AC 33.70-5 Process control Definition Credit Used for:
Factor Cutup: A representative periodic test 2 Risk reduction sample cutup to ensure the surface metallography of the axial slot or slot edge remains within the acceptable range to meet the manufacturing intent.
Etch: Etchants able to detect distorted 2 Risk reduction surface metallography when combined with an appropriate visual standard reference. Caution: An etched surface can reduce the fatigue capability of the material and should be evaluated as part of the approved lifing method of the component.
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AC 33.70-5
Figure F-1. Number of Effective Slots as a Function of Automated Manufacturing Credits for Axial Blade Slot (Edges and Surfaces) F.3 Rules for Applying Credits.
F.3.1 Rules.
• Process validation is considered mandatory on machining processes on all future products when using the methods provided by this AC. Use of process validation provides a 5X manufacturing credit as specified in tables F-1 and F-2.
• Use of manufacturing credits including process validation should be part of the certification data submittal.
• Process monitoring should be used in both roughing and finishing operations.
• Surface Credit for Fluid Monitoring cannot be claimed unless Machine Condition, Fluid & Fluid Condition, Tooling, and Setup Validation are also in use.
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AC 33.70-5
• Surface Credit for Process Monitoring cannot be claimed unless Fluid Monitoring and Machine Condition, Fluid & Fluid Condition, Tooling, and Setup Validation are also in use.
F.3.2 Restrictions.
• Use of Surface Credits requires a Process Validation and Control Plan.
• Use of Edge Credits requires a Process Validation and Control Plan.
• Surface Credits cannot be used for edges.
• Edge Credits cannot be used for surfaces.
• Non-destructive inspection(s) at manufacture cannot be accounted for in the analysis to lower the POF value before application of manufacturing credits.
F.4 Credit Application Process.
1. Decide which controls are to be applied.
2. Sum the credit values for the combination of controls.
3. Calculate the number of effective slots.
4. Calculate the probability of fracture for the axial slot feature in the disk.
5. Divide the probability of fracture by the sum of the credit values.
6. This reduced probability of fracture should be compared to the DTR.
F.5 Slot Surface Credit Examples.
F.5.1 An axial slot feature with 100 slots, N:
a. Applicable Credits:
Process Validation and Control Plan 5 Total Credit 5 Number of Effective Slots: a = 0, Neff = 90 + (1-0)*(N-90) = N = 100 Initial design POF using Neff: 5.0E-4 events per service life POF with controls is now: 5.0E-4 / 5 = 1.0E-4 events per service life This is not adequate against Axial Blade Slot DTR.
b. Applicable Credits:
Process Validation and Control Plan 5 Machine Condition, Fluid & Fluid condition, Tooling and Setup validation 5 F–8
AC 33.70-5 Fluid Monitoring 5 Surface Condition inspection – nondestructive 5 Total credit 20 Number of Effective Slots: a = 10/20=0.5, Neff = 90 – (1-0.5)*(N-90) = 95 Initial design POF using Neff: 4.5E-4 events per service life POF with controls is now: 4.5E-4 / 20 = 2.25E-5 events per service life This is not adequate against Axial Blade Slot DTR
c. Applicable Credits:
Process Validation and Control Plan 5 Machine Condition, Fluid & Fluid condition, Tooling and Setup validation 5 Fluid Monitoring 5 Process Monitoring 10 Surface Condition inspection – nondestructive 5 Total credit 30 Number of Effective Slots: a = 0.9, Neff = 90 – (1-0.9)*(N-90) = 91 Initial design POF using Neff: 4.0E-4 events per service life POF with controls is now: 4.0E-4 / 30 = 1.3E-5 events per service life This is acceptable to Axial Blade Slot DTR.
F.6 Slot Edge Credit Examples.
F.6.1 An axial slot feature with 100 slots, N:
a. Applicable Credits:
Process Validation and Control Plan 5 Total Credit 5 Number of Effective Slots: a = 0, Neff = 90 + (1-0)*(N-90) = N = 100 Initial design POF using Neff: 2.5E-4 events per service life F–9
AC 33.70-5 POF with controls is now: 2.5E-4 / 5 = 5.0E-5 events per service life This is not adequate against Axial Blade Slot DTR.
b. Applicable Credits:
Process Validation and Control Plan 5 Edge Processing: Manual Finishing 8 Total credit 13 Number of Effective Slots: a = 8/15, Neff = 90 + (1-8/15)*(N-90) = 95 Initial design POF using Neff: 2.0E-4 events per service life POF with controls is now: 2.0E-4 / 13 = 1.54E-5 events per service life This is acceptable against Axial Blade Slot DTR F–10
AC 33.70-5 APPENDIX G. SURFACE (MANUFACTURING INDUCED ANOMALY) DAMAGE
TOLERANCE ASSESSMENT FLOWCHARTS G.1 Overview.
Note: For more details on the steps described in the following figures, refer to chapter 3 and the appendices.
For the purposes of this section, the definition of a "blade slot set" is consistent with the description in appendix E, paragraphs E. 1 and E.2.
G.2 Process.
