JP2019118954A - Method for determining defect - Google Patents

Abstract

To provide a method for determining a defect, with which it is possible to easily determine whether processing of an article is normal or not.SOLUTION: A method for determining a defect comprises a good product data acquisition step, a displacement acquisition step, a differential acquisition step, a first calculation step, and a determination step. In the good product data acquisition step, current waveforms W1-Wn for an article 11 are acquired, which affect quality of the article 11 in a welding step for the article 11 and change with time. In the displacement acquisition step, a current waveform Xp is acquired, which affects the quality of the article 11 in the welding step for the article 11 and changes with time. In the differential acquisition step, a differential value Q of the current waveform Xp for the current waveforms W1-Wn in the welding step is acquired at each prescribed time T1-Tm. In the first calculation step, sums Rp and Rp(max) are calculated. In the determination step, it is determined whether or not the sums Rp and Rp(max) calculated in the first calculation step are outside of one range J1, which is a threshold.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a defect determination method.

  As a method of determining whether the welding performed on an article is normal or not, after welding is performed, the condition of the welding is visually confirmed, or a predetermined number of articles welded are taken out per lot for destructive inspection. It is known to On the other hand, Patent Document 1 discloses a method of scoring whether the welding performed is normal or not based on the current or voltage in welding on an article.

Japanese Patent Publication No. 2013-510725

  However, the defect determination method of Patent Document 1 calculates an average current value obtained by averaging the current within a predetermined time, subtracts the average current value and the normal data, and determines whether the difference value is equal to or more than a threshold value. It is used as one of the judgment factors of failure. However, in this determination method, when currents separated locally and generally close to the normal data are detected within a predetermined time, these currents are averaged to obtain locally separated currents. It can not be identified. Therefore, it is not possible to detect a welding defect or the like caused by a local increase or decrease in penetration depth generated due to local separation of the current, and there is a possibility that it can not be accurately determined whether welding is properly performed.

  The present invention has been made in view of the above-mentioned conventional situation, and has an object to be solved to provide a defect determination method capable of easily determining whether or not the processing of an article is normal.

  The defect determination method of the present invention includes a non-defective item data acquisition process, a displacement amount acquisition process, a difference acquisition process, a first calculation process, and a determination process. The non-defective item data acquisition step affects the quality of the article in any processing step of the article and acquires the normal displacement amount of the article which changes with time. The displacement amount acquisition step affects the quality of the article in any processing step of the article and acquires a processing displacement amount that changes with time. A difference acquisition process acquires the difference value of the processing displacement amount with respect to the normal displacement amount in a processing process for every unit time. The first calculation step obtains the sum of the absolute values of the difference values and the maximum value of the absolute values of the difference values. The determination step determines whether the sum of the absolute values of the difference values obtained in the first calculation step and the maximum value of the absolute values of the difference values deviate from the threshold value.

  In this defect determination method, in processing displacement amounts that change with time, a difference value for the normal displacement amount is acquired for each unit time, and the sum of absolute values of the difference values and the maximum value of the absolute values of the difference values are obtained. It is determined whether the sum of the absolute values of the difference values and the maximum value of the absolute values deviate from the threshold value. That is, the whole difference value is calculated by the sum of the absolute values of the differences, the maximum difference value is calculated by the maximum value of the absolute values of the differences, and the calculation displacement is determined by the threshold value. On the other hand, it is possible to detect as an abnormality not only in the case of being separated entirely but also in the case of being separated locally from the amount of normal displacement. Therefore, it can be accurately determined whether or not arbitrary processing has been normally performed on the article.

  The failure determination method of the present invention may include a difference value exclusion step of excluding a predetermined range from the difference value among the difference values. In this case, the defect determination method does not extract the difference value when the difference value generated in arbitrary processing of the article is not separated from the predetermined range. By this, it is possible to prevent a normal processing from being erroneously determined as an abnormal processing. For example, if a minute deviation is also accumulated as a difference value, a large difference will result. However, this minute deviation is within the range of normal processing and is a value that should not be extracted. By excluding the predetermined range from the difference value generated in the normal processing, it is possible to prevent the erroneous determination that the normal processing is the abnormal processing. Then, the sum of the absolute values of the difference values is obtained in this manner, and the maximum value of the absolute values of the difference values is obtained, so that it can be accurately determined whether or not the processing is normally performed.

