A method to improve the working efficiency of production line robots based on machine tool material change time model

Abstract

The present invention discloses a method for improving the working efficiency of production line robots based on a machine tool refueling time model, comprising the following steps: obtaining parameter information, external information, and position information of all machine tools in an automated production line; constructing a refueling time model for each machine tool based on the parameter information and position information of the machine tools; parsing the processing formula files of all parts, monitoring the current processing progress in real time, and predicting the remaining processing time in real time; performing a real-time comparison between the remaining processing time of the part and the refueling time model to determine whether the remaining processing time of the part is greater than the refueling time of the machine tool; if so, the production line robot performs other tasks; if not, sending the refueling task of the part to the production line robot task queue for execution. The present invention optimally schedules the production line robots according to task category rules, thereby significantly improving the processing efficiency of the production line robots.

Description

Technical Field

[0001] The present invention belongs to the field of automated processing of production line robots, and specifically relates to a method for improving the working efficiency of production line robots based on a machine tool material change time model. Background Art

[0002] In the high-variety, low-batch manufacturing industry, due to the frequent change in parts variety and the complex number of process steps involved, the machine tool's processing program must be frequently switched during the machining process to keep up with the changes in parts. Furthermore, the same part often requires multiple steps on various machine types. Due to these characteristics, the processing time for each part in the automated production process varies, forcing production line robots to receive restocking information only after the part has been processed. Furthermore, the inconsistent restocking times of different types of equipment result in long waiting times for the machine tool during restocking, significantly reducing the automation line's processing efficiency and machine tool utilization. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for improving the working efficiency of production line robots based on a machine tool material change time model, and to optimally schedule the production line robots according to task category rules, thereby significantly improving the processing efficiency of the production line robots.

[0004] To solve the above technical problems, the technical solution of the present invention is: a method for improving the working efficiency of production line robots based on a machine tool material change time model, comprising the following steps:

[0005] Obtain parameter information, external information and location information of all machine tools in the automated production line;

[0006] Constructing a material change time model for each machine tool according to the type information, working parameter information and physical location information of the machine tool;

[0007] Analyze the machining formula files of all parts, monitor the current machining process in real time and predict the remaining machining time in real time;

[0008] A real-time comparison is performed based on the remaining processing time of the part and the material change time model to determine whether the remaining processing time of the part is greater than the material change time of the machine tool; if so, the production line robot performs other tasks; if not, the material change task of the part is sent to the production line robot task queue for execution.

[0009] The steps for constructing the material change time model of machine tools include: (1) collecting the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and preprocessing the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; preprocessing includes data redundancy processing of the motion parameter data of the machine tool when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) according to the physical position of each machine tool in the production line and calculating the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) establishing a many-to-many mapping set of the spindle return to zero time of different types of machine tools and the material change time of production line robots at different positions, and clustering them by machine tool type, and finally obtaining the material change time model of all machine tools in the production line.

[0010] The specific execution steps of the machine tool reloading time model are as follows: the position information of the computer tool and the current position of the production line robot are used to calculate the running distance of the production line robot, and the time for the production line robot to reload parts is calculated based on the rate at which the production line robot moves parts of different weights.

[0011] The execution steps of the machine tool material change time model also include: for parts that need to be turned, considering the clamping and turning time of the production line robot, and the replacement time of parts and electrodes.

[0012] The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

[0013] When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

[0014] A system for improving the working efficiency of production line robots based on a machine tool refueling time model is also provided, comprising a wirelessly connected main controller and a production line robot, wherein the production line robot works in an automated production line with a machine tool; wherein,

[0015] The main controller obtains parameter information, external information, and location information of all machine tools in the automated production line through the production line robot;

[0016] The main controller constructs a material change time model for each machine tool according to the type information, working parameter information and physical position information of the machine tool;

[0017] The main controller parses the machining formula files of all parts, monitors the current machining process in real time, and predicts the remaining machining time in real time;

[0018] The main controller compares the remaining processing time of the part with the material change time model in real time to determine whether the remaining processing time of the part is greater than the material change time of the machine tool; if so, the production line robot is controlled to perform other tasks; if not, the material change task of the part is sent to the production line robot task queue for execution.

[0019] The steps for constructing the material change time model of machine tools include: (1) collecting the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and preprocessing the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; preprocessing includes data redundancy processing of the motion parameter data of the machine tool when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) according to the physical position of each machine tool in the production line and calculating the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) establishing a many-to-many mapping set of the spindle return to zero time of different types of machine tools and the material change time of production line robots at different positions, and clustering them by machine tool type, and finally obtaining the material change time model of all machine tools in the production line.

