JP3302148B2 - Assembling work time prediction apparatus and method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an assembling work time estimating apparatus and method for estimating the assembling work time of a new product which is the basis of various production planning work.

[0002]

2. Description of the Related Art Conventionally, the prediction of the assembly work time of a new product is as follows.
Estimate the product of the number of parts and the standard working time of the assembling person or machine, or, if you have assembled a product with the same number of parts in the past and know the working time at that time, the working time You are using However, since such a prediction method has a large error, the predicted value is multiplied by a coefficient larger than 1 to obtain a final predicted value of the assembly work time so that the capability of a person or a machine does not actually occur.

In the case of an assembly machine, a program creation support system such as an offline teaching system of a robot having an operation time calculation model of the assembly machine is used to input part data of a product, and to assemble a part assembly sequence and an operation program. , And assembling work time.

[0004]

However, the above conventional example has the following problems.

(1) The estimated value of the assembly operation time based on the product of the number of parts and the standard operation time of the assembly operator or the assembly machine does not take into account the differences in the various assembly operations of the assembly operator or the assembly machine. The error from the actual assembly work time is large. For this reason, the obtained assembly work time is simply multiplied by a coefficient larger than 1 to obtain a final estimated value of the assembly work time so that the capacity shortage does not occur. Therefore, it is not possible to accurately predict the assembly operation time.

(2) There is no product assembled in the past with exactly the same number of parts and the same working conditions as a new product, and even if assembling work time for a similar product as close to the new product as possible is obtained, Large difference from the assembly time. For this reason, the obtained assembly work time is simply multiplied by a coefficient larger than 1 to obtain a final estimated value of the assembly work time so that the capacity shortage does not occur. Therefore, it is not possible to accurately predict the assembly operation time.

(3) A program creation support system such as an off-line teaching system for a robot having an operation time calculation model of an assembly machine can usually use only one operation time calculation model for one type of assembly machine. Even if there are a plurality of assembling machines of the same model, and there are machine differences that cannot be ignored between these assembling machines, calculation cannot be performed in consideration of the machine differences. Also, in order to build a model including machine differences for the same machine, it is necessary to actually measure the dynamic characteristics of all machines under various conditions,
For this reason, a great amount of time was required and it was impossible.

The present invention has been made in view of the above-described conventional problems, and has an assembling work time estimating apparatus capable of accurately estimating an assembling work time without spending a great deal of time and calculating the estimated value quickly. The purpose is to provide.

[0009]

In order to achieve the above object, an assembling work time estimating apparatus according to the present invention comprises a predicting object data inputting means for inputting data relating to an assembling work time estimating target product; types of parts, the performance data, including the type-specific points and assembly work time of the parts, double
Means for storing performance data for each of the number of mounting lines, and selecting one of the plurality of mounting lines.
Selecting means for-option, with reference to the data regarding the product of the object, the stored in the actual data storage means
From the actual data of the selected mounting line, selecting means for selecting a track record data which satisfies a predetermined similarity, based on actual data wherein the selected, the selection of the products
And a prediction processing unit for predicting the assembly work time in the completed mounting line . Preferably, the prediction processing unit creates an assembly work prediction model based on the selected actual data, and predicts the assembly work time with reference to the assembly work prediction model. Preferably, the prediction processing section, the actual data storage unit, if the actual data that satisfies the similarity of the plant constant is not stored, using the standard time of the mounting apparatus in the selected mounting line Thus, the assembly work time of the target product is predicted.

[0010] The assembly work time prediction method according to the present invention, the over
The type of parts of the product in the past, the number of parts by type and the set
The mounting data, including the stand-by time, is
The data is stored in the actual data storage means for each mounting line ,
One mounting line is selected from the mounting lines, and the selected mounting line stored in the performance data storage means is selected.
The actual data satisfying a predetermined similarity with the data on the target product is selected from the actual result data of the target, and the assembly work time of the target product is predicted based on the selected actual data. Also, the actual data storage means, if the actual data that satisfies the similarity of the plant constant is not stored, using standard work time of mounting apparatus of the selected mounting line, of the product of the target Perform assembly work time prediction.

[0011]

[0012]

[0013]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a block diagram showing a schematic configuration of an embodiment of an assembling work time estimating apparatus according to the present invention. The assembling work time estimating apparatus according to this embodiment estimates the mounting time of electronic components on a printed circuit board. Is what you do.

