Apparatus for nondestructive on-line inspection of electric resistance welding states and a method thereof

Apparatus for inspecting an electric resistance welding state including a first electrode connected to a power source, a second electrode connected to another terminal of the power source, and a welding object interposed between the first and the second electrodes. A voltage waveform measuring system includes a first analog-to-digital converter for detecting voltage applied, during a welding process, to both ends of the welding object. An electrode movement measuring system includes a sensor for detecting a change of a gap between the first and the second electrodes during the welding process, and a second analog-to-digital converter for receiving an output of the sensor. A computer system which includes a neural network inspection system for receiving outputs from the voltage waveform measuring system and from the electrode movement measuring system is provided.

BACKGROUND OF THE INVENTION 
1. Technical Field 
This invention relates generally to an apparatus and a method for 
inspection of an electric resistance welding state, and more particularly 
to an on-line nondestructive inspection apparatus and a method thereof 
making use of a neural network and carrying out the inspection 
simultaneously with the welding operation. 
2. Description of the Prior Art 
An electric resistance welding is that joins two parts to be melted and 
fused under pressure by making use of heat developed by an electric 
current flowing through them. The electric resistance heat can be 
determined by 
EQU Q=0.24 I.sup.2 Rt (1) 
where, 
Q is amount of heat, 
I is current flowing through welding objects, 
R is resistance of the contact point of welding objects, and 
t is time in seconds 
Generally, in electric resistance welding, high resistive materials are 
used for load site, and a low voltage and high current source is employed 
for power site so that heat generated when the high current flows through 
the high resistive material can be used for joining the materials. The 
current is as high as up to 80,000 Amperes and the voltage dropped to the 
both sides of the load is as low as 1-10 Voltages. This large 
current/small voltage power source can be easily obtained by employing an 
alternating current source and a transformer. 
Electric resistance welding can be accomplished by many methods including 
butt welding, spot welding, seam welding, projection welding and so on. 
These methods have an advantage that the welding temperature is somewhat 
low and the welding takes a short operation time; further, the welding 
reliability is improved. 
In particular, spot welding is a low-cost and mass productive operation and 
has performance benefits such as very strong joining strength, light 
weight, savings of materials and simple structure. And how much the 
operator is skilled or trained cannot influence the spot welding since the 
condition of the operation is determined by the welding machine used. As a 
result, the spot welding is broadly and commonly used in the metallurgical 
industry. 
Accordingly, the description below will focus on spot welding. 
FIG. 1 is a schematic diagram of a spot welding machine. In FIG. 1 are 
shown a welding transformer 1, a control part 2 connected to the first 
winding of the transformer 1, electrodes 3a and 3b (also referred to as 
`welding rods`) connected to the second winding of the transformer 1, a 
compressing part 5 and parent base metals 7 (welding objects) interposed 
between the two electrodes 3a and 3b. 
When a current begins to flow through the base metals 7 from the electrodes 
3a, 3b with the base metals being under pressure by the compression part 
5, the contact area of the metals will locally glow red with the heat. By 
pressing again the base metals with a suitable compression force, spot 
welding can be accomplished with using only small circular area 
corresponding to the contact area of the electrodes to the base metals. 
The welding point has a circular form called as a negget. 
Spot welding is also called point welding since the welded area seems like 
a point, and can join the welding objects without perforating any holes 
unlike a riveted joint in which two metal plates are permanently joined 
together by forming many rivet holes and by inserting rivets through the 
aligned holes. 
It should be noted that any defect of the welded area will degrade the 
reliability of the spot welding, in particular when pressure is applied 
repeatedly or impulsively. And safety of the work becomes very important 
factor in case of high pressure and load used. Accordingly, defects of the 
welding area should be eliminated. 
The method for inspection of the welded area is divided into destructive 
testing and non-destructive testing. In the destructive testing method, 
the welded area of a selection of an certain collection or of a test 
sample manufactured for a specific purpose is used in the inspection. 
Destructive testing includes a punching test, a fracture surface test, a 
macro organization test and a micro organization test, etc. In general, a 
direct destructive testing which destroys the selected test sample after 
carrying out the spot welding is broadly used. If defects or problems are 
found, all of the foregoing operations should be deleted or reworked. 
Accordingly, the destructive testing is limited in automatic operation 
because the reliability of the product is poor and the test procedure is 
not efficient. Further the destructive testing gives rise to a great loss 
of samples as well as a loss of manpower. 
Alternative to destructive testing is non-destructive testing or 
inspection. Non-destructive inspection does not change the shape, 
dimension of the product nor damage or destroy test sample in estimating 
the integrity of the welded area. The non-destructive inspection makes use 
of x-rays, ultrasonics, radiography, magnetic flux, paint penetration, and 
so on. 
X-ray inspection, which is a kind of radiograph test, can utilize a 
difference of light and darkness in image of the rays in order to inspect 
the failure of the welding because the segregation of the defects and 
impurities are easy to project the x-rays and are highly photo-sensitive. 
However, an apparatus used in x-ray inspection is very expensive and 
causes a security problem in a working gap, and uncomfortably the operator 
needs to always watch and handle the apparatus as the inspection operation 
is performed. 
In ultrasonics inspection, it is difficult to test a sample having a very 
complex shape or a very coarse surface, and the ultrasonic wave cannot 
penetrate through a high density material such as a steel plate. 
And the magnetic flux test can be applied only to ferromagnetic materials 
and presently remains in theoretical study. 
The paint penetration test cannot detect the defects of the sample which do 
not reach to the surface, and has a drawback that the surface of the 
sample should be grounded before welding. 
As a result, the above mentioned non-destructive inspection methods are not 
commonly used, and they need, like the destructive test, additional 
inspection steps after the welding operation, and accordingly produce 
losses of time and manpower. 
As perceived from the above description, it is required to test or inspect 
the defects of the welded area simultaneously with the welding operation 
rather than after the welding operation. In order to meet the requirement, 
a new and non-destructive test method needed to be developed. However, the 
welding state varies upon several parameters such as the amount of the 
applied current, operation time, the condition of the welding electrode 
tip, the pressure and the choice of a material for the base metal. 