  The predetermined range of the defect determination method of the present invention may be a range based on the standard deviation obtained from the normal displacement amount. In this case, although the range of variation of the difference value generated in normal processing is generally different for each facility, this defect determination method uses a standard deviation to make a predetermined range desired for the entire variation. It can be set to a percentage. As a result, it is possible to accurately determine whether or not arbitrary processing of an article has been normally performed without changing the predetermined range, even for equipment having different ranges of variation in difference value.

  The failure determination method of the present invention may include a second calculation step of dividing the sum of the absolute values of the difference values obtained in the first calculation step by the processing time in the processing step. In this case, in the defect determination method, it is not necessary to consider the length of the processing time in any processing step for the article (that is, the length of the waveform of the displacement amount from the processing start to the processing end).

It is the schematic which shows the welding apparatus which enforces the defect determination method of this invention. It is a flowchart which shows the pre-processing for enforcing the defect determination method of this invention. (A) is a graph showing current values per unit time of a plurality of current waveforms in welding processing of an article and a plurality of current waveforms, and (B) is a current per unit time of each of a plurality of current waveforms It is an expression showing an average value. It is a table which shows the difference value for every unit time of each of a plurality of current waveforms which are data of good goods. (A) is a table | surface which shows each standard deviation of several current waveform which is data of non-defective goods, (B) is a type | formula which shows an average standard deviation. It is a flowchart which shows the machine learning process for enforcing the defect determination method of embodiment. It is a table which shows the difference value for every unit time of each of a plurality of current waveforms which are data for study. (A) is a table which shows the difference value extracted in each of a plurality of current waveforms which are study data, and (B) is a table which shows each feature quantity of a plurality of current waveforms which are study data. It is a flowchart which shows the determination processing of the defect determination method of this invention. It is a table | surface which shows the difference value for every unit time of the current waveform which is data to determine. (A) is a table which shows the difference value extracted in the current waveform of the data to be determined, (B) is a table which shows the feature quantity of the current waveform of the data to be determined. A graph in which the horizontal axis is the sum of absolute values of difference values, and the vertical axis is the maximum value of absolute values of difference values, and a line segment which is a separated hyperplane (determinator) is an absolute value of difference values. A state in which an area in which each of the sum of and the maximum value of the absolute value of the difference value is positive is divided into two areas is shown.

  Embodiments embodying the defect determination method of the present invention will be described with reference to the drawings.

Embodiment
First, a welding apparatus 1 in which the defect determination method is used will be described with reference to FIG. The welding apparatus 1 includes a torch unit 10, a power supply unit 10A, a current value detection unit 10B, a first voltage value detection unit 10C, a second voltage value detection unit 10D, and a control unit 10E. In the welding device 1, the article 11 can be obtained by welding the welded portions 11C of the member 11A and the member 11B.

  The torch unit 10 constitutes an electrode for performing arc welding, and feeds out the welding member 10F formed in a bar shape. The welding member 10F is continuously fed out of the torch portion 10 during welding. Further, the torch unit 10 can be moved along the welded portions 11C (hereinafter referred to as welded portions 11C) of the member 11A and the member 11B by a moving mechanism (not shown). When a voltage required to generate an arc between the torch portion 10 and the portion to be welded 11C is applied by a power source portion 10A described later, an arc is generated between the torch portion 10 and the portion to be welded 11C Then, an electric current flows between the torch portion 10 and the portion to be welded 11C. Thereby, welding of the to-be-welded part 11C is performed. In accordance with this, the welding portion 11C is continuously welded by moving the torch portion 10 along the welded portion 11C by the moving mechanism while the welding member 10F is continuously drawn out from the tip of the torch portion 10 It can be performed.

  The power supply unit 10A supplies the torch unit 10 with power used for welding. The power supply unit 10A is electrically connected to the torch unit 10 via the first conductive path 10G. Further, the power supply unit 10A is electrically connected to the member 11A and the member 11B via the second conductive path 10H.

  Further, a current value detection unit 10B that detects a current flowing through the first conductive path 10G is provided in the first conductive path 10G. The current value detection unit 10B detects the current flowing through the first conductive path 10G, and outputs a value corresponding to the magnitude of the detected current to the control unit 10E described later.

  The control unit 10E executes the defect determination method of the present invention. Specifically, the control unit 10E includes a ROM (read only memory), a CPU (central processing unit), a RAM (random access memory), and the like (not shown). The ROM previously stores control programs and the like executed by the CPU. The CPU executes a program or the like stored in the ROM. The RAM operates in accordance with a program executed by the CPU.