The assembling work time estimating apparatus comprises an assembling work time estimating apparatus 1 having a configuration to be described later, and a terminal 2 realized by a display and a keyboard. The performance data 3 indicates performance data in a management system of an electronic component mounting line (hereinafter, referred to as a mounting line). In the present embodiment, the performance data 3 is input to the assembly work time prediction device 1 via a network. Reference numeral 4 in the drawing denotes a printed circuit board design CAD, which designs the circuit design of various printed circuit boards incorporated in the electronic component and the mounting layout of the electronic component on the printed circuit board. This printed circuit board design CA
From D4, component data of each printed circuit board mounted on the assembling work time estimation device 1 is input via a network or a magnetic recording medium.

The assembling work time predicting apparatus 1 includes a performance data editing processing unit 101, a performance data storage unit 102, a mounting line data editing processing unit 103, and a mounting line data storage unit 10.
4. It has a mounting machine data edit processing unit 105 and a mounting machine data storage unit 106.

The result data edit processing unit 101 fetches result data 3 in response to a processing request from the terminal 2 and stores it in the result data storage unit 102. The performance data 3 is, for example, the type of mounting parts for each product of each mounting machine stored in the mounting line management system, the number of mounting points, and the mounting work time data for each product. The data is input by the unit 101 via a network or a magnetic recording medium, and is stored in the performance data storage unit 102.

Further, the performance data editing processing unit 101
Result data storage unit 102 in response to a processing request from terminal 2
Display and change the contents of the data stored in. FIG. 2 shows the contents of the performance data storage unit 102. The data contents in FIG. 2 show a case where a plurality of mounting machines are connected to form a line. The actual line data edit processing unit 103 adds and corrects the mounting line name and the name of the mounting machine constituting the line in response to the processing request from the terminal 2, stores the data in the mounting line data storage unit 104, and The data stored in the line data storage unit 104 is displayed and corrected. Mounting line data storage unit 104
3 is shown in FIG.

The mounting machine data editing processing unit 105
The display, correction, and addition of the data such as the mountable components and the mounting standard time of each mounting machine stored in the mounting machine data storage unit 106 in response to a processing request from. FIG. 4 shows the contents of the mounter data storage unit 106. Here, the mountable component type 401 in FIG. 4 describes the types of components that can be mounted by the mounting machine, and the mounting standard time section 402 groups component types that can be mounted in the same mounting time. It is. The mounting standard time 403 corresponds to the mounting standard time for the identification symbol of each mounting standard time section 402.

Further, the assembling work time estimating apparatus 1 includes a board process data editing unit 107, a board process data storage unit 1
08, a component data edit processing unit 109, a component data storage unit 110, a component priority data edit processing unit 111, and a component priority data storage unit 112.

The board process data edit processing section 107 is provided with a board process data storage section 108 in response to a processing request from the terminal 2.
The display, correction, and addition of the mountable lines of the various calculation target boards stored in. FIG. 5 shows the contents of the substrate process data storage unit 108.

In response to a processing request from the terminal 2, the component data edit processing unit 109 inputs, via a network, component data used for mounting a calculation target board from the printed circuit board design CAD 4 and stores it in the component data storage unit 110. . When the printed circuit board design CAD 4 is not connected by a network, the component data is output from the printed circuit board design CAD 4 to the magnetic recording medium, and the data of the magnetic recording medium is input from the component data editing processing unit 109.

The component data edit processing unit 109 displays, corrects, and adds component data of a board to be calculated stored in the component data storage unit 110 in response to a processing request from the terminal 2. FIG. 6 shows the contents of the component data storage unit 110. As shown in FIG. 6, the data stored in the component data storage unit 110 includes a part number, a component type, and the number of components to be mounted for each surface of each board to be calculated.

The component priority data editing unit 111 responds to a processing request from the terminal 2 and
The display, correction, and addition of data relating to the component type and the mounting priority stored in the storage unit 12 are performed. FIG. 7 shows the contents of the component priority data storage unit 112. In FIG. 7, the mounting priority indicates that the smaller the value is, the higher the priority is, and that a component type with a higher priority must be mounted before a component type with a lower priority. This priority is X
-When mounting one surface of one board using multiple Y-table moving mounters, large
-When the Y-tape is moved, the inertia of the component becomes larger than the adhesive force of the cream solder holding the component to the board,
The components are provided to prevent the components from being displaced or falling off on the substrate. That is, the constraint that the high-priority component is mounted by driving the XY table at a high speed, and the next low-priority component is mounted by driving the XY table at a lower speed than before. It is.

