Tool taking control method, device and system for storage tool magazine

Abstract

The invention relates to the technical field of tool control, in particular to a tool taking control method, device and system for a storage tool magazine, and the working performance and economical efficiency of a numerical control machine tool are remarkably improved. The storage tool magazine comprises a plurality of tool rows and a tool taking mechanism, each tool row is provided with a plurality of tool containing grooves used for storing tools, each tool containing groove corresponds to a unique position number, and the tool taking mechanism is used for taking the tools in the tool containing grooves according to machining requirements. According to the processing technology data information, the position number of a to-be-disassembled tool used in the current procedure and procedure feature information of the next procedure are extracted; extracting the dimension specification of a to-be-mounted cutter used in the procedure from the procedure feature information of the next procedure; traversing the tool database, and extracting tool feature portrait information of all tools with the same size specification as the to-be-installed tool; and performing cutter wear analysis on the process feature information.

Description

Technical Field

[0001] The present invention relates to the technical field of tool control, and in particular to a tool retrieval control method, device and system for a storage tool magazine. Background Art

[0002] In modern manufacturing, CNC machine tools (CNC) are widely used in various precision machining tasks. In order to improve production efficiency and machining accuracy, CNC machine tools are usually equipped with a storage tool magazine for storing and managing a variety of tools. The storage tool magazine can automatically retrieve and replace tools to reduce manual intervention.

[0003] Existing tool retrieval control methods are usually based on preset tool positions and simple sequential retrieval strategies. They do not take into account the real-time wear of the tool, resulting in the use of excessively worn tools, affecting the processing quality and tool life. They also do not have the function of optimizing the tool retrieval path, which increases the energy consumption of the tool retrieval mechanism and affects the overall efficiency. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a tool retrieval control method for a storage tool magazine, which significantly improves the working performance and economy of a CNC machine tool.

[0005] In a first aspect, the present invention provides a tool retrieval control method for a storage tool magazine, the method comprising:

[0006] Acquire machining process data information, and extract the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the machining process data information; the process characteristic information includes tool size specifications, cutting parameters, tool materials, workpiece materials and cooling conditions;

[0007] Extract the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traverse the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount;

[0008] Perform tool wear analysis on the process characteristic information to predict the tool wear amount caused by the next process to the tool to be installed; extract cutting parameters that affect tool wear from the process characteristic information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; use a preset tool wear database to find tool wear data under the same cutting parameters as the current process characteristic information; estimate the tool wear amount in the next process based on the tool wear data;

[0009] For each tool with the same size and specification as the tool to be installed, the tool wear amount is used to verify the wear of its tool feature profile information, and the tool that cannot independently complete the next process is eliminated, and the remaining tools with the same size and specification as the tool to be installed are marked as first-order tools to be installed; the elimination condition is: the tool wear amount plus the tool real-time wear amount exceeds the tool wear threshold;

[0010] For each first-order tool to be installed, perform three-dimensional route splitting to obtain a three-dimensional feature vector of the tool picking route corresponding to the first-order tool to be installed;

[0011] Collect the translation drive energy consumption information of each dimension in the tool taking mechanism, and perform data conversion to obtain the translation drive energy consumption feature vector of the tool taking mechanism;

[0012] The three-dimensional feature vector of the tool picking route and the translation drive energy consumption feature vector are input into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool picking path is replaced as the optimal tool to be installed.

[0013] Furthermore, the mathematical calculation formula of the comprehensive calculation model of the tool path is:

[0014]

[0015] Among them, I represents the comprehensive index of the tool path, d x Indicates the travel distance on the X axis, d y Indicates the travel distance on the Y axis, d z Indicates the travel distance on the Z axis, e x represents the energy consumption when moving along the X axis, e y represents the energy consumption when moving along the Y axis, e z Represents the energy consumption when moving along the Z axis.

[0016] Furthermore, the calculation formula for the tool wear caused by the installed tool is:

[0017]

[0018] Among them, W represents the expected tool wear, k is the empirical coefficient based on tool material, workpiece material and processing conditions, V represents cutting speed, a represents an index related to cutting speed, f represents feed speed, b represents an index related to feed speed, d represents cutting depth, c represents an index related to cutting depth, and t represents processing time.

[0019] Furthermore, the method for constructing the tool database includes:

[0020] Select a database management system and configure the database server, including setting the database name, user permissions, and backup strategy;

[0021] Enter the information of existing tools into the database;

[0022] Verify the entered data to ensure its accuracy and completeness;

[0023] Create indexes for fields in the database;

[0024] Regularly optimize the database, formulate database backup strategies, and regularly back up database data;

[0025] Implement database security measures, set up access controls, and encrypt sensitive data;

[0026] Integrate the tool database with the control system of the CNC machine tool.

[0027] Furthermore, factors affecting the setting of the tool wear threshold include tool material properties, workpiece material properties, cutting parameters, processing conditions and economic benefits.

[0028] Furthermore, the method for obtaining the three-dimensional feature vector of the tool path includes:

[0029] For each candidate tool marked as a first-order tool to be installed, a path from the current tool picking mechanism position to the tool position is calculated; the three-dimensional feature vector of the tool picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance;

[0030] After the three-dimensional route splitting is completed, a three-dimensional feature vector of the tool picking route is generated for each first-order tool to be installed.

