FEATURE SELECTION FOR IDENTIFICATION OF SPOT

WELDING PROCESSES

Eija Haapalainen, Perttu Laurinen, Heli Junno, Lauri Tuovinen and Juha R

oning

Intelligent Systems Group,Department of Electrical and Information Engineering

P.O. Box 4500, FIN-90014 University of Oulu, Finland

Keywords:

Feature selection, process identiﬁcation, k-nearest neighbour classiﬁer, resistance spot welding.

Abstract:

Process identiﬁcation in the ﬁeld of resistance spot welding can be used to improve welding quality and to

speed up the set-up of a new welding process. Previously, good classiﬁcation results of welding processes have

been obtained using a feature set consisting of 54 features extracted from current and voltage signals recorded

during welding. In this study, the usability of the individual features is evaluated and various feature selection

methods are tested to ﬁnd an optimal feature subset to be used in classiﬁcation. Ways are sought to further

improve classiﬁcation accuracy by discarding features containing less classiﬁcation-relevant information. The

use of a small feature set is proﬁtable in that it facilitates both feature extraction and classiﬁcation. It is

discovered that the classiﬁcation of welding processes can be performed using a substantially reduced feature

set. In addition, careful selection of the features used also improves classiﬁcation accuracy. In conclusion,

selection of the feature subset to be used in classiﬁcation notably improves the performance of the spot welding

process identiﬁcation system.

1 INTRODUCTION

Resistance spot welding is one of the most important

methods for joining metal objects. It is in widespread

use in, for example, the automotive and electrical in-

dustries, where more than 100 million spot welding

joints are produced daily in European vehicle indus-

try only (TWI, 2004).

In resistance spot welding, two or more metal

sheets are joined together by passing an electrical cur-

rent through them. The current is conducted through

two electrodes pressed against the metal surfaces to

hold the parts to be welded tightly together. The

heat produced by the ﬂowing current melts the met-

als, and a welding spot is formed. The amounts of

current, pressure and time are all carefully controlled

and matched to the type and thickness of the material.

After cooling, the quality of the welding joint can

be estimated by measuring its diameter. In general,

the bigger the diameter is, the ﬁrmer is the welding

joint. Some other factors, such as faults and embrit-

tlement in the welding joint, also affect its strength.

The most reliable and commonly used method to ver-

ify the quality of a welding joint is to tear the welded

parts apart and to measure the spot diameter. How-

ever, the welding joint is thereby destroyed. Some

nondestructive methods for estimating the spot diam-

eter also exist, but so far, no real-time, nondestructive

method for online use on production lines has been

developed. The two most common methods of nonde-

structive testing are radiographic and ultrasonic weld

inspection (Anderson, 2001). These methods can also

be used to detect discontinuities within the internal

structure of a weld. Another example of nondestruc-

tive quality control methods of spot welding is the

method based on primary circuit dynamic resistance

monitoring by (Cho and Rhee, 2002).

Different combinations of welding machines used

and materials welded constitute distinctive welding

processes. In other words, welding processes could

also be called production batches. In this study, the

properties of welding experiments that distinguish

different processes are the type of welding machine

used, the materials welded, the thicknesses of the

materials and the welding time. However, changes

in current, electrode force and electrode wear are

thought to be internal changes of processes. Recogni-

tion of the most similar process from a pool of previ-

ously stored processes is called process identiﬁcation.

This study is a follow-up on a previous article by

the authors, in which different classiﬁcation methods

were evaluated for use in the identiﬁcation of spot

welding processes (Haapalainen et al., 2005). That

study showed that welding processes can be reliably

Haapalainen E., Laurinen P., Junno H., Tuovinen L. and Röning J. (2006).

FEATURE SELECTION FOR IDENTIFICATION OF SPOT WELDING PROCESSES.

In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, pages 40-46

DOI: 10.5220/0001209100400046

 SciTePress

identiﬁed by extracting certain statistical and geomet-

rical features of current and voltage signals measured

during welding and performing classiﬁcation based

on these features. The k-nearest neighbour classiﬁer

(kNN) with the parameter value k = 3 was found to

be the most suitable method for the classiﬁcation of

spot welding processes.