Further, the assembling work time prediction device 1 is provided with an assembling work time prediction processing unit 113 and an assembling work time prediction result storage unit 114. The assembling work time prediction processing unit 113 responds to a processing request from the terminal 2 to store the actual data storage unit 102, the mounting line data storage unit 104, the mounting machine data storage unit 106, the board process data storage unit 108,
The respective data of the component data storage unit 110 and the component priority data storage unit 112 are input, a mounting time prediction value of the designated calculation target board is obtained, and the result is stored in the assembly work time prediction result storage unit 114. Or displayed on the terminal 2. FIG. 8 shows an outline of a processing flow of the assembly work time prediction processing unit 113.

In the figure, first, a board whose work time has not been predicted is selected from the designated boards to be calculated (step S801), and then a plurality of mountable lines of the selected board are calculated. If so, a line that has not been examined is selected (step S802). afterwards,
The component mounting data of the specified board is stored in the component data storage unit 1.
10 and the result data mounted on the same line from the result data storage unit 102.

Then, the similarity of the component configuration between the calculation board and the actual data is evaluated using a similarity function (step S8).
03), similarity functions include a Pearson correlation coefficient, a Euclidean square distance, a standardized Euclidean distance, and a Mahalanobis general distance. Here, the similarity dij is calculated using the Euclidean square distance. Here, the Euclidean square distance is defined as follows.

[0028]

(Equation 1) Here, Xki is the number of components in the mounting standard time section Xk of the calculation target board i, and Xkj is the number of components in the mounting standard time section Xk of the actual board j. The sum of squares of the difference in the number of components is the Euclidean square. Distance. The smaller the Euclidean square distance is, the higher the similarity between the calculation target substrate and the actual substrate is.

Next, as for the similarity obtained between the calculation target substrate and the actual substrate, it is determined whether or not the actual data having a value equal to or less than a prescribed value is obtained by a sufficient number for performing multiple regression analysis (step S804). . It is generally said that the number of pieces of actual data sufficient to perform the multiple regression analysis is n ≧ p + 11. Here, n is the number of data, and p is the number of explanatory variables, and here corresponds to the number of mounting standard time segments of the mounting machine.

If the conditions are not satisfied, calculation is performed using the standard working time of the mounting machine on the mounting line. If these conditions are satisfied, a mounting time prediction model is created by a multiple regression analysis (step). S805). The multiple regression analysis is performed on a multiple regression model using the mounting standard time section of each mounting machine of the selected mounting line as an explanatory variable and the mounting time as an objective variable. For this reason, first, the actual data is processed, and multiple regression analysis input data as shown in FIG. 9 is created for each mounting machine.

In FIG. 9, the time 901 is the actual value of the assembly work time required for mounting,
Is the number of mounting points of a component type corresponding to a certain mounting standard time division of the mounting machine.

Subsequently, a multiple regression model for the following assembly work time calculation is assumed for each mounting machine, and A0, A1, A2, and A3 are estimated by multiple regression analysis. The following equation is an example of a multiple regression model for assembling work time calculation.

Y = A0 + A1 * X1 + A2 * X2 + A3 * X3 Here, Y is called an objective variable and represents an assembly work time.
X1, X2, and X3 are called explanatory variables, and in this case, represent the mounting points of each mounting standard time section. A0 is called the intercept and in this case represents the bias term of the working time. A1, A
2, A3 is called a partial regression coefficient, and in this case, represents an assembling time per part in each mounting standard time section.

Next, whether or not the assembly operation time calculation model obtained by the multiple regression analysis is valid for each mounting machine is determined by checking the sign of the coefficient of the multiple regression model and determining the statistical superiority ( Step S806). Checking the sign of the coefficient of the multiple regression model is to check whether the sign of the partial regression coefficient of the obtained partial regression model of the multiple regression model is positive. Therefore, the obtained assembly work time model becomes invalid.

As a method of judging the statistical superiority of the multiple regression model, a generally used analysis of variance is used. As a result of these checks, if the multiple regression model is invalid even for one of the mounting machines on the specified mounting line, all the mounting machines on the specified mounting line do not use the multiple regression model Calculated by the assembly work model using. Therefore, when the multiple regression model becomes invalid, the process jumps to step S810.

When the multiple regression model for assembling time calculation obtained for all the mounting machines on the designated line is valid, the standard working time model of the mounting machine is replaced with the obtained assembling time calculation model. (Step S80
9).

Next, the components of the board to be calculated are allocated to the mounting machine of the designated line to determine the assembly work time ( step
S810 ). In allocating components, component priority data is input from the component priority data storage unit 112 so as to satisfy component priorities and to make the load of the mounting machine in the designated line as uniform as possible. The assembly work time prediction value of the calculation target board obtained by the component allocation is stored in the assembly manual work time prediction result processing unit 114.

