PICASSO Improving PoD through simulation Tim Barden 2010

Transcription

1 PICASSO Improving PoD through simulation Tim Barden Rolls-Royce plc The information in this document is the property of Rolls-Royce plc and may not be copied or communicated to a third party, or used for any purpose other than that for which it is supplied without the express written consent of Rolls-Royce plc. This information is given in good faith based upon the latest information available to Rolls-Royce plc, no warranty or representation is given concerning such information, which must not be taken as establishing any contractual or other commitment binding upon Rolls-Royce plc or any of its subsidiary or associated companies.

2 Contents 2 Overview of Probability of detection The PICASSO project Aims Motivation Progress Summary

3 Probability of Detection Statically approach to understand the minimum reliability detectable 3 2 main types Hit-miss - Pass/ fail with no quantitative information a vs a-hat ( vs response) - Quantitative vs response data used

4 Probability of Detection Statically approach to understand the minimum reliability detectable defect 4 Example : a vs a-hat ( vs response) Log(Response) Noise Log()

5 Probability of Detection Statically approach to understand the minimum reliability detectable defect 5 Example : a vs a-hat ( vs response) Log(Response) Threshold Noise Log()

6 Probability of Detection Statically approach to understand the minimum reliability detectable defect 6 Example : a vs a-hat ( vs response) Log(Response) Best fit 90% PoD Threshold Noise Log()

7 Probability of Detection Statically approach to understand the minimum reliability detectable defect 7 Example : a vs a-hat ( vs response) Log(Response) Probability of detection (%) PoD(a) PoD(a) with 95% confidence Log() a90 a90/95

8 Probability of Detection Increasing requirement to qualify inspection capability by probability of detection 8 Manufacture defect samples (40 to 60 crack sites minimum) Obtain NDT response from defects Set accept/ reject threshold level Calculate PoD curves Minimum reliable detectable

9 Probability of Detection Increasing requirement to qualify inspection capability by probability of detection 9 Manufacture defect samples (40 to 60 crack sites minimum) High cost not always possible Obtain NDT response from defects Set accept/ reject threshold level Calculate PoD curves Obtain accurate PoD results without manufacturing a complete set of defect samples Minimum reliable detectable

10 PICASSO Aims: Increase the accuracy and reduce the cost of PoD trials by simulating results Modelling response from real defect shaped flaws Uncertainty management - Understanding factors that influence the inspection process Partners Industrial 10 - Snecma, Rolls-Royce, MTU, Turbomeca, VolvoAero, EADS Research/SME - BAM, CEA, Chalmers, IZFp-D, HTS, PHIMECA, Technic control, TWI

11 Modelling approach 11 Validation cases Determine material and equipment properties Determine defect geometry Calculate simulated defect response Calculate and compare PoD data Measure NDT response

12 Modelling approach 12 Validation cases Determine material and equipment properties Determine defect geometry Calculate simulated defect response Calculate and compare PoD data Measure NDT response Requires models that can simulate responses from real defects

13 Uncertainty management 13 Modelled data Response

14 Uncertainty management 14 Modelled data Actual data Response Response

15 Uncertainty management 15 Modelled data Actual data Response Response Understand function that creates the uncertain/random component of the result

16 Uncertainty management Hand scanning eddy current inspection 16 Determine factors affecting inspection Measure influence of factors Understand operating limits Model influence factor and determine result on inspection sensitivity Probe tilt Quantify probe tilt vs response relationship During inspection probe may vary between +/-??? Deg Using EC model to create randomised data

17 Uncertainty management 17 Modelled data Actual data Response Response

18 Validation cases Validation case Ultrasonic inspection of Ti Billet (including defects in glass) Industrial sponsor MTU Technique UT Radiographic inspection of forged Ti MTU RT 18 4 Ultrasonic 3 Radiographic 3 Eddy current Eddy current of bolt holes TM ET Radiography of electron beam welds TM RT Ultrasonic inspection of Forged Ti Snecma UT Radiographic inspection of single crystal Ni turbine blades Snecma Eddy current on flat surfaces R-R ET Ultrasonic inspection of Ni alloy R-R UT RT Ultrasonic PA inspection of electron beam welds Manual eddy current inspection of aluminium plates EADS EADS UT ET

19 PICASSO Aims: Increase the accuracy and reduce the cost of PoD trials by simulating results Modelling response from real defect shaped flaws Uncertainty management 19 Current progress Validation cases chosen Preliminary discussions with EASA Initial data being gathered - Material properties, defect dimensions, etc