[0031] Furthermore, the method for obtaining the translation drive energy consumption characteristic vector includes:

[0032] The sensor is used to monitor the translation drive energy consumption data of the tool taking mechanism in real time during each axial movement;

[0033] Preprocess the collected translation drive energy consumption data, including data cleaning, data conversion and data standardization;

[0034] Extracting the preprocessed data to extract features that can reflect the translational drive energy consumption of the tool picking mechanism in different dimensions; the features include average energy consumption, maximum energy consumption, energy consumption change rate and total energy consumption;

[0035] The extracted energy consumption features of each dimension are combined into a feature vector, namely, the translation drive energy consumption feature vector.

[0036] On the other hand, the present application provides a tool retrieval control device for a storage tool magazine, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any one of the above-mentioned methods when executed by the processor.

[0037] In a third aspect, the present application further provides a tool retrieval control system for a storage tool magazine, the system comprising:

[0038] The processing technology data analysis module obtains the processing technology data information, and extracts the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the processing technology data information; the process characteristic information includes the tool size specification, cutting parameters, tool material, workpiece material and cooling conditions; the tool position number is composed of three coordinates of X, Y and Z;

[0039] The tool feature image query module extracts the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traverses the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount;

[0040] The tool wear analysis module performs tool wear analysis on the process characteristic information, predicts the tool wear amount caused by the next process to the tool to be installed; extracts cutting parameters that affect tool wear from the process characteristic information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; uses a preset tool wear database to find tool wear data under the same cutting parameters as the current process characteristic information; and estimates the tool wear amount in the next process based on the tool wear data;

[0041] The tool screening module uses the tool wear amount to verify the wear of each tool with the same size and specification as the tool to be installed, removes the tools that cannot independently complete the next process, and marks the remaining tools with the same size and specification as the tool to be installed as first-order tools to be installed; the removal condition is: the tool wear amount plus the real-time tool wear amount exceeds the tool wear threshold;

[0042] The three-dimensional route planning module performs three-dimensional route splitting for each first-order tool to be installed, and obtains a three-dimensional feature vector of the tool picking route corresponding to the first-order tool to be installed; the three-dimensional feature vector of the tool picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance;

[0043] The energy consumption information conversion module collects the translation drive energy consumption information of each dimension in the tool taking mechanism, and performs data conversion to obtain the translation drive energy consumption feature vector of the tool taking mechanism;

[0044] The comprehensive optimization decision module inputs the three-dimensional feature vector of the tool picking route and the translation drive energy consumption feature vector into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and replaces the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool picking path as the optimal tool to be installed.

[0045] Compared with the prior art, the invention has the following beneficial effects: by considering the wear of the tool in real time and eliminating the tools that cannot meet the requirements of the next process due to wear during the tool selection process, the tools used in the processing process are ensured to be in good condition, thereby improving the processing quality and extending the service life of the tools;

[0046] This method realizes the full automation and intelligence from processing technology data analysis to optimal tool selection, reduces manual intervention, improves production efficiency and operation convenience; through three-dimensional route splitting and tool path comprehensive calculation model, it can comprehensively consider the length and energy consumption of the tool path, and select the path with the minimum comprehensive energy consumption, thereby reducing the energy consumption of the tool picking mechanism and improving the overall efficiency;

[0047] This method can flexibly select the most suitable tool according to different process feature information, enhancing the adaptability and flexibility of the system; by reducing the processing interruption and tool replacement frequency caused by tool wear, and optimizing the tool path to reduce the tool picking time, this method significantly improves the production efficiency of CNC machine tools;

[0048] This method helps reduce production costs by improving machining quality and tool life, reducing tool replacement times and rework due to machining quality issues. Based on a large amount of tool feature profile information and real-time wear data, data analysis and modeling are used to guide tool selection and tool path optimization, enhancing the scientificity and accuracy of decision-making.

[0049] In summary, the tool retrieval control method of the storage tool magazine has significant advantages in improving processing quality, optimizing tool management, reducing energy consumption and improving production efficiency. It not only solves the problems existing in the traditional method, but also significantly improves the working performance and economy of CNC machine tools by introducing advanced information technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a tool retrieval control method of a storage tool magazine of the present invention;

[0051] Figure 2 It is a structural diagram of a tool retrieval control device of a storage tool magazine of the present invention;

[0052] Figure 3 The present invention is a structural diagram of a tool retrieval control system for a storage tool magazine. DETAILED DESCRIPTION

[0053] In the description of this application, those skilled in the art should know that this application can be implemented as a method, device, or system. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0054] This application describes the provided methods, devices, and systems through flowcharts and / or block diagrams.

[0055] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0056] The present application is described below in conjunction with the drawings in the present application.

[0057] Embodiment 1: Figure 1 to Figure 2 As shown, a tool retrieval control method for a storage tool magazine of the present invention specifically includes the following steps:

[0058] S1. Obtain processing data information, and extract the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the processing data information;

[0059] The process characteristic information includes tool size specifications, cutting parameters, tool material, workpiece material and cooling conditions; the tool position number consists of three coordinates: X, Y and Z;

[0060] CNC machine tools are usually programmed using G codes or M codes, which contain detailed information such as processing steps and cutting parameters;

[0061] Each tool's position in the storage tool magazine has a unique identifier, which is represented by three coordinates: X, Y, and Z;

[0062] Read the position number of the tool currently in use from the control system;

[0063] Tool size specifications include tool diameter, length and number of edges;

[0064] Cutting parameters include spindle speed, feed rate and cutting depth;

[0065] Tool material refers to the type of material used for the tool, such as carbide, high-speed steel, etc.

