Tool retrieval control method, device and system for a storage tool magazine

Abstract

The present invention relates to the technical field of tool control, and particularly to a tool retrieval control method, device and system for a storage tool magazine, which significantly improves the working performance and economy of a numerical control machine tool; the storage tool magazine includes a plurality of tool rows and a tool picking mechanism, each tool row is provided with a plurality of tool slots for storing tools, and each tool slot corresponds to a unique position number, and the tool picking mechanism is used to extract the tools in the tool slots according to the processing requirements. The method includes: obtaining the processing process data information, and extracting the position number of the tool to be disassembled used in the current process and the process feature information of the next process according to the processing process data information; extracting the size specification of the tool to be installed used in the process from the process feature information of the next process; and traversing the tool database to extract the tool feature portrait information of all tools with the same size specification as the tool to be installed; performing tool wear analysis on the process feature information.

Description

Technical Field

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

[0002] In modern manufacturing, numerically controlled machine tools (CNCs) are widely used in various precision machining tasks. To improve production efficiency and machining accuracy, CNCs 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, reducing manual intervention.

[0003] Existing tool retrieval control methods usually rely on preset tool positions and simple sequential retrieval strategies, without considering the real-time wear condition of the tools, resulting in the use of overly worn tools, which affects machining quality and tool life. Moreover, they do not have the function of optimizing the tool retrieval path, leading to increased energy consumption of the tool retrieval mechanism and affecting the overall efficiency. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for controlling the retrieval of tools in a storage tool magazine, which significantly improves the working performance and economy of a numerically controlled machine tool.

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

[0006] Obtaining processing technology data information, and extracting the position number of the tool to be disassembled used in the current process and the process feature information of the next process according to the processing technology data information; the process feature information includes tool size specifications, cutting parameters, tool materials, workpiece materials, and cooling conditions;

[0007] Extracting the size specifications 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 portrait information of all tools with the same size specifications as the tool to be installed; the tool feature portrait information includes the tool position number, tool size specifications, tool wear thresholds, and tool real-time wear amounts;

[0008] Performing tool wear analysis on the process feature information to predict the tool wear amount caused by the tool to be installed in the next process; extracting the cutting parameters affecting tool wear from the process feature information, including cutting speed, feed rate, cutting depth, cutting width, and spindle speed; using a preset tool wear database to find the tool wear data under the cutting parameters the same as the current process feature information; and estimating the tool wear amount in the next process according to the tool wear data;

[0009] For each tool with the same dimensional specifications as the tool to be installed, the tool wear amount is used to verify the wear of its tool characteristic portrait information, and the tools that cannot independently complete the next process are eliminated, and the remaining tools with the same dimensional specifications 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 real-time tool wear amount exceeds the tool wear threshold;

[0010] For each first-order tool to be installed, three-dimensional route splitting is performed to obtain the three-dimensional feature vector of the tool-taking 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] Input the three-dimensional feature vector of the tool-taking route and the translation drive energy consumption feature vector into the comprehensive calculation model of the tool-taking path to obtain the comprehensive index of the tool-taking path; and the first-order tool to be installed with the minimum comprehensive energy consumption index of the tool-taking path is used as the optimal tool to be installed for replacement.

[0013] Further, the mathematical calculation formula of the comprehensive calculation model of the tool-taking path is:

[0014]

[0015] Among them, I represents the comprehensive index of the tool-taking path, d x represents the travel distance on the X-axis, d y represents the travel distance on the Y-axis, d z represents 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] Further, the calculation formula for the tool wear amount caused by the tool to be installed is:

[0017]

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

[0019] Further, the construction method of 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 cutting tools into the database;

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

[0023] Create indexes for the fields in the database;

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

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

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

[0027] Further, the influencing factors for setting the cutting tool wear threshold include cutting tool material characteristics, workpiece material characteristics, cutting parameters, machining conditions, and economic benefits.

[0028] Further, the method for obtaining the three-dimensional feature vector of the tool picking route includes:

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

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

[0031] Further, the method for obtaining the feature vector of the translation drive energy consumption includes:

[0032] Real-time monitor the translation drive energy consumption data of the tool picking mechanism when moving in each axis through sensors;

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

[0034] Extract the data after preprocessing to extract the features that can reflect the translation 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] Combine the energy consumption features extracted from each dimension into a feature vector, that is, the feature vector of the translation drive energy consumption.

[0036] On the other hand, the present application provides a tool retrieval control device for a warehousing tool magazine, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, it implements the steps in any one of the above methods.

[0037] In a third aspect, the present application further provides a tool retrieval control system for a warehousing tool magazine. The system includes:

[0038] A processing technology data analysis module, which acquires processing technology data information and extracts the position number of the tool to be disassembled used in the current process and the process feature information of the next process according to the processing technology data information. The process feature information includes tool size specifications, cutting parameters, tool materials, workpiece materials, and cooling conditions. The tool position number is composed of three coordinates X, Y, and Z.

[0039] A tool feature portrait query module, which extracts the size specifications 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 portrait information of all tools with the same size specifications as the tool to be installed. The tool feature portrait information includes tool position number, tool size specifications, tool wear thresholds, and real-time tool wear amounts.

[0040] A tool wear analysis module, which conducts tool wear analysis on the process feature information to predict the tool wear amount caused by the next process to the tool to be installed. It extracts the cutting parameters affecting tool wear from the process feature information, including cutting speed, feed rate, cutting depth, cutting width, and spindle speed. It uses a preset tool wear database to find the tool wear data under the same cutting parameters as the current process feature information. According to the tool wear data, it estimates the tool wear amount in the next process.

