precise defect detection with sensor data fusion · pdf fileprecise defect detection with...

© WZL/Fraunhofer IPT

Precise defect detection

with sensor data fusion

Composite Europe 2016

Dipl.-Ing. Philipp Nienheysen

Laboratory for Machine Tools and Production Engineering

(WZL) of RWTH Aachen University, Germany

Chair of Metrology and Quality Management

Prof. Dr.-Ing. Robert Schmitt

November 30th 2016, Düsseldorf, Germany

Seite 2 © WZL/Fraunhofer IPT

WZL Forum Offers further educational measures and

workshops (e.g. Executive MBA)

Production Engineering at RWTH Aachen University

Fraunhofer Institute of Production-

Technology (IPT) Institute of the Fraunhofer-Corporation

established in 1980

about 450 employees

(about 124 research assistants)

3 000 m² office and laboratory area

Associate-Institute in Boston/USA: CMI

Fraunhofer Center for Manufacturing Innovation

Machine Tools Laboratory (WZL) Institute of the RWTH Aachen

established in 1906

about 840 employees

(about 250 research assistants)

10 000m² office and laboratory area


Chair of Metrology and Quality Management Department for Model-Based Systems

Laser-based metrology in large volumes, computer vision, thermography, computer tomography,

ultrasonic testing, quality assurance along the FRP process chain

Fixtureless assembly, assembly in motion, robotics, positioning systems, process and capability

analysis, industrial statistics

Real

world

Virtual

world

Modell der

Arbeitszelle

Roboter-

Systeme

Simulation der Sichtverbindung

Signalstabilität

Simulation der Messunsicherheit

Signalqualität

Optimierungsstrategien

0 10 20 30 40 50 60 70 800

50

100

150

200

250

300

350

400

450

Target index

Uncert

ain

ty/m

m

x

y

z

Analyse der

Sichtbarkeit

Modell der

Sichtlinien

Unsicherheit des verwendeten

Messsystems

Simulation roboterbasierter

Produktionsprozesse

Mobile Coordinate and Machine Vision Systems

Metrology assisted assembly


Quality assurance

Semi-finished

production process Preforming

Finishing

processes

Trimming,

joining Repair

Chair of Metrology and Quality Management Quality assurance along the FRP process chain and product life cycle

Delamination and inner folds

Impact damage detection

and evaluation

Thermography

system Optical sensor

systems

Fiber orientation

Defect

detection

Area density

X-ray sensor Ultrasonic

system

Testing of adhesive joints

Sensor-assisted

handling

Digitization


CCD matrix

Laser

Optics

Cylindrical lens

Workpiece

Laser light section principle

Steinbichler T-scan TS

NDT technologies 3D digitization

Laser beam is expanded to a line with the help of a

cylindrical lens

The diffuse light reflected by the work piece is projected

on a CCD matrix

The projected image on the CCD changes according to

the distance between the workpiece and the lens

From multiple CCD images a 3D height profile of the

workpiece can be calculated

Steinbichler T-scan TS digitizing device

– Consists of a hand-held laser light section scanner and a

tracking unit

– Resolution: 0,1 mm

Source: optotechnik.zeiss.com


NDT technologies Applications for 3D digitization

CFRP body of the BMW i3

3D model created with

Steinbichler T-scan TS


NDT technologies Ultrasonic testing

High-frequency sound waves (20 KHz – 2 GHz)

generated by a piezoelectric element or a laser

Reflection and transmission of sound at any material

interface (CFRP ↔ steel ↔ air)

Pulse-echo: Measuring of the reflected sound

Runtime based depth (d) determination of defects

𝑑 = 𝑐 𝑚 ∗ 𝑡2

𝑐 𝑚: Average sound velocity of the material t: Sound propagation time

A-scan: Sound intensity plotted against time

B-scan: Vertical cut through the workpiece

C-scan: Horizontal cut through the workpiece

D-scan: TOF of the first back wall echo

Defect

Transmitter/Receiver

Gel coat

Pulse-echo testing

B-scan vs. C-scan

Surface echo

Defect echo

A-scan


NDT technologies Applications for ultrasonic testing

Testing of adhesive hybrid joints (such as CFRP & steel)

– Adhesive size and spread is not predictable

– How big is the area of the adhesion?

Detection of voids and inner cavities in FRP parts

Detection and classification of delamination caused by

impact defects

– How big is the defective area?

– Which layers are affected?

(depth of the defect)

Wall-thickness measurement with one-sided accessibility

Detection of impact defects

43 mm 42,83 mm

Photo US C-scan

Measurement of adhesive hybrid joints


NDT technologies Lockin-thermography

All materials with T > 0K emit thermal radiation

Stefan-Boltzmann law (radiated power from a black body)

𝑃 = 𝜎 ∗ 𝐴 ∗ 𝑇4

𝜎: Stefan-Boltzmann constant 𝐴: surface area

Sinusoidal thermal excitation

Pixel-wise Fourier transform of image sequences

In the resulting phase images, all effects due to

illumination inhomogeneity and infrared surface emission

are canceled out

Visualization of thermal wave propagation by the phase

images

Stefan-Boltzmann law

Lockin-thermography


Ultrasonic testing and Lockin-thermography Comparison

Ultrasonic testing Lockin-thermography testing

Axial resolution

Lateral resolution

Testing range

Testing speed

Penetration depth

Thermography suited for a fast, extensive near surface defect detection

Ultrasonics most suitable for a high-resolution defect classification even in deeper layers

Conclusion: 1.) Fast large area thermography scan for ROI determination

2.) Ultrasonic scan of the ROI for defect detection and classification

3.) Fusion of NDT data sets


NDT data fusion General approach

Generation of multiple

NDT data sets

Fusion of damage data

and classification

3D damage detection

and visualization

Source: olympus-ims.com, flir.de

Registration of damage

data in the same

coordinate system

Damage information:

- size, shape

- position, orientation


NDT data fusion I 3D digitization of the external geometry

Using of a tracked laser light section scanner

Detection and localization of external defects

Generation of a reference model for locating the ultrasound data

Cameras

IR-LEDs

Laser light section scanner


NDT data fusion I Ultrasound testing for internal defects

Using of a tracked ultrasound probe

Detection and localization of internal defects

Generation of ultrasound D-scan data

Camera IR-LEDs

Ultrasound system

Ultrasound probe


NDT data fusion I Automated damage detection and 3D visualization

Automated detection of the damage out of the conventional ultrasound D-scan data

Generation of a 3D model of the damaged volume


NDT data fusion I Fusion of the damage data with the geometric model

Localization of the damaged volume inside the geometric volume

Fusion of the external and the internal geometric data


NDT data fusion I Analyzing the affects of the damage on the structure

FEM simulation for analyzing the affects of the damage on the structure

Building use cases for quick damage evaluations in the future


NDT data fusion II Machine Vision for CFRP textiles and preforms

Surface

3D-fiber orientation

Geometry Robotically guided

Source: Mersmann, C.: Industrialisierende Machine-Vision-Integration im Faserverbundleichtbau. Aachen: Apprimus-Verlag, 2012


NDT data fusion II 3D preform digitization with the WZL Fiber Measurement System

Model based analysis

Local regression

Geometry profile

1

2

3

Raw scanning data 3D-mesh interpolation

Surface mapping

Seite 19 © WZL/Fraunhofer IPT Dipl.-Ing. Philipp Nienheysen

+49 241/ 80 27573

[email protected]

Thank you for your attention.

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