Automatic production line multi-model formula classification calling method and system

Abstract

The invention discloses an automatic production line multi-model formula classification calling method and system. The method comprises the following steps: analyzing formula parameters of various product models; the formula parameters are divided into common parameters and independent parameters; the formula is divided into a long bearing plate formula and a short bearing plate formula according to the independent parameters; generating a new formula containing common parameters and independent parameters; and calling the corresponding new formula according to the product model in the production process. The invention also provides a system for realizing the method. According to the method and the system, the remodeling debugging time can be greatly shortened, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of automated production technology, and in particular to a method and system for classifying and calling multi-model recipes for an automated production line. Background Art

[0002] In an automated production line, it is usually necessary to call corresponding recipe parameters according to different product models. At present, the commonly used method is to have one product correspond to one recipe, that is, a one-to-one recipe calling method. For example, for an automated production line that can produce more than 50 types of products, more than 50 corresponding recipes need to be set.

[0003] This one-to-one recipe calling method has the following technical problems:

[0004] 1) When changing product types, the corresponding formula needs to be changed and called synchronously, which is cumbersome;

[0005] 2) When the parameters in a recipe (such as servo parameters) need to be corrected, the parameters at the same position in other recipes also need to be corrected synchronously, which is a lot of work;

[0006] 3) The debugging burden during the changeover is heavy and the changeover time is long;

[0007] 4) High skill requirements for operators.

[0008] In summary, the existing technology takes a long time to debug and change models, the burden on employees to change models independently is large, and the skills required of employees are high. The above factors directly determine the production output requirements for the day, reduce the monthly planned yield, and the overall production efficiency declines. In terms of structural characteristics, the existing technology formula is called one-to-one. In a certain formula, the Z-axis servo position of the support plate is adjusted from 12.5 to 12.0, then all other formulas with the same structure need to adjust this servo position from 12.5 or other uniformly to 12.0. If there are many product models involved, the debugging workload will increase immediately.

[0009] These problems directly affect production efficiency and reduce equipment utilization and production capacity. Therefore, a more efficient recipe calling method is urgently needed to meet the needs of multi-model product production and improve the production efficiency of automated production lines. Summary of the invention

[0010] The purpose of the present invention is to provide a method and system for classifying and calling multi-model recipes of an automated production line, aiming to solve the technical problems existing in the one-to-one recipe calling method in the prior art and improve the production efficiency of the automated production line.

[0011] To achieve the above-mentioned object, in a first aspect, the present invention provides a method for classifying and calling multi-model recipes of an automated production line, comprising the following steps:

[0012] S1, analyze the formulation parameters of various product models;

[0013] S2, divide the recipe parameters into two categories: common parameters and independent parameters;

[0014] S3, dividing the formula into a long carrier plate formula and a short carrier plate formula according to independent parameters;

[0015] S4, generating a new recipe including shared parameters and independent parameters;

[0016] S5, during the production process, the corresponding new formula is called according to the product model.

[0017] Preferably, the step S2 specifically includes:

[0018] S21, analyzing the differences of the same servo position parameters in different recipes;

[0019] S22, setting the parameters whose differences are within a preset range as common parameters;

[0020] S23, setting the parameters whose differences exceed the preset range as independent parameters.

[0021] Further preferably, in step S22, the preset range is ±10.

[0022] Further preferably, the step S22 further includes:

[0023] S221, actual position calibration and verification of the shared parameters;

[0024] S222, taking the average value of the shared parameters as the final shared parameters.

[0025] Preferably, the step S3 specifically includes:

[0026] S31, the independent parameters are divided into two categories according to the size of the difference;

[0027] S32, performing actual position calibration and verification on each type of independent parameter;

[0028] S33, taking the middle value of each type of independent parameter as the final independent parameter of that type.

[0029] Preferably, the step S4 specifically includes:

[0030] Combine the common parameters with the independent parameters of the long support plate to generate a long support plate formula;

[0031] The common parameters are combined with the short support plate independent parameters to generate the short support plate formula.