  Next, the operation of the welding device 1 will be described. First, the member 11A and the member 11B are disposed in the welding device 1. At this time, the member 11A and the member 11B are disposed in the welding apparatus 1 so that the torch unit 10 is positioned in the vicinity of the portion where welding of the portion to be welded 11C starts.

  Next, a voltage required to generate an arc is applied from the power supply unit 10A between the torch unit 10 and the welding portion 11C. Then, the welding member 10F is fed out from the torch unit 10. Then, the current value detection unit 10B starts detection of the current value flowing between the torch unit 10 and the member 11A and the welded portion 11C of the member 11B. Then, when a voltage necessary to generate an arc is applied between the torch portion 10 and the welded portion 11C, an arc is generated between the torch portion 10 and the welded portion 11C, and the torch portion 10 is generated. A current flows between the and the portion to be welded 11C. Thus, welding starts (processing starts) at the portion where welding of the welded portion 11C is started.

  Then, while the welding member 10F is continuously fed out from the torch portion 10, the torch portion 10 is moved along the portion to be welded 11C by the moving mechanism. Thereby, welding part 11C is welded continuously. And the torch part 10 reaches the part which complete | finishes welding of 11 C of to-be-welded parts. Then, the welding device 1 stops the application of the voltage applied between the torch portion 10 and the portion to be welded 11C. Thus, the welding of the portion to be welded 11C is completed (processing end), and the article 11 is obtained. Then, the article 11 is taken out of the welding device 1, and the member 11A and the member 11B to be welded next are arranged in the welding device 1. Thus, welding of the plurality of members 11A and the members 11B is repeatedly performed using the welding device 1 to obtain a plurality of articles 11.

  Next, a procedure for determining whether the quality of welding of the article 11 obtained by the welding apparatus 1 is normal or not will be described with reference to FIGS. 2 to 12 using the defect determination method of the present invention. The defect determination method is executed by the control unit 10E.

«Pre-processing»
The pre-processing is processing for obtaining the waveform of the current value flowing through the first conductive path 10G when welding is normally performed in the portion to be welded 11C, and calculating and obtaining the value used when executing the defect determination method It is.

  First, as shown in FIG. 2, data of a non-defective item is acquired (step S1). Here, the non-defective product data is a waveform of a current value flowing through the first conductive path 10G when welding is normally performed in the portion to be welded 11C. That is, the waveform of this current value is a normal displacement amount. Specifically, in the plurality of welded parts 11C, waveforms (hereinafter referred to as a plurality of current waveforms W1 to Wn) corresponding to the current value detected by the current value detection unit 10B from the start to the end of welding are acquired Do. Here, n is the number of acquired non-defective data. The plurality of current waveforms W1 to Wn are normal displacement amounts, which affect the quality of the article 11 and change with time. That is, step S1 is a non-defective item data acquisition process, and acquires a plurality of current waveforms W1 to Wn (normal displacement amounts) of the article 11 from the start of welding to the end of welding in the welding process which is an optional processing process of the article 11.

  Next, the average value of the acquired non-defective data is calculated (step S2). Specifically, the average value of the plurality of current waveforms W1 to Wn acquired in step S1 is calculated. As shown in FIG. 3A, the plurality of current waveforms W1 to Wn change substantially similarly to one another over time, but they are not identical. That is, the plurality of current waveforms W1 to Wn have variations.

  In step S2, an average value of the magnitudes of current values for each unit time of each of the plurality of current waveforms W1 to Wn is calculated. Specifically, in welding time Tend, which is a processing time from the start to the end of welding, the magnitudes of the current values of the plurality of current waveforms W1 to Wn for each predetermined time (T1 to Tm) which is a unit time Calculate the average value of Here, m is an integer greater than one. The current values of the current waveforms W1 to Wn at a predetermined time T1 are A11 to An1, and the current values of the current waveforms W1 to Wn at a time Tm are A1 m to Anm. Here, the average of the magnitudes of the current values A11 to An1 at a predetermined time T1 is taken as an average value H1, and the average of the magnitudes of the current values A1m to Anm at a time Tm is taken as an average value Hm (see FIG. 3B). ). Thereby, average values H1 to Hm of the plurality of current waveforms W1 to Wn are obtained. That is, the average values H1 to Hm are average values of the plurality of current waveforms W1 to Wn.

