Int. Journal of Applied Sciences and Engineering Research, Vol. 4, Issue 4, 2015
© 2015 by the authors – Licensee IJASER- Under Creative Commons License 3.0
Research article
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ISSN 2277 – 9442
Practical aspects for development of add-in solid works
applications on example of optimization problems
Mikhail V. Chugunov 1, Akexander V. Schekin 2
1- Asst. Professor, Department of Mechanical Engineering Ogarev Mordovia State University, Russia
2- Engineer, Department of Mechanical Engineering Ogarev Mordovia State University, Russia
DOI: 10.6088.ijaser.04050
Abstract: This paper proposes some practical approaches that are intended to provide Add-In
SolidWorks applications development. Problem includes two parts: design tree analysis for an existing
model, including design history and parametric information; modification of the SolidWorks model.
The both parts are integrated in one system, based on SolidWorks API, COM-interface and special user
interface, built into SolidWorks windows.
Keywords: Add-In SolidWorks Application, Parametric Model, Meta-model, User Interface,
COM-Interface, API, Optimization Problem
1. Introduction
Add-In Application has a purpose to improve and to extend a basic functionality of modern software.
CAD/CAE-systems are knowledge-intensive, high-technology software that has powerful API. It is best
opportunity to focus on creativity (“Do not reinvent a wheel”), using well-known CAD/CAE-systems as the
template. SolidWorks is one of the powerful CAD/CAE-software (from our point of view). So, those
problems that are not included into the basic SolidWorks functionality are looking as the most interesting
(Jun Wang, 2011). We offer one problem for example used in this context.
An optimization problem for which objective and constraint functions are approximated, using step by step
method with mid-range approximations (Toropov V.V. and Polynkin A.A., 1992, 2012) and is considered.
This optimization functions are represented as analytical expressions, formulated by the least-squares
method for small sub-regions of the design space (metamodel functions). The FEM-experiments (or another
“accurate” but computationally expansive method) are carried out at some points at the design sub-region
for the parameters of the analytical expressions determining. Then the partial optimization problem is
solving within the sub-region, using simplified objective and constraints metamodel functions. The choice
of the next search sub-region is dependent on previously obtained optimal results for the sub-region. So, the
optimization procedure is sequential solving of the partial less computationally expensive problems, moving
the sub-regions toward optimum.
Another approach is the fully-stressed design (FSD), it is often not optimal (Rozvany G. I. N., 2009), but at
the same time, it is a popular alternative method to perform structural redesign that does not use the
expensive optimization algorithms. A fully stressed state is reached when all its substructures (or small
elements, FEs, for example) are utilized to their full strength capacity. In any case it is necessary to solve
some common problems, concerning Add-In software development aspects.
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*Corresponding author (e-mail: m.v.chugunov@mail.ru)
Received on April 2014; Published on August, 2015
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Practical aspects for development of Add-In SolidWorks applications on example of optimization problems
2. SolidWorks design tree analysis and modification of the model
The analysis of the SolidWorks design tree is carried out in the enclosed cycles as it are depicted in Figure
1. As a result, all information of the model is provided in some intermediate format, and it is ready for
further using regarding for definition of the optimizing problem, and regarding for solving of the
optimization task.
Figure 1: Solid Works construction tree analysis
In Figure 1 also some fragments of the C++ code (MS Visual Studio C++ and COM) for extraction of the
model data are depicted. So, in particular, shell and equation managers, GetSpecificFeature2 and
QueryInterface functions are used for extraction of the following parameters: dimensions, equations, and
also thicknesses and physic-mechanical properties of the shells.
Mikhail V. Chugunov1, Akexander V. Schekin
Int. Journal of Applied Sciences and Engineering Research, Vol. 4, No. 4, 2015
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Practical aspects for development of Add-In SolidWorks applications on example of optimization problems
As the algorithm of discretely full-stressed design is applied, as a rule, to thin-walled parts, all shells are
being selected from design tree and tension sensors are being placed on them automatically (Chugunov M.
V. et al, 2014)
3. User interface
The user interface is released in two SolidWorks windows: Property Manager Page and Task Pane. The
formulation of the optimization problem is depicted in Figure 1, Figure 2 and Figure 3: to maximize a
volume (Figure 2) of the ellipsoid, using its semi-axises as the control parameters (variables) (Figure 3) so,
that the ellipsoid’s surface area isn’t changed (Figure 4).
Figure 2: Objective function defining
Figure 3: Control variables defining
Figure 4: Constraint functions defining
So, “Objective function” property page includes Bitmap Picture, Combo Boxes, and List Box. In the case
selection “Face”, “Edge” or “Vertex”, the user can define the entity by mouse clicking in the graphics area.
List Box, placed on Control Variables Page lists all model dimensions, global variables and the equations.
User selects the necessary parameter, pressing button ∇ and removing it to second List Box. Geometry
Constraints are defined in Edit Boxes (on the right).