[0032] In a second aspect, the present invention provides a system for classifying and calling multi-model recipes for an automated production line, comprising:

[0033] Parameter analysis module, used to analyze the formulation parameters of various product models;

[0034] Parameter classification module, used to classify recipe parameters into common parameters and independent parameters;

[0035] A recipe generation module, used for generating a new recipe according to the common parameters and the independent parameters;

[0036] The recipe calling module is used to call the corresponding new recipe according to the product model during the production process.

[0037] Preferably, the system further comprises a parameter calibration module for performing actual position calibration and verification on the common parameters and the independent parameters.

[0038] Preferably, the system further comprises a human-computer interaction module for displaying recipe information and receiving operation instructions.

[0039] The beneficial effects of the present invention are:

[0040] 1) By classifying recipe parameters and generating new recipes, many-to-one recipe calls are achieved, greatly reducing the debugging time for model change;

[0041] 2) The setting of common parameters reduces the workload of parameter modification and improves operational efficiency;

[0042] 3) Simplifies the recipe calling process and reduces the skill requirements for operators;

[0043] 4) Improved the production efficiency and equipment utilization of automated production lines.

[0044] The present invention is suitable for the classification of formulas of automated production lines of multiple models. Combining more than 50 models of products and the corresponding formula characteristics, more than 50 formulas are classified and sorted into two categories of formulas. Thus, a leapfrog transformation from one-to-one calling to many-to-one calling is formed. The changes in servo parameters of formulas with the same structure are eliminated, and a unified adjustment of formula parameters is formed. By classifying and integrating existing formulas, the workload of model change debugging between different product models can be reduced, thereby improving production efficiency; at the same time, it also solves the problem of needing to modify all formulas one by one when certain common parameters change, further reducing the debugging workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments disclosed in the present invention, the drawings of the embodiments will be briefly introduced below. These drawings are only used for illustrative purposes and are not intended to limit the protection scope of the present invention.

[0046] Figure 1 This is a flow chart of a method for classifying and calling multiple models of recipes for an automated production line provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention (including the preferred technical solution) is further described in detail below by means of the accompanying drawings and by listing some optional embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1 This embodiment provides a method for classifying and calling multi-model recipes of an automated production line, comprising the following steps:

[0050] S1, analyze the formulation parameters of various product models.

[0051] The original formulas correspond to the product models one by one, with more than 50 kinds. For the convenience of expression, the whole text will be represented by codes: A0, A1, A2, A n+1 , B0, B1, B2, B n+1 .

[0052] When a servo parameter in recipe A0 changes, the same servo parameter in other recipes A1, A2, A3, etc. needs to be changed accordingly. This change process is called debugging process.

[0053] After the formula of the present invention is divided into two formulas, namely, A series_long support plate and B series_short support plate, the corresponding formula changes are directly eliminated.

[0054] Specifically, first, the recipe parameters of all product models on the automated production line are collected. In this embodiment, the automated production line is compatible with the production of more than 50 types of products, and there are more than 50 recipes accordingly. Each recipe contains multiple servo position parameters, such as the Z-axis position of the take-up plate, the Z-axis position of the place-down plate, the X-axis position of the take-up plate, the X-axis position of the place-down plate, etc.

[0055] S2, divides recipe parameters into two categories: common parameters and independent parameters.

[0056] This step specifically includes:

[0057] S21, analyzing the differences of the same servo position parameters in different recipes. For example, comparing the differences of the Z-axis position parameters of the take-up plate and the Z-axis position parameters of the place-down plate in all recipes.

[0058] S22, setting the parameters whose differences are within a preset range as common parameters. In this embodiment, the preset range is ±10. That is, if the maximum difference of a servo position parameter in all recipes does not exceed 20, the parameter is set as a common parameter.

[0059] S23, setting the parameters whose differences exceed the preset range as independent parameters.

