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Automated Registration for 3D Inspection of Complex Shapes. Xinju Li, Igor Guskov, Jacob Barhak EECS & ERC/RMS University of Michigan. Challenge. Develop methodology for inspection of surfaces with complex geometry . Challenge in Inspection: Part Alignment.

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Automated Registration for 3D Inspection of Complex Shapes

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Automated registration for 3d inspection of complex shapes l.jpg

Automated Registration for 3D Inspection of Complex Shapes

Xinju Li, Igor Guskov, Jacob Barhak

EECS & ERC/RMS

University of Michigan

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Challenge l.jpg

Challenge

  • Develop methodologyfor inspection of surfaces withcomplex geometry.

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Challenge in inspection part alignment l.jpg

Challenge in Inspection: Part Alignment

  • Coordinate systemregistrationisrequiredsince measured data and the CAD model arenot in the same coordinate system

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Part alignment from common practice to computing power l.jpg

Common Practice

PhysicalPart Alignment:

Fixture dependant

A calibration-like process prior to the physical inspection

The part coordinate system is established by measuring locators

Computing power allows

ComputationalPart Alignment:

Fixtureless

The Alignment is preformed after measurement acquisition

The nominal shape establishes the part coordinate system

Part Alignment - From Common Practice to Computing Power

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Approach advantages l.jpg

Approach Advantages

  • No fixturerequired for inspection

    • Free-orientationinspectionpossible

    • Save design time

    • Save in manufacturing resources

    • Save time allotted for mounting the part in the fixture

    • Increased part exposure during inspection

  • A simplified inspection plan

    • Does not require prior knowledge of the inspected part

    • Save time in designing the inspection plan

    • Decoupleacquisition and alignmentstages

    • Less physicalinteraction

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


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More Advantages

  • Improvedinformation flowbetween processes in modern environment

    • Noneed inDatumdefinition for inspection

    • TheCAD modelis the nominal shape

    • Rapid prototypedparts can be easily inspected

  • Inspectionmachine capabilities increase

    • Part size is not limited toinspection volume

    • Systematic errorscan becompensatedfor

    • Suitsvarious machinesemploying non-contact probes

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Inspection and alignment methodology l.jpg

Inspection and Alignment Methodology

3D CAD model

ComputationalPart Alignment

Inspection

Initial Pose Estimation (Approximate solution)

Acquired 3D Data

Solution Refinement Iterate Closest Point (ICP) algorithm

Shape Deviation (Manufacturing error)

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Inspection approach is similar to reverse engineering l.jpg

Inspection Approach is Similar to Reverse Engineering

Measurement from a single direction

Multi-scan from 12 directions

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Alignment by registration for reverse engineering and for inspection l.jpg

Alignment by Registration for Reverse Engineering and for Inspection

Inspection:

Registration between a point cloud and the CAD model

Reverse Engineering:

Registration between clouds of points acquired from different vantages

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Inspection and alignment methodology10 l.jpg

Inspection and Alignment Methodology

3D CAD model

ComputationalPart Alignment

Inspection

Initial Pose Estimation (Approximate solution)

Acquired 3D Data

Solution Refinement Iterate Closest Point (ICP) algorithm

Shape Deviation (Manufacturing error)

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Overview l.jpg

Overview

  • Scans and models are point clouds with normals

  • A scan is considered part of its model

  • Feature points are detected and matched

Before matching

After initial matching

Method by Xinju Li and Igor Guskov

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Point selection l.jpg

Point Selection

  • Multi-scale feature points are used to minimize the matching effort using:

    • Xinju Li, Igor Guskov, “Multi-scale Features for Approximate Alignment of Point-based Surfaces” (SGP05) http://graphics.eecs.umich.edu/dgp/mrfet-electronic.pdf

  • Build multi-scale representation of the surface by a smoothing procedure

  • Compute the normal difference between neighbor levels

  • Feature points are local maximal or minimal of normal difference

Scan and model : feature points are marked with yellow circles

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Point selection13 l.jpg

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Point Selection

Multi-scale representation

Normal difference

Feature points are local maxima or minima on the normal difference of the surface

