A multi sensor approach for reverse engineering of an object

A MULTIPLE-SENSOR APPROACH FOR REVERSE ENGINEERING OF AN OBJECT BY TAN HWEE LYNN CYRENE (B.Eng.) DEPARTMENT OF MECHANICAL ENGINEERING A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2003 ACKNOWLEDGMENTS The author would like to express her sincere appreciation to A/Prof. Zhang Yun Feng and A/Prof Loh Han Tong, from the Department of Mechanical Engineering at the National University of Singapore for their invaluable guidance, advice and discussion in the entire duration of the project. It has been a rewarding experience under their supervision. She would also like to express her gratitude to A/Prof Wong Yoke San for his valuable suggestions and guide in the project. She would also like to acknowledge the financial support, the research scholarship from the National University of Singapore. Special thanks are given to professional officer Mr Neo Ken Soon of the Advanced Manufacturing Laboratory for his aid in handling the equipment, fellow graduate student Wu Yi Feng for his guidance and encouragements, and final-year student Lee Lye Peng in his contribution to the project. Finally, the author would express her sincere gratitude to her family, Dominic Cheong and Lord Jesus for their constant support and love. i TABLE OF CONTENTS ACKNOWLEDGMENTS i TABLE OF CONTENTS .ii LIST OF FIGURES .v SUMMARY ix Chapter Introduction .1 1.1 Reverse Engineering……………………………………………………… 1.2 Objectives………………………………………………………………… 1.3 Overview of Thesis………………………………………………………… Chapter Background and Literature Review .4 2.1 New Product Development……………………………………………… . 2.2 Reverse Engineering……………………………………………………… 2.3 Literature Review………………………………………………………… . 2.4 Error from Non-contact Laser Scanner……………………………………. 2.5 Error from Contact Digitizer- CMM……………………………………… 10 2.6 Sensor Integration………………………………………………………….11 2.7 Outline of Methodology………………………………………………… . 13 Chapter Methodology for Part Digitization 15 3.1 Processing of Measurement Data…………………………………………. 15 ii 3.2 Determination of edge segments from the triangular mesh………………. 16 3.2.1 Identification of boundary…………………………………………… 20 3.3 Surrounding Topology of Edge…………………………………………… 25 3.4 Transformation of the Reference Systems……………………………… . 26 3.4.1 3.5 Use of Base Plate for Transformation Process……………………… 29 Path Planning of Digitization Process using Touch Probe……………… 32 3.5.1 Determining the Direction of Digitization………………………… . 33 3.6 Cavities…………………………………………………………………… 39 3.7 CMM Probe Path Generation…………………………………………… 41 3.8 Minimization of Error by Adaptive Re-digitization……………………… 43 3.9 3.8.1 Determination of Error Points……………………………………… 44 3.8.2 Determination of the Position of Error Points………………………. 46 3.8.3 Rectification…………………………………………………………. 49 Intersection of Edges……………………………………………………… 53 Chapter Implementation 55 4.1 Minolta Vivid 900 Laser Scanner……………………………………… . 55 4.2 Minolta Polygon Editing Software………………………………………. 57 4.3 Scanning of Object using VIVID 3D Laser Digitizer…………………… 58 4.4 RapidForm2000………………………………………………………… 60 4.5 Use of Program…………………………………………………………… 62 4.6 Coordinate Measuring Machine CMM………………………………… 63 iii 4.7 Flowchart of Implementation Process……………………………………. 66 Chapter Case Studies. .67 5.1 5.2 Case Study 1……………………………………………………………… 67 5.1.1 Results on a Straight Edge……………………………………………71 5.1.2 Results on a Curved Edge…………………………………………… 75 5.1.3 Result on a Cavity…………………………………………………… 78 5.1.4 Rectification of Points From Manufacturing Errors………………… 83 Case Study 2……………………………………………………………… 84 Chapter Conclusion and Future Work 86 Reference .87 APPENDIX A 91 APPENDIX B 93 APPENDIX C 94 APPENDIX D 100 iv LIST OF FIGURES Figure 2.1 Realization of a product design Figure 2.2 Outline of methodology 13 Figure 3.1 Triangular mesh of cloud data 16 Figure3.2 Deviation of normal from inaccurate edge point .17 Figure 3.3 A Pair of neighboring elements 19 Figure 3.4 Classification of edges 21 Figure 3.5 Method of reduction of edge points 23 Figure 3.6 Reduced edge points .24 Figure 3.7 Neighboring points for two regions with different curvatures .26 Figure 3.8 Model with four distinct points for transformation 27 Figure 3.9 Flow diagram of the transformation process 29 Figure 3.10 Baseplate used for transformation 30 Figure 3.11 Four chosen points for transformation .31 Figure 3.12 Characteristics of the planes for identification .31 Figure 3.13 Vertex obtained from the intersection of planes .32 Figure 3.14 Wrong probe direction 34 Figure 3.15 Approximated local surface 35 v Figure 3.16 Cross product of