[0066] Workpiece material refers to the material of the part being machined, as different materials require different types of cutting tools;

[0067] Cooling conditions indicate whether coolant is required and its type, which affects tool wear and life.

[0068] In this step, the accuracy and real-time nature of the data are ensured by directly extracting the position number of the tool to be disassembled used in the current process and the detailed process feature information of the next process from the processing technology data information; this provides a reliable basis for tool wear analysis and path optimization in subsequent steps; since the system can quickly identify the specifications of the tool currently in use and the tool to be used, it can immediately start preparing for the tool replacement process, reducing the time delay caused by manual query and confirmation of tool information, thereby improving production efficiency; by obtaining detailed process feature information, the system can ensure that the selected tool matches the processing task and avoid processing quality problems caused by inappropriate tools; by monitoring the real-time wear of the tool, the system can replace the tool in time before it wears to a certain extent, avoiding the use of excessively worn tools for processing, thereby extending the service life of the tool and reducing the frequency and cost of tool replacement; the tool position number and process feature information obtained in step S1 provide necessary data support for tool path optimization in subsequent steps; by considering the position of the tool and processing requirements, the system can calculate the optimal tool path and reduce energy consumption and time costs in the tool picking process.

[0069] S2, extracting the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traversing the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount;

[0070] The method for constructing the tool database comprises:

[0071] Clarify the functions and performance requirements that the tool database needs to support, including storage, query, update and deletion of tool information;

[0072] Determine the tool feature profile information that needs to be recorded, including tool position number, tool size specification, tool wear threshold and real-time tool wear amount;

[0073] Select a database management system that suits the needs of the enterprise and configure the database server, including setting the database name, user permissions and backup strategy to ensure the security and stability of the database;

[0074] Enter the information of existing tools into the database through manual entry and data import;

[0075] Verify the entered data by using the data verification function provided by the database management system to ensure the accuracy and completeness of the data;

[0076] Create indexes for key fields in the database to improve query efficiency;

[0077] Regularly optimize the database, formulate a database backup strategy, and regularly back up database data to prevent data loss and damage;

[0078] Prepare a database recovery plan to quickly restore data in the event of data loss and system crash;

[0079] Implement database security measures, set up access control, and encrypt sensitive data; regularly conduct security audits and vulnerability scans on the database to promptly identify and fix potential security issues;

[0080] Integrate the tool database with the control system of the CNC machine tool to ensure that tool information can be obtained and updated in real time.

[0081] In this step, by accurately matching the size specifications of the tool to be installed, it is possible to ensure that the most suitable tool is selected for processing, thereby avoiding processing errors and unnecessary downtime caused by tool mismatch, and significantly improving processing accuracy and production efficiency; the tool feature portrait information contains the tool wear threshold and real-time wear amount, so that the system can evaluate the remaining service life of the tool and avoid using excessively worn tools for processing, thereby extending the overall life of the tool and reducing the frequency and cost of tool replacement; the construction and use of the tool database realizes centralized management and rapid query of tool information; it not only improves the efficiency of tool management, but also makes the inventory, usage and maintenance records of the tool clear at a glance, which is convenient for enterprises to optimize resources and control costs; by integrating the tool database with the control system of the CNC machine tool, real-time updating and intelligent scheduling of tool information are realized; The machine tool can automatically select the appropriate tool according to the processing requirements, reduce manual intervention, and improve the automation and intelligence level of the production line; by implementing database security measures, the security of the tool database is ensured; this prevents data leakage and illegal access, and protects the company's core assets and competitive advantages; the construction and use of the tool database provides data support for the company's continuous improvement; by recording and analyzing the use of tools, the company can discover problems and bottlenecks in the processing process, and take targeted measures to optimize and improve them, promoting the continuous improvement of product quality and continuous growth in production efficiency; in step S2, by traversing the tool database to extract the feature portrait information of the tool to be installed, not only the processing accuracy and efficiency are improved, but also the tool life is extended, tool management is optimized, system intelligence is enhanced, data security is improved, and strong support is provided for the company's continuous improvement.

[0082] S3, performing tool wear analysis on the process characteristic information, and predicting the tool wear amount caused by the tool to be installed in the next process;

[0083] The method of performing tool wear analysis on process feature information includes:

[0084] Extract cutting parameters directly related to tool wear from process feature information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; these parameters are the main factors affecting tool wear;

[0085] Consider the characteristics of the tool material and the workpiece material; different tool materials have different wear resistance, thermal stability and chemical stability; similarly, the workpiece material's hardness, toughness, thermal conductivity and other characteristics will also affect the friction, heat generation and tool wear during the cutting process;

[0086] Use the existing tool wear database to find tool wear data under cutting conditions similar to the current process feature information;

[0087] According to the information in the tool wear database and the specific conditions of the current process, the wear coefficient is applied to estimate the tool wear in the next process;

[0088] In addition to cutting parameters and material properties, other factors that affect tool wear need to be considered, including the initial state of the tool, the dynamic characteristics of the machine tool, and environmental factors; these factors can be evaluated through empirical judgment or experimental data to assess their impact on tool wear and given appropriate consideration in the prediction process;

[0089] The analysis results are synthesized to determine the expected wear of the tool to be installed in the next process;

[0090] The calculation formula for the tool wear caused by the installed tool is:

[0091]

[0092] Among them, W represents the expected tool wear, k is the empirical coefficient based on tool material, workpiece material and processing conditions, V represents cutting speed, a represents an index related to cutting speed, f represents feed speed, b represents an index related to feed speed, d represents cutting depth, c represents an index related to cutting depth, and t represents processing time.

