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MultiScience - XXXIII. microCAD International Multidisciplinary Scientific Conference
University of Miskolc, 23-24 May, 2019, ISBN 978-963-358-177-3
WELDING POINT OPTIMIZATION
Máté Ternay
Junior researcher
Smart division, Bay Zoltán Nonprofit Ltd. for Applied Research, Miskolc
ABSTRACT
In the XXI century, the welding point optimization has a significant role in the
machine industry, especially in the automotive industry. Nowadays, the production
of a car is mainly done by robots. Robots weld the elements, assemble the parts and
perform all the tasks that make the manufacturing process easier and faster. By
making the bulk of the work robots perform an essential task of accuracy. Robots
must be programmed as accurately as possible to ensure that a car can be used safely
by people after it is completed.
INTRODUCTION
The established hypothesis is as follows: In the automotive industry, there are
also a lot of spot welding errors. During these errors, the position (coordinate) of the
spot welding may differ and may fall outside the tolerance limit. Finding and defining
a point-to-point fault position can take several hours for engineers and operators.
To reduce point welding errors, you need to optimize the manufacturing
process by following these steps:
Collecting input data in a database
Definition of the ICP matrix
NX Integration Weld Point finder application
Welding debugging and displaying application
DOI: 10.26649/musci.2019.030
1. Figure – Optimization process
COLLECTING INPUT DATA
Input data is mostly saved in Excel, Pdf and CAD formats. The primary goal
is to connect the robots to a network and store the input data in a database, replacing
the aforementioned files. The collection and storage of the data is done by a prepared
application.
In addition to the database, needs to be for a development environment where
we can manage to the data collected. This development environment is NX but can
be created in any CAD / CAM design program.
In NX 11.0 we created an internal module. This internal module can import all
the information stored in the database. With this all information about the component
and welding points from the NX program can be retrieved for engineers and
operators. I will discuss this in more detail in the NX Integrated Welding Point Search
Application.
DEFINITION OF THE ICP MATRIX
Utilizing references [1], [2], [3], [4], [5], [7] to draw conclusions and correlations
that determined this ICP matrix.
The relationship between engineering and robot coordinates was determined
by an ICP (Iterative closet point) matrix. The transformation required to match the
set of points in two different coordinate systems is determined by the ICP matrix.
The ICP (nearest iterative point) algorithm is a comparison of three-
dimensional geometric models according to the "de facto" standard, as we can
determine the initial estimated position. ICP uses k-d trees to speed up the search.
We have done a lot of research about this. This study describes a new search
procedure, exploiting the iterative behavior of cached k-d trees, the ICP algorithm.
This results in a significant acceleration of about 50%, as shown in the evaluation
with different data sets.
The algorithm calculates the registration in an iterative way. At each iteration
step, the algorithm selects the nearest points as the equations and calculates the
transformation, ie rotation and translation (R, t) to minimize the equation.
where Nm and Nd are the number of points in the M model set and the D
data set, and wji are the weights for a point match. The weights are assigned as
follows: wji = 1 if we are the closest point to dj, wji = 0 anyway. Eq. (1) can be
reduced to:
since the matching matrix can be represented by a vector v containing the
point pairs, i.e. v = ((d1, mf (d1)), (d2, mf (d2)),…, (dNd, mf (dNd)), and f (x) the
search function returns to the nearest point. The assumption is that the last iteration
step is the matching of the points, ie the point pair is correct.
In each ICP iteration, the transformation can be calculated by any of the four
methods:
1. SVD based on the Arun et al method
2. Horn et al.
3. an algorithm using the Horn et al orthonormal matrix
4. Calculations based on dual quads of Walker et al.
These algorithms show similar performance and stability with regard to noisy
data. In recent years, we have chosen the high-speed ICP version of the various
strategies and suggestions for reducing the number of points, ie selecting, fitting and
weighting points, in the knowledge of many versions of the ICP algorithm. This
ICP version with point-to-plane error size and projection-based method helps you
choose the point match. In addition, we have concluded that the other stages of the
ICP process have little effect on the convergence level, so we choose the simplest
ones, namely random sampling, constant weighting, and rejection of point pairs
depending on their distance threshold.
An error occurs in the ICP algorithms if we choose too many points from an
unspecified area. In this case, the algorithm slowly converges, finds a bad position,
or diverges. The purpose of the standard spatial sampling presented by
Rusinkiewicz and Levoy is to limit the input network generated from the point
cloud. The algorithm tries to ensure that the normal points of the selected points
spread evenly across the sphere of directions. The covariance sampled by Levoy is
expanded by the normal space approach. It is determined whether a pair in the net
becomes unstable in the ICP algorithms by estimating the covariance matrix from
the input rarely sampled.