two vectors 36 Figure 3.17 Calculation of normal vector of point 37 Figure 3.18 Determination of approach direction 38 Figure 3.19 Profile of the a cavity .40 Figure 3.20 Approach point for cavity .41 Figure 3.21 Retract and approach position 42 Figure 3.22 Touch probe movement 43 Figure 3.23 Inaccurate edge points 43 Figure 3.24 Determination of position of error point (outer plane) .47 Figure 3.25 Error points on the outside of top and bottom plane 48 Figure 3.26 Error points on the inside of top and bottom plane 49 Figure 3.27 Local accessibility range 50 Figure3.28 Angle for rectification .50 Figure 3.29 Approach direction after rectification 52 Figure 3.30 Mid-point of two skew lines .53 Figure 4.1 Minolta VIVID 900 3D laser scanner 56 Figure 4.2 Prepared object on rotating table 58 Figure 4.3 Interface for polygon editing tool .59 Figure 4.4 Surface geometry of one data set 60 Figure 4.5 Registration of two different data sets 61 Figure 4.6 Complete 3D profile of the object 62 vi Figure 4.7 Determining vertices for transformation 63 Figure 4.8 Mahr multisensor CMM .64 Figure 4.9 KMESS software 65 Figure 4.10 Flowchart of implementation process 66 Figure 5.1 Case model .67 Figure 5.2 Top and side profile of model 68 Figure 5.3 Edge points of the model 69 Figure 5.4 Accessible and non-accessible edges .70 Figure 5.5 Approach and edge points 71 Figure 5.6 Straight edge on model .71 Figure 5.7 Plot of points from first digitization .73 Figure 5.8 Plot of deviation of points from edge .73 Figure 5.9 Plot of points from second digitization 74 Figure 5.10 Plot of points of straight edge from second digitization .74 Figure 5.11 Results from the two digitization processes .75 Figure 5.12 Curved edge of model 75 Figure 5.13 Result of curved edge from first digitization process .76 Figure 5.14 Deviation plot of points of curved edge of the first digitization process .76 Figure 5.15 Result of curved edge from second digitization process 77 Figure 5.16 Deviation plot of points of curved edge of the second digitization process 78 vii Figure 5.17 Result of curved edge .78 Figure 5.18 Cavity of model 79 Figure 5.19 Result of cavity from first digitization process 79 Figure 5.20 Plane fitted through the points from first digitization 80 Figure 5.21 Deviation plot of points of cavity from the first process 80 Figure 5.22 Result of cavity from second digitization process 81 Figure 5.23 Plane fitted through the points from second digitization 82 Figure 5.24 Deviation plot of points of cavity from the second process .82 Figure 5.25 Sharp and rounded edge of triangular cavity 83 Figure 5.26 Coordinates of constructed edge points of triangular cavity 84 Figure 5.27 A freeform surface 84 Figure 5.28 Approach and edge points on surface .85 Figure 5.29 Result of the freeform surface 85 viii SUMMARY This thesis describes an integrated reverse engineering approach for scanning freeform objects using a 3D scanner and a coordinate measuring machine (CMM). The aim is to achieve a more efficient digitization and obtain more accurate results by taking the advantages of both the laser and mechanical sensors with minimum human intervention. The whole process is divided into four stages: the acquisition of a set of point cloud by the use of a scanner and the planning of the boundary digitization by a program, the digitization of important features of the object using a coordinate measuring machine CMM and lastly the adaptively re-digitization process of the error points. The planning of the boundary digitization defines the edges of the object where two surfaces meet. This approach reduces the product development lead time and obtains a set of data with good accuracy. In this thesis, the combined system is described and the case studies are presented. ix Chapter Case Studies Only the top surface of the model is used. Only one edge is illustrated here. The blue points shown in Figure 5.28 are the approach points of the touch probe. The red points are the edge of the surface to be digitized. The direction of the touch probe is determined by the summation of the normals of the planes which forms the edge. Figure 5.28 Approach and edge points on surface Figure 5.29 Result of the freeform surface 85 Chapter Conclusion and Future Work Chapter Conclusions and Future Work In this report, a combined approach for the reverse engineering of objects has been presented, based on the integration of a 3D laser digitizer system with CMM digitization. The advantages and disadvantages of the contact and non-contact sensors are explored. The combination of the