AC 33.70-34/17/23 C HAPTER 3. ENHANCED LIFE MANAGEMENT PROCESS
3.1 Damage Tolerance Assessment for Critical Titanium Rotating Parts.
Section 33.70 requires applicants to perform appropriate damage tolerance assessments (see figure 3-1). Damage tolerance assessments are fracture-mechanics based probabilistic risk assessments that predict the relative probability of failure (POF) for each part. The predicted POF is compared to an allowable DTR. Designs that satisfy the allowable DTR will be considered compliant with the titanium melt anomaly damage tolerance requirements as set forth in § 33.70.
3.2 Options to Reduce the POF.
Engine manufacturers have several options available to reduce the POF to meet the allowable DTR. They include, but are not limited to, the following options:
• Component redesign.
• Material changes.
• Material process improvements.
• Manufacturing inspection improvements.
• In-service inspections.
• Life limit reductions.
Chapter 4 presents an overview of the methodolog y for conducting the fracturemechanics based probabilistic analyses mentioned above.
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SAFE LIFE
PREDICTION
METHODS AND DAMAGE
MATERIAL TOLERANCE
DATA ASSESSMENT
ELEMENT ADDED
TO EXISTING SAFE
LIFE LIFE PROCESS
MANAGEMENT
PROCESS
DEVELOPMENT SERVICE LIFE
AND VALIDATION AND PRODUCT
TESTS ASSURANCE Figure 3-1. Life Management Process 3-2
4.1
4.1.1
4.1.2
4.1.3
4.1.4
4.2
4.2.1
4.2.2
4.2.3 C HAPTER 4. DAMAGE TOLERANCE ASSESSMENTS A pproach.
Pr obabilistic Damage Tolerance Risk Assessment (PDTRA).
As described in AC 33.7 0-1, a PDTRA is an acceptable method to assess a part’s ability to tolerate anomalies. The assessment results provide the basis to evaluate the relative damage tolerance capabilities of candidate part designs. The results also allow the engine manufacturer to balance enhanced reliability and customer impact.
These resul ts are compared to the allowable DTR to determine if the design meets the DTR criteria (see paragraph 4.6).
While the DTR was selected to limit the probability of hard alpha anomaly failures in titanium rotor parts, its selection was restricted to risk levels that were technologically achievable. The use of probabilistic risk assessment tools and compliance with the DTR is acceptable for undesirable part conditions that cannot be eliminated by the best manufacturing and inspection processes available. Satisfactory compliance with the DTR should not be used to reject or eliminate manufacturing and inspection techniques capable of limiting or reducing the number of anomalies.
Further Risk Reduction Actions.
The need to reduce a part’s risk will be based on whether or not the design under consideration satisfies the desired DTR at both the individual component level and the engine level. If the targets are met, then the design is considered compliant with the damage tolerance requirements, as defined in § 33.70. The manufacturer has several options available to achieve the required DTRs and may conduct quantitative parametric studies to determine the influence of key variables, such as inspection methods and frequency, hardware geometry and processing, material selection, and life limit reduction.
The manufacturer may then make changes to the design or the part’s field management procedures, or both, to achieve the allowable DTR (see figure 4-1). This approach gives the engine manufacturer the flexibility to develop an optimal engine design solution consistent with customer requirements, company policies and procedures, and available resources. An example assessment using this methodology is described in paragraph 4.3 and appendix A.
Probabilistic damage tolerance risk assessments are usually performed during the detail design phase of the engine component. Paragraph 4.3 defines an assessment methodology applicable to material melt-related induced anomalies. It contains a standardized list of inputs for conducting these assessments and a process for refining the design to meet the allowable DTR.
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---- AC 33.70-34/17/23
4.3
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5 Methodology.
Manufacturers ma y conduct probabilistic risk assessments using a variety of methods such as the Monte Carlo simulation or numerical integration techniques.
Figure 4-1 conceptually depicts a melt-related anomaly probabilistic assessment process. Standardized inputs and default data are contained in the appendices of this AC. Applicants should use them to perform the PDTRA. The use of standardized inputs and default information is necessary to achieve consistent industry-wide assessment results, which can then be compared to the allowable DTRs.
A list of standardized inputs is provided below. Default input data is described in paragraphs 4.3.5 and 4.3.6. The use of default data in probabilistic assessments requires no further demonstration of applicability or accuracy. However, if this data is used, the manufacturer should follow the guidelines accompanying this data to ensure applicability. The use of input data, other than the default information, may require additional validation to verify applicability, adequacy, and accuracy.
Probabilistic risk assessments should incorporate the following inputs as part of a basic approach:
•
Anomaly distribution.
•
Inspection POD.
•
Maintenance exposure rate.
•
Component stress and volume.
•
Material properties.