[0060] For the servo position parameters set as common parameters, the following processing is required:

[0061] S221, actual position calibration and verification of common parameters. The specific method is: first calculate and debug to find the "0" position of the servo motor, take the "0" position as the origin, and "position start" in sequence according to the recipe parameters, and record the position each time.

[0062] S222, taking the average value of the common parameters as the final common parameters. For example, the values ​​of the Z-axis position parameters of a carrier plate in different recipes are 11, 12, 12.5, 13, and 14. After removing the maximum and minimum values, the average value (12+12.5+13) / 3=12.5 is taken as the final common parameter.

[0063] S3, divide the recipe into long carrier recipe and short carrier recipe according to independent parameters.

[0064] This step specifically includes:

[0065] S31, classify the independent parameters into two categories according to the difference. For example, for the X-axis position parameter of the support plate, there may be multiple values ​​such as 680, 684, 688, 690, 694, 698, etc., which can be divided into 680, 684, 688 as one category and 690, 694, 698 as another category.

[0066] S32, performing actual position calibration and verification on each type of independent parameter.

[0067] S33, taking the middle value of each type of independent parameter as the final independent parameter of the type. For example, for the type 680, 684, 688, the middle value 684 is taken as the X-axis position parameter of the long support plate for placing the support plate; for the type 690, 694, 698, the middle value 694 is taken as the X-axis position parameter of the short support plate for placing the support plate.

[0068] S4, generating a new recipe including common parameters and independent parameters.

[0069] Specifically, the common parameters are combined with the independent parameters of the long support plate to generate the long support plate formula; the common parameters are combined with the independent parameters of the short support plate to generate the short support plate formula. In this way, the original more than 50 formulas are integrated into two new formulas.

[0070] S5, during the production process, the corresponding new formula is called according to the product model.

[0071] When a certain type of product needs to be produced, the corresponding new recipe is called according to the type of carrier plate used by the product (long carrier plate or short carrier plate).

[0072] Example 2

[0073] Based on Example 1, this embodiment also provides an automated production line multi-model recipe classification calling system for implementing the above method. The system includes:

[0074] Parameter analysis module, used to analyze the formulation parameters of various product models;

[0075] Parameter classification module, used to classify recipe parameters into common parameters and independent parameters;

[0076] A recipe generation module, used for generating a new recipe according to the common parameters and the independent parameters;

[0077] The recipe calling module is used to call the corresponding new recipe according to the product model during the production process.

[0078] The system may further include a parameter calibration module for performing actual position calibration and verification on the common parameters and the independent parameters.

[0079] In addition, the system may also include a human-computer interaction module for displaying recipe information and receiving operation instructions. The operator can view the recipe information currently in use through the human-computer interaction module and can also manually adjust certain parameters when necessary.

[0080] Example 3

[0081] The application process of the present invention is described in detail below in conjunction with specific production scenarios.

[0082] A certain automated production line mainly produces various types of carrier plates. The carrier plates are sent to the vibration plate via a conveyor belt, and after going through steps such as 3D camera recognition, robot grasping, and 2D camera judgment, they are finally placed in the assembly mold.

[0083] In actual production, different types of carriers have slightly different sizes, mainly in terms of length. This results in different servo position parameters being required when placing and picking up the carriers.

[0084] The original recipe setting method is to set a recipe for each type of carrier plate separately, resulting in a large amount of repeated parameter setting work. By applying the method of the present invention, the recipe setting and calling process can be greatly simplified.

[0085] The specific steps are as follows:

[0086] 1. Parameter analysis

[0087] First, the formula parameters of all types of carrier plates are collected, and key parameters such as the Z-axis position of the carrier plate, the Z-axis position of the carrier plate, the X-axis position of the carrier plate, and the X-axis position of the carrier plate are analyzed in detail.