[0020] The specific execution steps of the machine tool material change time model are as follows: the production line robot's running distance is calculated by calculating the position information of the computer tool and the current position of the production line robot; the time for the production line robot to change materials is calculated based on the rate at which the production line robot moves parts of different weights; for parts that need to be turned, the clamping and turning time of the production line robot and the replacement time of parts and electrodes are taken into account.

[0021] The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

[0022] When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention predicts the remaining processing time of parts and generates material change tasks in advance, and optimally schedules production line robots according to task category rules, thereby greatly improving the processing efficiency of production line robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0027] To address the efficiency issues associated with flexible automated production lines for various types of equipment in high-variety, low-batch industries, a method for improving robot productivity based on a machine tool retooling time model is proposed. First, a retooling time model for each machine tool within the automated production line is constructed by acquiring the parameter and position information of all machine tools within the production line. During the actual processing phase, all part processing formulas and simulation files are parsed, and the current program execution progress is monitored in real time. The remaining processing time for each part is compared with the retooling time model in real time, allowing accurate prediction of part completion events and pre-generated retooling tasks. Robots are then optimally scheduled based on task category rules, significantly improving the processing efficiency of industrial robots.

[0028] Example 1:

[0029] The technical solution of the present invention is: a method for improving the working efficiency of production line robots based on a machine tool material change time model, comprising the following steps:

[0030] Obtain parameter information, external information and location information of all machine tools in the automated production line;

[0031] Constructing a material change time model for each machine tool according to the type information, working parameter information and physical location information of the machine tool;

[0032] Analyze the machining formula files of all parts, monitor the current machining process in real time and predict the remaining machining time in real time;

[0033] A real-time comparison is performed based on the remaining processing time of the part and the material change time model to determine whether the remaining processing time of the part is greater than the material change time of the machine tool; if so, the production line robot performs other tasks; if not, the material change task of the part is sent to the production line robot task queue for execution.

[0034] The system can obtain parameter information of all machine tools in the automation line, including machine tool type, spindle zeroing time, automatic door / oil tank opening and closing time, etc., and also obtain the position information of all machine tools in the automation line.

[0035] The steps for constructing the material change time model of machine tools include: (1) collecting the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and preprocessing the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; preprocessing includes data redundancy processing of the motion parameter data of the machine tool when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) according to the physical position of each machine tool in the production line and calculating the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) establishing a many-to-many mapping set of the spindle return to zero time of different types of machine tools and the material change time of production line robots at different positions, and clustering them by machine tool type, and finally obtaining the material change time model of all machine tools in the production line.

[0036] The specific execution steps of the machine tool reloading time model are as follows: the position information of the computer tool and the current position of the production line robot are used to calculate the running distance of the production line robot, and the time for the production line robot to reload parts is calculated based on the rate at which the production line robot moves parts of different weights.

[0037] The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

[0038] When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

[0039] Example 2:

[0040] In another feasible solution, the main method is consistent with that of Example 1, and a machine tool material change time model is added to determine the parts that need to be turned as follows:

[0041] The execution steps of the machine tool material change time model also include: for parts that need to be turned, considering the clamping and turning time of the production line robot, and the replacement time of parts and electrodes.

[0042] Example 3:

[0043] In another feasible solution, a system for improving the working efficiency of a production line robot based on a machine tool material change time model is provided, comprising a main controller and a production line robot connected wirelessly, wherein the production line robot works in an automated production line having a machine tool; wherein,

[0044] The main controller obtains parameter information, external information, and location information of all machine tools in the automated production line through the production line robot;

[0045] The main controller constructs a material change time model for each machine tool according to the type information, working parameter information and physical position information of the machine tool;

[0046] The main controller parses the machining formula files of all parts, monitors the current machining process in real time, and predicts the remaining machining time in real time;

[0047] The main controller compares the remaining processing time of the part with the material change time model in real time to determine whether the remaining processing time of the part is greater than the material change time of the machine tool; if so, the production line robot is controlled to perform other tasks; if not, the material change task of the part is sent to the production line robot task queue for execution.

[0048] The steps for constructing the material change time model of machine tools include: (1) collecting the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and preprocessing the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; preprocessing includes data redundancy processing of the motion parameter data of the machine tool when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) according to the physical position of each machine tool in the production line and calculating the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) establishing a many-to-many mapping set of the spindle return to zero time of different types of machine tools and the material change time of production line robots at different positions, and clustering them by machine tool type, and finally obtaining the material change time model of all machine tools in the production line.

[0049] The specific execution steps of the machine tool material change time model are as follows: the production line robot's running distance is calculated by calculating the position information of the computer tool and the current position of the production line robot; the time for the production line robot to change materials is calculated based on the rate at which the production line robot moves parts of different weights; for parts that need to be turned, the clamping and turning time of the production line robot and the replacement time of parts and electrodes are taken into account.

