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