Process identiﬁcation is needed to be able to utilise

information collected from previously run processes

to produce new welding spots of good quality.

The characteristics of a sample from a new weld-

ing process can be compared to information col-

lected from previously run processes to ﬁnd a similar

process. After that, the process parameters of the pre-

vious process already proven to lead to high-quality

welding joints can be applied to the new process.

With this approach, good welding results are achieved

right from the beginning, and the time needed for the

set-up of a new process can be signiﬁcantly reduced.

In addition, if a similar process is found, the quality

control methods that proved viable for that process

can also be used for the new process.

In the previous study by the authors, classiﬁca-

tion of welding processes was performed using alto-

gether 54 distinctive features extracted from the signal

curves recorded during welding. The aim of this study

was to reduce the dimension of the feature space by

eliminating features with less classiﬁcation-relevant

information and to consider the usefulness of the in-

dividual features. This was expected to cut down the

time needed for classiﬁcation and, most importantly,

to further improve the classiﬁcation accuracy. Various

feature selection methods were tested to ﬁnd the min-

imal feature set yielding good classiﬁcation results.

Previously, feature selection in the ﬁeld of spot

welding has been studied by (Stoppiglia et al., 2003).

In that study, however, only one process was consid-

ered at a time, and the study concentrated exclusively

on nondestructive estimation of the diameter of the

welding spot. The existing feature selection methods

have been extensively reviewed in the studies (Dash

and Liu, 1997) and (Kudo and Sklansky, 2000). Es-

pecially, in (Kudo and Sklansky, 2000) and (Jain and

Zongker, 1997), the Sequental Floating Feature Se-

lection methods used in this study have been shown

effective and suitable for feature selection problems

with the dimension of the feature data of the same

magnitude as the data used in this study.

2 THE DATA

The data used in this study were supplied by two

welding equipment manufacturers. There were alto-

gether 20 processes, of which 11 had been welded

at Harms+Wende GmbH & Co.KG and 9 at Stanz-

biegetechnik. A total of 3879 welding experiments

were covered. The experiments were done by weld-

ing two metal objects together using a resistance spot

welding machine. Each of the observations contained

measurements of current and voltage signals recorded

during welding.

The raw signal curves contained plenty of oscilla-

tory motion and a pre-heating section, and they were

therefore pre-processed. The pre-heating parts of the

curves were cut off, so that all that remained was

the signal curves recorded during the actual welding

phase. In addition, the curves were smoothened using

the Reinsch algorithm (Reinsch, 1971). An example

of a signal curve before and after pre-processing is

shown in Figs. 1 a) and b).

3 THE FEATURES

Altogether 54 geometrical and statistical features

were extracted from the two signal curves relating to

a single welding experiment. The geometrical fea-

tures were chosen to locate the transition points of the

curves as precisely as possible. The statistical features

included the median of the signal and the arithmetic

means of the signal values calculated on four intervals

based on the transition points. In addition, the signal

curve was divided into ten intervals of equal length,

and the means of the signal values within these inter-

vals were used as features. There were altogether 12

geometrical and 15 statistical features extracted from

both signal curves. The features are demonstrated in

Figs. 2 a) and b).

In practice, it often happens that some of the geo-

metrical features overlap, and that the overlapping

features vary from one curve to another. However,

this can also be regarded as a characteristic of the

curve. In Fig. 2 a), all the geometrical features are

demonstrated on an artiﬁcial curve simulating the real

data. On this curve, the features do not overlap, but

the curve is otherwise notably similar to genuine sig-

nal curves. Figure 2 b) shows an example of the fea-

tures calculated on a real signal curve.

The ten means of a signal curve have earlier been

used as features in the articles (Junno et al., 2004a),

(Junno et al., 2004b), (Junno et al., 2005) and (Haa-

palainen et al., 2005). It has been discovered that they

present the main characteristics and differences of the

curves very well and are therefore suitable to be used

in the quality control and identiﬁcation of welding

processes.