[0093] In this step, by predicting the amount of tool wear, the over-worn tools can be replaced in time to avoid the reduction of machining accuracy and product quality problems caused by tool wear; it helps to maintain stable machining accuracy and improve the overall quality level of the product; through reasonable tool management and replacement strategies, the situation of premature scrapping of tools due to excessive wear can be reduced, thereby extending the service life of the tools and reducing the cost of tool replacement; reducing the downtime for tool change caused by tool wear can improve the continuous operation capacity of the production line, shorten the production cycle, and improve the overall production efficiency; by predicting and optimizing the tool picking path, the energy consumption of the tool picking mechanism can be reduced, and the indirect costs caused by frequent tool replacement can be reduced; in addition, reasonable tool management can also reduce tool inventory costs and improve capital utilization; this step is an important part of the intelligent management of CNC machine tools and storage tool magazines. By introducing data analysis and prediction technology, the intelligence level of the manufacturing process is improved, laying the foundation for the further development of intelligent manufacturing; it provides scientific and accurate tool wear prediction data for production managers, which helps them make more reasonable and efficient tool management and replacement decisions, thereby optimizing production plans and resource allocation.

[0094] S4. For each tool with the same size and specification as the tool to be installed, use the tool wear amount to verify the wear of its tool feature profile information, eliminate the tool that cannot independently complete the next process, and mark the remaining tools with the same size and specification as the tool to be installed as first-order tools to be installed; wherein the elimination condition is: the tool wear amount plus the tool real-time wear amount exceeds the tool wear threshold;

[0095] Add the predicted wear amount to the current real-time wear amount of the tool to obtain the total wear amount of the tool after executing the next process;

[0096] The calculated total wear is compared with the tool wear threshold; the tool wear threshold represents the degree of wear at which the tool performance will be significantly reduced and it is no longer suitable for continued use;

[0097] For tools whose total wear exceeds the wear threshold, they are deemed unable to complete the next process independently and should be removed from the candidate list;

[0098] The remaining tools, i.e., those whose total estimated wear does not exceed the wear threshold, are marked as first-order tools to be installed, and they are eligible as candidate tools for the next operation;

[0099] The factors affecting the setting of the tool wear threshold include:

[0100] Tool material characteristics: Tools made of different materials have different wear resistance; tools with high wear resistance can withstand more cutting loads, so their wear threshold is set relatively high; the stability of the tool in a high temperature environment also affects its wear threshold; tools with good thermal stability are not easy to fail during high-temperature cutting, and the wear threshold can be appropriately increased; the chemical reaction between the tool and the workpiece material may also lead to increased wear; therefore, the chemical stability of the tool is also a factor that needs to be considered when setting the wear threshold;

[0101] Workpiece material characteristics: The hardness of the workpiece material directly affects the cutting force and tool wear; workpiece materials with higher hardness require higher cutting forces and more wear-resistant tools, so the wear threshold is relatively low; the toughness of the workpiece material also affects the wear of the tool; materials with good toughness will generate greater cutting forces and vibrations during cutting, thereby accelerating tool wear; the thermal conductivity of the workpiece material affects the heat transfer during the cutting process; materials with low thermal conductivity may cause the temperature of the cutting area to rise, exacerbating tool wear;

[0102] Cutting parameters: Too high cutting speed will lead to increased cutting temperature and accelerated tool wear; therefore, the impact of cutting speed needs to be considered when setting the wear threshold; the feed rate also directly affects the wear of the tool; too high a feed rate will increase the cutting load and cutting force, thereby accelerating tool wear; the cutting depth has a significant impact on tool wear; deeper cutting depths require greater cutting forces and more wear-resistant tools to withstand;

[0103] Processing conditions: The use of coolant can effectively reduce the temperature of the cutting area and reduce tool wear; therefore, the use of coolant needs to be considered when setting the wear threshold; the stability of the machine tool will also affect the wear of the tool; excessive machine tool vibration will accelerate tool wear, so the stability of the machine tool needs to be considered when setting the wear threshold;

[0104] Economic benefits: The cost of the tool is one of the economic factors that need to be considered when setting the wear threshold. High-cost tools may require a higher wear threshold to fully utilize their value. The frequency of tool replacement directly affects production efficiency. Too low a wear threshold may lead to frequent tool replacement and reduce production efficiency. Therefore, when setting the wear threshold, it is necessary to maximize production efficiency while ensuring processing quality.