  Next, the average values H1 to Hm are subtracted from the non-defective product data (step S3). Specifically, as shown in FIG. 4, the average obtained in step S2 from the current values (A11 to An1) to (A1m to Anm) (hereinafter referred to as current value A) of the plurality of current waveforms W1 to Wn Reduce the values H1 to Hm. Thereby, the difference value from average value H1-Hm of current value A in predetermined time T1-Tm of each of a plurality of current waveforms W1-Wn is obtained. Here, difference values in the current waveform W1 are difference values D11 to D1m, and difference values in the current waveform Wn are difference values Dn1 to Dnm. Hereinafter, the difference values (D11 to D1m) to (Dn1 to Dnm) are referred to as a difference value D.

  Next, the average standard deviation σ is obtained (step S4). Specifically, as shown in FIG. 5A, the standard deviations S1 to Sn of the plurality of current waveforms W1 to Wn are calculated from the difference value D obtained in step S3. Here, the standard deviation S1 corresponds to the current waveform W1, and the standard deviation Sn corresponds to the current waveform Wn. Then, the average value of the standard deviations S1 to Sn thus obtained is the average standard deviation σ (see FIG. 5 (B)). Thus, the pre-processing ends. Alternatively, instead of the standard deviations S1 to Sn, the unbiased standard deviations of the plurality of current waveforms W1 to Wn may be calculated, and the mean standard deviation may be calculated using the unbiased standard deviations. In step S4, the standard deviation of the current value A at each of the predetermined times T1 to Tm may be calculated, and the average value of the standard deviations obtained in this manner may be used as the average standard deviation σ.

<< machine learning processing >>
The machine learning process is a process of generating a separation hyperplane (determinator) for determining whether the welding is normal or not based on the value obtained by the pre-processing. Generally, every time equipment and processing conditions are changed, it is necessary for the operator to change the program and to calculate and set the separation hyperplane (threshold) again, but by executing the machine learning process, the operator can execute the program. It can respond to changes in equipment and processing conditions without changing it.

  First, as shown in FIG. 6, learning data is acquired (step S11). Here, the learning data is data for calculating a separation hyperplane (threshold value) for determining whether welding is normal or not, and a current value when welding is normally performed in the portion to be welded 11C Not only the waveform of the above, but also the current value when the abnormal welding is performed in the portion to be welded 11C is acquired. Specifically, in step S11, a plurality of current waveforms M1 to Mi are acquired (see FIG. 3). Here, i is the number of acquired learning data.

  Next, the average values H1 to Hm are subtracted from the learning data (step S12). Specifically, as shown in FIG. 7, the average values H1 to Hm obtained in step S2 are converted to current values (B11 to Bi1) to (B1m to Bim) for each unit time of the plurality of current waveforms M1 to Mi. (Hereafter referred to as current value B). Thereby, the difference value from average value H1-Hm of current value B in each predetermined time T1-Tm of a plurality of current waveforms M1-Mi is acquired. Here, difference values in the current waveform M1 are difference values E11 to E1m, and difference values in the current waveform Mi are difference values Ei1 to Eim. Hereinafter, the difference values (E11 to E1m) to (Ei1 to Eim) are referred to as a difference value E.

  Next, a difference value E larger than the value based on the average standard deviation σ is extracted (step S13). Specifically, the magnitudes of the difference value E and the value based on the average standard deviation σ obtained in step S4 are compared. Here, the value based on the average standard deviation σ is obtained by changing the size of the average standard deviation σ to an arbitrary size (for example, 1 time, 2 times, 3 times, etc.).

  Then, when the difference value E is smaller than the value based on the average standard deviation σ, this difference value E is made zero. Further, when the difference value E is larger than the value based on the average standard deviation σ, the difference value E is held. Thus, the difference value E larger than the value based on the average standard deviation σ is extracted. An example of the difference value E of each of the plurality of current waveforms M1 to Mi thus extracted is shown in FIG.

  The number of differential values E to be extracted can be adjusted by changing the magnitude of the value based on the average standard deviation σ. For example, when the value based on the average standard deviation σ is increased, the number of extracted difference values E decreases, and when the value based on the average standard deviation σ is decreased, the number of extracted difference values E increases. That is, the number of difference values E to be extracted can be adjusted to a desired number by changing the magnitude of the value based on the average standard deviation σ to any size. Thereby, the degree of abnormality determination in the determination processing described later can be adjusted. Specifically, when the value based on the average standard deviation σ is increased, it is determined that a large difference value E (that is, one with a large fluctuation) is abnormal, and when the value based on the average standard deviation σ is decreased, the value is small. Even the difference value E (that is, one with a small variation) can be determined to be abnormal.