FEATURE SELECTION FOR IDENTIFICATION OF SPOT WELDING PROCESSES

Figure 1: a) A raw signal curve. The pre-heating section is outlined with a rectangle. b) The same curve after pre-processing.

a) b)

Figure 2: a) The geometrical features on an artiﬁcial voltage curve. The line segments A-D below the curve demonstrate the

intervals based on the transition points on which means were calculated. b) An example of how the geometrical features often

partially overlap in practice. On this voltage curve, the features named ’peak’ and ’max’ overlap.

4 FEATURE SELECTION

Of the classiﬁcation methods evaluated in the previ-

ous article by the authors (Haapalainen et al., 2005),

the k-nearest neighbour classiﬁer proved to be opti-

mal for the identiﬁcation of spot welding processes.

The parameter value k = 3 was selected based on the

comparative study. The classiﬁcation was performed

using alternatively either all the 54 features extracted

from the curves or only the ten mean values calcu-

lated on both signal curves (altogether 20 features).

The best results were obtained using the ten means.

In that case, a classiﬁcation accuracy of 98.53 per-

cent was obtained, while the classiﬁcation based on

all the 54 features yielded notably inferior results with

a classiﬁcation accuracy of only 84.13 percent.

Although good classiﬁcation results were obtained

using the ten means as features, there was still an in-

terest to study the usability of the other features. Also,

the amount of classiﬁcation-related information car-

ried by each of the ten means was unknown. There-

fore, various feature selection methods were applied

to the entire feature set to ﬁnd the optimal feature sub-

set to be used in classiﬁcation. It was studied whether

classiﬁcation accuracy could be further improved by

discarding redundant features. Since dimension re-

duction of the feature set also reduces the computa-

tional time required for classiﬁcation, a minimal sub-

set was searched for.

Five different feature selection methods were

tested: Sequential Forward Selection (SFS), Se-

quential Backward Selection (SBS), Sequential For-

ward Floating Selection (SFFS), Sequential Back-

ward Floating Selection (SBFS) and n Best Features

Selection.

SFS is a simple bottom-up search procedure in

which one feature at a time is added to the current fea-

ture set. At each stage, the feature to be included is

selected from the set of remaining available features,

so that the new extended feature set yields a maxi-

mum value of the criterion function used (Devijver

and Kittler, 1982). The SBS method is the top-down

counterpart of the SFS algorithm.

The Floating Forward and Backward Feature Se-

lection methods, SFFS and SBFS, introduced in

(Pudil et al., 1994), are based on the plus l-take away

r method (Stearns, 1976), in which the feature set

is alternately enlarged by l features using the SFS

method and reduced by discarding r features applying

the SBS algorithm. In the Floating Feature Selection

methods, however, the number of forward and back-

ward steps is dynamically controlled instead of being

ﬁxed in advance. The conditional inclusion and ex-

clusion of features is controlled by the value of the

criterion function. In the bottom-up algorithm, SFFS,

after each forward step, a number of backward steps

ICINCO 2006 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION

are applied as long as the resulting subsets yield bet-

ter values of the criterion function than the previously

evaluated ones of the same dimension. In the top-

down counterpart, SBFS, an exclusion of a feature

is followed by a series of successive conditional in-

clusions if an improvement to the previous sets can

be made. The feature to be included into the current

feature set or excluded from it is always the one that

improves the set most or degrades the value of the cri-

terion function least (Pudil et al., 1994).

The n Best Features Selection method simply

means selection of the n individually best features in

the sense of maximizing the criterion function. It is

the simplest alternative for feature subset selection,

but also the most unreliable since the features may

correlate with each other. Therefore, it was only used

for comparison in this study.

The best possible way to design the process identi-

ﬁcation system would have been to select the feature

set and the classiﬁcation method used simultaneously.

However, because the kNN classiﬁer had previously

been found suitable for the process identiﬁcation task

(Haapalainen et al., 2005), the effectiveness of the

different feature subsets produced by the feature se-

lection methods were evaluated using the classiﬁca-

tion accuracy of the 3N N classiﬁer as the criterion

function. In addition, the k-nearest neighbour method

has been used to measure the goodness of a feature

set also in the studies (Jain and Zongker, 1997) and

(Kudo and Sklansky, 2000).