Accordingly, it is difficult to develop a non-destructive test system, 
capable accommodating the various signals of applied voltage waveforms 
which are fluctuated corresponding to the variation of the welding state. 
SUMMARY OF THE INVENTION 
Accordingly, it is an object of this invention to provide a new apparatus 
and a method for non-destructive inspection of the defects and the 
integrity of joined materials welded using electric resistance welding 
technology. 
It is another object of the present invention to provide a new apparatus 
and a method for non-destructive on-line inspection of the defects and the 
integrity of joined materials, which can be carried out simultaneously 
with the electric resistance welding operation. 
In order to accomplish the objects, the present invention is characterized 
in that the voltage waveform applied to both sides of the base material 
which is varied in accordance with the progress of the welding operation 
is utilized in the inspection of the welding. 
And in order to accomplish the objects of the present invention, both the 
voltage waveform which is applied to both sides of the base material and 
varied in accordance with the progress of the welding operation, and the 
electrode movement waveform which is displacement of the distance between 
two welding electrode tips are utilized in the inspection of the welding. 
Further, in order to accomplish the objects of the present invention, an 
apparatus for electric resistance welding and inspection comprising the 
first electrode connected to one end of a power source, the second 
electrode connected to ground terminal, and a load interposed between the 
first and second nodes is characterized in that A) an analog to digital 
(A/D) converter for converting voltage signals detected from an electrode 
into digital signals, B) software for receiving the digital signals 
converted by the A/D converter and for detecting maximum values at each 
cycle of the digital signals, and C) a computer system comprising a 
multi-layered neural network for receiving the data from the software and 
for inspecting the welding state of the load are provided. 
According to one aspect of the present invention, a method for inspecting 
an electric resistance welding state, comprises an off-line learning stage 
including: 1) a first step comprising the substeps of A) applying a 
voltage signal to both ends of each of at least two welding objects 
through a first and a second welding electrodes, so that the welding 
objects can be joined together, B) converting said voltage signal into 
digital data, C) detecting a variation of peak values of said converted 
digital data, D) saving said detected variation in a computer system, 2) a 
second step comprising a sub-step of monitoring a welding state of said 
welding objects, 3) a third step comprising the sub-steps of A) repeating 
the first and the second steps, B) inputting actual output data and a 
predetermined desired output data into a neural network contained in the 
computer system, said actual output data representing the variation of 
peak values and said desired output data corresponding to respective 
welding state, C) determining weight values of the neural network by using 
a backpropagation algorithm, and an inspection stage including: 1) 
inputting said detected variation in the first step into the neural 
network having the weight values determined in the sub-step C) of the 
third step, 2) performing on-line inspection of the welding state. 
According to another aspect of the present invention an apparatus for 
electric resistance welding state inspection comprising the first node 
connected to one end of a power source, the second node connected to other 
end of the power source, and a load interposed between the first and 
second nodes is characterized in that A) an analog to digital (A/D) 
converter for converting voltage signals detected from sensor system which 
detects the distance between the first and second nodes into digital 
signals, B) software for receiving the digital signals converted by the 
A/D converter and for collecting data at each cycle of the digital 
signals, and C) a computer system comprising a multi-layered neural 
network for receiving the data from the software and for inspecting the 
welding state of the load, further comprises a sensing means for measuring 
the distance between the first and the second welding electrodes, and 
wherein the converting means is connected between the sensing means and 
the computer system. 
According to still another aspect of the present invention, a method for 
inspecting an electric resistance welding state, comprises an off-line 
learning stage including: 1) a first step comprising the sub-steps of A) 
applying a voltage signal to both ends of each of at least two welding 
objects through a first and a second welding electrodes, so that the 
welding objects can be joined together, B) converting said voltage signal 
and an electrode movement pattern into digital data, C) detecting a 
variation of peak values of said converted digital data, D) saving said 
detected variation in a computer system, 2) a second step comprising a 
sub-step of monitoring a welding state of said welding objects, 3) a third 
step comprising the sub-steps of A) repeating the first and the second 
steps, B) inputting actual output data and a predetermined desired output 
data into a neural network contained in the computer system, said actual 
output data representing the variation of peak values of the voltage 
signal and the electrode movement pattern, and said desired output data 
corresponding to respective welding state, C) determining weight values of 
the neural network by using a backpropagation algorithm, and an inspection 
stage including: 1) inputting said detected variation in the first step 
into the neural network having the weight values determined in the 
sub-step C) of the third step, 2) performing on-line inspection of the 
welding state.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION 
Prior to describing the structure of the spot welding apparatus according 
to the present invention, waveforms of applied voltage to both ends of 
base metals, electrode movement patterns, and factors affecting the 
integrity of the welding will be explained. 
Driving modes of a spot welding machine can be classified into a mode using 
a DC power source which employs an invertor and a mode using an AC power 
source. The DC driving spot welding machine can estimate the welding state 
by directly receiving voltage values and analyzing the waveform obtained 
from the values. 
FIG. 2 shows a voltage waveform during welding operation of a spot welding 
machine adopting the AC power source as a driving mode. In this Figure, 
the voltage wave form of the spot welding machine is a sine wave housing a 
cycle of, e.g., 60 Hz. 
Because low voltage values do not have a great influence on the welding, a 
thyristor of a controller is adjusted so that electrical potential can be 
applied only during a few milliseconds when the voltages represent maximum 
or minimum values of the sine wave. The applying time of voltage is 
allowed, by adjusting the controller having the thyristor, to be in a 
range from several milliseconds to several hundreds millisconds. 
Referring to FIG. 2, the values of the applied voltage range from 2.5 to 
3.0 V and gradually go down as the welding is progressed. The time 
required in analog-digital converting is changed, in a software, based 
upon the duration of voltage applied by a welding machine, and the start 
of the analog-digital conversion is initiated by a trigger of input 
voltage. 
For example, when the voltage applying time is 150 msec. and a sampling 
time is 0.1 msec., the number of data is 1500. However, these 1500 data 
are difficult to be analyzed in real time on-line method. And it is peak 
values of each of sine waves, i.e., maximum and minimum voltages to 
directly affect the spot welding. Accordingly only the max/min voltages 
are enough to be used in the inspection of the welding. 