two sensors at the information level makes use of the benefits of the systems. A digitizing strategy is explored to enable an CAD model to be re-engineered with high accuracy and minimal human intervention. A dense cloud point is first obtained using a laser scanner. This set of data is obtained in a very short time and the information obtained is useful to attain a more accurate model. The necessary information is extracted from this point cloud and a CMM is then used to digitize the important points. Error points were flagged and adaptively redigitized until the required accuracy. While conventional methods which solely depend on the CMM and it require days to complete the entire digitization process, this new method eliminates many intermediate steps and hence leading to a reduction in time. As shown in the case studies, the part digitization process is carried out until the user defined tolerance is achieved and hence, a CAD model with satisfactory accuracy can be generated rapidly. Future development will be aimed at increasing the level of automation of the whole process, by providing procedures that recognize varied features in the point cloud and accuracy in the transformation of the reference systems of the two machines. 86 Reference Reference Carbone, V. and Carocci, M., Combination of a vision system and a coordinate measuring machine for the reverse engineering of freeform surfaces, International Journal of Advanced Manufacturing Technology, 2001, 17, pp. 263-271 Chan, V.H and Bradley., C, A multi sensor approach to automating co-ordinate measuring machine-based reverse engineering, Computers in Industry, 2001, 44 (2), pp. 105-115 Chen, L.C. and Lin, C.I. 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Academic Press, New York, 1990 Hamann, B., Curvature approximation of 3D manifolds in 4D space, Computer Aided Geometric Design, 1994, 11, pp. 621-632 Hamann, B., Curvature approximation for triangulated surfaces, Computer Suppl, 1993, 8, pp. 139-153 Huang, J. and Menq,, C.H., Automatic data segmentation for geometric feature extraction from unorganized 3-D coordinate points, IEEE Transactions on Robotics and Automation, 2001, 17(3) Huang, M.C and Ching, C.T, Pre-processing of data points for curve fitting in reverse engineering” Lee, L.P., Point data transformation from a laser scanner to a CMM”, Bachelor of Engineering Thesis, National University of Singapore, Singapore 2002 Liu, G. H., Segmentation of cloud data for reverse engineering and direct rapid prototyping. Master of Engineering Thesis, National University of Singapore, Singapore 2001 Lo, S.H. and Wang, W.X., An algorithm for the intersection of quadrilateral surfaces by tracing of neighbours, Computational. Methods Appl. Mech. Engrg, 2003, 192, pp. 2319–2338 88 Reference Menq, C. and Chen, F.L., Curve and surface approximation from CMM measurement data, Computers & Industrial Engineering, 1996, 30(2), pp. 211-225 Milroy, M.J. and Bradley, C., Segmentation of a wrap-around model using an active contour, Computer-Aided Design, 1997, 29(4), pp. 299-320 Nashman, M., An integrated vision touch probe system for dimensional inspection task, Proc. SME Applied Machine Vision, 1996 Conference, Society of Manufacturing Engineers, pp. 243-255 Piegel, L., Fundamental developments of computer-aided geometric modelling” Razdan, A and Bae, M.S., A hybrid approach to feature segmentation of triangle meshes, Computer-Aided Design, 2003, 35, pp. 783–789 Shi, M., Triangular mesh generation employing a boundary expansion technique, Master of Engineering Thesis, National University of Singapore, Singapore 2001 Sohb, T.M and Owen J.C, A sensing strategy for the reverse engineering of machined parts, Journal of Intelligent and Robotic Systems, (1995), pp. 1-18 Song, C. K. and Kim, S. W, Reverse engineering: autonomous digitization of freeformed surfaces on a CNC coordinate measuring machine. International Journal of Machine Tools and Manufacture, 1997, 37(7), pp. 1041-1051 Woo, H. and Kang, E. , A new segmentation method for point cloud data, International Journal of Machine Tools & Manufacture, 2002, 42, pp. 167-178 Yau, H.T and Menq C.H, Reverse engineering in the design of intake and exhaust ports, ASME PED Manufacturing Science and Engineering, 1993, 64, pp. 139-148 89 Reference Yau, H.T. and Menq, C., Automated CMM path planning for dimensional inspection of dies and molds having complex surfaces, Int Mach.Tools Manufact, 1995, 35(6), pp. 861-876 Zhang, S.G and Ajmal, A., Feature-based inspection process planning system for CMM, Journal of Materials