NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 1 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 2 Challenge Develop methodology for inspection of surfaces with complex geometry.

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 3 Challenge in Inspection: Part Alignment Coordinate system registration is required since measured data and the CAD model are not in the same coordinate system

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 4 Part Alignment - From Common Practice to Computing Power Common Practice Physical Part Alignment: Fixture dependant A calibration-like process prior to the physical inspection The part coordinate system is established by measuring locators Computing power allows Computational Part Alignment: Fixtureless The Alignment is preformed after measurement acquisition The nominal shape establishes the part coordinate system

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 5 Approach Advantages No fixture required for inspection –Free-orientation inspection possible –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 –Decouple acquisition and alignment stages –Less physical interaction

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 6 More Advantages Improved information flow between processes in modern environment –No need in Datum definition for inspection –The CAD model is the nominal shape –Rapid prototyped parts can be easily inspected Inspection machine capabilities increase –Part size is not limited to inspection volume –Systematic errors can be compensated for –Suits various machines employing non-contact probes

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 7 Computational Part Alignment Inspection Inspection and Alignment Methodology Solution Refinement Iterate Closest Point (ICP) algorithm Shape Deviation (Manufacturing error) Acquired 3D Data 3D CAD model Initial Pose Estimation (Approximate solution)

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 8 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 9 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 10 Computational Part Alignment Inspection Inspection and Alignment Methodology Solution Refinement Iterate Closest Point (ICP) algorithm Shape Deviation (Manufacturing error) Acquired 3D Data 3D CAD model Initial Pose Estimation (Approximate solution)

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 11 Overview Scans and models are point clouds with normals A scan is considered part of its model Feature points are detected and matched Before matchingAfter initial matching Method by Xinju Li and Igor Guskov

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 12 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) 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 13 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 14 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 15 Computational Part Alignment Inspection Inspection and Alignment Methodology Shape Deviation (Manufacturing error) Acquired 3D Data 3D CAD model Initial Pose Estimation (Approximate solution) Solution Refinement Iterate Closest Point (ICP) algorithm

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 16 Solution Refinement: ICP Algorithm For every cloud point p i find the closest point q i on the model  Find the transformation T to minimize distance sum  || p i - T q i || 2 Iterate the process until it converges Output the Deviation Given a point cloud {p i } and a CAD model  of the part Graphics by: Liang Zhu

NSF Engineering Research Center for Reconfigurable Manufacturing Systems College of engineering, University of Michigan 17 Output: Shape Verification Model: 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 18 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 19 Output: Shape Verification Model: 530,168 Vertices; 1,060,346 Faces Simplified: 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 20 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, 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 21 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 22 Acknowledgements Research supported by the NSF Engineering Research Center for Reconfigurable Manufacturing Systems (ERC/RMS) under the grant EEC This work was also supported in part by NSF CAREER award (CCR ) Prof. Yoram Koren for supporting these projects Geoffrey Blake and Sher 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 23 Thank you for your attention!! Your feedback and questions are welcome