2013 DMC presentation - NLign

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3D Process Analy0cs for Carbon Composite Manufacturing Tom Sharp

NLign Analy0cs A Division of Etegent Technologies, Ltd.

Copyright 2013 Etegent Technologies, Ltd. 1

3D Process Analy0cs for Carbon Composite Manufacturing

Outline: •  Composite Manufacturing Process Data •  NLign Overview •  Discussion of Data Mapping Techniques •  Case Studies

Overview

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An Unprecedented Amount of Process Data is Generated and Collected

Composite Manufacturing Process Data

Process Data

Material Machine People Inspec0on Models


•  This Data is Difficult to use for Process Analysis/Improvement •  In many different forms

•  Paper, Text files, Proprietary data formats, etc.

•  Not meaningfully organized •  File cabinets, File systems •  Excel spreadsheets, Legacy Databases, ERP/MES system

•  Large volume

•  What if this Data Could be Meaningfully Tied to a 3D Model?

Composite Manufacturing Process Data


NLign Overview

Data is then automa0cally aligned to CAD model and stored in an archive database

Models Structural Molds Fixtures

FEA Models Specifica0ons

Inspec0on Data

Process Data Tool Set Number Machine Log Files Resin Proper0es

Etc.

Digital Pictures

Dimensional Data

Manufacturing

Maintenance

Align & Archive

Process Improvement/

RCCA

MRB Process Improvement

Aircraft Structural Integrity

Structural Repair


Analysis Tools

Visualize ultrasound C-‐Scan results

Visualize digital radiography on 2D Model

Overlay various models

Data Export: •  Excel • Minitab •  FEA •  Per0nence PRD

Integrated Analysis Tools •  Plo\ng •  Basic sta0s0cs

Analyze

•  Repor0ng •  Visualiza0on •  Trending •  Root Cause Analysis •  Process Improvement •  Coverage checking •  Analysis package integra0on


Interac?ve Analysis Environment


3D Process Analy0cs for Carbon Composite Manufacturing Outline: •  Composite Manufacturing Process Data •  NLign Overview •  Discussion of Data Mapping Techniques •  Interac0ve Analysis Environment •  Case Studies

Overview

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•  Goal is to Automa0cally Map all Data Back to a 3D Model

•  This is Difficult Because: •  Data contains minimal “loca0on” informa0on •  Range of data from text, images, 0me histories, etc. •  Range of image forma0on techniques

•  Single image/Mul0ple images •  Overlap/No overlap •  Clear features/No clear features

•  One Approach Will not Handle all Data Types

Discussion of Data Mapping Techniques


Configurable Alignment System

•  A Configurable System that Selects the Proper Alignment Algorithm for the Par0cular Data

•  U0lizes a Library of Data Alignment Techniques

Incoming Data

Iden0fy Data Type

Alignment Method Database

Data/Image Alignment

Metadata Alignment

Archive


•  Data Alignment Library has Numerous Techniques •  Will Discuss Several in Detail

•  Robo0c •  Turntable •  Limited trajectory informa0on

•  Dimensional data •  Text transla0on

Data Mapping Techniques


•  Goal: determine x, y, z loca0on of each C-‐Scan pixel

•  Handle two degrees of freedom case •  Rota0on •  Height

•  x,y,z Determined by “Rolling” Aligned UT Data onto Model

Mapping Turntable Data


•  Goal: determine x, y, z loca0on of each C-‐Scan pixel

•  Limited informa0on available on trajectory of sensor head

•  Use Nominal Trajectory to Develop 3D -‐> 2D map

•  Image Features Used to Op0mize Alignment in 2D

•  Map 2D Aligned Data to 3D

Limited Trajectory Informa?on


•  Goal: Improve Visualiza0on of Varia0ons in Dimensional Data

•  Aggregate Dimensional Measurements Across Mul0ple Parts

•  Problem: Posi0on Error can be Mul0ple Orders of Magnitude Smaller than Dimension of Parts •  Error vector created between nominal and actual

•  Error vector scaled (e.g., 100 X) •  Measurement shown on model at scaled offset from nominal

Dimensional Data


•  Goal: Convert Text Based Loca0on Informa0on into X, Y, Z

•  Loca0on Can be Specified in Mul0ple Ways •  Hole number •  Stringer/Body sta0on •  Distance from some point

•  Each Type Requires Different Approach •  Look up tables •  Parameterized curves •  Ad-‐Hoc

Text Data Mapping


3D Process Analy0cs for Carbon Composite Manufacturing Outline: •  Composite Manufacturing Process Data •  NLign Overview •  Discussion of Data Mapping Techniques •  Case Studies

Overview

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•  Six Case Studies •  Monitor tool degrada0on •  Porosity root cause analysis •  Track thickness/compac0on over units •  Missed inspec0ons •  Track thickness/compac0on •  MRB Process Improvement •  MRB Analysis

•  All Data Shown is Fabricated •  The Case Studies do Represent How NLign is Being Used

Case Studies


•  Porosity indica0ons shown as red dots on model

•  Spa0al filter set to “watch” tool at known trouble area

•  Report shows majority of porosity indica0on along seam are associated with one tool

Case Study – Monitor Tool Degrada?on


Case Study – Root Cause Analysis

Cluster of porosity Findings (red)

Model showing location of tool seam

Point showing location of seam rework (green)

•  Root cause of tool seam leaks were correlated to rework in the area •  Rework process was corrected


Case Study: Missed Inspec?ons


•  Ability to Track NDI Results at Specific Loca0on Over Time

•  Select Loca0on on Model •  Select NDI Parameter •  Plot Shows Time-‐of-‐Flight Es0mate of Part Thickness

•  Could Also be Used to Track Low Level Porosity

Case Study: Track Thickness/Compac?on

0.17

0.175

0.18

0.185

0.19

32 33 34 35 36 37 38 39 40 41 46


Case Study: MRB Process Improvement

•  OEM disposi0on taking over a month •  Reduce risk of con0nued manufacturing

•  Repository of acceptable defects •  Confident decision to either

•  Con0nue processing part while MRB analysis is being performed

•  Stop processing part un0l MRB analysis is complete

•  Yield: a significant rate increase while managing risk


•  Combine Non-‐conformance with design models •  FEM Models •  Ply Models •  Historical Non-‐conformances

•  Enable quick analysis of Non-‐conformance

Case Study: MRB Analysis


•  Aligning Process Data to 3D Models Makes this Data Much More Valuable •  Visualizing data in 3D allows for spa0al pamerns to be quickly iden0fied

•  Several Case Studies Discussed •  Process monitoring •  Root cause analysis

•  The Tool Has Demonstrated that it can: •  Reduce Scrap and Rework •  Improve MRB processes •  Improve In-‐service composite repair •  Ensure Inspec0on coverage

Conclusions


Ques?ons

Documents

2013 DMC presentation - NLign