Processing Technology, 2000, 107, pp. 111118 www.rapidform.com, 2003 www.mathworld.com,2003 http://www.tasc.com/projects/catt/re/CMM.htm,2003 90 Appendix APPENDIX A Specification of Coordinate Measuring Machine 91 Appendix 92 Appendix APPENDIX B System Block Diagram of Vivid Laser Scanner 93 Appendix APPENDIX C Specification of RapidForm 2000 System Requirements Microsoft Windows NT 4.0, Windows 95/98/Me/2000/XP Pentium -based Computer ( Pentium-II minimum or higher recommended ) 256 MB RAM minimum or more recommended 350 MB hard disk space minimum for installation OpenGL acceleration graphic board CD-ROM drive Basic Functionalities Windows® OLE automation-based API (RapidForm2002 Developer) Customizable GUI Layer management Wide variety of real-time selection tools Dimension measuring / Auto measuring Transformation Various reference geometry construction Unlimited undoing SpaceMouse(from 3D Connexion) interface 3D data compression Direct interface for CAD data (Exchange), etc. 94 Appendix Support File Formats Proprietary file format MDL(model), FCS(face), PTS(point), ICF(INUS Compression Format), TPL(INUS Template format) 3D scanner file format VVD(Minolta), AC(Steinbichler), View/Cloud(GOM), CBK/GRK(Kreon), PMJ(3DD), BRE(Breuckmann), XYZ(EOIS), SAB(3D Scanners), Colored ASC(Arius3D), HYM(Hymarc), PLY(Cyberware), NRF(NEC), STB(Scantech), PTS(Cyra), SWL/Bin(Perceptron), Rtpi/Xyzi/Xyzrgb(3rd Tech) Standard file format ASC, 3DS, DXF, IV, STL, WRL, OBJ, POV, VDAFS, IGES, STEP(AP203), 3DM(Rhino), MTS(Viewpoint), RP Slice format(SLC, CLI, SLI, DXF) Functions of Scan Workbench Customizable ASC (point cloud) file format parser Intelligent point cloud shading Curvature-based/Uniform Point cloud sampling Noisy point filtering Point cloud smoothing Point-to-Polygon triangulation (2D/3D) Multiple scan view registration for either polygonal model or point cloud (alignment) Rotary table registration Merging polygon / point cloud models, etc. 95 Appendix Functions of Polygon Workbench Decimation (Polygon reduction) Curvature-based smoothing Local smoothing with paintbrush Subdivision Global/local Remeshing Curvature-based hole filling Dividing and mirroring Zipping gaps Trimming Shape fitting to points, polygon, and surface 3D photography tools (Virtual painting & Color micro-tuning) Texture mapping automation Intelligent texture mapping Mesh template for predefined model animation Edge fitting along curves (Sharp edge reconstruction) Automatic healing for bad data Freeform deformation Medial axis transformation Polygon morphing Real-time collision detection Automatic symmetric model recognition & management, etc. 96 Appendix Functions of Rapid Prototyping Workbench Working volume management Offsetting or thickening polygon Boolean operation (union/intersection/subtraction) Interference checking Exporting slicing data in CLI, DXF, SLI, and SLC format Overhang region searching, etc. Functions of Curve Workbence Designing curves on polygon/point cloud in various ways Automatic global trimming Automatic loop finding & management Offsetting and extending on polygon Polygon boundary fitting Point set generation from curve Extraction of feature curves Editing and deformation of curves Curve template Fully-associative with reference polygon/point model Shape analyzing, etc. 97 Appendix Functions of Surface Workbench Polygon-to-trimmed/untrimmed NURBS Automatic polygon-to-NURBS conversion Blending/Lofting/Sweeping/Extending Exact geometry generation with NURBS Intersection and trimming N-boundary filling Smoothing Rebuilding & Refitting Matching continuity Editing and deformation Intelligent surface healer for imported geometries Tessellation Fully-associative with reference polygon/point model Shape analyzing, etc. Functions of Inspect Workbench Free space alignment for Point cloud-to-CAD model CAD data/STL comparison with 3D-scanned point cloud Machining error analysis with various color map Geometry tolerance analysis (Flatness/Straightness/Circularity/Cylindricity/Concentricity) 2D/3D annotation Sectional analysis 98 Appendix GD & T (Geometric Dimensioning & Tolerance) functionalities Surface re-fitting to original scan data Report generation in MS document file format & HTML, etc. Functions of 3D Imaging Workbench MRI/CT image thresholding/segmentation VOI(Volume of Interest) management Real-time volume rendering Qualified polygonal model generation, etc. 99 Appendix APPENDIX D CAD drawing of Model Used For Case Study 100 [...]