The SFS, SBS and n Best Features methods were

selected to be used in this study because of their

easy application and relatively short calculation time.

Compared to the basic sequential feature selection

methods, the main advantage of the ﬂoating meth-

ods is that the resulting feature sets of different di-

mensions are not necessarily nested, as in the case

of the SFS and SBS methods. This is because the

ﬂoating methods are able to correct the erroneous de-

cisions made at the previous steps of the algorithm.

Therefore, these methods provide a close to optimal

solution to the problem of feature subset selection

(Pudil et al., 1994). Because of this characteristic,

they are also highly applicable to problems involving

nonmonotonic feature selection criterion functions,

which was the case in this study. In addition, even

though the ﬂoating feature selection methods are only

nearly optimal, they are much faster than the optimal

but computationally prohibitive Branch and Bound

algorithm (Narendra and Fukunaga, 1977).

In order to evaluate classiﬁcation accuracy when

using different feature sets, the data were divided into

training and test data sets, which consisted of 2/3 and

1/3 of the data, respectively. The training data set was

used to train the 3N N classiﬁer, and the test data set

was used to evaluate the classiﬁcation accuracy.

5 RESULTS

The best possible feature subsets for maximizing

the 3N N classiﬁers classiﬁcation accuracy were

searched for. The feature selection methods were ap-

plied to both the original and a normalized feature set.

The latter was formed by normalizing the feature val-

ues of the original feature set to have an average of

zero and a standard deviation of one. The results of

the classiﬁcation using feature subsets constructed by

the various feature selection methods are presented in

Tables 1 a) and b).

The tables show the best classiﬁcation accuracy ob-

tained using feature sets formed by each of the feature

selection methods. The feature subsets of all dimen-

sions between 1 and 54 (dimension of the original fea-

ture set) were formed with each of the feature selec-

tion methods. Classiﬁcation using each of these sets

was performed, and the best classiﬁcation accuracy

obtained was recorded in the tables. The percentages

in the middle row indicate the ratios of correctly clas-

siﬁed processes and the numbers in the bottom row

stand for the dimension of the feature set used in clas-

siﬁcation. It should be noted that the best feature sub-

sets produced by the different feature selection meth-

ods are composed of unequal numbers of features.

The classiﬁcation results of feature subsets formed

from the unnormalized features are presented in Ta-

ble 1 a), and the results of the subsets constructed

from the normalized features are shown in Table 1

b). It can be seen that the subsets of the set of nor-

malized features are notably larger than the subsets of

the set of original features. However, the dimensions

and classiﬁcation accuracies of the different feature

subsets are difﬁcult to compare since only the sub-

sets yielding the best classiﬁcation results were con-

sidered at this point. Only the backward methods,

SBS and SBFS, seem to yield better feature subsets

when applied to normalized data. Nevertheless, the

dimensions of these sets, 17 and 29, are much larger

than those of the subsets formed from the unnormal-

ized feature data, which are both of dimension 7. For

comparison, it can be studied what the classiﬁcation

accuracies would be for smaller subsets of the set of

normalized features formed with the backward meth-

ods. These results are presented in Table 2. It can

be seen that quite good classiﬁcation results are also

obtained by using the smaller feature sets. However,

these results do not compare with the classiﬁcation re-

sults of the subsets produced from the unnormalized

feature set by the forward methods, SFS and SFFS.