FIG. 3 is a voltage waveform used in inspection of the spot welding and 
shows the variation of peak values of the applied voltages. Assuming that 
the voltage is applied for 150 msec., a 60 Hz sine wave has 9 cycles for a 
single spot welding operation, and accordingly 18 peak values can be 
obtained. 
In order to obtain 18 data as shown in small circular in FIG. 3, analog 
signals generated during the welding operation are converted into digital 
signals, and all negative values in the digital signals are changed into 
their absolute values by a software in a computer system. Then by 
selecting peak values from the positive and absolute values and by 
plotting the peak values we can obtain a graph showing the variation of 
the peaks as shown in FIG. 3. 
From FIG. 3 it can be understood that since the joining of the base metal 
welding objects is not yet perform in the early stage, the resistance is 
so high that the voltages have peak values. However, the values of the 
voltages decrease as the welding operation is progressed. The plot of FIG. 
3 represents the variation of peak voltage values when the welding is 
normally carried out, and is differentiated from the waveforms when the 
welding is excessively or insufficiently performed. 
FIG. 4 is an electrode movement pattern which represents a moving of the 
welding electrodes while the welding is operated. The electrode movement 
pattern utilizes the fact that the distance between two welding electrodes 
becomes far by the heat of the first stage of the welding operation, while 
the welding electrodes are extracted after welding. In this figure, the 
initial distance of the electrodes is set zero (0), and variations of the 
electrode distance are measured. Since the electrode movement pattern of 
FIG. 4 is obtained by using an optical sensor system which outputs the 
measured distance variation in the form of voltages and by using a 
computer system which reads data converted from the output of the optical 
sensor system by an A/D converting, it can be seen from the pattern that 
the vibration of the welding machine itself and external noises have great 
influence on the pattern. 
FIG. 5 is an electrode movement pattern in which the vibration of the 
welding machine and the external noises are removed by a digital filter. 
FIG. 6 is an electrode movement pattern transformed into more smooth curved 
pattern by use of a moving average method. 
FIG. 7 is a plot which is obtained by synchronizing the waveform of FIG. 6 
to a frequency of a voltage waveform, and then by sampling the 
synchronized waveform. The electrode movement pattern of this figure shows 
an overall shape of the variation of the electrode distance, and is used 
in inspection of the welding state. 
The voltage waveforms as shown in FIG. 3 and the electrode movement pattern 
for respective welding states will be utilized in a welding inspection of 
the present invention. 
The factors affecting the welding states of an electric resistance welding 
are the variations of applied current, compression force of the electrodes 
and the change of cross-sectional area of electrodes due to abrasion by 
long use of the electrodes. These three factors will change the welding 
states, and may result in failure or error of the welding. The change of 
the welding states due to the variation of each of three factors can be 
found out and estimated by the waveform of voltages applied to both sides 
of the welding objects during the welding is performed and by the waveform 
of electrode expansion. 
First, an influence of the current variation on the welding state will be 
explained. 
Because a driving mode of the spot welding machine presently used commonly 
uses of constant current source, the current has a fixed value when the 
welding is performed. Accordingly, as shown in the below equation, the 
variation of applied voltages is proportional to only a variation of 
resistance of the welding objects. 
EQU E=I.times.R (2) 
where, E is voltage, I is current, R is resistance. 
Although current is fixed in the spot welding machine using the constant 
current driving mode, the amount of current may be changed by an error of 
operator who handles a controller of the spot welding machine. If all 
other conditions are constant the resistance of the welding objects is not 
varied by the current variation. 
The heat generated in the electric resistance welding can be obtained by 
Equation 1, and can be rewritten by 
EQU Q=0.24 I.sup.2 R t=0.24 I E t (3) 
From Equation 3, it can be understood that increase of current (I) will 
generate much heat and cause an excessive over welding, and that decrease 
of current will produce little heat and cause an insufficient under 
welding. 
Second, an influence of variations of the compression force of the 
electrodes will be explained. 
The spot welding is performed by compressing the welding objects by using 
the electrode, and by applying a current to flow through the objects. At 
this time, it is preferable to make constant the pressure of the electrode 
to the welding objects. However, as the pressure is a parameter having a 
physical property it cannot easily controlled and easy to vary in 
accordance with time variation. This variation of the pressure greatest 
influence on the welding state, since it has close relation with the 
contact resistance of the welding objects. 
An increase of the pressure reduces the contact resistance between two base 
metal welding objects, and a decrease of the pressure increases the 
contact resistance. In other words, the pressure is in inverse proportion 
to the contact resistance. And the pressure is also inversely proportional 
to the thickness of the welding objects. 
The contact resistance can be expressed by: 
EQU R=(.rho.t/A) (4) 
where, 
.rho. is a resistivity of the welding objects, 
t is thickness of the welding objects, and 
A is an contact area of the electrode and the object. 
As apparent, Equation 4, an increase of the thickness (t) results in an 
increase of the contact resistance. The increases of the thickness and the 
contact resistance will cause the increase of applied voltage. 
In conclusion, when the compression force of the electrodes, i.e., the 
pressure is increased, the resistance is reduced resulting in an 
insufficient welding. And when the pressure is decreased, the resistance 
is increased resulting in an excessive welding. 
Now will be explained an influence of a variation of an cross-sectional 
area of welding rods i.e. of contact area of the electrodes to the welding 
objects. 
In the spot welding, the electrodes are worn out by continuous use, and the 
contacting area of the electrodes will be broader than that of the initial 
used. This variation of an increased area of the electrodes changes both 
the resistance and the applied voltage by two conflicting parameters: an 
increase of the contact resistance due to a decrease of the pressure per 
unit area, and a decrease of the resistance due to the increase of the 
contact area (see Equation 4). The relationship of the contacting area of 
the welding rods to the pressure can be expressed by: 
EQU F=P/A (5) 
where, 
F is a compressing force per unit area, 
P is a pressure, and 
A is a contacting area of the welding rods. 