1. Assess the Probability of Fracture for One Blade Slot.
2. Calculate the Probability of Fracture for the Entire Blade Slot Set.
Blade slot set list Identify all blade slot sets in the engine Slot set 1 Slot set 2 Slot set 3 For each blade slotset:
Improve:
propagation Crack Blade slot set risk -Slot manufacturingor Slot analysis/design or analysis assessment Blade slot set risk < DTR ? Slot in service (see Figure G-2) (see Figure G-3 inspection or and Figure G-4) no Inspection trequency yes End J Figure G-1. Surface Damage Risk Assessment Work Flow G-1
AC 33.70-5
Blade slot set Identify critical locations on blade slot edges and surface Determine crack propagation model (plate, crack prop.
law) Temperature & Stress results Run crack propagation analyses on edges and surface Crack size vs number of flights for corner crack "a vs Ne" Crack size vs number of flights for surface crack "a vs Ns" Figure G-2. Crack Propagation Analysis
3. Begin by selecting one slot from the blade slot set.
4. Calculate the probability of fracture for this single blade slot using the established damage tolerance criteria.
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Crack size vs number of flights for corner crack "a vs Ne" Crack size vs number of flights for surface crack "a vs Ns" Calculate probability of fracture (figure G-6) Slot edge probability of fracture "sepf1" for 1 slot of set i (figure G-6) Expected component service life Inspection plan and appropriate POD curves Anomaly distribution (appendix B) Slot surface probability of fracture "sspf1" for 1 slot of set i (figure G-6) Figure G-3. Feature Risk Assessment (Part 1 of 2)
5. Use the probability of fracture for one slot (from s tep 1) to calculate the probability of fracture for the entire blade slot set.
6. Apply the appropriate manufacturing credits and effective slot numbers as defined in previous sections to complete the assessment.
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AC 33.70-5 Manufacturing Credits for Effective number of slots for edges "emc" edges "nese" (see Figure G-5) Determine the probability of fracture "sepf" for the Slot edge probability edges of the slot set of fracture "sepf1" for (see Figure G-6) 1 slot of seti sepf = 1 - (1 - sepf1 / emc) nese Keep the worst probability of fracture between sepf and sspf values for the slot Slot surface Determine the probability "sspf" of set to obtain the probability of fracture "sspf1" for 1 slotof fracture for the surface of the slot set blade slot set risk.
set i (see Figure G-6) sspf = 1 - (1 - sspf1 / smc) ness Manufacturing Credits for Effective number of slots for surfaces "smc" surfaces "ness" (see Figure G-5) Figure G-4. Blade Slot Set Risk Assessment (Part 2 of 2) G-4
AC 33.70-5 Manufacturing Credits associated with Automated Edge Processing being applied "emac" Determine the effective number of slots for edges "nese" if n <= 90 : nese = n Effective number of slots for if n > 90 : edges "nese" nese = 90 + (1-(emac/emac_max)* (n - 90) emac_max = max possible manufacturing credit for Automated Edge processing Number of slots "n" Determine the effective number of slots for surfaces "ness" if n <= 90 : ness = n ness = 90 + (1-(smac/smac_max)* (n - 90) if n >90 : Effective number of slots for surface "ness" smac_max = max possible manufacturing credit for Automated Surface processing Manufacturing Credits associated with Automated Surface Processing being applied "smac" Figure G-5. Determination of the Number of Effective Slots in the Blade Slot Set G-5
AC 33.70-5 The POF calculation is described here following a Monte Carlo simulation process. A large sample of anomalies should be simulated. Each simulation in the sample follows the flowchart below to determine if it:
- fractures before the expected component service life meets or exceeds the expected component service life
- is detected with a crack Probability of fracture before N flight cycles (sepf1, sspf1 from Figure G-3 = (number of fractured slots before N flight cycles) / (total sample size) manufacturing anomaly? Is there a yes What "ao"? size life from initial flaw size, Calculate crack growth inspection(s)? frequency of Number and no Success Any inspection no >= the expected Is anomaly life yes during anomaly component Success life? service life?
no yes Fracture (1) Is anomaly detected at Calculate anomaly size inspection(s) using yes at each inspection(s) applicable POD curve? Detection (1) Notes : no
- Each requires a random pick from the pertinent random variable distribution Fracture (1)
- (1) record the number of flights at fracture or detection for each slot simulated Figure G-6. Probability of Fracture Calculation G-6
AC 33.70-5 APPENDIX H. DE FINITIONS The following definitions apply for the purpose of this AC.
H.1 Axial Blade Slot.
Axial blade slots are nominally identical features, with typically one per blade root.
They are located at the same nominal radius, usually in the rim of a disk, and their geometry provides a mechanical constraint that positions the blades radially. Appendix E of this AC provides more information.
H.2 Component.
A part that may contain multiple sets of component features.
H.3 Component Feature.
A unique location, structural shape, or surface of a component or part. For a disk, component features are blade slots, bore, or web locations. Other examples are grooves, slots, or holes. Usually, different machining processes and practices are required to produce each unique feature.
H.4 Damage Tolerance.
An element of the life management process that recognizes the potential existence of component imperfections that are the result of inherent material structure, material processing, component design, manufacturing, or usage. Damage tolerance addresses this situation through the incorporation of fracture resistant design, fracture mechanics, process control, or non-destructive inspection.
H.5 Default Probability of Detection (POD) Values.
Values representing mean probabilities of detecting anomalies of various types and sizes. These values are accepted as valid provided the inspection is conducted consistent with applicable industry specifications.
H.6 Design Target Risk (DTR).
The relative risk of failure caused by material, manufacturing, and service induced anomalies and the standard against which probabilistic assessment results (stated in terms of feature, component, and engine level event rates) are compared.
H.7 Event.
A rotor structural part separation, failure, or burst, with no assessment of consequence.
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