[0105] In this step, by eliminating those tools that cannot complete the next process independently, it is ensured that only tools in good condition are used for processing, thereby avoiding the decline in processing quality and premature scrapping of tools due to excessive wear of the tools, and effectively extending the service life of the tools; by calculating the total wear of each candidate tool and comparing it with the wear threshold, this step can accurately select the first-order tools to be installed, that is, those tools that are expected to maintain good performance after completing the next process; although this step itself does not directly involve energy consumption management, it indirectly reduces the energy consumption in the production process by reducing the number of shutdowns and replacements caused by tool wear; at the same time, by quickly and accurately finding the optimal tool to be installed, the moving distance and time of the tool picking mechanism are reduced, further improving production efficiency; this step introduces A prediction and verification mechanism for tool wear is established, making the tool retrieval control of the storage tool magazine more intelligent; by updating the tool feature portrait information in real time, the tool wear threshold and selection strategy can be dynamically adjusted to meet the needs of different processing tasks, enhancing the adaptability and flexibility of the system; by using tools rationally, reducing waste and downtime, it helps to reduce production costs and improve economic benefits; at the same time, setting a higher wear threshold for high-cost tools can give full play to their value and further improve the overall economic benefits; this step plays a vital role in the tool retrieval control of the storage tool magazine, which not only improves the processing quality and tool life, but also optimizes tool selection, reduces energy consumption, improves production efficiency, and enhances the intelligence and adaptability of the system, ultimately improving the overall economic benefits.

[0106] S5. For each first-order tool to be installed, perform three-dimensional route splitting to obtain a three-dimensional feature vector of the tool-picking route corresponding to the first-order tool to be installed; the three-dimensional feature vector of the tool-picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance;

[0107] The method for obtaining the three-dimensional feature vector of the tool path comprises:

[0108] For each candidate tool marked as a first-order tool to be installed, the path from the current tool picking mechanism position to the tool position is calculated; this path involves movement in three dimensions: X, Y, and Z; the process of three-dimensional route splitting is to decompose this continuous movement path into a series of independent travel distances in the X, Y, and Z axes;

[0109] The X-axis travel distance indicates the distance that the tool picking mechanism needs to move in the horizontal direction to reach the X-coordinate position of the target tool;

[0110] The Y-axis travel distance indicates the distance that the tool removal mechanism needs to move in another horizontal direction perpendicular to the X-axis to further approach the Y coordinate position of the target tool;

[0111] The Z-axis travel distance indicates the distance the tool removal mechanism needs to move in the vertical direction to finally reach the storage position of the target tool;

[0112] After completing the three-dimensional route splitting, a three-dimensional feature vector of the tool picking route is generated for each first-order tool to be installed. The vector contains three components: X-axis travel distance, Y-axis travel distance and Z-axis travel distance. This feature vector not only describes the total moving distance required for the tool picking mechanism to reach the target tool, but also implies the direction changes during the movement and possible obstacle avoidance requirements.

[0113] In this step, the three-dimensional route splitting refines the continuous tool picking path into independent travel distances in each dimension, making the path planning more accurate; it helps to reduce the extra movement and energy consumption caused by inaccurate path estimation; by obtaining the three-dimensional feature vector of the tool picking path for each tool, it can comprehensively evaluate the complexity and energy consumption of different tool picking paths, so as to select the optimal tool picking sequence; it helps to reduce the energy consumption of the tool picking mechanism and improve the overall processing efficiency; the acquisition of the three-dimensional feature vector takes into account the direction changes and obstacle avoidance requirements during the movement process, so that the system can be flexible when facing different tool magazine layouts and working environments The tool picking path can be adjusted flexibly to ensure the safety and smoothness of the tool picking process; the three-dimensional feature vector of the tool picking path can be further used as input data in the intelligent decision-making system to automatically select the optimal tool picking path; it helps to improve the intelligence level of warehouse tool storage management and reduce manual intervention and errors; by accurately planning the tool picking path, the potential damage to the tool and workpiece caused by improper tool picking process is reduced, which helps to improve the processing quality and extend the tool life; the three-dimensional route splitting in step S5 and the acquisition of the three-dimensional feature vector of the tool picking path are the key links in optimizing the tool retrieval process in the warehouse tool storage.

[0114] S6, collecting the translation drive energy consumption information of each dimension in the tool taking mechanism, and performing data conversion to obtain the translation drive energy consumption characteristic vector of the tool taking mechanism;

[0115] The method for obtaining the translation drive energy consumption characteristic vector includes:

[0116] The sensor is used to monitor the translation drive energy consumption data of the tool taking mechanism in real time during each axial movement;

[0117] Preprocess the collected translation drive energy consumption data, including data cleaning, data conversion and data standardization;

[0118] Extracting the preprocessed data to extract features that can reflect the translational drive energy consumption of the tool picking mechanism in different dimensions; the features include average energy consumption, maximum energy consumption, energy consumption change rate and total energy consumption;

[0119] The average energy consumption indicates the average energy consumption of each movement in each dimension, reflecting the average energy consumption during the movement process;

[0120] Maximum energy consumption represents the maximum energy consumption of a single movement in each dimension, caused by factors such as acceleration, load, or friction;

[0121] The energy consumption change rate indicates the energy consumption change rate between adjacent moves, reflecting the fluctuation of energy consumption;

[0122] Total energy consumption refers to the total energy consumption during the entire movement from the starting point to the end point;

[0123] The extracted energy consumption characteristics of each dimension are combined into a feature vector, namely, a translation drive energy consumption feature vector; the translation drive energy consumption feature vector contains the energy consumption information of the tool picking mechanism when it is translationally driven in different dimensions.