As seen from Equation 5, the contacting area (A) is inversely proportional 
to the compressing force per unit area. 
And a resistance considering the contact resistance coefficient (.rho. ) 
can be determined by: 
EQU R=(.rho.+.rho. )t/A (6) 
EQU .rho.'.varies.A/P (7) 
In Equations 6 and 7, the contact resistance coefficient (.rho.') is 
proportional to the contacting area (A), and serves to increase the total 
resistance (R). Accordingly, an increase of the contacting area results in 
a decrease of resistance according to Equation 4. 
As a result, the variation of the applied pressure according to the change 
of the contacting area of welding rods is affected by both conflicting 
parameters of 1) increase of the contact resistance resulted from a 
decrease of pressure per unit area, and of 2) decrease of the resistance 
resulted from an increase of contacting area. However, since the decrease 
of the resistance due to the increase of the contacting area is direct 
while the increase of the resistance due to the decrease of pressure per 
unit area is indirect, the resistance is in actuality reduced. However, in 
the inspection of the welding state based upon the variation of the 
contacting area of the welding electrode tips, it is difficult to 
differentiate the voltage waveform obtained when the welding is good from 
the voltage waveform obtained when the welding is poor, since the two 
voltage waveforms are analogous. 
In order to overcome this difficulty, the present invention adopts an 
inspection technology using a waveform of the expansion of the gap between 
two electrodes in the inspection performed based upon the variation of the 
contacting area of the electrode tips. The waveform of the electrode gap 
expansion can be plotted by introducing the concept that the gap between 
two electrodes will be expanded by heat of the first stage of the welding, 
but the gap will be shrunken when the welding is over. If the contacting 
area of the electrode tip becomes larger, the widened heat area will 
result in very minute expansion of the gap and the shrinkage of the gap 
cannot be observed since no welded area exists. By utilizing this 
principle, we can establish the relationship between the waveform of the 
electrode gap expansion and the contacting area of the electrode tips. 
Because, the gap expansion is very small, as small as approximately 0.1 mm 
in case of a steel plate of 1 mm thickness used, an optic sensor of 
non-contact type is used in measurement of the variation of the gap. 
In conclusion of the above described electric resistance welding, when the 
current is reduced, heat produced becomes small and an insufficient 
welding happens, and also when pressure put on the welding objects or a 
contacting area of the electrode tips becomes larger, the contact 
resistance is decreased resulting in an insufficient welding. And it 
should be noted that the welding state is dependent upon an unexpected 
change of applied current, the variation of the compressing force, and the 
variation of the contacting area of the electrode tip resulted from a 
constant use of the same electrode, and that the variations of the above 
parameters are detected by the variations of the waveforms of the applied 
voltage and the gap between the electrodes. 
According to the present invention, a multi-layered neural network which 
adopts a learning method suitable for mapping the variation signals 
changed by various factors is included for a pattern recognition of each 
of the welding states. With use of the neural network in analysis of the 
waveforms of the applied voltage and of the electrode gap expansion, 
causes of the welding failure or error can be detected in real time, and 
the welding states can be immediately estimated in the working place of 
the electric resistance welding. 
Now will be detailed described the preferred embodiments of the present 
invention. 
FIG. 8 is a schematic diagram of a welding state monitoring system using a 
neural network, which comprises a spot welding machine 11, a voltage 
waveform measuring system 12, an electrode movement measuring system 14, a 
neural network monitoring system 16a, and a welding state inspection 
system 16b. 
FIGS. 9A to 9C are block diagrams of an illustrative embodiment of the spot 
welding machine according to the present invention, which utilizes 
waveforms of the voltages applied to both ends of base metal welding 
objects in inspection of the welding states. 
Referring to FIG. 9A, a spot welding machine 11 of the present invention 
having first and second electrodes (11a and 11b) connected to respective 
terminals of a power source (not shown), and a load 12 interposed between 
the two electrodes is provided with an analog/digital converter 15 for 
detecting the analog voltage signals from the nodes 11a and 11b and for 
converting the detected voltage signals into digital data, and with a 
computer system 17 for receiving the digital data from the analog/digital 
converter 15. In this embodiment, the second node, i.e., the lower 
potential point 1b is grounded. The converter 15 is activated by the 
computer system 17 in order to convert the analog voltage signals to 
digital data. 
In the embodiment of the present invention, as the analog/digital converter 
15 there is used a high performance DA&C carrier board, e.g. an AX5611c-L 
available from Axom Inc. The carrier board has up to 16 inputs and can 
perform sampling of 1 Mhz using DMA (Direct Memory Access) technology. 
However, single channel is used and sampling time is set 10 Khz in order 
to read out voltage waveforms corresponding to a single welding point. 
The computer system 17 comprises software for processing the digitalized 
voltage data and a neural network. The software in the computer system 17 
may be constituted by a program converting the digital voltage data into 
absolute value data, and a program for detecting peak values from the 
absolute value data. 
The neural network adjusts weight values through a predetermined learning 
method and stores the waveforms of standard welding voltage and of 
standard electrode gap expansion. Thus an inspection of the welding is 
performed by using the stored weight values. 
FIG. 9B is a block diagram of another embodiment of the present invention. 
Compared to the embodiment shown in FIG. 9A, a noise filter 13 is further 
provided between the spot welding machine 11 and A/D converter 15. The 
noise filter 13 receives a voltage dropped between the first electrode 11a 
and the second electrode 11b which is lower potential point and grounded. 
Actually, many high frequency noises are generated from the spot welding 
machine as well as from the noisy environment of a factory processing the 
spot welding. Further, the supply voltage has a cycle of about 60 Hz. 
Accordingly, in the FIG. 9B embodiment of the present invention the noise 
filter 13 is a low pass filter (LPF) which can not pass a high frequency 
noise signal. 
However, the noise filter may be properly chosen based upon what noises are 
generated and what forms of voltages are supplied. 
FIG. 9C is a block diagram of FIG. 9B. 
FIGS. 10A and 10B are block diagrams illustrating the situation when the 
electrode movement patterns occurring at both ends of the welding objects 
during the welding operation are utilized in the welding state inspection. 
