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Graph-based feature recognition and suppression on the solid models
A. Malyshev1, S. Slyadnev1, V. Turlapov1
al.s.malyshev@rambler.ru|sergey.slyadnev@gmail.com|vadim.turlapov@itmm.unn.ru
1 Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
We consider one of the main problems in the field of CAD / CAM, which is feature recognition for CAD models represented in
boundary representation form. We take into account volumetric features only. We use the recognition results for CAD model
simplification. Our recognition procedures are based on detection of specific patterns in adjacency graphs constructed for the input
CAD parts. Recognition procedures for bosses, holes, pockets, and for arbitrary cavities are proposed. Simplification is conducted by
suppression of the detected features using a dedicated topological operator.
Keywords: feature recognition, feature suppression, boundary representation, CAD model simplification, graph-based methods.
1. Introduction
The problem of feature recognition and suppression from the
so-called "dumb" model is one of the most frequently
encountered problems in the CAD field. “Dumb” means that
digital input model does not contain a history of construction:
only pure geometric and topological data are present.
The vast majority of CAD-systems implement history-based
modeling. However, a way of storing this information is
vendor-specific and is not readily available for integration with
third-party software. A “dumb” model may emerge in various
scenarios like a lossy translation from one format to another or
reverse engineering. Although the international STEP standard
(ISO 10303) supports features as a part of AP224 protocol,
usually, CAD-systems do not fully use this possibility.
Features are widely covered by academic literature. However, a
“feature” term may have a different meaning in different
engineering domains. In our paper, the “feature” term is used in
a way proposed by Shah and Mantyla [9]. The description,
properties, and semantics of the most commonly used features
can be found in their fundamental book.
Feature-based modeling is close in nature to the constructive
solid geometry (CSG).The main difference between the two
techniques is how a building tree is organized. In the feature-
based modeling the building tree consists of various features,
while in the CSG, it consists of Boolean operators and
primitives. The concept of the building tree can be found in the
article [1], where the problem of reconstructing three-
dimensional models from two-dimensional drawings is
considered.
Feature-based modeling is a more advanced design technique
compared to other design methods. In feature-based modeling
paradigm, an engineer works in terms of meaningful groups of
geometric elements (features), and not in terms of vertices,
edges, and faces. Feature recognition is opposed to design-by-
features paradigm in sense that it does not rely on a construction
tree. Feature recognition allows performing the following
conversions:
● Defeaturing (feature suppression) on a model.
● Creation of feature model associated with a “dumb”
model.
The defeatured model can be used in the following contexts:
● Protection of the intellectual property when
exchanging data.
● Preparation of a model to engineering analysis.
● Creating levels of details for efficient visualization of
massive data (e.g. large mechanical assemblies).
● Preparation of a model for visualization on mobile
devices.
2. Related work
A general overview of recognition methods is given by Han,
Pratt, and Regli in [3]. Joshi and Chang proposed graph-based
methods of recognition in [5]. The main idea besides the graph-
based methods is to analyze connectivity between the boundary
elements of a model, these methods convert the input model to
one or several graphs, assign some attributes to the vertices and
edges of these graphs and after that perform recognition
procedure.
Joshi and Chang propose to use attributed adjacency graph
(AAG). Its structure is presented below:
● Vertices - represent faces of the input model.
● Edges - represent adjacency relationship between
faces.
● Attributes - represent convexity of dihedral angles.
Gao and Shah [2] introduce new graphs into recognition
process. They use manufacturing face adjacency graph, minimal
condition sub-graph, concave adjacency graph, and other
medium structures. The main goal of their work is to recognize
interacting features which is the key problem in feature
recognition field.
Verma and Rajotia in [11] propose to use a matrix
representation of a graph instead of isomorphism in the
recognition process. The so-called feature vector is calculated
for each feature of interest. The feature vector is then used in
recognition heuristic. This transformation decreases recognition
complexity from 𝑜(𝑛𝑘) to𝑜(𝑛3).
Lockett and Guenov in [6] propose to build AAG on the mid-
surface representation of a geometric model. The main problem
of their approach is to adapt AAG concept to the mid-surface
representation. This technique significantly reduces a number of
nodes and vertices in the graph and improves recognition speed.
Hayasi and Asiabanpour [4] consider the platform-dependent
approach of conversion design features into manufacturing
features.
Nasr and others [7] unite different recognition procedures into
the sequential workflow to construct building tree for a model.
The major drawback of graph-based methods is the
impossibility of guaranteed separation of interacting features.
This problem is partially solved by Gao and Shah, but it
remains actual.
Usually, the result of recognition is presented as a set of
faces, which is an advantage, since it allows using this method
for recognition of volumetric and surface features. The concept
of volumetric and surface features can be found in [10].
The method, which is proposed in this paper, is based on the
original attributed adjacency graph. Therefore it can be
implemented in any CAD-system since no specific functionality
is required. The recognition procedures presented below are
based on the rule-based approach, initially invented in [10].
This approach reduces complexity of the problem and allows
flexible tuning of the recognition rules.
3. Recognition procedures
The development of efficient feature recognition algorithm is
hardly possible without constraining the input. In this work, the
following assumptions are made:
● Input model consists of one or more valid solids.
This restriction enables convexity analysis for
dihedral angles. Such analysis relies on proper
orientations of faces in a solid.
Face maximization was applied to the input model.
The definition of the maximization procedure can be
found in [8].