Further, it is checked whether or not all the mountable lines of the calculation target board have been examined (step S).
811) If there is an unexamined line, step S802
Do from.

When the estimation of the assembly work time is completed for all the mountable lines of a certain board to be calculated, it is checked whether there is a board to be calculated that has not been studied (step S812). Returning, the next calculation target board is selected and the assembly work time is estimated.

The predicted assembly work time for the specified mountable line of the specified board to be calculated is stored in the assembly work time prediction result storage unit 114. FIG. 10 shows data contents of the assembly work time prediction result storage unit 114.

In FIG. 10, a line tact 1001 is a maximum value of an assembly operation time (tact) in a plurality of mounting machines in a mounting line, and this value determines a production capacity of a substrate to be calculated. Will be. Regression 1002 indicates whether the predicted value of the assembly work time was calculated by the multiple regression model,
It indicates whether the calculation was performed using the mounting standard time of the mounting machine. When the symbol in this column is ○, the multiple regression model is used, and when the symbol is ×, the mounting standard time of the mounting machine is used.

The assembly work time prediction result stored in the assembly work time prediction result storage unit 114 is displayed on the terminal 2 according to an instruction from the terminal 2 or printed on paper by a printing machine when a printing machine is connected. Shall be performed.

According to the present embodiment, if the newly mounted board has a high similarity in component configuration to the previously mounted printed circuit board, the component distribution of each mounting machine of the previously mounted printed circuit board and the Just collect the implementation time of
The mounting time of a newly mounted board can be predicted with very high accuracy.

Thus, the equipment load can be predicted with high accuracy, the operating rate of the mounting machine can be improved, and a great effect can be exerted on improving the productivity and reducing the production cost. In addition, since the person in charge does not need to spend a long time calculating the mounting time from the number of components and the standard mounting time of the mounting machine, and because the dynamic characteristic data of each mounting machine is included in the program creation support system, A high-accuracy mounting time model can be created and mounting time can be predicted with high accuracy without collecting dynamic characteristic data for a huge number of mounting machines at a great cost and time.

Next, another embodiment of the present invention will be described.

In the above embodiment, the board to be calculated and the board similar to the board are obtained from the actual data storage unit 102, but the whole mounted parts of the board to be calculated and the whole parts of a certain board in the mounting machine data are targeted. It is also possible to obtain a similar allocation state for each mounting machine from the performance data storage unit 102 using the allocation component data of the mounting machine to be mounted, create an assembly work time prediction model by multiple regression analysis, and predict the mounting time. . Hereinafter, this embodiment will be described in detail.

First, any one of the mountable lines of the board to be calculated is selected, and the mounting machine data storage unit 106 is selected.
From the mounting machine data of the line. The man-hour of the mounting machine data is a mounting standard man-hour, and the components of the board to be calculated are allocated to each mounting machine using the mounting standard man-hour. This allocation method is the same as step S810 in FIG.

Next, performance data having a component assignment similar to that of the mounting machine of the board to be calculated for each mounting machine is obtained from the performance data storage unit 102 using the Euclidean square distance. This process is the same as step S803 in FIG.
If the similar mounting allocation state is obtained equal to or more than the reference value, multiple regression analysis is performed for each mounting machine, and the validity of the obtained mounting time prediction model is determined in the same manner as in step S806 in FIG.

If valid, the standard work time of the mounting machine is replaced with a mounting time prediction model, and then the components of the board to be calculated are again allocated to the mounting machine of the selected line to calculate the mounting time. If a similar mounting machine allocation state is not sufficiently obtained, or if the mounting time prediction model obtained by multiple regression analysis is not valid, the mounting time based on the mounting machine standard man-hour calculated first is used for mounting the target board. Time.

The estimated mounting time thus obtained is shown in FIG.
It is stored with the same contents as 0, and can be displayed and printed according to an instruction from the terminal 2. According to this embodiment, the same effects as those of the above embodiment can be obtained. That is,
Of the assembly work time by the product of the number of parts and the standard operation time
Search for predictions and past assembly times for similar products
Using a method that increases the error, such as using
Can accurately predict the assembly work time in a short time
it can. As a result, the operating rates of equipment and
Productivity can be improved. In addition, future facilities
Accurate grasp of the number of equipment by accurate load forecast
Create efficient and efficient capital investment plans
Becomes possible. In addition, assembly work including machine differences between assembly machines
Dynamic characteristics of all assembly machines to build a working time calculation model
Without spending a lot of time measuring and counting
Types of parts assembled by machine and assembly work time at that time
Only the very simple task of collecting
It is possible to predict the assembly work time.