•
Crack gr owth lives.
• Design ser vice life.
Input.
4.3.5.1 Anomaly Distribution.
For melt-related (hard alpha) assessments, use one of the following:
• The default anomaly distributions outlined in paragraph 4.4; or
• FAA-approved company-specific data.
Manufacturers should develop company-specific data with the same process used to develop the default anomaly distributions. This process is described in GT2012 68987 and AIAA-97-1068. Manufacturers should also treat anomalies as sharp propagating cracks from the first stress cycle.
Inspection of titanium billets should comply with FAA Policy ANE 2002
33.15-R0. This policy states in part– 4-2
AC 33.70-34/17/23
• “Require the billet UT inspection system output to be electronic c-scan data, which can be acquired, retained, stored, and retrieved electronically.”
• “Perform the UT inspection in accordance with SAE Document AMS2628 [Class A], sections 3 and 4,” using the following sensitivity standards or an equivalent FAA accepted procedure.
Table 4-1. Billet Inspe ction Standards Billet Inspection Standards Billet Dia. – Inches FBH Size – Inches Standard 1 >5 but <10 2/64 Standard 2 >10 3/64
4.3.5.2
4.3.5.3
4.3.5.4 To comply with the FAA inspection policy, manufa cturers should use the default anomaly distributions listed in appendix C to conduct the PDTRA.
Manufacturers using forging input material < 5 in ches in diameter should provide data to substantiate the use of the default anomaly distribution selected.
Probability of Detection.
Manufacturers should ensure the subsurface assessments consider the effe cts of subsurface inspection techniques, such as ultrasonic inspection or X-ray inspection only. Additionally, they should verify the surface assessm ents also consider the effects of fluorescent penetr ant i nspection (FPI) and eddy current inspection (ECI), as applicable. Paragraph 4.5 of this section contains default PODs and instructions on the use of c ompany-specific value s.
Maintenance Exposure Interval.
When assessing inspection benefits, the exposure interval curves for the engine, module, or component in question, may be modeled in the analysis as appropriat
e.
Stress.
A part’s operating stress is a variable that has a major influence on crack propagation life. Manufacturers should base this input on the most limiting operational principal stresses, as follows:
• Subsurface assessments should incorporate the appropriate subsurface and near-surface stress distributions.
• Surface assessments should incorporate the appropriate surface stress distributions, including the effects of stress concentrations.
Manufacturers should also:
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AC 33.70-34/17/23
4.3.5.5
4.3.5.6
4.3.5.7
• Use certification or actual usage flight cycles, if known, to establish stress variation during the flight profile.
• Consider the influence of major and minor flight cycles since the cyclic damage accumulati on can differ dramatically between crack propaga tion and crack initiation.
Note: The method described in this AC has been calibrated against industry experience without considering the surface enhancement effects, such as shot peening on predicted crack propa gation lives. Therefore, it is inappropriate to include the beneficial effects of such enhancements .
Volume.
This variable (volume) is re quired to predict the probability of having a defect in a part. The part is divided into multiple smaller sub-volumes;
each sub-vol ume represents the volume of material at a specific stre ss level. Sub-volumes are required to assess the risk associated with subsurface anomalies.
Manufacturers should su b-divide the part’s surface into thin surface volumes (“onion skin”). Where a non-axisymmetric feature, such as a series of holes in a disk web, has a localized stress concentration, manufacturers should decide whether it makes a significant contribution to the probability of failure. This decision should be based on a combination of:
• The ma ss of material at high stress; and
• The size of the anomaly that would cause the part’s failure, prior to reaching i ts safe-life.
While the method described in this AC assumes axisymmetric features, a non-axisymmetric fe ature can also be included. To do this, reduce the cross-sectional area to ensure that the total volume, when integrated around the whole circumference, is equal to the volume at high stress.
Material Data.
Manufacturers should use the average, cyclic crack growth rate properties of the base mate rial generated in an air environment as the default condition to calculate anomaly propagation life.
Propagation Life.
Propagation life is defined as the number of cycles for a given size anomaly to grow to a critical size. It is based on all of the following factors:
• Knowledge of part stress,
• Temperature,
• Geometry, 4-4
AC 33.70-34/17/23
• Stress gradients,
• Anomaly orientation, and
• Material properties.
Manufacturers should use linear elastic fracture-mechanics for calculating propagation life. Default conditions should assume anomalies to be in the worst orientation to the stress field.
4.3.6 Calibration.
Engine manufacturers should conduct the industry test case detailed in appendix A of this AC to calibrate their analytical predicti on tools. The test case consists of a probabilistic analysis of an ideal titanium ring disk, using specified inputs and scenarios.
Test case results considered acceptable are in the ranges below:
• 1.27E-09 to 1.93E-09 (for the “no inspection” case).
