Simulation Assisted ADR for Digital Radiography

NDE @ IITM

Krishnan BalasubramaniamChair Professor and Head

Centre for Nondestructive EvaluationIndian Institute of Technology, Chennai

balas@iitm.ac.inwww.cnde.in

NDE@CNDE

• Non-destructive Imaging & Evaluation of Materials, Structures, Products

• Structural Health Monitoring using in-situ Sensor Systems

• Measurements of Material Properties and In-Process Parameters.

Testing, Monitoring, and Imaging Techniques using

Models & Experiments

IN-PROCESS monitoring of Cure Properties of Melts, Concrete,

Polymers, and Joints

Material Property Measurements at Ambient Temperatures and Elevated Temperatures up to 1500 C

Applying Acoustic and Electromagnetic Spectrum for Industrial Measurements

People @CNDE

Students PHD=23MS=28

Others=20

StaffTech 18

Non-Tech 4

FacultyCore 4

Post Doc 2Assoc 8

Collaborations@CNDEIndian Industries

• Tata Steel• BHEL• Indian Oil Corporation• Reliance Industries Ltd • NTPC• BPCL• Indian Railways• DAE• DST• ISRO• DRDO• Tech Development Board• Sieger Spintech• Ordinance Factory Cossipore• Mahindra Intertrade• Sundaram Clayton• Sundaram Brake Linings

Foreign Industries

• US Air-force Res Lab,• AFOSR, USA• General Electric, USA• Corning Inc., USA• Boeing, USA.• Timken, USA• LAM Research, USA• NSF. USA• Pratt & Whitney, Canada.• Airbus (Space), France• EPSRC, UK• Viswalab, Singapore• United Tech Aerospace, USA• United Tech Aerospace, Singapore• St. Gobain, USA, India• IGSTC (Indo German)• CEFIPRA (Indo French)

Academic Partners

• Michigan State Univ, USA• Northwestern Univ, USA• Auburn Univ, USA• Penn State Univ, USA• Iowa State Univ, USA• Imperial College, UK• Univ of Warwick, UK• Newcastle University, UK• Univ of Bordeaux, France• CEA, France• IzfP,, Germany• BAM, Germany• Tomsk Univ, Russia• West Pomeranian Univ, Poland• Nanyang Tech Univ, Singapore• Concordia University, Canada• Swinburne Univ of Tech, Australia• University of Nairobi Kenya• Drexel University, USA

Sectors & Spin-offs @IITM

CNDE@ IITM

Industry Sector

Social Sector

Spin-outs

Strategic Sector

DAEDRDOISRO

Oil & GasBHELNTPC

RailwaysSpinning Mills

IITM technology for Tank Inspection in Korea

IITM technology for Pipe Inspection

IITM technology for Health Monitoring of Aircrafts

IITM technology for Monitoring of Fast Breeder Reactor Core

IITM technology for Contamination Detection in Cotton

Rapid Inspection of Rails

• Dhvani Research• Planys Tech• Detect Tech• Maximl Labs

• Solinas Integrity• XYMA Analytics

Power Plants

Defence &Aerospace

Oil & Gas

Transportation

Manufacturing

What is NEW in Radiography & CT ??

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Sim

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Assis

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Rapi

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CT

CMOSDirect ConversionHigher Efficiency

Cone BeamLaminographyLimited Angle Helical CTMulti-Energy VisualisationADRs

Micro-focalNano-FocalHybrid FocalOpen Tube

SimXRAYArtstCIVASimDR

GPU ComputationProcess Integrated CT

Automatic Defect Recognition ADR

Automated Defect

RecognitionADR

DHVANI RESEARCH

ADR for Turbine Blade

Inspection

ADR for Digital X-ray

Simulation Assisted Deep Learning Tools

Infra-red Imaging

FPIMPIVisual

RadiographyUltrasonics

Automation and ML/DL Algorithms

Thermal Images

ADR Levels

July 13, 2019

Level 1Rules based on Experts

Level 2Rules based on Experts & SimADR

Level 3 SimADRSimulation based ML/DL

DHVANI RESEARCH

Automated Defect Recognition

RTR of Plate butt weld joint

RTR of Valve Body yoke weld jointDigital Flat panel with lead shutters

RTR IMAGES OF VALVE BODY YOKE JOINTS WITH DIGITAL FLAT PANEL

Gas holesLack of fusion

ADR Results

The results from the ADR software with the images on left and ADR results superimposed on the images on the right for different valve images (A) No Defect, (B) Cluster of porosity, (C) Planar Defect and gas hoes, (D) Slag, (E) Planar Defect.