All features are placed in interior of faces. It is a
principal constraint which is actively used in the
recognition and suppression procedures.
The host geometry of faces is canonical. This
limitation is used in the first three procedures and
does not apply to the last method.
The common stage which is shared by all recognition
procedures proposed in our paper is detection of a base face. In
general, a base face is a face where features can exist. In our
study, the base faces are the faces with inner wires (Figure 1).
Figure 1: Cube model and base face (yellow color).
Here and later, it is assumed that attributed adjacency graph
is built. Particular features can be recognized using additional
constraints, which are proposed for each feature type.
3.1 Holes recognition
Hole is cavity feature, so the dihedral angle between base
face and feature faces should be convex. Feature faces which
are adjacent to the base face should have cylindrical host
geometry. After that, the list of feature faces can be extended by
adjacent faces with non-cylindrical host geometry. For instance,
it is possible to allow transition from cylindrical geometry to
the planar to handle nested holes case. For clarity, the simplest
case is considered – the stopping criterion for the recognition
procedure is another base face. The main parameter determining
the hole is radius. It seems reasonable to filter out all holes with
radius bigger than some given value. Modified recognition
procedures can be proposed using the requirements of a
particular subject area.
The figure below shows the detected holes on the ANC101
test model with a radius not exceeding a given threshold (Figure
2):
Figure 2: ANC101 model and recognized holes (yellow
color).
3.2 Pockets recognition
Pockets are another machining features, the main difference
between holes and pockets is that the pocket is always blind.
The dihedral angles on the edges of the inner contour must also
be convex. The underlying geometry on the feature faces which
are adjacent to the base face can have any type. The feature is
constructed by adding adjacent faces. The recognition process
stops when a plane face without internal contours is detected,
parallel to the base face. The pocket can be defined by two
parameters – the depth and the contour that forms it. For
example, the filtering can be carried out using the area of the
figure bounded by the contour of the pocket. The following
illustration shows the result of this recognition rule (Figure 3):
Figure 3: ANC101 model and recognized pockets (yellow
color).
3.3 Bosses recognition
A boss is a protrusion feature in a work piece. Therefore, the
dihedral angles between the boss faces and the base face are
concave. The faces constituting a boss feature yield an isolated
connected component in the adjacency graph with eliminated
base face (as in the case of a pocket). New faces are added to
the feature similarly to the case with pockets. Filtering bosses
by size can be conducted using the diagonals of their
corresponding bounding boxes.
Recognition speed can be improved by limiting the search depth
in the adjacency graph. For instance, the search depth equal to
two can be used to find the simplest bosses (the search begins
from the base face). Figure 4 shows the boss feature found in
the MBB Gehause Rohteil model.
Figure 4: Modified MBB Gehause rohteil model and
recognized boss (yellow color).
3.4 General holes recognition
All the procedures considered earlier are designed to operate on
canonical geometry. In this section, a more general method is
proposed. The features detected by the following procedure do
not correspond to simple machining operations such as drilling
or milling. We call this new feature type a “general hole” since
it represents an arbitrary cavity. Like in the case with a pocket
or a hole feature, the dihedral angles for general holes are
convex. More faces are added to the feature according to the
procedure already described. There are two stopping criteria:
● For through general holes, the feature faces are
located between two base faces.
● For blind general holes, the feature faces yield an
isolated connectivity component in the adjacency
graph.
Filtering by size can be based on diagonal of a bounding box
calculated for the detected feature faces.
The described recognition procedure covers both the procedure
for holes and the procedure for pockets. Figure 5 and Figure 6
show the recognition result for ANC101 model.
Figure 5: ANC101 model and recognized general holes.
Figure 6: Gear model and recognized general holes (yellow
color).
4. Feature suppression
When all the features that should be suppressed are recognized,
they can be suppressed to simplify a model. This process
involves two main steps:
1. Removing of the feature faces from B-Rep
representation.
2. Updating all affected faces to get valid B-Rep model
after suppression.
In general, the update procedure is very difficult since it
requires both topology and geometry modification. Restrictions
introduced earlier significantly reduce the complexity of this
step. It is necessary to delete only inner wires on the base faces,
and no additional actions are needed to bring geometry into
valid state. It is the reason to detect isolated non-interacting
features which represented as isolated connectivity component
on the graph.
5. Conclusion and further work
Feature recognition and suppression procedures were proposed
for holes, pockets, bosses, and general holes. These methods are
implemented using OpenCascade geometric modeling kernel.
The algorithms were tested on a wide set of cases, including the
well-known benchmarking models (MBB Gehause Rohteil,
ANC101) and real industrial parts.
Figure 7 and Figure 8 demonstrate the defeaturing process
based on the proposed recognition procedures.
Figure 7: Feature suppression on the ANC101 model.
Figure 8: Feature suppression demonstration, from left to
right, from top to bottom.
In future, we plan to improve the recognition methods in the
following aspects:
● Extension of the supported feature types.
● Recognition of non-isolated features.
● Migration from explicit-based rules to the
parameterized rules.
● Automatic generation of the building tree.
6. References
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About authors
Vadim Turlapov (vadim.turlapov@itmm.unn.ru): professor
of Computer Science, Lobachevsky Nizhny Novgorod
University.
Alexander Malyshev (al.s.malyshev@rambler.ru): PhD
student, Lobachevsky Nizhny Novgorod University.
Sergey Slyadnev (sergey.slyadnev@gmail.com): PhD
student, Lobachevsky Nizhny Novgorod University.
