This information sheet identifies specifications and requirements for various metallurgical quality grades for through hardened, carburize and hardened, induction and flame hardened, and nitrided gearing. Characteristics covered include raw material, heat treatment and post heat treat processing, and their associated inspections. Topics related to gear design and rating, such as case depth, stress numbers, and quality control sampling plans are not included in this document. Effort was made to reference ISO specifications where possible. The consolidated tables were submitted to the gear rating committees for their agreement and are published here for reference by other standards.

Author: | Nikolrajas Mazunris |

Country: | Saint Lucia |

Language: | English (Spanish) |

Genre: | History |

Published (Last): | 6 August 2017 |

Pages: | 83 |

PDF File Size: | 6.63 Mb |

ePub File Size: | 20.90 Mb |

ISBN: | 935-9-84924-434-7 |

Downloads: | 54973 |

Price: | Free* [*Free Regsitration Required] |

Uploader: | Akinojar |

A method is presented to characterize premium quality clean steels using statistics of extreme values SEV and quantitative stereology. 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 density 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 VAR steels at a fraction of the cost.

The ability to fully characterize the geometric and chemical characteristics of micro and macro oxide inclusion populations using automated image analysis SEM allows the steelmaker to understand 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.

See Table 1. The AGMA information sheet on steels for gears requires that oxide inclusion ratings meet certain limits in order to meet grade 2 or 3 gear steel 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 inclusion type A, B, C. For D type globular oxides, the worst 0. 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 2, lower-left are recorded as micro inclusions, and their geometry is reported by square root area. Stringers of continuous or intermittent oxide particles see Figure 2, upper-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 3a shows the raw data from automated SEM image analysis in the form of a histogram for micro globular oxide inclusion per square millimeter. Figure 3b 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 3a to Figure 3b, 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.

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. The 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 value. 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:. Equation 1. A total reference area A tot is selected, which is 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:.

Equation 2. Equation 3. As illustrated in Figure 6, a 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 likely 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 gears can be 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. Equation 4. 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 density 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 V , for each range N V j is then:. Equation 5. 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 gear is determined to have 0. 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 cannot 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].

DIALUX 4.10 MANUAL PDF

## Popular Publishers

AGMA A06 Any person who refers to any AGMA technical publication should be sure that the publication is the latest available from the Association on the subject matter. The AGMA gear rating standards identify performance levels of gearing by heat treatment method and grade number. For each heat treatment method and AGMA grade number, acceptance criteria are given for various metallurgical characteristics identified in this document.

LIBRO COMPLETO LA META DE ELIYAHU GOLDRATT PDF

## AGMA-923-B05

This document identifies metallurgical quality characteristics which are important to the performance of steel gearing. Your Alert Profile lists the documents that will be monitored. If the document is revised or amended, you will be notified by email. You may delete a document from your Alert Profile at any time. This standard is also available to be included in Standards Subscriptions.

ADA COLAU VIDAS HIPOTECADAS PDF

## AGMA 923-B05 Metallurgical Specifications for Steel Gear

A method is presented to characterize premium quality clean steels using statistics of extreme values SEV and quantitative stereology. 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 density 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 VAR steels at a fraction of the cost. The ability to fully characterize the geometric and chemical characteristics of micro and macro oxide inclusion populations using automated image analysis SEM allows the steelmaker to understand 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.

MAPAS ESTRATEGICOS KAPLAN Y NORTON PDF

## Gear Design Relevant Cleanness Metrics

Any person who refers to any AGMA technical publication should be sure that the publication is the latest available from the As- sociation on the subject matter. The AGMA gear rating standards identify performance levels of gearing by heat treatment method and grade number. For each heat treatment method and AGMA grade number, acceptance criteria are given for various metallurgical characteristics identified in this document. Published by. No part of this publication may be reproduced in any form, in an electronic retrieval system or otherwise, without prior written permission of the publisher. Contents Page Foreword.