From the point of view of this study, it was consid-

ered more important to ﬁnd a moderately small fea-

ture set that yields excellent classiﬁcation results than

to reduce the dimension of the feature set used to the

absolute minimum. It can be stated, however, that the

best classiﬁcation results are obtained with small fea-

FEATURE SELECTION FOR IDENTIFICATION OF SPOT WELDING PROCESSES

Table 1: Classiﬁcation results of the 3N N classiﬁer using different feature subsets formed by the feature selection methods

together with the number of features included in each feature set. a) Subsets formed from the original feature set. b) Subsets

formed from normalized feature data.

a) Feature selection method SFS SBS SFFS SBFS nBEST

Classiﬁcation accuracy 98.92 84.67 99.30 84.75 95.28

Number of features used 6 7 11 7 10

b) Feature selection method SFS SBS SFFS SBFS nBEST

Classiﬁcation accuracy 98.30 97.14 98.45 97.45 95.12

Number of features used 18 17 19 29 52

Table 2: Classiﬁcation accuracies of subsets of different dimensions formed by the SBS and SBFS methods from the normal-

ized feature set.

Method/Dim. 6 7 8 10 15 20

SBS 95.74 96.28 96.44 96.83 96.75 97.06

SBFS 95.67 96.21 96.52 96.90 97.14 97.21

ture subsets formed from the original feature set. The

best of all feature subset with a classiﬁcation accuracy

of 99.30 percent was produced by the SFFS method.

The dimension of this set was 11. The SFS method

also yielded a feature subset with almost equally good

classiﬁcation accuracy of 98.92 percent. And what is

remarkable about this result is that this set consists of

only 6 features.

The observation that, in general, the subsets gener-

ated from the set of original features yield better clas-

siﬁcation results than the subsets formed from the nor-

malized data alludes that the measurements of current

contain more information related to process identiﬁ-

cation than the measurements made of voltage. This

is because the original range of current measurement

values was wider than the range of voltage values.

Therefore, in the case of the original data, the fea-

tures extracted from the current signals affect the clas-

siﬁcation more than the features calculated from the

voltage signals. Since the inﬂuence of the two sig-

nals is equalized in normalized data, this implies that

the features calculated from current signals are more

signiﬁcant to process identiﬁcation than the features

extracted from the voltage signals. From experience,

however, it is known that only a rough classiﬁcation

of welding processes can be made based on the cur-

rent signals alone, and the information carried by the

voltage signals is also needed to get more precise re-

sults.

The signiﬁcance of the features extracted from the

current signals can also be established by comparing

the amount of classiﬁcation-related information con-

tained in each of the features individually. Table 3 a)

presents the 20 individually best features of the nor-

malized feature set. The column in the middle shows

the accuracy of classiﬁcation based on only one fea-

ture. The classiﬁcation results of the feature subsets

formed with the n Best Features Selection method are

presented in the column on the right. It can be seen

that all the 20 individually best features are extracted

from the current curves. (The ﬁrst feature extracted

from voltage signals would be the 22th on this list.)

However, the individual goodness of the features is

not a sufﬁcient criterion for feature selection since the

features may correlate with each other. Because of

this, the feature subsets produced by the n Best Fea-

tures Selection method are generally inferior to the

subsets formed by the other methods. The best sub-

set formed from the normalized feature set yielded a

classiﬁcation accuracy of 95.12 percent, and the sub-

set consisting of unnormalized features yielded an ac-

curacy of 95.28 percent as seen in Table 1.

It should also be noted in Table 3 that all of the

ten means calculated on the current signal (called in-

terval means) are among the 18 individually best fea-

tures. After this, it only seems logical that remarkably

better classiﬁcation results were obtained in the previ-

ous study using a feature subset consisting of the ten

means than using the entire feature set in classiﬁca-

tion.

In Table 4, the features selected by the SFS, SFFS

and n Best Features Selection methods are presented.

These feature subsets of the dimensions 6, 7 and 10

were formed from the unnormalized feature set. It can

be seen that several of the features have been selected

by two or all three of the methods. Again, the ten

means are well represented in the set of features se-

lected. It should also be noted that the SFS and SFFS

methods have selected approximately evenly features

extracted from the current and the voltage signals. As

the best classiﬁcation results are obtained using fea-

ture subsets formed by these methods, it can be con-

cluded that the use of features extracted from both the

signals is necessary to obtain excellent classiﬁcation

results.

ICINCO 2006 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION

Table 3: The 20 individually best features of the normalized

feature set. A c at the end of a feature name means that the

feature has been extracted from the current signal, while a v

stands for voltage signal.