FIG. 10A is a schematic diagram of a subsystem for measuring electrode 
movement pattern which is provided with a non-contacted optical sensor 27 
(also referred to as an optical distance sensor) for detecting changes of 
the gap between the two welding electrodes which supply voltage potentials 
to the welding objects. 
FIG. 10B shows another embodiment of a subsystem for measuring electrode 
movement pattern, which comprises a non-contacted optical sensor 27 for 
detecting the expansion of the gap between two welding electrodes, an 
analog/digital converter 28 for receiving the output of the sensor 27, and 
a digital filter 29 for receiving the output of the analog/digital 
converter 28. 
Since the above-described embodiment of the present invention uses an 
inexpensive optical sensor which can detect any variation without contact 
with the sensing object, it is possible to measure the variation with no 
information of the standard distance. In most of the present research 
through many experiments, a contacted optical sensor is used which 
requires information about the standard distance of the welding 
electrodes, and which outputs inexact measurement results. The standard 
distance information can be normalized in laboratory, but in actual 
industrial use the variation of parameter due to the constant use of the 
welding electrodes makes it impossible to set up the standard distance of 
the electrodes. Further if a distance or gap measurement uses a laser, 
very exact and precise sensing is possible, but a sensor able to measure a 
micrometer unit is very expensive. 
The measured data of the electrode gap expansion is input into and used in 
causing the neural network to be learned. A waveform of the electrode gap 
expansion can be plotted by continuously reading out, through a channel of 
the A/D converter 28, voltage signals measured by the optical distance 
sensor 27 of the electrode gap expansion measuring subsystem. 
In this embodiment, the measuring subsystem is a PT-165 Optical Sensor 
System form Keyence Inc., Japan. using the PT-165 optical sensor, the 
measuring range is 2.5 centimeter, the accuracy is 5 micrometer, and -5 to 
+5 volt signals are linearly output. These output voltage signals, after 
being converted by the A/D board 28, are plotted as shown in FIG. 4. 
From the waveform of the electrode gap expansion of FIG. 4, it can be 
understood that there is noise generated by vibration of the welding 
machine itself and irregular vibration of the electrode movement pattern. 
The noise due to these vibrations, if a digital filter 30 is disposed 
between the A/D converter 29 and the computer system 17 as shown in FIG. 
10B, can be eliminated and the electrode movement pattern will be plotted 
as shown in FIG. 5. 
Referring to FIG. 7, it is apparent that the noise is greatly reduced. The 
digital filter 29 is for filtering out the abrupt vibration of the welding 
machine. The filtered waveform can be transformed, by a moving average 
method, into a more smooth curve, which is plotted in FIG. 6. 
Although a neural network can obtain data from the transformed electrode 
movement pattern, these data are too many to process in real time. 
Accordingly, the data must be sampled on a time base, e.g., a time 
corresponding to one wavelength of welding cycle. The sampled electrode 
movement pattern is illustrated in FIG. 7. 
In FIG. 9, reading time of the all data is adjusted twice of the welding 
time in order to monitor the course of electrode movement (expansion and 
shrinkage). For example, when 6 cycles are used in welding, 12 data are 
read into and used in analysis of the characteristic of the electrode 
movement pattern. 
As explained here-in-before, it becomes possible to inspect the welding 
states in an on-line nondestructive test mode by interconnecting the 
voltage waveform measuring subsystem and the electrode movement measuring 
subsystem to the spot welding machine, and by processing, with a computer 
system which comprises a neural network system, the variations of the 
voltage applied to both ends of the welding base metals and changes of the 
electrodes distance. 
The term "on-line" means that the varying voltage signals and the changes 
of the welding electrode movement can be processed by computer system at 
the time of the act of welding by the spot welding machine. 
Now will be explained the software and neural network which are contained 
in the computer system. The software is for activating the A/D board, and 
the neural network is for monitoring the welding state using signals 
processed by the A/D board. 
FIG. 11 is a flow chart for receiving the voltage waveform according to the 
present invention so as to drive the hardware of the A/D board. Referring 
to FIG. 11, the flow consists of initiating the A/D board (step 31), 
selecting a suitable channel of the A/D board (step 33), setting sampling 
time (step 35), selecting hardware so that the A/D converter can start to 
run only when the voltage value is greater than a specific value (step 
37), storing the converted digital data into a memory of the computer 
(step 39), selecting predetermined data (step 41), and saving the finally 
processed voltage data into a file which can be used in the learning 
procedures of the neural network (step 45). In step 41, data required 
respectively in the subsystems for measuring the voltage waveforms and the 
electrode movements are selected. In other words, for the voltage 
waveforms measuring subsystem, peak values which can influence the welding 
states are selected from the absolute values of the converted digital data 
in above described steps, while for the electrode movement measuring 
subsystem, electrode movement data are selected by removing vibration and 
noises with use of the digital filter and moving average technology, and 
by synchronizing the converted signals to a frequency of the voltage 
waveform. 
Although as one embodiment of the present invention a single channel is 
selected in the step of selecting of a suitable channel, the AX 5611C 
board can input data through up to 16 channels. And in the step of setting 
the sampling time, maximum sampling time is 1 MHz. When the sampling time 
is set too small, although the accuracy of the data will be increased, the 
amounts of data are too great to be properly processed. Accordingly, in 
one embodiment of the present invention, 0.1 msec (i.e., 10 Khz) of 
sampling time is chosen for the voltage waveform measuring subsystem, and 
for the electrode movement measuring subsystem the sampling time 
corresponds to one wavelength of welding frequency. 
On the other hand, sequential reading of data is adjusted to be carried out 
by initiating the A/D converter when voltages are over at specific value, 
because memory of computer is used and because other operations will be 
otherwise adversely troubled. The A/D board is started by an input trigger 
of, e.g., 0.7V. Though the steps 31, 33, 35, 37, the A/D board is set up. 
In the next step, 39, for more speedy data converting, the A/D board is 
driven by using DMA (Direct Memory Access) technology. 