[0088] Recipe Model Get the Z-axis position of the carrier plate Z-axis position of the support plate Take the X-axis position of the carrier plate X-axis position of the support plate …… A0 12 362 -60 682 …… A1 12.5 366 -63 684 …… A2 13 368 -66 685 …… An+1 …… …… …… …… …… B0 12 362 -70 690 …… B1 12.5 365 -72 692 …… B2 13 366 -74 696 …… Bn+1 …… …… …… …… ……

[0089] 2. Parameter classification

[0090] By comparing the differences of the same parameters in different recipes, the parameters are divided into common parameters and independent parameters.

[0091] For example, after analysis, it is found that the difference between the Z-axis position of the take-up plate and the Z-axis position of the place-down plate in different recipes is small (within the range of ±10), and can be set as a common parameter.

[0092] However, the X-axis position of the take-up plate and the X-axis position of the place-down plate vary greatly in different recipes and need to be set as independent parameters.

[0093] 3. Parameter integration

[0094] For common parameters, such as the Z-axis position of the carrier plate, there may be multiple values ​​such as 12, 12.5, 13, etc. in different recipes. After removing the maximum and minimum values, the average value is taken to obtain the final common parameter value of 12.5.

[0095] For independent parameters, such as the X-axis position of the support plate, there may be multiple values ​​such as 680, 684, 688, 690, 694, 698, etc. They can be divided into two categories: 680, 684, 688 is one category (corresponding to the long support plate), and the middle value is 684; 690, 694, 698 is another category (corresponding to the short support plate), and the middle value is 694.

[0096] According to the actual situation on site, the servo motor used on site is Siemens V90 series. The servo motor drives the ball screw to reciprocate through pulse control, which has the advantage of high positioning accuracy.

[0097] Moving torque T f =P / 2πη(μgW+F), acceleration torque T a =(J L +J M )*2πN / t+T f ;

[0098] Where W: mass, P: lead, F: external force, η: mechanical efficiency, μ: friction coefficient, g: gravitational acceleration, J L : Load inertia, J M : Motor inertia, t: acceleration time, N: rotation speed.

[0099] According to the operation of the V90 series servo motor, first calculate and debug to find its "0" position, take the "0" position as the origin, and "position start" in sequence according to the recipe parameters, and record the position each time. For example: the Z-axis position of the carrier plate in different recipes is: 12, 12.5, 13..., etc., then position the Z-axis servo to the above positions respectively, and mark and record them. After confirming all the records of the same servo parameters involved, remove the maximum and minimum values, and take the average of the remaining parameters to obtain the common parameters. For example: the Z-axis servo parameters of the carrier plate are: 11, 12, 12.5, 13, 14, etc. According to the debugging method: (12+12.5+13) / 3=12.5 (that is, the Z-axis position of the carrier plate in the recipe), and the same applies to the other servo common position parameters.

[0100]

[0101] For the same servo position parameters with large differences (error beyond ±10), actual position calibration and verification are carried out in two cases, and two optimal positions suitable for device compatibility are obtained and integrated into two categories.

[0102] The "0" position is the original position point, which will not change after the setting is completed. After dividing the data with large differences in the same servo position parameters into two categories, the positioning is also started to the recipe parameter position, and marked for record. According to the actual position difference, the error is less than 10 in one category and the error is greater than 10 in another category. Then sort out the middle values ​​in each category. For example: the X-axis servo position parameters of the support plate are: 680, 684, 688, 690, 694, 698, then the first category is: 680, 684, 688. Take the middle value as 684 (the long support plate is placed on the support plate X-axis position). The second category is: 690, 694, 698. Take the middle value as 694 (the short support plate is placed on the support plate X-axis position).

[0103]

[0104] 4. New recipe generation

[0105] The integrated common parameters and independent parameters are combined to generate two new recipes: a long carrier recipe and a short carrier recipe.