• 8.36E-10 to 1.53E-09 (for the “with in-service inspection” case).
Test case results outside these ra nges may indic a te problems with either the pr obabilistic asse ssment technique or the assumptions.
4.3.7 Output.
4.3.7.1 Com ponent Level Asses sments.
Manufacturers should calculate the probabilistic assessments and prediction of event potential over the entire anticipated service life of a part. This result should be expressed as the number of predicted events for each cycle and designated as the predicted “component event rate.” For multiple stage components, such as spools, manufacturers should conduct the assessment for each stage. The predicted component event rate should then be compared to the component-level allowable DTR to assess design acceptability.
4.3.7.2 Engine Level Assessme nts.
When all critical titanium rotors in a given engine configuration have satisfied the allowable component level DTR, the cumulative event rate for these components should then be calcul ated and compared to the allowable engine-level DTR for acceptability.
4.3.7.3 All owable Design Target Risks.
Paragraph 4.6 specifies the allowable DTRs for titanium.
4.3.8 Gener al Comments.
Standardized inputs and defau lt data for melt related PDTRA assessments are available for titanium material, melt-related (h ard alpha) anomalies. Industry and the FAA are collaborating to develop the following:
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AC 33.70-34/17/23
• Material anomaly information for other rotor grade materials (for example, nickel).
• Inputs and default data for manufacturing and maintenance-induced anomalies.
4.3.9 The FAA strongly encourages engine manufacturers to incorporate fracture-resistant design concepts, when possible. The FAA may give credit for fracture or burst-resistant engine design features that clearly demonstrate a reduction in rotor failure due to the presence of melt induced anomalies from unanticipated material. Manufacturers should demonstrate the reduction in rotor failure through analysis and test.
4.3.10 The design of an aircraft turbine-engine rotor is a lengthy process involving numerous iterations, each of which can substantially alter the initial calculated predicted risk.
Therefore, it is important that the DTR values be satisfied at the time of engine certification.
4.3.11 Risk assessments may also be conducted several years after the engine enters service because of changes. These changes could come from design changes associated with inservice problems or changes in the analytical results from evolving predictive capability.
Note: The allowable D TRs should be satisfied at both the individual component and overall engine levels throughout the life of the part.
4.4 Anom aly Distributions.
4.4.1 Ingot Distributions.
Key inputs associated with PDTRA assessments are the size and rate of occurrence of the anomalies. This type of information is statistical and can be presented in a form that plots the number of inclusions that exceed a particular size in a specified amount of material. Anomaly distributions may be referred to as “exceedance curves.” At a highlevel, a similar approach was used to develop the distributions in AC 33.14-1 and this AC. However, some of the detailed assumptions that went into defining the underlying ingot distribution were different for this AC based on updated information available since AC 33.14-1 was issued. Publications AIAA-97-1068 and ASME GT2012-68987 (see paragraph 1.3.3 of this AC for full citation) provide detailed information covering the development of the ingot distributions and downstream exceedance curves, including how the POD data were used and verification against fleet experience.
Therefore, although both sets of curves are meaningful for assessing risk from hard alpha in titanium, manufacturers should not use them in direct comparison to draw conclusions about the billet or forging inspection techniques, technology, or capability.
4.4.2 Titanium Melt-Re lated (Hard Alpha) Distributions.
The hard alpha anomaly distributions contained in appendix C of this AC apply to fully machined components. The exceedance curves assume that:
• The ma terial is triple vacuum arc remelt (3VAR), or cold hearth melt (CHM), plus vacuum arc remelt (VAR) melted material.
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• The material has been inspected at the billet and forging stages according to the standards specified on the curve and are consistent with the POD curves as described in paragraph 1.5.8.
For example, figure C-1 represents the anomaly distribution for a component produced from >10-inch diameter billet an d is:
• Inspected at the billet stage using a zoned ultrasonic inspection performed in accordance with AMS2628 Class A.
• Calibrated to a #3 flat bottom hole (FBH).
• Calibrated at the sonic shape using a conventional (non-zoned) ultrasonic probe calibrated to at least a #3 FBH.
Manufacturers may use the anomaly distributions contained in appendix C to determine compliance with the allowable DTR options listed in paragraph 3.2. The background associated with the development of these distributions is contained in GT2012 68987 and AIAA-97-1068. The distributions were developed by modeling a complex series of interrelated steps that simulated the entire component manufacturing and inspection process, from billet conversion to final part machining.
