As-Built Modeling of Ojbects for Performance Assessment

The goal of ''as-built'' computational modeling is to incorporate the most representative geometry and material information for an (fabricated or legacy) object into simulations. While most engineering finite element simulations are based on an object's idealized ''as-designed'' configuration with information obtained from technical drawings or computer-aided design models, ''as-built'' modeling uses nondestructive characterization and metrology techniques to provide the feature information. By incorporating more representative geometry and material features as initial conditions, the uncertainty in the simulation results can be reduced, providing a more realistic understanding of the event and object being modeled. In this paper, key steps and technology areas in the as-built modeling framework are: (1) inspection using non-destructive characterization (NDC) and metrology techniques; (2) data reduction (signal and image processing including artifact removal, data sensor fusion, and geometric feature extraction); and (3) engineering and physics analysis using finite element codes. We illustrate the process with a cylindrical phantom and include a discussion of the key concepts and areas that need improvement. Our results show that reasonable as-built initial conditions based on a volume overlap criteria can be achieved and that notable differences between simulations of the as-built and as-designed configurations can be observed for a given loadmore » case. Specifically, a volume averaged difference of accumulated plastic strain of 3% and local spatially varying differences up to 10%. The example presented provides motivation and justification to engineering teams for the additional effort required in the as-built modeling of high value parts. Further validation of the approach has been proposed as future work.« less

[1]  Ralph Müller,et al.  Smooth surface meshing for automated finite element model generation from 3D image data. , 2006, Journal of biomechanics.

[2]  R. Garimella,et al.  Untangling of 2D meshes in ALE simulations , 2004 .

[3]  Ralph R. Martin,et al.  Algorithms for reverse engineering boundary representation models , 2001, Comput. Aided Des..

[4]  Harry E. Martz,et al.  Application of 3D X-Ray CT Data Sets to Finite Element Analysis , 1996 .

[5]  G. Michael,et al.  X-ray computed tomography , 2001 .

[6]  Robert West,et al.  Opportunities for Data Fusion in Multi-Modality Tomography , 1999 .

[7]  Mark F. Horstemeyer,et al.  Numerical, experimental, nondestructive, and image analyses of damage; progression in cast A356 aluminium notch tensile bars , 2013 .

[8]  J. Kinney,et al.  On the importance of geometric nonlinearity in finite-element simulations of trabecular bone failure. , 2003, Bone.

[9]  I Naert,et al.  Individualised, micro CT-based finite element modelling as a tool for biomechanical analysis related to tissue engineering of bone. , 2004, Biomaterials.

[10]  Luca Antiga,et al.  Geometric reconstruction for computational mesh generation of arterial bifurcations from CT angiography. , 2002, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[11]  Marshall W. Bern,et al.  Mesh Generation , 2020, Handbook of Computational Geometry.

[12]  Yongjie Zhang,et al.  3D Finite Element Meshing from Imaging Data. , 2005, Computer methods in applied mechanics and engineering.

[13]  Mathew Koshy,et al.  On the application of discrete tomography to CT‐assisted engineering and design , 1998 .

[14]  Mark F. Horstemeyer,et al.  Three dimensional void analysis of AM60B magnesium alloy tensile bars using computed tomography imagery , 2000 .

[15]  M Lengsfeld,et al.  Development of a hybrid finite element model for individual simulation of intertrochanteric osteotomies. , 2001, Medical engineering & physics.

[16]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[17]  P. Menegazzi,et al.  A New Approach to the Modelling of Engine Cooling Systems , 1997 .

[18]  Pierre Moulin,et al.  Cramer-Rao bounds for parametric shape estimation , 2002, Proceedings. International Conference on Image Processing.

[19]  Alla Sheffer,et al.  Hexahedral meshing of non-linear volumes using Voronoi faces and edges , 2000 .

[20]  K. M Abd El-Ghany,et al.  Expert system to automate the finite element analysis for non-destructive testing , 2000 .

[21]  David H. Chambers,et al.  As-built model generation for a cylindrical test object , 2003 .

[22]  Angelo Cappello,et al.  Automatic generation of accurate subject-specific bone finite element models to be used in clinical studies. , 2004, Journal of biomechanics.

[23]  Nelson L. Max,et al.  A contract based system for large data visualization , 2005, VIS 05. IEEE Visualization, 2005..

[24]  J. M. Sullivan,et al.  Automatic finite element mesh generation from MRI scans for breast cancer investigations , 2001, Proceedings of the IEEE 27th Annual Northeast Bioengineering Conference (Cat. No.01CH37201).

[25]  A. Willsky,et al.  Reconstruction from projections based on detection and estimation of objects--Parts I and II: Performance analysis and robustness analysis , 1984 .

[26]  Thomas J. R. Hughes,et al.  Finite element modeling of blood flow in arteries , 1998 .

[27]  Tait S. Smith,et al.  Techniques for the analysis of aging signatures of silica-filled siloxanes , 2003 .

[28]  Yongjie Zhang,et al.  Adaptive and Quality Quadrilateral/Hexahedral Meshing from Volumetric Data. , 2006, Computer methods in applied mechanics and engineering.

[29]  N. Bayley,et al.  Failure , 1890, The Hospital.