  Next, the feature amount of each learning data is obtained (step S14). Specifically, as shown in FIG. 8B, the values of the root sum of squares are summed at difference values E extracted in each of the plurality of current waveforms M1 to Mi. As a result, sums G1 to Gi (hereinafter referred to as sums G1 to Gi) of the absolute values of the difference values E extracted in each of the plurality of current waveforms M1 to Mi are obtained. Also, among the values of the square sum root found in the difference values E extracted in the plurality of current waveforms M1 to Mi, the largest value is the maximum value E1 (max) to Ei (max) (hereinafter, the maximum value E1 (max) ) To Ei (max)). These totals G1 to Gi and E1 (max) to Ei (max) thus obtained are the feature quantities K1 to Ki of the plurality of current waveforms M1 to Mi (see FIG. 8 (B) and FIG. 12). . The characteristic amounts Ki (sum total Gi and maximum value Ei (max)) indicate that the current values Bi1 to Bim of the current waveform Mi more deviate from the average values H1 to Hm as the values become larger.

  Next, a separation hyperplane (determinator) for determining whether or not welding is normal is generated (step S15). Specifically, with the feature amounts K1 to Ki and the maximum values E1 (max) to Ei (max) obtained in step S14 as the explanatory variables, any determination result of whether the welding is normal or not is regarded as the objective variable. Do. Then, by performing machine learning using these explanatory variables and the objective variables, a separated hyperplane L (threshold value) that determines whether welding is normal or not is calculated and generated as a determiner (see FIG. 12). Here, machine learning is a well-known method of realizing the same function as learning ability naturally performed by humans with a computer. Machine learning analyzes a set of data and extracts significant rules and judgment criteria. As methods of machine learning, for example, supervised learning and unsupervised learning are known. Supervised learning is a method of generating a function that links an explanatory variable and a target variable corresponding to the explanatory variable. Unsupervised learning is a method of generating a function from only explanatory variables. The separation hyperplane L (determinator) generated in this manner is a function of a computer program, and is stored, for example, in the RAM of the control unit 10E. Thus, the machine learning process ends.

<< judgment processing >>
In the defect determination method of the present invention, it is processing to determine whether welding is normal or not by using values obtained in the pre-processing and the machine learning processing.

  First, as shown in FIG. 9, data to be determined is acquired (step S21). Specifically, in step S21, a current waveform Xp which is a processing displacement amount is acquired. The current waveform Xp affects the quality of the article 11 and changes over time (see FIG. 3). That is, step 21 is a displacement amount acquisition step, and acquires a current waveform Xp (processing displacement amount) from the start of welding to the end of welding in the welding step which is an arbitrary processing step of the article 11. Here, p is an arbitrary integer and is the number of data to be determined. In the current waveform Xp, for example, all data from the start of welding to the end of welding are temporarily stored in the RAM or the like of the control unit 10E.

  Next, the average values H1 to Hm are subtracted from the data to be determined (step S22). Specifically, as shown in FIG. 10, the average values H1 to Hm obtained in step S2 are converted to current values Cp1 to Cpm per unit time of the current waveform Xp temporarily stored in the RAM or the like of the control unit 10E (hereinafter referred to as It is reduced from the current value C). Thereby, the difference value from the average value H1 to Hm of the current value C in the predetermined time T1 to Tm of the current waveform Xp is obtained. Here, the difference values in the current waveform Xp are referred to as difference values Qp1 to Qpm. Hereinafter, it is called difference value Q. That is, step S22 is a difference acquisition step, and the difference value Q of the current waveform Xp (processing displacement amount) with respect to the current waveforms W1 to Wn (normal displacement amount) from welding start to welding end in the welding process is predetermined time T1 Acquire every Tm.