To calculate the nearest points, k-d trees are the general search form. Simon
et al achieved high speed from the nearest point caching, which reduced the number
of required k-d searches. The recommended cached k-d tree search requires O ((I +
logNm) Nd) at best. This performance can be achieved if the constant time for
retrieval results in Nd logNm time to create the tree, and I · Nd to search if no
retrieval is required. Obviously, the retrieval time depends on the calculated ICP
transformation (R, t). For small transformations, time is almost constant.
NX INTEGRATED WELD POINT FINDER APPLICATION
The NX Diagnostic Data Collection Software is a NX 11.0 Block Styler
application. It is designed to save NX-designed components in the ".stl" format and
the welding points in ".txt" format so that we can use it later in the NX welding
debugging application. In addition to exporting, we can also add new welding
points, and we get all the information about existing welding points: which robots
belong to, exact coordinates, welding punch ID.
2. Figure – Integrated application
Create weld point
With the help of the application, we can create a welding point. Here we can
configure weld ID for which robot and which part/element to belong.
3. Figure – Point create
Points Search
With point search, we get all the information about welding points on the
model. The points found here are compared to the points in the Mongo database by
distance calculation and are used to assign their weld IDs to them.
Here are some of the most important information about the points:
coordinates (X,Y,Z)
weld ID
Robot name
Schedule
For a selected point, the camera view brings the current point to the center of
the screen and focuses on it.
Export
There are four ways to import:
Mongo database: Here you can export the new welded points with the related
information
STL Export: We export the model in “.stl” format, which we load with the
weld point debugger and display application later
Excel export: Information about the welding points is exported to an excel
BOM export: you can export welding point maps
4. Figure - Exporting
WELDING DEBUGGING AND DISPLAYING APPLICATION
The application of the welding fault point application is designed to allow
engineers and operators to see faulty welding points on the workstations. The
application reads the exported models and the related information. It is worth noting
here that this information I s not extracted from the database, but obtained from NX.
Components exported in .stl format are read and selected from a specific
folder. It then loads all the information that belongs to the welding points on the
model. Then, after selecting a welding point, it will be displayed on the display and
we will know exactly where the spot welding error is and which robot weld it and
what is the error code.
5. Figure – Display error welding point
When you type ".stl" files, the list is first updated, followed by the model, ie the
loaded image. We then select one of the ".stl" files that we import from the folder we
want to work with. The selected item is then added to the "view port". For the selected
list item, you can access the camera for that point on the model.
The [7] reference was used for 3D rendering, but I have referred to this simpler
and more obvious solution by referring to visual difficulties.
CONCLUSION
With these applications, we will have to opportunity to receive as many and as
accurate information as possible from each welding point, keep it in consistent
manner and reduce point defects to find them as quickly as possible.
REFERNCES
[1] Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., and Stuetzle, W., ” A
Review of Point Cloud Registration Algorithms for Mobile Robotics”,
Foundations and Trends in Robotics Vol. X, No. X (2014)
[2] Hanan Samet, An Overview of Quadtrees, Octrees and Related
Hieachical Data Scructures, NATO ASI Series, Vol. F40, pp. 51-68, DOI:
10.1007/978-3-642-83539-1_2
[3] Blinn, J. F. (1982). A Generalization of Algebraic Surface Drawing.
ACM Transactions on Graphics, 235-256, Volume 1, Issue 3., DOI:
10.1145/357306.357310
[4] Kohonen, T. "Self-organized Formation of Topologically Correct
Feature Maps", Biological Cybernetics, Vol. 434.. pp. 59-69. 1982,
DOI: 10.1007/BF00337288
[5] Binary space partitioning,
https://en.wikipedia.org/wiki/Binary_space_partitioning
[6] Schmidt, R., & Simari, P. (2012). Consensus meshing. Computers &
Graphics, 488-497, Volume 36, Issue 5. doi:10.1016/j.cag.2012.03.030
[7] R. Beleznai, G. Dobos, Sz. Szávai, Z. Bézi, Development of
Training Platform for Power Plant Application Using Virtual
Reality Tools pp. 442- 447.Proceedings of TEAM 2015: 7th
International Scientific and Expert Conference of the International
TEAM Society Beograd, Szerbia: University of Belgrade, Faculty of
Mechanical Engineering, (2015) p. 650, DOI: 10.26649/musci.2016.098