Method by: Xinju Li and Igor Guskov

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Matching and transformation l.jpg

Matching and Transformation

  • Calculate transform for all feature pairs

  • Select best transform according to distance criteria

Normal

Principal Curvature Direction

Method by: Xinju Li and Igor Guskov

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Inspection and alignment methodology15 l.jpg

Inspection and Alignment Methodology

3D CAD model

ComputationalPart Alignment

Inspection

Initial Pose Estimation (Approximate solution)

Acquired 3D Data

Solution Refinement Iterate Closest Point (ICP) algorithm

Shape Deviation (Manufacturing error)

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Solution refinement icp algorithm l.jpg

Solution Refinement: ICP Algorithm

Given a point cloud {pi} and a CAD model of the part

For every cloud point pi find the closest pointqi on the model 

Find the transformationT to minimize distance sum  || pi - T qi||2

Iterate the process until it converges

Output the Deviation

Graphics by: Liang Zhu

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Output shape verification l.jpg

Output: Shape Verification

Model: 69668 Vertices; 139,336 Faces

Scan: 26,757 sampled points in 12 scans

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Output shape verification18 l.jpg

Output: Shape Verification

Model: 882,954 Vertices; 1,765,388 Faces

Simplified: 50,054 Vertices, 100,000 Faces

Scan: 87,903 sampled points in 12 scans

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Output shape verification19 l.jpg

Output: Shape Verification

Model: 530,168 Vertices; 1,060,346 Faces

Simplified: 49996 Vertices; 100,000 Faces

Scan: 31,677 sampled points in 12 scans

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Additional information l.jpg

Additional Information

  • J. Barhak, “Utilizing Computing Power to Simplify the Inspection Process of Complex Shapes”. The 2004 Israel-Italy Bi-National Conference on Measurements and Uncertainty Evaluation in Coordinate Measuring Machine (CMM) and Scanners and their Implication on Design and Reverse Engineering. Haifa, Israel, November 29-30, 2004.

  • L. Zhu, J. Barhak, V. Srivatsan, R. Katz, “Error Analysis and Simulation for Four-Axis Optical Inspection System”, Digital Enterprise Technology, September 13-15, 2004, Seattle, Washington, USA.

  • L. Zhu, J. Barhak, V. Srivatsan, R. Katz, “Efficient Registration for Precision Inspection of Free-Form Surfaces”, Accepted by the International Journal of Advanced Manufacturing Technology.

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


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Conclusions

  • Multi-view inspection offers many advantages, especially in conjunction with contemporary non-contact devices.

  • Computing power is an essential component in dealing with multi-view inspection.

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


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Acknowledgements

  • Research supported by the NSF Engineering Research Center for Reconfigurable Manufacturing Systems(ERC/RMS) under the grant EEC-9529125

  • This work was also supported in part by NSF CAREER award (CCR-0133554)

  • Prof. Yoram Koren for supporting these projects

  • Geoffrey Blake andSher Jun Tan for programming

  • Dr.Liang Zhu and Vijay Srivatsan for their help in developing the 3D inspection approach

  • Special thanks to Steve Erskine for his aid in system construction

  • Neil Craft from Williams International for his consultation

  • Szymon Rusinkiewicz for his consultation at early stages of the work

  • Additional thanks to the UM3D Lab director Dr.-Ing. Klaus-Peter Beier and Brett Lyons for manufacturing the models

  • Cyberware.com web site for the hip bone model

  • Large Geometric Models Archive at Georgia Institute of Technology for the Turbine blade model

  • The Arrigo dataset are courtesy of the Visual Computing Lab of CNR-IS TI, Pisa, Italy

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


Thank you for your attention l.jpg

Thank you for your attention!!

Your feedback and questions are welcome

NSF Engineering Research Center for Reconfigurable Manufacturing Systems

College of engineering, University of Michigan


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