FIG. 12A shows the structure of a neural network which can perform learning 
and on-line inspection and consists of multi-layered perceptrons. The 
multi layered perceptron model which includes input layer 21a, hidden 
layer 23a and output layer 25a can solve a problem which cannot be 
separated linearly. One of the learning methods adapted to the perceptron 
model is a backpropagation learning algorithm developed by D. Rumelhart in 
1986. 
How many is suitable for the hidden layer and what number is adequate for 
the process unit of each hidden layer should be found out through a series 
of repeated experiments. 
Here-in-after, a backpropagation algorithm which can learn the above 
described multi-layered perceptrons will be explained. The backpropagation 
algorithm is designed to minimize errors between actual outputs and 
desired outputs by using a gradient descent method. The outputs are 
represented by a non-linear function which can be differentiated. 
Generally as the nonlinear function, sigmoidal function is used and can be 
expressed by 
##EQU1## 
FIG. 12B is a graph of the sigmoidal function. The backpropagation 
algorithm can be summarized as follows: First, a weight is set up. An 
input pattern is given together with desired output values. And then 
actual outputs are calculated. In order to minimize the error between the 
actual calculated outputs and the desired outputs, the weights are 
adjusted to be minimized. 
The weights can be adjusted by: 
EQU W.sub.ij (n+1)=W.sub.ij (n)+.DELTA.W.sub.ij (9) 
where, W.sub.ij is an weight between the present layer j and the former 
layer i, W.sub.ij (n+1) is a new weight between layers j and i, and 
.DELTA.wij is an amount of variation after learning. 
Equation 9 is induced as follows: 
EQU Net j=.SIGMA.ioj W.sub.ij (10) 
where, Net j is a product of the outputs of the lower-most layer and the 
weights, 0j is an output of the j-th neuron, and if j is an input stage 0j 
becomes Ij. 
The output 0j can be expressed by: 
EQU 0j=f(Net j) (11) 
The f(Net j) is a sigmoidal function and can be written as: 
##EQU2## 
where, .theta.j is a threshold. 
.delta.j of the output layer can be expressed by: 
EQU .delta.j=(Tj-0j)f'(Net j) (13) 
EQU f'(Net j)=f(Net j)(1-f(Net j)) (14) 
where, Tj is desired output of j-th neuron, and 0j is the actual calculated 
output of j-th neuron. 
And .delta.j of hidden layer can be expressed by: 
##EQU3## 
In Equation 15, m is the number of process unit of upper layer of a layer 
in which j is placed. Thus weight is adjusted by Equation 9. 
That is 
##EQU4## 
.eta. is a rate of learning, and can be defined as 0&lt;.eta.&lt;1. The larger 
is .eta. the greater is the variation of weight. However, .eta. is 
commonly selected from a range of 0.1&lt;.eta.&lt;0.75 because the overall 
neural network may become unstable when .eta. is greater than 0.75. Within 
such a range, the rate of learning becomes rapid as .eta. is larger. 
When the net error between the actual outputs and desired outputs is 
optimized to be minimum by adjusting weight values in the above-described 
manner, input patterns and desired outputs are given again and another 
pattern is subjected to a learning process. In order to make a convergency 
rate to be more speedy, momentum terms or bias terms may be added. 
FIG. 12C shows the structure of a neural network according to the present 
invention. 
In one embodiment of the present invention, the number of data for input is 
eighteen when a sine wave of 60 Hz is applied for 150 msec. Accordingly, 
20 input electrodes 21b are established in consideration of two extra 
electrodes. The electrodes of hidden layers 23b are forty, which is twice 
of the number of the input electrodes and is empirically determined. Two 
electrodes are established as output 25b. When the values of the two 
outputs are `11`, the welding state is set to be `over welded`, when the 
values are `00`, the welding state is set to be `under welded`, and when 
the values are `10`, the welding state is set to be `standard welding`. 
Although three states are defined in one embodiment of the present 
invention, the number of the welding states can be increased or decreased 
as necessary. The software system is so structured that the numbers of 
electrodes of the input layer, hidden layer and output layer can be 
adjusted in accordance with the variation of setting conditions of the 
spot welding machine. 
The above-described neural network monitoring system can be utilized in the 
waveform inspector which is for grouping the patterns of the welding 
voltages by using as inputs the voltage waveforms of FIG. 3 or utilized in 
the waveform inspector, which can classify the patterns of the electrode 
movement by using as inputs the voltage waveforms of FIG. 7. 
FIG. 13 is a flow chart of the operation of the neural network monitoring 
system used in the present invention. The neural network monitoring system 
comprises a learning part 51 for learning the welding states, and an 
output generation part 53 for actual inspection after the learning. A 
selection of the learning part and the output generation part is performed 
in program software. The monitoring system is designed to use a pointer 
which can select the number of input layers and output layers so that the 
other pattern of learning and inspection can be applied. The learning 
method of the preferred embodiment of the present invention is a 
backpropagation. 
First, an explanation of the learning part 51 will be given. 
The first step 55 is setting up the structure of the neural network, at 
which the number of input and output electrodes by a program and the 
learning rate (.eta.), and momentum rate are determined. Then, data and 
weight are initialized, and data are read for learning in steps 57 and 59. 
In these stages, the beginning of completely new learning may be chosen or 
subsequent learning with adding previous weights of foregoing learning may 
be selected. Data can be read out from the file stored in step 45 of FIG. 
10. 
Next is step 61 for calculating actual outputs of the multi-layered 
perceptron in order to carry out the learning of the neural network. The 
actual outputs for respective data sets are calculated by using the data 
file generated in step 45 of FIG. 11. 
In step 63, weights are modified and adjusted by utilizing the errors 
between the desired outputs and the actual outputs calculated in step 61. 
The modification of weight is consistently performed until the learning is 
stopped when the errors are within a predetermined range through step 65 
for checking the error limit. 
When the learning is over, the modified weight value should be saved to be 
used in the output generation process. The saved data is transformed into 
a file in step 67. Through this learning procedure, a standard waveform 
pattern can be recognized with respect to each of the standard, under 
welded and over welded cases. At this time, the learning is carried out 
off-line. 