[0106]

[0107] 5. Recipe call

[0108] In the actual production process, the long carrier plate formula or the short carrier plate formula is selected according to the carrier plate model to be produced. For example, when the carrier plate of model A needs to be produced, the system will automatically recognize that model A uses a long carrier plate, so the long carrier plate formula is called.

[0109] By applying this method, more than 50 recipes that originally needed to be set up and maintained were simplified to 2 recipes, greatly reducing the complexity of recipe management. At the same time, due to the setting of common parameters, when a common parameter needs to be adjusted, it only needs to be modified in one place to affect all related product models at the same time, avoiding repeated adjustment work.

[0110] In addition, this method simplifies the changeover process. Operators only need to select the long carrier plate formula or the short carrier plate formula according to the length of the carrier plate, without having to search for the corresponding model in more than 50 formulas, which greatly improves the changeover efficiency and reduces the possibility of operating errors.

[0111] In practical applications, this method can be implemented through an automated control system. The system can include functional modules such as a parameter analysis module, a parameter classification module, a recipe generation module, and a recipe call module. Operators can easily view and manage recipe information through a human-computer interaction interface.

[0112] In general, the method and system provided by the present invention realize the transformation of the recipe calling mode from "one-to-one" to "many-to-one" through intelligent classification and integration of recipe parameters, effectively improving the production efficiency and flexibility of the automated production line.

[0113] Those skilled in the art will readily appreciate that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, combinations, replacements, improvements, etc. made within the spirit and principles of the present invention are included within the protection scope of the present invention.

Claims

1. A classification and calling method for multi-model recipes of automated production lines. It is characterized in that The following steps are involved: S1, analyze the formulation parameters of various product models; S2, divide the recipe parameters into two categories: common parameters and independent parameters; S3, dividing the formula into a long carrier plate formula and a short carrier plate formula according to independent parameters; S4, generating a new recipe including shared parameters and independent parameters; S5, during the production process, the corresponding new formula is called according to the product model.

2. The method for classifying and calling multi-model recipes of an automated production line according to claim 1, It is characterized in that The step S2 specifically includes: S21, analyzing the differences of the same servo position parameters in different recipes; S22, setting the parameters whose differences are within a preset range as common parameters; S23, setting the parameters whose differences exceed the preset range as independent parameters.

3. The method for classifying and calling multi-model recipes of the automated production line according to claim 2, It is characterized in that In the step S22, the preset range is ±10.

4. The method for classifying and calling multi-model recipes of an automated production line according to claim 2 or 3, It is characterized in that The step S22 further includes: S221, actual position calibration and verification of the shared parameters; S222, taking the average value of the shared parameters as the final shared parameters.

5. The method for classifying and calling multi-model recipes of an automated production line according to claim 1, It is characterized in that The step S3 specifically includes: S31, the independent parameters are divided into two categories according to the size of the difference; S32, performing actual position calibration and verification on each type of independent parameter; S33, taking the middle value of each type of independent parameter as the final independent parameter of that type.

6. The method for classifying and calling multi-model recipes of an automated production line according to claim 1, It is characterized in that The step S4 specifically includes: Combine the common parameters with the independent parameters of the long support plate to generate a long support plate formula; The common parameters are combined with the short support plate independent parameters to generate the short support plate formula.

7. An automated production line multi-model formula classification calling system, It is characterized in that include: Parameter analysis module, used to analyze the formulation parameters of various product models; Parameter classification module, used to classify recipe parameters into common parameters and independent parameters; A recipe generation module, used for generating new recipes according to common parameters and independent parameters; The recipe calling module is used to call the corresponding new recipe according to the product model during the production process.

8. The automated production line multi-model recipe classification calling system according to claim 7, Features: The system further comprises a parameter calibration module for performing actual position calibration and verification on the common parameters and the independent parameters.

9. The automated production line multi-model recipe classification calling system according to claim 7 or 8, Features: The system also includes a human-computer interaction module, which is used to display recipe information and receive operation instructions.

10. Application of the method according to any one of claims 1 to 6 in an automated production line.

Images