitative stereology [7], [8] can be used to predict the mean-free path between inclusions or the volumetric concentration of inclusions based on the measured unit area data. Mean-free path can quickly be used to consider the likelihood of an inclusion being present in a component and is valid for both globular oxides and stringers.

The volumetric inclusion concentration can be considered against the stressed volume of a gear, and the probability of encountering a critically sized inclusion in a gear or a population of aagma can 92-3b05 estimated. Similarly, volumetric inclusion populations can be used to populate loading models, in combination with Monte Carlo simulation, to predict relative fatigue life between different populations.

Figure 7 shows the calculated mean-free paths for the globular oxide inclusions greater than 10 mm in square root area and stringers longer than mm in length. Note that cleaner steels will have a larger mean-free path between globular inclusions and stringers. These data can quickly be considered with respect to a gear size or a test coupon size to get a sense of the probability of inclusion-related fatigue failures.

The Saltykov [9] method is a popular and frequently used method to convert area agmaa of spheres to volume density. In this method, the three-dimensional distribution of spheres is approximated by first dividing the two-dimensional frequency per unit area, na, data in to K discrete size sets of integer values between 7 and The data presented in Figure 3 meet these criteria. A series of multipliers is generated based on the number of size ranges selected.

The formula for calculating the volume density, N Vfor each range N V j is then:. Figure 8 shows a comparison of the number of inclusions greater than 10 mm and greater than 20 mm in square root area per cubic centimeter.

If it is determined that inclusions greater than 10 mm represent a risk for failure at stress levels exceeding MPa, then the gear design engineer can assess the design and determine what volume of gear is exposed to principal stresses of MPa and higher.

For example, an automotive ring gear might see stresses at and near the surface in the contact region in excess of MPa. If that volume stressed in excess of MPa is determined to be 0. Similarly, if the same automotive ring aagma is determined to have 0.

### AGMA B05 – Metallurgical Specifications for Steel Gearing

As noted previously, macro stringer inclusions are typically an order of magnitude less frequent than globular oxides and therefore much less likely to be in a critical area compared to one or more critically sized globular oxides. The Saltykov method assumes that the features being addressed are all spherical. This is a good assumption with micro globular oxide inclusions but clearly does not work for macro stringer oxide inclusions.

As a result, the current work agmq provide a figure for oxide stringers equivalent to Figure 8. Linear elastic fracture mechanics is a field developed in the solid mechanics and materials science communities [12], [13].

This well-established science applies the physics of solid mechanics stress and strain and the physics of energetic fracture processes to calculate 923–b05 driving force for propagation of cracks in materials and predict crack behavior.

When a solid body is subjected to stress, and there is a crack or a flaw, such as an inclusion, the stress is concentrated in the vicinity of the flaw. In the laboratory, one can apply known cyclic stress intensity to a crack and evaluate the threshold stress intensity, K thwgma the minimum stress intensity required to begin to drive crack growth.

One can also assess the rate of crack growth rate when K th is exceeded and the plane strain fracture toughness, or K 1Cwhere a crack becomes unstable and the component or test coupon fails.

Figure 9 illustrates the progression of a gear tooth bending fatigue failure as a result of a globular oxide micro inclusion. The leftmost photo shows a gear tooth that has fractured off of the gear. Further evaluation at higher magnifications shown in the progression of pictures moving forward to the right shows a fatigue thumbnail, and in the middle of the thumbnail is a globular oxide micro inclusion that measures approximately 20 mm in square root area.