Feature % n

median c 72.45 72.45

start value c 72.29 89.47

mean C c 71.67 90.40

interval mean 5 c 71.36 92.57

mean D c 69.51 92.57

interval mean 6 c 69.20 92.26

interval mean 4 c 68.65 92.57

interval mean 2 c 68.50 94.58

interval mean 7 c 68.19 94.74

mean A c 66.33 94.97

interval mean 8 c 65.87 94.58

interval mean 9 c 65.71 94.50

interval mean 10 c 64.94 94.35

interval mean 3 c 64.63 94.43

peak value c 62.38 94.50

mean B c 61.84 94.50

max value c 61.77 93.96

interval mean 1 c 61.53 94.43

last value c 60.06 94.81

min value c 59.68 94.66

6 CONCLUSION

In this study, various feature selection methods were

discussed with an aim to improve the performance

of a spot welding process identiﬁcation system. The

methods were applied to a set of features extracted

from current and voltage signals recorded during

welding. The classiﬁcation accuracy of the 3NN clas-

siﬁer found suitable for the process identiﬁcation task

in a previous study was used as the criterion func-

tion for the feature subsets produced by the meth-

ods. Altogether ﬁve different feature selection meth-

ods were tested on both the original and a normalized

feature set. These methods were the Sequential For-

ward Selection (SFS), the Sequential Backward Se-

lection (SBS), the Sequential Forward Floating Se-

lection (SFFS), the Sequential Backward Floating Se-

lection (SBFS) and the n Best Features Selection. In

general, the subsets generated from the set of original

features yielded better classiﬁcation results than the

subsets formed from the normalized data.

It was discovered that classiﬁcation accuracy can

be improved from the 84.13 percent obtained previ-

ously to 99.30 percent simultaneously reducing the

dimension of the feature set from 54 to 11. This

reduction in the feature set size facilitates both the

extraction of features and the actual process identi-

ﬁcation. The best feature subset (classiﬁcation accu-

racy of 99.30 percent) was obtained using the SFFS

Table 4: The features selected by the SFS, SFFS and n Best

Features Selection method from the original feature set.

Feature SFS SFFS nB

start value c x x x

interval mean 2 c x x x

interval mean 1 v x x

interval mean 5 v x x

median c x x

interval mean 5 c x x

interval mean 6 c x x

interval mean 10 v x

last value v x

peak value v x

median v x

interval mean 3 v x

interval mean 6 v x

mean A c x

mean C c x

mean D c x

interval mean 4 c x

interval mean 7 c x

method. The feature subset produced by the SFS

method also yielded a very good classiﬁcation re-

sult. The classiﬁcation accuracy obtained using this

set was 98.92 percent. And what is most important

about this result is that the dimension of this set was

notably smaller than the dimension of the set pro-

duced by the SFFS method being only 6. Hence, it

was shown, that the dimension of the feature subset

used in classiﬁcation can be signiﬁcantly reduced and

the performance of the spot welding process identiﬁ-

cation system notably improved.

By considering the amount of classiﬁcation-related

information contained in each of the features individ-

ually, it was conﬁrmed that the ten means calculated

on intervals of equal length of a signal curve are in-

deed very good features to be used in classiﬁcation. In

addition, it was discovered that the features extracted

from current signals contain more information related

to process identiﬁcation than those calculated from

voltage signals. Nevertheless, the use of both of these

is necessary to obtain excellent classiﬁcation results.

ACKNOWLEDGEMENTS

We would like to thank our partners in coopera-

tion Fachochschule Karlsruhe, Stanzbiegetechnik and

Harms + Wende GmbH & Co.KG. Furthermore, we

would like to express our gratitude for the ﬁnancial

support provided by the Commission of the European

Communities, speciﬁc RTD programme ”Competi-

tive and Sustainable Growth”, G1ST-CT-2002-50245,

”SIOUX”, and Infotech Oulu, an umbrella organiza-

tion for information technology research in Oulu re-

gion.

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