The term "off-line" is used, because data, or standard, under and over 
welded states are obtained by destructive inspection, when each welding is 
finished, and the learning of the neural network is accomplished by using 
the data and by determining weight. The voltage waveforms for each case 
are saved in the computer system according to the flow shown in FIG. 11. 
The learning data of the neural network system are obtained and collected 
through repeated experiments by changing the current, the compression 
force or contacting surface of the welding tip. Among the obtained 
waveforms, proper bounds in which the welding is best performed are 
determined and the standard waveform is set. Then the features of under 
welding and over welding are determined so as to teach the neural network. 
Now will be explained the output generation part 53. The initial step 71 of 
the neural network makes use of data regarding the structure data of the 
neural network stored in the learning process. In step 73, weight values 
modified by the learning are read from the saved file in order to provide 
for an inspection by the neural network. 
The next step 75 is activating the A/D board according to the flow of FIG. 
11 to provide for reading the digitalized voltage waveform. Inputs from 
the activated A/D board, which consists of max and min voltage values, are 
read in step 77. After then actual data are generated, in step 79, by the 
voltage signal data processed in the above described steps. The results 
and causes of comparison of the actual data with desired data are 
displayed as the welding state in step 81. When the welding state is poor, 
the inspection is stopped. If the welding is acceptable and good, the 
progress is return to step 77 and inputs are read and the inspection goes 
on. 
The output generation part works at the same time as the welding job, 
leading to on-line inspection. If the inspection or monitoring is carried 
out under a condition similar to that of the learning process, the 
inspection operation can be repeatedly performed by using the weights 
determined through the learning operation, without doing over again 
another learning operation. 
FIG. 14 is a table of learning data according to one embodiment of the 
present invention. The learning data are used in off-line learning of the 
neural network which utilizes the backpropagation explained above with 
reference to FIG. 13. Data sets 1,2,3 represent that the welding state is 
standard, data sets 4,5,6 show the under-welded state, and over-welded 
state is designated by data sets 7,8,9. The values of output electrode 
representing the welding states are set, at the same time of the welding 
operation, to correspond each of the data sets. In one embodiment of the 
present invention, the output node values are respectively `10`, `00`, and 
`11` which correspond to data sets for 1,2,3!, 4,5,6!, and 7,8,9!. Each 
of the data sets are input to the neural network, and by comparing these 
data sets with desired output (`11`, `00`, or `10`) weights can be 
properly adjusted. 
When the rates of learning and of momentum are all given by 0.5, the error 
is 0.001 in 1500 learning cycles and the learning takes about 6 minutes 
with a 486DX-2 computer system. The values of rates of the learning and of 
momentum are constant through the below-explained other embodiments of 
this invention. 
When the learning is completed, the inspection of the monitoring system is 
performed on-line. The time taken in monitoring a single welding point is 
about 20 msec, which enables real time inspection. The learning-over 
neural network employs an A/D converting system for receiving a continuous 
voltage waveform and uses a computer system for analyzing the converted 
voltage waveform data. 
FIG. 15A is an AC voltage waveform of the first illustrative embodiment of 
the present invention. In the first embodiment, the spot welding machine 
is a 19V-SCF spot welding machine which is developed by Orion Electric 
Co., Korea, and used in welding a frame of a TV CRT (Cathode Ray Tube). 
The welding machine is set at 6-3-10 cycles. The welding objects (i.e., 
base metals) are either 1.0 t bimetals available from Toshiba Co., Japan 
or surface coated 1.2 t SPC1 frames produced by Pohang Iron Manufacturing 
Co., Korea. 
An applied voltage waveform consisting of three continuous AC waveforms as 
shown in FIG. 15A reveals that impurities which are from the surface 
coating process, exist on the surface of the base metals. 
The first period corresponding to 6 cycles is a preliminary current for 
burning the coating of the welding area when the base metal has been 
coated. The active welding current is supplied in 10 cycles after 3 cycles 
of cooling period. Accordingly, the welding inspection only uses the 
voltage waveform corresponding to the 10 cycles. 
FIG. 15B is a table of standard data according to the first embodiment of 
the present invention, which shows the influence of the current variation 
on the welding state. The standard data are obtained by the off-line 
learning using a backpropagation algorithm, which follows the flow chart 
of the learning part 51 as shown in FIG. 13. In the table of FIG. 14B, 
data series `1,2` represents a standard welding state, while series 
`3,4,5` represents an under welded state. In the first embodiment, the 
current of the standard welding state is 4100 A and the under welded 
current is 3000 A. 
FIG. 15C is a voltage waveform used in the welding inspection of the first 
embodiment of the present invention, which is plotted by making use of the 
data of FIG. 15B. Referring to FIG. 15C, the waveform 85 represents the 
standard welding state and the waveform 87 is related to the low current 
state. 
FIG. 15D is an electrode movement pattern used in the inspection of the 
first embodiment of the present invention. The three waveforms 89, 91 and 
93 correspond to electrode movement patterns of a low current (3000 A), a 
standard current (4100 A), and a high current (5000 A), respectively. 
FIG. 16A is an AC voltage waveform of the second illustrative embodiment of 
the present invention. In the second embodiment, the spot welding machine 
is a SP5361-CH spot welding machine which is developed by Orion Electric 
Co., Korea. The welding machine is set to 9 cycles. The welding objects 
(i.e., base metals) are either 0.76 t bimetals available from Toshiba Co., 
Japan or 0.65 t SVS 304 frames of Poong-San Metal Co., Korea. 
FIG. 16B is a table of standard data according to the second embodiment of 
the present invention. Data sets 1,2 correspond to a case of good welding 
state under standard pressure, while data sets 3,4, and data sets 5,6 
represent an under welded state under high pressure and an over welded 
state under low pressure, respectively. In the second embodiment, the 
standard pressure is 4 Kg/m.sup.2, the low pressure is 3 Kg/m.sup.2, and 
the high pressure is 5 Kg/M.sup.2. 
The data of FIG. 16B can be obtained by performing a destructive direct 
inspection after every welding operation, and by extracting samples to the 
standard welding, the under and over welded states. At this time, the 
voltage waveforms during welding operation corresponding to each of three 
welding states are saved into a computer system simutaneously with the 
welding process. The voltage waveforms corresponding to each of the 
extracted samples are the desired output which is used in the learning of 
the neural network. 