  Next, a difference value Q larger than the value based on the average standard deviation σ is extracted (step S23). Specifically, the magnitudes of the difference value Q and the value based on the average standard deviation σ obtained in step S4 are compared. Then, when the difference value Q is smaller than the value based on the average standard deviation σ, this difference value Q is made zero. If the difference value Q is larger than the value based on the average standard deviation σ, the difference value Q is held. Thus, the difference value Q larger than the value based on the average standard deviation σ is extracted, and the difference value Q smaller than the value based on the average standard deviation σ is excluded. That is, the differential value Q thus extracted is an average value of standard deviations S1 to Sn obtained from the current waveforms W1 to Wn with respect to the average values H1 to Hm obtained from the current values A of the current waveforms W1 to Wn. It is further than the value based on the average standard deviation σ. An example of the difference value Q of the current waveform Xp thus extracted is shown in FIG. That is, step S23 is a difference value exclusion step, and based on the average standard deviation σ obtained from the current waveforms W1 to Wn within a predetermined range with respect to the current waveforms W1 to Wn (normal displacement amount) among the difference values Q. Range is excluded every predetermined time T1 to Tm.

  Next, the feature amount of the data to be determined is obtained (step S24). Specifically, as shown in FIG. 11B, the sum of squares is summed at the difference value Q extracted as the extraction value in the current waveform Xp. As a result, a sum Rp (hereinafter referred to as a sum Rp) of the absolute values of the difference values Q extracted in the current waveform Xp is obtained. Further, among the values of the root-sum-of-squares calculated in the difference value Q extracted in the current waveform Xp, the largest value is the maximum value Qp (max) (hereinafter referred to as the maximum value Qp (max)). The sum total Rp and the maximum value Qp (max) thus obtained are the feature amount Up of the current waveform Xp (see FIG. 11B). That is, step S24 is a first calculation step, and the sum total Rp and the maximum value Qp (max) are obtained. The feature amount Up (sum total Rp and maximum value Qp (max)) indicates that the current values Cp1 to Cpm of the current waveform Xp deviate more from the average values H1 to Hm as the values become larger.

  Next, the separated hyperplane L (determinator) generated in step S15 is read (step S25). Specifically, a separation hyperplane L (determinator) which is a function of a computer program stored in the RAM or the like of the control unit 10E is read into the CPU.

  Next, the separation hyperplane L (determiner) is compared with the feature amount Up to determine whether the data to be determined is normal (step S26). Specifically, as shown in FIG. 12, the separation hyperplane L (determinator) is a plane J having the sum total Rp on the horizontal axis and the maximum value Qp (max) on the vertical axis, and the sum Rp , And the maximum value Qp (max) is expressed as a line segment that divides the area where each becomes positive into two areas J1 and J2. In this case, when the feature amount Up (the sum Rp of the current waveform Xp and the maximum value Qp (max)) is located in one region J1 divided by the separation hyperplane L (threshold), the current waveform Xp is good. When the characteristic amount Up (the sum Rp of the current waveform Xp and the maximum value Qp (max)) is located in the other region J2 divided by the separation hyperplane L (threshold), the current waveform Xp is defective. It is determined that That is, step S26 is a determination step, and it is determined whether or not the total sum Rp and the maximum value Qp (max) obtained in step S24 deviate from the separation hyperplane L (threshold value). The form of the separation hyperplane L is not limited to the form disclosed in FIG.

  In step S26, the total sum Rp is divided by the welding time Tend. The value F thus obtained indicates the magnitude of the total sum Rp with respect to a unit time (for example, one second). That is, step S26 also includes a second calculation step, and the sum total Rp obtained in step S24 is divided by the welding time Tend in the welding step. Thereby, for example, when welding at different welding times is performed using this welding apparatus 1, it is possible to handle the values F of the other without being distinguished from each other without being influenced by the welding time. Thus, the determination process ends.

  As described above, in the defect determination method, in the current waveform Xp that changes with time, the difference value Q for the current waveforms W1 to Wn is obtained every predetermined time T1 to Tm, and the sum of the absolute values of the difference values Q is obtained. The maximum value Qp (max) of the absolute values of Rp and difference value Q is determined, and the sum Rp of the absolute values of difference value Q and the maximum value Qp (max) of the absolute values of difference value Q are separated into hyperplane L ( It is determined whether or not it deviates from the threshold). That is, the entire difference value is calculated by the sum Rp of the absolute values of the difference values Q, the maximum difference value is calculated by the maximum value Qp (max) of the absolute values of the difference values Q, and the calculation result is Not only the current waveform Xp is entirely separated from the current waveforms W1 to Wn, but also the case where the current waveform Xp is locally separated from the current waveforms W1 to Wn by determining based on the threshold). Can also be detected as abnormal. Therefore, it can be accurately determined whether or not the welding process is normally performed on the article 11.