[0050] The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

[0051] When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

[0052] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for improving the working efficiency of production line robots based on a machine tool material change time model, characterized in that: The following steps are involved: Obtain parameter information, external information and location information of all machine tools in the automated production line; Constructing a material change time model for each machine tool according to the type information, working parameter information and physical location information of the machine tool; Analyze the machining formula files of all parts, monitor the current machining process in real time and predict the remaining machining time in real time; A real-time comparison is performed between the remaining processing time of the part and the reloading time model to determine whether the remaining processing time of the part is greater than the reloading time of the machine tool; if so, the production line robot performs other tasks; If not, the part replacement task is sent to the production line robot task queue for execution; The steps to construct the material change time model of the machine tool include: (1) Collect the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and pre-process the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; the pre-processing includes data redundancy processing of the motion parameter data of the machine tools when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) Based on the physical location of each machine tool in the production line and the calculation of the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) A many-to-many mapping set of the spindle zero return time of different types of machine tools and the material change time of production line robots at different positions is established, and clustered by machine tool type to finally obtain a material change time model for all machine tools in the production line.

2. The method for improving the working efficiency of production line robots based on a machine tool material change time model according to claim 1 is characterized in that: The specific execution steps of the machine tool material change time model are as follows: the production line robot's running distance is calculated by calculating the position information of the computer tool and the current position of the production line robot; the time for the production line robot to change materials is calculated based on the rate at which the production line robot moves parts of different weights; for parts that need to be turned, the clamping and turning time of the production line robot and the replacement time of parts and electrodes are taken into account.

3. The method for improving the working efficiency of production line robots based on a machine tool material change time model according to claim 1 is characterized in that: The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

4. The method for improving the working efficiency of production line robots based on a machine tool material change time model according to claim 1 is characterized in that: When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

5. A system using the method for improving the working efficiency of production line robots based on a machine tool material change time model as claimed in claim 1, characterized in that: It includes a wirelessly connected main controller and a production line robot. The production line robot works in an automated production line with a machine tool. The main controller obtains parameter information, external information, and location information of all machine tools in the automated production line through the production line robot; The main controller constructs a material change time model for each machine tool according to the type information, working parameter information and physical position information of the machine tool; The main controller parses the machining formula files of all parts, monitors the current machining process in real time, and predicts the remaining machining time in real time; The main controller compares the remaining processing time of the part with the refueling time model in real time to determine whether the remaining processing time of the part is greater than the refueling time of the machine tool; if so, it controls the production line robot to perform other tasks; if not, it sends the refueling task of the part to the production line robot task queue for execution; The steps to construct the material change time model of the machine tool include: (1) Collect the motion parameter data of all machine tools, including the time for the machine tool spindle to return to zero position, the opening and closing time of the automatic door or oil tank, the worktable movement time, the oil draining time, and the oil tank lifting time, and pre-process the motion parameter data to obtain the spindle return to zero position time of each type of machine tool; the pre-processing includes data redundancy processing of the motion parameter data of the machine tools when processing the same type of parts, filtering out duplicate sample data, and classifying and merging the filtered data according to the machine tool motion type; (2) Based on the physical location of each machine tool in the production line and the calculation of the motion trajectory and time of the production line robot starting from different origins to perform the material change task for each type of machine tool, the material change time of each production line robot at different positions is obtained through data mining; (3) A many-to-many mapping set of the spindle zero return time of different types of machine tools and the material change time of production line robots at different positions is established, and clustered by machine tool type to finally obtain a material change time model for all machine tools in the production line.

6. The system according to claim 5, characterized in that The specific execution steps of the machine tool material change time model are as follows: the production line robot's running distance is calculated by calculating the position information of the computer tool and the current position of the production line robot; the time for the production line robot to change materials is calculated based on the rate at which the production line robot moves parts of different weights; for parts that need to be turned, the clamping and turning time of the production line robot and the replacement time of parts and electrodes are taken into account.

7. The system according to claim 5, characterized in that The specific steps for predicting the remaining processing time are as follows: simulate the processing of the part according to the geometric model and processing formula of the part, monitor the processing progress in real time, and dynamically compare it with the simulation process to predict the remaining processing time of the part.

8. The system according to claim 5, characterized in that When a production line robot receives a part reloading task, it first adds the reloading task to the current task queue. At the same time, it traverses the existing task queue and sorts all tasks according to task type and scheduling rules. The machine tool reloading task has higher priority than the scanning task and other types of tasks. If there are multiple reloading tasks at the same time, the reloading task with the minimum reloading time calculated according to the reloading time model will be executed first.

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