ADR of Hancock Valve GUI

Radiography & CT SimulatorsSimXRAYSimCT

SimXRAY & SimCT – Virtual Tools

ImportedPart in the SimXRayGUI

Real-time X-ray Image

Import component geometry from 3D CAD

Full Element Attenuation library and Compounds

Source and Detector configuration

Multithreading enabled kernel

GPU based parallel processing enabled CT Reconstruction kernel

Computed Tomography Simulator

Geometry Visualisation using surface extraction from CT Data

Dual Energy Simulation and Material Identification

SimXRAY Forward Simulation – Sinogram

SimXRAY Simulation

DHVANI RESEARCH

SIMXRAY Quick Tour – Simulation and Defect Insertion

Simulation Assisted ADRSimADR

Simulation Assisted ADRTo employ simulations to assist the Automated Defect Detection and RecognitionConcept

• SimXRAY is a software that has been developed for Simulation of X-ray images• IMAGIN is a software that has been developed for pre-processing and ADR of images• SimADR combines these experiences to provide a powerful new tool for automation of

Digital Radiography for industrial applications

The X-ray images obtained on very complex configurations must be automatically analyzed.Need

• Data sets available in the industry on a component, with defects, is limited.• The images are of sub-optimal quality due to variations in the material absorption across

the sample.• Defects such as porosity, cracks, and interfacial disbonds must be automatically detected.

SIMXRAY Validationson

Images provided by a CLIENT

Aluminium Castingsw w w . d h v a n i - r e s e a r c h . c o m

DHVANI RESEARCH

DHVANI RESEARCH

SimXRAY Validations

Aluminium Casting ComponentSWING ARM

SimXRAY Validations

DHVANI RESEARCH

SWING ARMPosition 3 - Image No. 16_47_12

XRAY Image SimXRAY Simulation

SimXRAY Validations

DHVANI RESEARCH

SWING ARMPosition 9

XRAY Image SimXRAY Simulation

XRAY_IMAGINE

DHVANI RESEARCH

Aluminium Casting Component : Diffuser Plate SimXRAY simulated image

XRAY_IMAGINE

DHVANI RESEARCH

Aluminium Casting ComponentDiffuser Plate

SimXRAY Simulated Digital XRAY Image

SimXRAY is used to evaluate rotation and tilt in the Digital XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Aluminium Casting ComponentDiffuser Plate

Customer requirement for this component:· No individual cavity shall exceed 1.5 mm in any direction.· A maximum of two defects 1.0-1.5 mm long provided they are more than 2.0 mm apart.· A maximum five defects above 0.76 mm long.· Defects less than 0.76 mm are acceptable provided no section is reduced by or more than

20% by their presence

NDE FOR AUTOMOBILE INDUSTRYAutomatic defect detection system

• Automatic detection system for castings.

• Detection according to ASTM 2422.

• Automatic classification of gas holes, porosity and shrinkage.

• Material certification as L1,L2 … based on the area of the defect.

• Automatic rejection threshold on each shots for sentencing

• ASTM Standard comparison for verification

DHVANI RESEARCH

XRAY|IMAGINE|ADR

DHVANI RESEARCH

Development of ADR Software ‘XRAY|IMAGINE|ADR’

for Aluminium CastingsDefects are identified from the radiography images of Aluminiumcasting component using Dhvani’s ADR software XRAY_IMAGINE_ADR.

As per the Customer’s requirement, defects are classified as ‘L1’, ‘L2’, ‘L3’,’L4’ upto ‘L8’. ASTM Reference images are used to classify the defect levels. Defect levels L4 and above are considered as ‘Rejected’

The defects clusters are colored as: L1-green, L2-Blue, L3-Yellow and L4 and above in Red.

One of the ASTM Reference images of Shrinkage, Gas Hole or Gas Porosities is used for the defect classification based on the rules defined by the customer.

ASTM reference images used in the software are shown in the following slides.

DHVANI RESEARCH

200 micron ASTM Reference Image for Gas Holes

200 micron ASTM Reference Image for Gas Holes converted to Black and White image using Dhvani’s Imagine Software

XRAY|IMAGINE|ADR

DHVANI RESEARCH

200 micron ASTM Reference Image for Gas Porosity

200 micron ASTM Reference Image for Gas Porosity converted to Black and White image using Dhvani’s Imagine Software

XRAY|IMAGINE|ADR

DHVANI RESEARCH

200 micron ASTM Reference Image for Shrinkage Cavity

200 micron ASTM Reference Image for Shrinkage Cavity converted to Black and White image using Dhvani’s Imagine Software

