International Academy for Production Engineering 66th General Assembly – Guimaraes, Portugal, Aug.21-27, 2016
From reverse engineering to shape engineering in mechanical design by N. Anwer and L. Mathieu Presenting author: N. Anwer, LURPA, ENS Cachan, Univ. Paris-Sud, Université Paris-Saclay, 94235 Cachan, France. Email:
[email protected] CIRP Annals - Manufacturing Technology Volume 65, Issue 1, 2016, pp. 165–168 CIRP office: 9 rue Mayran, 75009 PARIS – France, E mail:
[email protected],
http://www.cirp.net
Motivation and Objective Reverse Engineering (RE) in the Product Design Process • Capture of technical product data • Reinvention, reconstruction and reproduction • Geometry-centric: Geometric Reverse Engineering (GRE) • Research issue addressed by CIRP (Since 90’s; > 60 papers)
Computational models
Cover other phases of the Product Life Cycle
Shape Engineering Perspective (Virtual/Physical) Digitizing technologies
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Outline • • • • •
Reverse Engineering Geometric Reverse Engineering (GRE) Shape Engineering Shape Processing for GRE Applications – Test cases – Multiple-sensor measured part – NC Simulation
• Conclusions and Outlook
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Reverse Engineering Fundamentals Object Physical part/product Hardware Software Biological System
Process Measuring and Testing Deduction Backward chaining Inference Inverse Problem Solving Decomposition Collection Comparison
Concept Abstraction (higher level) Representation (new) Data Information Knowledge Categorization Ontology
(Rekoff 1985), (Chikofsky et al. 1990), (Otto et al. 1998), (Ullman 2010), (Wang 2010)
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Reverse Engineering in Mechanical Design Object Physical part Product
Process Digitizing Preprocessing Segmentation Reconstruction Recognition
Concept Point/Mesh Structure Feature-Based Parameters/Relations Design intent
Equal Radii
Same Orientation
Coaxial Parallel Axes Coaxial Coplanar
Coaxial
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Geometric Reverse Engineering Workflow Product Digitization
Shape reconstruction
Cloud of points -Size -Unstructured -Noisy -Outliers -Digitization holes
3D CAD Modelling
I
II
III
Preprocessing
Reconstruction
Characterization
Outliers Removal Noise Filtering Normal Estimation
Meshing Registration Segmentation
Filtering Recognition
Fitting Known model
Unknown model
Orthogonal distance (L2 or
norms)
L∞
Model parameters Geometric parameters Transformation parameters
step slot open pockets
Measurement Mechanical simulation Manufacturing simulation Topology optimization
Preprocessing • Noise and outliers • Registration and fusion Reconstruction • Point to Mesh to Surface • Topology guarantees Characterization/Evaluation • Estimate model parameters • Quantify Uncertainty
round holes
B-Rep/Solid Model Geometric Features Domain-oriented and knowledge features Parameters and constraints PMI data 6
Shape Engineering ‘‘Shape is all the geometrical information that remains when location, scale, and rotational effects are filtered out from an object.’’ (Kendall 1984) • • •
Develop computational structures to capture, model, analyze and control shape and underlying geometrical variability during the whole product lifecycle. Correlate the shape and variability information with other functional and structural information. Mutidisciplinary research at the interface of mechanical engineering, modern geometry, computer science, and statistics.
Shape Acquisition
Shape Representation
Shape Description
Shape Processing
Shape Perception
Statistical Shape Analysis Shape Mining
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Shape Engineering Framework for GRE Local Surface Type recognition
Type: Discrete Str. Exp.: Point/Mesh Scheme: Cell complex
• (S,C) curvature measures • Labeling
Manifolds Curvatures
111.417
Representation
Description
Shape Mining
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Boundary Identification • Edge Points • High curvature Points
Vertex Clustering Processing
• Initial Clustering • Cluster Refining
Connected Region Generation Classification Clustering
Segmentation Recognition
• Connected region labeling • Region Refining
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Curvature measures •
Shape index – a single value within [-1,1] to measure the local shape type of a surface
•
Curvedness – A positive value to specify the intensity of surface curvatures (sharp edges, high curvature points)
– Visual Indicator (colour map) 111.417
0
26.237
0
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Vertex Clustering Based on local surface types (1) Querying surface types (2) Evaluating the possibilities of the surface types 4 2 3
3 -2
4
2 2
2 2
(3) Adjusting
2
3 • Cluster distance - Non-planar clusters
-2
2
- Planar cluster
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Examples Discrete model
Initial clustering
Cluster refining
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Conneted Region Generation • Connected region labeling • Vertices in the connected region with the same surface type are labeled with the same region label • Generates the initial segmentation result
• Region refining • For each pair of adjacent regions, The similarity is evaluated considering three indicators: - Local surface type - Perimeter - Area • For each iteration, the region pair with maximal similarity are merged together
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Test cases
Tessellated
Tessellated
Tessellated
Art. Noise
Scanned
Scanned
#V: 7041
#V: 4209
#V: 20713
#V: 3330
#V: 5086
#V: 14315
#R: 11
#R: 9
#R: 126
#R: 38
#R: 49
#R: 57
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Application : Multiple-sensor measured part
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Feature Recognition from In-Process Model
step slot open pockets round holes
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Conclusions and Outlook •
New insights of Geometric Reverse Engineering from Shape Engineering perspective – Extension of classical GRE scope – Highlights of the potential of Shape Engineering
• The proposed methods have been successfully tested on engineering products with freeform geometries •
Limitations : – – – –
•
Sampling from rough surfaces Noise and sparsity of data Processing large flow of data Lack of external source of information and knowledge
Future work : – Robust clustering method from spatial data mining domain – Reverse engineering topology optimization outputs
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From reverse engineering to shape engineering in mechanical design
Thank you Questions? Comments?
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