[0051]

As described in detail above, according to the present invention, a highly accurate assembly time can be predicted.
As a result, the operation rates of facilities and personnel are improved, and productivity can be improved. Further, since the future facility load is accurately predicted, the number of facilities can be accurately grasped, and a lean and efficient facility investment plan can be created.

In other words, the conventional technique of estimating the assembly work time based on the product of the number of parts and the standard operation time, and the method of increasing the error such as retrieving the past assembly work time of similar products and using the same value are used. , It is possible to accurately predict the assembly work time in a short time. This allows
The operation rate of equipment and personnel is improved, and productivity can be improved. Further, since the future facility load is accurately predicted, the number of facilities can be accurately grasped, and a lean and efficient facility investment plan can be created.

In order to construct an assembly work time calculation model including machine differences between assembling machines, a large amount of time is not required for measuring and totaling dynamic characteristics of all the assembling machines. It is possible to predict the assembly work time with high accuracy only by a very simple work of collecting only the type of the component and the assembly work time at that time.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of an embodiment of an assembly work time prediction device according to the present invention.

FIG. 2 is a diagram showing the contents of a performance data storage unit 102;

FIG. 3 is a diagram showing the contents of a mounting line data storage unit 104;

FIG. 4 is a diagram showing the contents of a mounting machine data storage unit 106;

FIG. 5 is a diagram showing the contents of a substrate process data storage unit 108;

FIG. 6 is a diagram showing contents of a component data storage unit 110;

FIG. 7 is a diagram showing the contents of a component priority data storage unit 112;

FIG. 8 is a diagram showing an outline of a processing flow of an assembly work time prediction processing unit 113;

FIG. 9 is a diagram showing multiple regression analysis input data.

FIG. 10 is a diagram showing data contents of an assembly work time prediction result storage unit 114;

[Explanation of symbols]

 REFERENCE SIGNS LIST 1 Assembly work time prediction device 2 Terminal 3 Actual data 101 Actual data editing processing unit 102 Actual data storage unit 103 Mounting line data editing processing unit 104 Mounting line data storage unit 105 Mounting machine data editing processing unit 106 Mounting machine data storage unit 107 Substrate Process data edit processing unit 108 Board process data storage unit 109 Component data edit processing unit 110 Component data storage unit 111 Component priority data edit processing unit 112 Component priority data storage unit 113 Assembly work time prediction processing unit 114 Assembly work time prediction result Storage

Continuation of the front page (58) Field surveyed (Int. Cl. ⁷ , DB name) B23P 21/00 G05B 19/418 G06F 17/60

Claims (5)

(57) [Claims] 1. A prediction target data input means for inputting data on a product whose assembly work time is to be predicted, and a plurality of actual data including past types of parts of the product, points of each type of parts, and assembly work times. Of the line
Result data storage means for storing each mounting line, and selecting means for selecting one from the plurality of mounting lines
And referring to the data related to the target product , and selecting the selected mounting line stored in the performance data storage unit.
From the emission actual data, selecting means for selecting a track record data which satisfies a predetermined similarity, based on actual data wherein the selection, the said products
A prediction processing unit for predicting an assembly operation time in a selected mounting line . 2. The method according to claim 1, wherein the prediction processing unit creates an assembly work prediction model based on the selected actual data, and predicts an assembly work time with reference to the assembly work prediction model. An assembling work time prediction device according to Claim 1. Wherein the prediction processing section, the actual data storage unit, if the actual data that satisfies the similarity of the plant constant is not stored, mounting apparatus in the selected mounting line <br/> The assembly work time prediction apparatus according to claim 1, wherein the assembly work time prediction of the target product is performed using the standard time. 4. The type of parts in a past product,
More than one type of mounting data, including points by type and assembly
In the actual data storage means for each mounting line of the mounting line
Storing, inputting data on the assembly work time prediction target product, and selecting one mounting line from the plurality of mounting lines
And the actual data storage means stored in the said selected real
From the actual data of the mounting line, selecting actual data that satisfies a predetermined similarity with the data on the target product, and predicting an assembly work time of the target product based on the selected actual data. Assembly work time prediction method. If the method according to claim 5, wherein the actual data storage means, actual data that satisfies the similarity of the plant constant is not stored,
The assembly work time prediction method according to claim 4, wherein the assembly work time of the target product is predicted using a standard work time of a mounting machine of the selected mounting line .