[0124] In this step, through real-time monitoring and precise measurement, the energy consumption data of the translational drive of the tool picking mechanism in different dimensions can be accurately obtained, which provides a solid foundation for the subsequent energy consumption analysis and optimization; the data preprocessing link effectively removes noise, outliers and problems of different dimensions, improves the accuracy and comparability of the data, and ensures the reliability of subsequent analysis; the extracted average energy consumption, maximum energy consumption, energy consumption change rate and total energy consumption and other characteristics fully reflect the energy consumption characteristics of the translational drive of the tool picking mechanism in different dimensions; it not only helps to understand the distribution and change law of energy consumption, but also provides rich information for optimizing the tool picking path; the construction of the translational drive energy consumption feature vector provides a key input for the comprehensive calculation model of the tool picking path By comprehensively considering the energy consumption costs of different tool picking paths, the model can select the path with the lowest energy consumption and the highest efficiency, thereby optimizing the tool picking path and reducing overall energy consumption; the optimized tool picking path can reduce the energy consumption and movement time of the tool picking mechanism, and improve the efficiency and accuracy of tool retrieval; it not only helps to improve the overall production efficiency of the production line, but also reduces the processing quality problems and downtime caused by improper tool replacement; the translation drive energy consumption feature vector can be used as part of the intelligent management system of the storage tool magazine, combined with other sensor data and process parameters to realize the automatic and intelligent management of tool retrieval; this helps to improve the management level of the storage tool magazine and reduce manual intervention and error rate.

[0125] S7, inputting the three-dimensional feature vector of the tool picking route and the energy consumption feature vector of the translation drive into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and replacing the first-order tool to be installed with the minimum comprehensive energy consumption index of the tool picking path as the optimal tool to be installed;

[0126] The structure of the comprehensive calculation model of the tool path includes:

[0127] Input layer: used to receive the three-dimensional feature vector of the tool picking path and the translation drive energy consumption feature vector, and input them into the model; the three-dimensional feature vector of the tool picking path includes the X-axis travel distance, the Y-axis travel distance, and the Z-axis travel distance, which reflects the geometric characteristics of the tool picking path; the translation drive energy consumption feature vector contains the energy consumption information of the tool picking mechanism when it is translated in each dimension, which is used to evaluate the energy consumption cost of the path;

[0128] Processing layer: According to preset rules, different weights are assigned to the geometric features and energy consumption features of the path; the weights reflect the different emphasis on path length and energy consumption in different production scenarios; according to the three-dimensional feature vector of the tool picking path, the total length cost of the path is calculated by weighted summing the travel distance of each axis; the weight of each axis can be adjusted according to the actual motion performance of the machine tool; the total energy consumption cost of the path is calculated by using the translation drive energy consumption feature vector and combining the specific energy consumption data of each segment in the tool picking path; the energy consumption data needs to be mapped to the specific path segment; the path length cost and energy consumption cost are weighted summed according to the assigned weights to obtain the comprehensive index of the tool picking path, which is used to quantitatively evaluate the advantages and disadvantages of different tool picking paths;

[0129] Optimization layer: According to the specific application scenario and requirements, select the appropriate optimization algorithm to search for the optimal tool path; the goal of the optimization algorithm is to find the path with the smallest comprehensive index among all possible tool paths; during the optimization process, it is necessary to consider constraints such as the motion restrictions of the machine tool, the accessibility of the tool, and the urgency of the processing task to ensure that the selected path is practical;

[0130] Output layer: Output the tool picking path with the smallest comprehensive index, including specific path coordinates, travel sequence and corresponding energy consumption prediction; at the same time, output the comprehensive energy consumption index of the path for reference and evaluation by production managers;

[0131] The mathematical calculation formula of the comprehensive calculation model of the tool path is:

[0132]

[0133] Among them, I represents the comprehensive index of the tool path, d x Indicates the travel distance on the X axis, d y Indicates the travel distance on the Y axis, d z Indicates the travel distance on the Z axis, e x represents the energy consumption when moving along the X axis, e y represents the energy consumption when moving along the Y axis, e z Represents the energy consumption when moving along the Z axis.

[0134] In this step, by comprehensively considering the geometric characteristics and energy consumption cost of the tool picking path, the model can calculate the optimal path with the smallest comprehensive index, reducing unnecessary movement of the tool picking mechanism, thereby shortening the tool replacement time and improving the overall production efficiency; when evaluating the path, the model not only considers the path length, but also combines the translation drive energy consumption characteristic vector, making energy consumption one of the important factors in path selection; selecting the path with the lowest energy consumption helps to reduce the energy consumption cost in the production process, which is in line with the concept of green manufacturing; by considering the real-time wear of the tool and avoiding the use of over-worn tools when selecting the tool picking path, the model helps to protect the tool, extend its service life, and reduce downtime and cost caused by tool damage; using tools in good condition for processing can reduce processing errors caused by tool wear, improve processing accuracy and Product quality meets the needs of precision machining tasks; the weight distribution in the model can be adjusted according to different production scenarios and needs, so that the system can adapt to different production environments and task requirements, enhancing the flexibility and adaptability of the system; by optimizing the tool path, the model helps to achieve the reasonable allocation and efficient utilization of production resources such as machine tools and cutting tools, reduce resource waste, and improve overall production efficiency; the tool path with the smallest comprehensive index output by the model and its comprehensive energy consumption index provide important decision-making basis for production managers; the comprehensive calculation model of the tool path in step S7 significantly improves production efficiency, reduces energy consumption costs, extends tool life, improves machining quality, and enhances the flexibility and resource utilization efficiency of the system through intelligent evaluation and optimization of the tool path, providing strong support for the intelligent upgrading of modern manufacturing industry.