Individual engine manufacturers who desire to use an alternate anomaly distribution or an improved inspection should use the methodology contained in GT2012 68987 and AIAA-97-1068 to crea te the alternate distributions. Manufactures must substantiate alternate distributions with the app ropriate background data. An alternate distribution should:
1. Include three dimensional inclusion data.
2. In clude inspection POD data.
3. Account for potential undetected, uncracked, and unvoided inclusions.
4. Be based upon substantial field experience.
4.5 Default Input - POD by Nondestructive Evaluation.
4.5.1 Detection of Local Material Anomalies.
The capability of individual nondestructive evaluation (NDE) processes, such as eddy current, penetrant, or ultrasonic inspection, to detect local material anomalies (discontinuities or potential anomalies) is a function of numerous parameters, including the size, shape, orientation, location, and chemical or metallurgical character of the anomaly. In addition, manufacturers should consider the following parameters when assessing the capabilities of an NDE process.
1. The material being inspected, such as its composition, grain size, conductivity, surface texture, etc.
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4.5.2
4.6
4.6.1
4.6.2
2. The inspection materials or instrumentation, such as specific penetrant and developer, inspection frequency, instrument bandwidth and linearity, etc.
3. The inspection par ameters, such as scan index.
4. The i nspector, such as visual acuity, attention span, training, etc.
Default P OD Data.
The “de fault” POD data supplied in appendix E are characteristic of inspection capability that has been measured under typical, well-controlled conditions. The FAA is providing these default POD values to assist with the selection of nondestructive inspection techniques that are best suited to support the completion of damage tolerance inspections. Although properly applied inspections should result in capability similar to these default values, they are strictly applicable only under the conditions in which they were acquired (see appendix E). Default POD curves are listed below:
1. F or a description of default POD curves, see appendix E.
2. F or an example of a calibration test case using this data, see appendix A.
3. F or NDE applicability of these POD curves, see appendix E.
Design Target Risk.
Allowable DTR .
The allowable D TR is a benchmark risk level selected to improve the overall safety of rotating titanium components that have been designed to the standards specified in this AC. The selected goal is expected to achieve a significant and distinct improvement over the 1990s rotor designs and represents a potential event rate reduction between 3x to 10x. The potential improvement for each OEM part will depend on each engine manufacture’s component design characteristics.
Allowable “Component Level DTRs” and “Engine Level DTRs.”
Allowable “component level DTRs” and an “engine level DTR” for titanium hard alpha anomalies were established to provide an event rate reduction relative to the baseline period presented in SAE report, SP-1270. The “component level DTR” corresponds to the maximum allowable predicted component event rate. The “engine level DTR” corresponds to the maximum allowable (cumulative) component event rate for all critical titanium rotating parts in a given engine. The allowable DTRs were developed based on assessments of representative component configurations using the methodology and inputs described in chapter 5 of this AC.
Note: The allowable DTRs apply only to the anomaly distributions contained within appendix C of this AC. This is primarily due to the selection method of allowable DTRs. Engine manufacturers desiring to use alternate company-specific anomaly distributions will be required to develop alternate allowable component and engine level 4-8
AC 33.70-34/17/23 DTRs. Alternate DTRs c an be shown to provide an event rate reduction consistent with paragraph 4.6.1 using the methodology in GT2012 68987 and AIAA-97-1068.
4.6.3 Rotor Titanium Life-Limited Parts.
Rotor titanium life-limited parts must satisfy the component level DTR and the engine level DTR to be considered acceptable.
4.6.3.1
4.6.3.2 Application.
Default DTR values have been established for melt related (hard alpha) anomalies. Calculated event rates should be assessed against appropriate allowable DTR values. For multiple stage components, such as spools, each individual stage must satisfy the component level DTR value.
Allowable DTR Values for Titanium Melt Related (Hard Alpha) Anomalies.
The allowable DTR values for titanium melt related (hard alpha) anomalies are as follows:
• Component level DTR: 1 x 10-9 events/flight cycle.
• Engine level DTR: 5 x 10-9 events/flight cycle.
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MISSION EXPOSURE INTERVAL MAINTENANCE INSPECTION POD TIO POWER SETTING IDLE CRUISE ENGINE MODULE MPONENT METHOD 1 METHOD 3 TIME
FLIGHT CYCLE CYCLES AREA
MATERIAL ANOMALY
DISTRIBUTION
PEAK STRESS CONDITION PROBABILISTIC PROBABILITY OF EXCE
ASSESSMENT
ANOMALY AREA
- PREDICTED EVENTS
- EVENTS/ENGINE FLIGHT CYCLE
PROPAGATION COMPONENT PART
LIFE DESIGN ITERATIONS DESIGN LIFE INITIAL ANAMOLY AREA ORDER DE MAGNITUR 1.0 RESULTS
0.5
- V2 _ DESIGN
- V6 -_V3 ACCEPTANCE REGION = TARGE CYCLES Figure 4-1. Typical Elements of a Titanium Melt-Related Anomaly Risk Assessment 4-10