  Therefore, the defect determination method of the present invention can easily determine whether the processing of the article 11 is normal.

  Further, the defect determination method includes a difference value exclusion step of excluding a range based on the average standard deviation σ obtained from the current waveforms W1 to Wn from the difference values Q with respect to the current values W1 to Wn. . Therefore, this defect determination method does not extract the difference value Q when the difference value Q generated in the welding process on the article 11 is not apart from the range based on the average standard deviation σ obtained from the current waveforms W1 to Wn. . By this, it is possible to prevent a normal processing from being erroneously determined as an abnormal processing. For example, if a minute deviation is also accumulated as the difference value Q, a large difference will result. However, this minute deviation is within the range of normal processing and is a value that should not be extracted. If the normal processing is abnormal processing by excluding the range based on the average standard deviation σ obtained from the current waveforms W1 to Wn with respect to the current waveforms W1 to Wn from the differential value Q generated in normal processing It is intended to prevent an erroneous determination. Then, the total sum Rp of the absolute values of the difference value Q is obtained in this way, and the maximum value Qp (max) of the absolute value of the difference value Q is obtained, so that it can be accurately determined whether or not the processing is normally performed.

  Further, the predetermined range of the defect determination method is a range based on the average standard deviation σ obtained from the current waveforms W1 to Wn. Generally, the range of variation of the difference value Q generated in normal processing is different for each facility, but in this failure determination method, a predetermined range is determined by using the average standard deviation σ obtained from the current waveforms W1 to Wn. It can be set to a desired ratio with respect to the overall variation. As a result, it is possible to accurately determine whether or not welding processing on the article 11 has been normally performed without changing the predetermined range, even for equipment having different ranges of variation in difference value Q.

  The defect determination method also includes a second calculation step of reducing the sum Rp of the absolute values of the difference values Q determined in step S24 by the welding time Tend in the welding process. Therefore, this defect determination method does not take into consideration the length of welding time Tend in the welding process of article 11 to weld portion 11C (that is, the length of the waveform of current waveform Xp from the start of welding to the end of welding). It's over.

The present invention is not limited to the embodiments described above with reference to the drawings. For example, the following embodiments are also included in the technical scope of the present invention.
(1) In the embodiment, the current waveform is acquired as the displacement amount, but the voltage waveform or the movement amount of the torch portion with respect to the welded portion may be acquired and used as the displacement amount. Also, these values may be used in combination.
(2) In the embodiment, the average standard deviation is changed to 1 ×, 2 ×, 3 ×, etc. to compare the magnitude with the difference value, but the average standard deviation may be changed to less than 1 × , May be changed more than three times.
(3) In the embodiment, the welding process is illustrated, but other processes such as cutting process and bending process may be used.
(4) In the embodiment, the movement of the torch portion along the portion to be welded is exemplified by the moving mechanism, but the portion to be welded may be moved along the torch portion.

  11 ... article, L ... separated hyperplane (threshold), Q (Qp1 to Qpm) ... difference value, Qp (max) ... maximum absolute value of difference value, Rp ... sum of absolute value of difference values, Tend ... welding Time (machining time), Xp ... current waveform (machining displacement amount), σ ... average standard deviation (standard deviation obtained from normal displacement amount), W1 to Wn ... multiple current waveforms (normal displacement amount)

Claims (4)

A non-defective item data acquisition step of acquiring a normal displacement amount of the article which affects the quality of the article in any processing step of the article and changes over time;
A displacement amount acquisition step of acquiring a processing displacement amount which affects the quality of the article in an arbitrary processing step of the article and changes over time;
A difference acquisition step of acquiring, for each unit time, a difference value of the processing displacement amount with respect to the normal displacement amount in the processing step;
A first calculation step of obtaining a sum of absolute values of the difference values and a maximum value of the absolute values of the difference values;
A determination step of determining whether or not the sum of the absolute values of the difference values obtained in the first calculation step and the maximum value of the absolute values of the difference values deviate from a threshold value;
The defect determination method characterized by having.   The defect determination method according to claim 1, further comprising: a difference value exclusion step of excluding a predetermined range from the difference value with respect to the normal displacement amount.   The defect determination method according to claim 2, wherein the predetermined range is a range based on a standard deviation obtained from the normal displacement amount.   The method according to any one of claims 1 to 3, further comprising a second calculation step of dividing the sum of the absolute values of the difference values obtained in the first calculation step by the processing time in the processing step. Defect judgment method described in.

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