... surfaces This does not include the physical integration of the two sensors but includes their combination at the operation level 1.2 Objectives The objectives of this project are: a) To look into the advantages and disadvantages of the 3D laser scanner and CMM b) To propose a digitizing strategy that combines the advantages of the optical and mechanical sensors c) To obtain a model of an object of complex... the part assuming that the breakage or wear was not too severe The geometry of the part can be digitized into the system and new part can be manufactured from the edited surfaces generated from the scanned data Reverse engineering can also be used as means to archive their outdated design data to obtain a database of their products This is done to repair and maintain facilities that require replacement... devices can be used to obtain dense measurement data in a relatively shorter time However, the accuracy of the scanning result is not as accurate as the contact digitizer Laser range sensors tend to generate very large data files, unstructured data that is not arranged in an orderly fashion In addition, there are other disadvantages of the non-contact digitizers too For example, many redundant points... the scan is estimated to select subsequent gazes for the subsequent gazes for the laser scanner A disadvantage is that the first scan is made manually and each additional scan is made after calculations is made of the previous scan 2.4 Error from Non-contact Laser Scanner Non-contact sensing systems work by projecting a laser beam onto the part surface and then inferring the location of the points through... testing As creating a prototype of the product is important for experimental evaluation, reverse engineering plays an important role to ensure that a dimensionally accurate prototype is created Traditional methods of reverse engineering of free form surfaces have relied heavily on digitization techniques utilizing coordinate measurement machines (CMM) For a large range of objects with planes and simple... diversity of information is used to overcome the limitations of the individual components In order to combine the advantages of a laser digitizer and a CMM , the strengths and the weakness of each sensor has to be compared The most obvious characteristic of the laser scanner is the fact that it is a non-contact sensor and as such, it is able to gather a large amount of data points in a short period of time... computer as a CAD representation At this point, alterations can be made to the CAD representation as desired by the design engineers There are numerous reasons as to why reverse engineering is employed An example is that when a working part of a part of a system breaks or wears out and no 6 Chapter 2 Background and Literature Review CAD file exists for that particular part, reverse engineering is carried... deduced from the earlier section This is done to create an approximate local surface to the surrounding area of the edge for the computation of the direction of approach in the digitization process A small local surface is approximated instead of using the normal of a large plane This is done so that edges which have high curvature and freeform characteristics can also be automatically digitized However,... precision of the individual mechanical parts that comprise the system The object itself can also impact the accuracy of the data A more accurate result can be obtained when the object has a white smooth matt surface A darker material tends to absorb more of the projected laser and hence, reflects less back to the sensor while smooth materials reflect the light strongly In addition, large inclination angle... through a set of ordered points generated by the CMM This approach can only be applied for smooth surface fitting Discontinuous features in part surfaces are not addressed in this research work An approach by Milroy (1997) automates a scanning process where the next scan is determined by a previous scan An initial scan is done where the range of possible orientations of the outer edges of the scan is . gazes for the subsequent gazes for the laser scanner. A disadvantage is that the first scan is made manually and each additional scan is made after calculations is made of the previous scan employed. An example is that when a working part of a part of a system breaks or wears out and no Chapter 2 Background and Literature Review 7 CAD file exists for that particular part, reverse engineering. scanner and a coordinate measuring machine (CMM). The aim is to achieve a more efficient digitization and obtain more accurate results by taking the advantages of both the laser and mechanical sensors