[0135] Embodiment 2: Figure 3 As shown, a tool retrieval control system for a storage tool magazine of the present invention specifically includes the following modules;

[0136] The processing technology data analysis module obtains the processing technology data information, and extracts the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the processing technology data information; the process characteristic information includes the tool size specification, cutting parameters, tool material, workpiece material and cooling conditions; the tool position number is composed of three coordinates of X, Y and Z;

[0137] The tool feature image query module extracts the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traverses the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount;

[0138] The tool wear analysis module performs tool wear analysis on the process characteristic information, predicts the tool wear amount caused by the next process to the tool to be installed; extracts cutting parameters that affect tool wear from the process characteristic information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; uses a preset tool wear database to find tool wear data under the same cutting parameters as the current process characteristic information; and estimates the tool wear amount in the next process based on the tool wear data;

[0139] The tool screening module uses the tool wear amount to verify the wear of each tool with the same size and specification as the tool to be installed, removes the tools that cannot independently complete the next process, and marks the remaining tools with the same size and specification as the tool to be installed as first-order tools to be installed; the removal condition is: the tool wear amount plus the real-time tool wear amount exceeds the tool wear threshold;

[0140] The three-dimensional route planning module performs three-dimensional route splitting for each first-order tool to be installed, and obtains a three-dimensional feature vector of the tool picking route corresponding to the first-order tool to be installed; the three-dimensional feature vector of the tool picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance;

[0141] The energy consumption information conversion module collects the translation drive energy consumption information of each dimension in the tool taking mechanism, and performs data conversion to obtain the translation drive energy consumption feature vector of the tool taking mechanism;

[0142] The comprehensive optimization decision module inputs the three-dimensional feature vector of the tool picking route and the translation drive energy consumption feature vector into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and replaces the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool picking path as the optimal tool to be installed.

[0143] The system uses the tool feature image query module and tool wear analysis module to monitor tool wear in real time, and screens tools based on tool wear in the tool screening module, avoiding the use of excessively worn tools for processing, thereby improving processing quality and extending tool life.

[0144] The entire system realizes the intelligent decision-making process from processing data analysis to optimal tool selection, reducing manual intervention and improving production efficiency and automation level. At the same time, through the three-dimensional route planning module and energy consumption information conversion module, the system can automatically plan the optimal tool picking path, reducing energy consumption and tool picking time.

[0145] The comprehensive optimization decision module calculates the comprehensive index of the tool path by comprehensively considering the three-dimensional feature vector and the translation drive energy consumption feature vector of the tool path, and selects the tool with the smallest comprehensive energy consumption index for replacement; it not only reduces the length of the tool path, but also reduces the energy consumption of the tool mechanism, thus improving the overall production efficiency; the system can flexibly select the most suitable tool according to different process feature information, thus enhancing the adaptability and flexibility of the system;

[0146] By reducing manual intervention, optimizing tool paths and reducing energy consumption, the system significantly improves production efficiency and machining accuracy, while reducing the frequency of tool replacement and machining quality problems caused by tool wear, thereby reducing production costs; based on a large amount of tool feature profile information and real-time wear data, the system provides data support for tool selection and tool path optimization through data analysis and modeling, enhancing the scientificity and accuracy of decision-making;

[0147] In summary, the tool retrieval control system of the tool storage magazine has significant advantages in improving processing quality, optimizing tool management, reducing energy consumption and improving production efficiency. It not only solves the problems existing in traditional methods, but also significantly improves the working performance and economy of CNC machine tools by introducing advanced information technology.

[0148] In addition, the present application also provides a tool retrieval control device for a storage tool magazine, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0149] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on a tool retrieval control device for a storage tool magazine. In other embodiments of the present invention, a tool retrieval control device for a storage tool magazine may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0150] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes a tool retrieval control method for a storage tool magazine in any embodiment of the present invention.

[0151] Specifically, a system or device equipped with a storage medium can be provided, on which software program code that implements the functions of any of the above-mentioned embodiments is stored, and a computer (or CPU or MPU) of the system or device can be enabled to read and execute the program code stored in the storage medium.

[0152] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0153] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0154] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A tool retrieval control method for a storage tool magazine, characterized in that: The storage tool magazine includes multiple tool rows and a tool taking mechanism, each tool row is provided with multiple tool placement slots for storing tools, wherein each tool placement slot corresponds to a unique position number, and the tool taking mechanism is used to extract the tools in the tool placement slot according to the processing requirements, and the method includes: Acquire machining process data information, and extract the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the machining process data information; the process characteristic information includes tool size specifications, cutting parameters, tool materials, workpiece materials and cooling conditions; Extract the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traverse the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount; Perform tool wear analysis on the process characteristic information to predict the tool wear amount caused by the next process to the tool to be installed; extract cutting parameters that affect tool wear from the process characteristic information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; use a preset tool wear database to find tool wear data under the same cutting parameters as the current process characteristic information; estimate the tool wear amount in the next process based on the tool wear data; For each tool with the same size and specification as the tool to be installed, the tool wear amount is used to verify the wear of its tool feature profile information, and the tool that cannot independently complete the next process is eliminated, and the remaining tools with the same size and specification as the tool to be installed are marked as first-order tools to be installed; the elimination condition is: the tool wear amount plus the tool real-time wear amount exceeds the tool wear threshold; For each first-order tool to be installed, perform three-dimensional route splitting to obtain a three-dimensional feature vector of the tool picking route corresponding to the first-order tool to be installed; Collect the translation drive energy consumption information of each dimension in the tool taking mechanism, and perform data conversion to obtain the translation drive energy consumption feature vector of the tool taking mechanism; The three-dimensional feature vector of the tool picking route and the translation drive energy consumption feature vector are input into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool picking path is replaced as the optimal tool to be installed.