XRAY|IMAGINE|ADR

Demo Video

DHVANI RESEARCH

XRAY|IMAGINE|ADR

USE OF ASTM STANDARDS

DHVANI RESEARCH

XRAY|IMAGINE|ADR

XRAY_IMAGINE

DHVANI RESEARCH

Diffuser Plate : Sample C1u063

Defect > 1.5mm0.75mm < Defect < 1.5mm

0.5mm < Defect < 0.75mmTo be decided whether Defect or FA

XRAY_IMAGINE

DHVANI RESEARCH

Diffuser Plate : Sample C1u067

Defect > 1.5mm0.75mm < Defect < 1.5mm

0.5mm < Defect < 0.75mmTo be decided whether Defect or FA

XRAY_IMAGINE

DHVANI RESEARCH

Diffuser Plate : Sample C1v2081

Defect > 1.5mm0.75mm < Defect < 1.5mm

0.5mm < Defect < 0.75mmTo be decided whether Defect or FA

XRAY_IMAGINE

DHVANI RESEARCH

Aluminium Casting ComponentSwing Arm: (654mm x 312mm x 130 mm)

XRAY_IMAGINE

DHVANI RESEARCH

Swing Arm Sample 16_48_16

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Swing Arm Sample 17_13_08

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Swing Arm Sample 06_34_27

Defect identified by ADR XRAY Image

Aluminium Casting ADR

X-Ray Image Defect identified by ADR

XRAY_IMAGINE

DHVANI RESEARCH

Swing Arm Sample 16_47_58

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Swing Arm Sample 16_48_08

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Wheel Sample 10_19_15

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Wheel Sample 10_19_22

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Wheel Sample 10_19_25

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Wheel Sample 10_19_37

Defect identified by ADR XRAY Image

XRAY_IMAGINE

DHVANI RESEARCH

Wheel Sample 10_20_00

Defect identified by ADR XRAY Image

Why Simulation Assisted ADR ?

Deep learning algorithms needs wide, unbiased, data sets for training.

The data has to represent most all cases that may be encountered.

Such data sets are difficult to obtain, particularly experimentally.

Hence, articifical data sets, that are experimentally validated, provide an excellent alternative.

SimXRAY is a virtual Digital X-ray imaging software developed by Dhvani Research in association with IIT Madras.

ADR rule based algorithms have been developed and implemented by Dhvani Research in industries.

Combining SimXRAY with ADR, a new data analytics software SimADR is developed, implemented and is currently under verification & validation phase.

Simulation Assisted ADR for Digital X-ray

DHVANIRESEARCH

Pros

Noise Simulation

Alignment Correction

Self Learning

ADRs

Model based Optimisation

of Data Collection

Model Based File Size

Reduction

Model based Training Database

XRAY|SimADR

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Alignment Issues

Current System @ Customer Site Robotic System @ Customer site

Alignment is an ISSUE in spite of robotic motion.

This leads to variability in the detection and classification

Xray|SimADR Engine

SIMXRAY

CAD MODEL

NOISE MODEL

DEFECT MODEL

REGISTRATION MODEL

Translation(x,y,z)Rotation(α,β,τ)

Geometric Scaling(Zz,Zy)Image Scaling(P)

TypeSize

ShapeLocation

Probability

Validation

Simulated Data Sets

Application CuratedData Sets

Deterministic/Probabilistic

TypePDF

DEEP LEARNING

ALGORITHMS

X%

(1-X)%

Y%

(1-Y)%TWEAK

EXPERT Annotation

CustomerDeployment

PRE-PROCESSOR

IIT Madras Proprietary Information

Image Models

55

Noise Models

56

Defect Models

57

Defect Parameters

58

Aspect Properties Measured

Geometry Major Axis Length, Eccentricity, Orientation

LocationCentroid X-Coordinate, Centroid Y-Coordinate

OrRadial Distance, Angular Position

Number Total number of Objects in the Image

Intensity Pixel Value

Defect Statistics

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Mean Standard Deviation Distribution Type

Radial Distance (547) 32 Normal

Angular Position 2.0056 120.33 Uniform

Major Axis 9.443 2. 863 Normal

Eccentricity 0.349 0.065 Normal

Orientation 15.428 70.172 Uniform

Intensity 0.0425 0.000833 Normal

Number 3.1 0.742 Normal

Defect Simulation

Typical Network architecture of CNN

61

Jae-Seo Lee; Shyam Adhikari; Liu Liu; Ho-Gul Jeong; Hyongsuk Kim and Suk-Ja Yoon. “Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study”

Verification (preliminary results)

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Results on the preliminary study

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SummaryRadiography and CT can a key role in QA in Manufacturing industries.

Improvements in detectors and sources is further increasing this opportunity.

Improved algorithms using parallel processing tools is becoming available.

Simulation and ADR tools will significantly assist the decision process.

Automation is the way forward in Digital Radiography and CT

CNDE

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5

Micro and NanoImaging Techniques

Structural Health Monitoring

Rapid & Automated NDT and Data Fusion

Materials Performance Prognosis

Advanced Computing for NDE Processes

Complex Shaped Component NDE

Hostile Environment Measurements