The desired output is compared with the calculated output which results 
from reading out of the voltage values corresponding to each of the states 
as shown in the learning part 51 of FIG. 7. After comparison of the 
desired output with the calculated output, weights are so controlled that 
the difference of two outputs is within a predetermined limit. 
FIG. 16C shows voltage waveforms for a welding inspection according to the 
second embodiment of the present invention, which is plotted by making use 
of the data of FIG. 16B. As apparent from FIG. 16C, the voltage is high at 
the first stage of the welding, and gradually decreases as the welding 
progresses when a waveform 97 corresponding to a standard welding state is 
considered. In the case of under welded state represented by a waveform 
99, the voltage maintains low values without showing significant 
variation, and an over welded state of a waveform 95 shows, as a whole, 
high values since the gradient of the waveform is greater than that of the 
standard welding state. 
FIG. 16D is an electrode movement pattern used in the inspection of the 
second embodiment of the present invention, of which waveforms 101 and 103 
correspond to electrode movement patterns under standard pressure and 
under low pressure, respectively. 
FIG. 17A is an AC voltage waveform according to another embodiment of the 
present invention. In this embodiment, the spot welding machine is a 
20V-CF spot welding machine which has been developed by Orion Electric 
Co., Korea. The welding machine is set at 12-4-12 cycles. The welding 
objects (i.e., base metals) are either 1.0 t bimetals available from 
Toshiba Co., Japan or 0.8t SPC1 frames produced by Pohang Iron 
Manufacturing Co., Korea. In FIG. 16A, only the last 12 cycles are shown, 
which is related to the actual welding current among the 12-4-12 cycles. 
FIG. 17B is a table of standard data according to the above described 
embodiment of the present invention. In the table, data sets 1,2 represent 
a standard welding state, the diameter of the tip of the welding electrode 
is 5 mm(.phi.5), and the current is within a range from 3700 to 4100 A. 
The data sets 3,4 relate to a low current state of 2500 A current, while 
data sets 7,8 relate to high current state of over 5000 A current. Data 
sets 5,6 correspond to a case where the diameter of the tip of the welding 
electrode grows to be e.g., 6 mm(.phi.6). 
FIG. 17C is a voltage waveform used in the inspection according to the 
embodiment of the present invention, which is plotted by making use of the 
data of FIG. 17B. In FIG. 17C, waveforms 105, 107, 109 and 111 are related 
to the states of high current, standard, wider contacting area of the tip 
of the welding electrode, and low current, respectively. 
FIG. 17D is an electrode movement pattern used in the inspection according 
to the embodiment of the present invention, of which a waveform 113 
represents a standard welding state, and a waveform 115 corresponds to a 
case where the contacting area of the welding electrode tip becomes wider. 
As apparent from the above description, the welding state is influenced by 
the initial condition and abrupt variation of the welding machine itself. 
In particular, abrupt environmental change such as variations of current 
or compression force, difference of contacting area due to a continuous 
use of the welding electrode are major causes of failure of electric 
resistance welding. 
The variation of the welding state resulting from the variation of the 
applied current and compression force can be easily detected by the 
voltage waveform measurement. However, in case of the difference of 
contacting area of welding electrodes, it is difficult to identify the 
features of the voltage waveforms, because increase of the contacting area 
results in two conflicting effects i.e., increase of contact resistance 
due to the variation of pressure per unit area, and decrease of resistance 
due to the increase of the contacting area. 
On the other hand, the electrode movement pattern has an excellent property 
to determine the welding state, however the characteristic curve is so 
simple that the cause of the failure can not be easily detected when the 
welding has failed. 
Accordingly, by simultaneously analyzing both the voltage waveform and the 
electrode movement pattern, it is possible to realize an inspection 
apparatus which can determine whether the welding operation has failed, 
and can detect the cause of failure of the welding so as to correct the 
failure on spot. 
When the outputs of the neural network comprising the voltage waveform 
monitoring system and the electrode movement pattern monitoring system as 
shown in FIG. 8 are analyzed, the following table presents the results in 
tabular form. 
______________________________________ 
voltage 
electrode 
waveform 
movement 
inspection 
pattern welding state 
causes 
______________________________________ 
1 10 standard welding 
low pressure, large 
tip area 
1 0 under welded low pressure, large 
tip area 
10 1 over welded small tip area 
10 10 standard welding 
proper condition 
10 0 under welded large tip area 
0 1 over welded high pressure, small 
tip area 
0 10 standard welding 
high pressure, small 
tip area 
0 0 under welded high pressure 
______________________________________ 
In the table, if the output of the neural network which has input data of 
outputs from the voltage waveform monitoring system is `11`, the state is 
over welded state. And, when the output is `00` or `10`, the state is 
under welded or standard welding state, respectively. On the other hand, 
when the output of the neural network which has input data of outputs from 
the electrode movement pattern monitoring system is `11`, `10` or `00`, 
the state is, respectively, over welded, standard welding, or under welded 
states. 
It can be seen from the above description that a decrease of the applied 
current and an increase of pressure causes high voltage resulting from the 
under welded state, and that an increase of the contacting area of the 
welding electrode tip is represented by a decrease of electrode movement, 
leading to an under welded state. 
As will be understood from the foregoing description, the on-line 
nondestructive inspection apparatus and method thereof is realized by 
utilizing the differences between voltage waveforms and electrode movement 
patterns of normal and abnormal welding states, which is conceived upon 
the fact that welding voltage goes down because contact resistance of the 
welding object is decrease as the welding is progressed, and that the 
distance of the welding electrode varies upon the advance step of the 
welding operation. 
As a result, instant and simultaneous inspection of the welding state is 
possible, and the causes of welding failures can be detected and corrected 
at the same time as the welding operation. 
Although the present invention has been described in connection with a spot 
welding machine, it will be apparent to those skilled in the art that 
other welding methods using electric resistance heat can utilize the 
preferred embodiments of the present invention without departing from the 
spirit and scope of the present invention.