Mikhail V. Chugunov1, Akexander V. Schekin
Int. Journal of Applied Sciences and Engineering Research, Vol. 4, No. 4, 2015
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Practical aspects for development of Add-In SolidWorks applications on example of optimization problems
Constraints Page is created similar to Objective Function Page and has Bitmap Picture, Spin Control,
Button List Box, two Combo Boxes and Edit Box. The most effective tool for dialogue realization is the
library of the classes MFC (Microsoft Foundation Classes) (Shepherd G.and Kruglinski D., 2003). For the
purpose of the dialogue realization in Property Page windows we developed a certain analog of MFC,
which structure is shown on Figure 5.
Figure 5: MFC analog structure
We have to define our own Page-classes, the message maps and the macros. The macro, declaring the
array, that will hold our message map entries and some pointers, used to find the message map of the base
class. The root of the class hierarchy is the base SolidWorks interface IpropertyManagerPage2Handler3.
We also use the DDX mechanism and overrided Do Data Exchange function. The following example
(Figure 6) shows the Do Data Exchange override function, in which five DDX functions are called for the
Constraints Property Page. This DDX functions shown are for a string, list-string, number-box and
edit-text box controls, respectively. In the Figure 7 the dialog realization in the TaskPane window is
depicted. Global variables are used: pSW is the smart SolidWorks (ISldWorks) interface pointer and wnd is
the Task Pane view window pointer. Two classes, derived from CDialog, using recourses
IDD_DIALOGTASKPANE and IDD_ SIMULWINDOWS respectively.
void CConstraintsPage::DoDataExchange(CDataExchangeSW* pDX)
{
DDX_CBStringSW(pDX,1001,m_ConstraintName);
DDX_CBStringSW(pDX,1005,m_ConstraintType);
DDX_CBStringSW(pDX,2005,m_EntityParameter);
DDX_LBStringSW(pDX,2003,m_EntityType);
DDX_NumboxSW(pDX,5001,m_NumberConstrdouble);
DDX_TextSW(pDX,1006,m_ConstrValue);
}
Figure 1: Do data exchange function
Mikhail V. Chugunov1, Akexander V. Schekin
Int. Journal of Applied Sciences and Engineering Research, Vol. 4, No. 4, 2015
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Practical aspects for development of Add-In SolidWorks applications on example of optimization problems
Figure 2: Task Pane dialog realization
The first is the dialog panel, having simple controls for the problem option defining and for property
manager page activation (top of the Task Pane) (Figure 8). The second is the window, created for the
plotting of the search process of the optimum (bottom of the Task Pane). Figure8 represents the solving of
the optimization problem in general; this problem is formulated by us for the ellipsoid’s volume
maximization. Here the curves are the approximated optimization functions (volume and surface area) on
2D section for the optimization space, blue biggest rectangle is the sub-region, corresponding for the last
search stage approximation, red small rectangle in the center of the sub-region is the optimum, green
rectangles inside are the points, used for the least-squares method realization. In these points the
optimization functions are defined exactly by SolidWorks. The search sub-region is drugged and dropped by
the user moving the corner points by mouse (small rectangles on sub-region vertexes) into direction of the
movement to the optimum, defined at the previous stage, and the black polyline is the path of the search. The
optimal ellipsoid is close to the sphere in this test problem (Chugunov M. V., Nebaykina J.A., 2011).
Figure 8: Optimization problem example
4. References
1. Chugunov M. V., Nebaykina J.A. 2011. Program module for solution of optimum design problem
in SolidWorks software, based on API. Science and Education. BMSTU, 9,
http://technomag.bmstu.ru/doc/206217.html (in Russia)
2. Chugunov M. V., Osyka V. V. Kudayev S. P., Kuzmichyov N. D., Klyomin V. V.,2014. Analysis
and design of force rolling stock elements. Science and Education. BMSTU. 9, 216-226. (in
Russia)
Mikhail V. Chugunov1, Akexander V. Schekin
Int. Journal of Applied Sciences and Engineering Research, Vol. 4, No. 4, 2015
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Practical aspects for development of Add-In SolidWorks applications on example of optimization problems
3. Jun Wang, Xinyuan Huang, and Shengju Sang. 2011. Using Multiple Methods for Rapid
Programming in SolidWorks. Applied Mechanics and Materials, 48-49, 1337-134.
4. Polynkin A.A. and Toropov V.V. 2012. Mid-range metamodel assembly building based on linear
regression for large scale optimization problems. Structural and Multidisciplinary Optimization.
45(4), 515-527.
5. Rozvany G. I. N., 2009. A critical review of established methods of structural topology
optimization. Struct Multidisc Optim. Springer-Verlag. 37(3), 217-237.
6. Shepherd G., Kruglinski D. 2003. Programming with Microsoft Visual C++. NET. Microsoft
Press.
7. Toropov V.V. 1992. Multipoint approximation method in optimization problems with expensive
function values /V.V. Toropov// In: Sydow, A. (ed.) Computational Systems Analysis, Elsevier,
207-212.
Mikhail V. Chugunov1, Akexander V. Schekin
Int. Journal of Applied Sciences and Engineering Research, Vol. 4, No. 4, 2015
499