2. The tool retrieval control method of the storage tool magazine according to claim 1, characterized in that: The mathematical calculation formula of the comprehensive calculation model of the tool path is: ； Among them, I represents the comprehensive index of the tool path, d x Indicates the travel distance on the X axis, d y Indicates the travel distance on the Y axis, d z Indicates the travel distance on the Z axis, e x represents the energy consumption when moving along the X axis, e y represents the energy consumption when moving along the Y axis, e z Represents the energy consumption when moving along the Z axis.

3. The tool retrieval control method of the storage tool magazine according to claim 1, characterized in that: The calculation formula for the tool wear caused by the installed tool is: ； Among them, W represents the expected tool wear, k is the empirical coefficient based on tool material, workpiece material and processing conditions, V represents cutting speed, a represents an index related to cutting speed, f represents feed speed, b represents an index related to feed speed, d represents cutting depth, c represents an index related to cutting depth, and t represents processing time.

4. The tool retrieval control method of the storage tool magazine according to claim 1, characterized in that: The method for constructing the tool database comprises: Select a database management system and configure the database server, including setting the database name, user permissions, and backup strategy; Enter the information of existing tools into the database; Verify the entered data to ensure its accuracy and completeness; Create indexes for fields in the database; Regularly optimize the database, formulate database backup strategies, and regularly back up database data; Implement database security measures, set up access controls, and encrypt sensitive data; Integrate the tool database with the control system of the CNC machine tool.

5. The tool retrieval control method of the tool storage magazine according to claim 1, characterized in that: The factors affecting the setting of the tool wear threshold include tool material properties, workpiece material properties, cutting parameters, processing conditions and economic benefits.

6. The tool retrieval control method of the tool storage magazine according to claim 1, characterized in that: The method for obtaining the three-dimensional feature vector of the tool path comprises: For each candidate tool marked as a first-order tool to be installed, a path from the current tool picking mechanism position to the tool position is calculated; the three-dimensional feature vector of the tool picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance; After the three-dimensional route splitting is completed, a three-dimensional feature vector of the tool picking route is generated for each first-order tool to be installed.

7. The tool retrieval control method of the tool storage magazine according to claim 1, characterized in that: The method for obtaining the translation drive energy consumption characteristic vector includes: The sensor is used to monitor the translation drive energy consumption data of the tool taking mechanism in real time during each axial movement; Preprocess the collected translation drive energy consumption data, including data cleaning, data conversion and data standardization; Extracting the preprocessed data to extract features that can reflect the translational drive energy consumption of the tool picking mechanism in different dimensions; the features include average energy consumption, maximum energy consumption, energy consumption change rate and total energy consumption; The extracted energy consumption features of each dimension are combined into a feature vector, namely, the translation drive energy consumption feature vector.

8. A tool retrieval control device for a storage tool magazine, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

9. A tool retrieval control system for a storage tool magazine, characterized in that: The system comprises: The processing technology data analysis module obtains the processing technology data information, and extracts the position number of the tool to be disassembled used in the current process and the process characteristic information of the next process according to the processing technology data information; the process characteristic information includes the tool size specification, cutting parameters, tool material, workpiece material and cooling conditions; the tool position number is composed of three coordinates of X, Y and Z; The tool feature image query module extracts the size and specification of the tool to be installed used in the next process from the process feature information of the next process; and traverses the tool database to extract the tool feature image information of all tools with the same size and specification as the tool to be installed; the tool feature image information includes the tool position number, tool size and specification, tool wear threshold and real-time tool wear amount; The tool wear analysis module performs tool wear analysis on the process characteristic information, predicts the tool wear amount caused by the next process to the tool to be installed; extracts cutting parameters that affect tool wear from the process characteristic information, including cutting speed, feed rate, cutting depth, cutting width and spindle speed; uses a preset tool wear database to find tool wear data under the same cutting parameters as the current process characteristic information; and estimates the tool wear amount in the next process based on the tool wear data; The tool screening module uses the tool wear amount to verify the wear of each tool with the same size and specification as the tool to be installed, removes the tools that cannot independently complete the next process, and marks the remaining tools with the same size and specification as the tool to be installed as first-order tools to be installed; the removal condition is: the tool wear amount plus the real-time tool wear amount exceeds the tool wear threshold; The three-dimensional route planning module performs three-dimensional route splitting for each first-order tool to be installed, and obtains a three-dimensional feature vector of the tool picking route corresponding to the first-order tool to be installed; the three-dimensional feature vector of the tool picking route includes an X-axis travel distance, a Y-axis travel distance, and a Z-axis travel distance; The energy consumption information conversion module collects the translation drive energy consumption information of each dimension in the tool taking mechanism, and performs data conversion to obtain the translation drive energy consumption feature vector of the tool taking mechanism; The comprehensive optimization decision module inputs the three-dimensional feature vector of the tool picking route and the translation drive energy consumption feature vector into the comprehensive calculation model of the tool picking path to obtain the comprehensive index of the tool picking path; and replaces the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool picking path as the optimal tool to be installed.

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