[0041] A tool screening module, for each tool with the same size specifications as the tool to be installed, uses the tool wear amount to verify the wear of its tool feature portrait information, eliminates the tools that cannot independently complete the next process, and marks the remaining tools with the same size specifications as the tool to be installed as first-order tools to be installed. The elimination condition is that the tool wear amount plus the real-time tool wear amount exceeds the tool wear threshold.

[0042] A three-dimensional route planning module, for each first-order tool to be installed, conducts three-dimensional route splitting to obtain a three-dimensional feature vector of the tool-taking route corresponding to the first-order tool to be installed. The three-dimensional feature vector of the tool-taking route includes the traveling distance along the X-axis, the traveling distance along the Y-axis, and the traveling distance along the Z-axis.

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

[0044] The comprehensive optimization decision-making module inputs the three-dimensional feature vector of the tool fetching route and the translational drive energy consumption feature vector into the comprehensive calculation model of the tool fetching path to obtain the comprehensive index of the tool fetching path; and takes the first-order tool to be installed with the minimum comprehensive energy consumption index of the tool fetching path as the optimal tool to be installed for replacement.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By considering the tool wear situation in real time and excluding those tools that cannot meet the requirements of the next process due to wear during the tool selection process, it ensures that the tools used in the machining process are in good condition, thereby improving the machining quality and extending the service life of the tools;

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

[0047] This method can flexibly select the most suitable tool according to different process characteristic information, enhancing the adaptability and flexibility of the system; By reducing machining interruptions and tool change frequencies caused by tool wear, and optimizing the tool fetching path to reduce the tool fetching time, this method significantly improves the production efficiency of the CNC machine tool;

[0048] Due to the improvement of machining quality and tool life, reduction of tool change times and rework caused by machining quality problems, this method helps to reduce production costs; Based on a large amount of tool characteristic portrait information and real-time wear data, through data analysis and modeling to guide tool selection and tool fetching path optimization, it enhances the scientificity and accuracy of decision-making;

[0049] In summary, the tool fetching control method of this storage tool magazine has significant advantages in improving machining quality, optimizing tool management, reducing energy consumption and increasing production efficiency. It not only solves the problems existing in the traditional method, but also significantly improves the working performance and economy of the CNC machine tool by introducing advanced information technology means. Brief Description of the Drawings

[0050] Figure 1 is the flowchart of the tool fetching control method of the storage tool magazine of the present invention;

[0051] Figure 2 is the structure diagram of the tool fetching control device of the storage tool magazine of the present invention;

[0052] Figure 3 It is a structural diagram of a tool retrieval control system for a storage tool magazine. Specific embodiments

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

[0054] The present application describes the provided method, device, and system through flowcharts and / or block diagrams.

[0055] It should be understood that each block of the flowchart and / or block diagram, as well as the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by the computer or other programmable data processing devices, resulting in a device that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0056] The present application will be described below in conjunction with the accompanying drawings in the present application.

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

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

[0059] The process feature information includes tool size specifications, cutting parameters, tool materials, workpiece materials, and cooling conditions; the tool position number is composed of three coordinates X, Y, and Z;

[0060] Numerical control machines usually use G codes or M codes for programming, and these codes contain detailed machining steps, cutting parameter information, etc.;

[0061] Each tool has a unique identifier in the storage tool magazine, 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] The tool size specifications include tool diameter, length, and number of flutes parameters;

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

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

[0066] Workpiece material represents the material of the part to be machined, as different materials require different types of tools;

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

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

[0069] S2. Extract the size specifications of the tool to be installed used in this process from the process feature information of the next process; and traverse the tool database to extract the tool feature portrait information of all tools with the same size specifications as the tool to be installed; the tool feature portrait information includes the tool position number, tool size specifications, tool wear threshold, and tool real-time wear amount;

[0070] The construction method of the tool database includes:

[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 portrait information to be recorded, including the tool position number, tool size specifications, tool wear threshold, and tool real-time wear amount;

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

[0074] Enter the information of existing cutting 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 integrity of the data;

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

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

[0078] Prepare a database recovery plan so that data can be quickly restored in case of data loss and system crashes;

[0079] Implement database security measures, set access controls, and encrypt sensitive data; conduct regular security audits and vulnerability scans of the database to promptly detect and fix potential security issues;

[0080] Integrate the cutting tool database with the control system of the CNC machine tool to ensure real-time acquisition and update of cutting tool information.

[0081] In this step, by precisely matching the dimensional specifications of the tool to be installed, it is possible to ensure the selection of the most suitable tool for machining, thereby avoiding machining errors and unnecessary downtime caused by tool mismatch, and significantly improving machining accuracy and production efficiency. The tool characteristic portrait information includes the wear threshold and real-time wear amount of the tool, enabling the system to evaluate the remaining service life of the tool and avoid using overly worn tools for machining, thus extending the overall life of the tool, reducing the tool change frequency and cost. The construction and use of the tool database achieve centralized management and quick query of tool information, not only improving the efficiency of tool management, but also making the inventory, usage, and maintenance records of the tools clear at a glance, facilitating resource optimization and cost control for the enterprise. By integrating the tool database with the control system of the CNC machine tool, real-time update and intelligent scheduling of tool information are realized, enabling the CNC machine tool to automatically select the appropriate tool according to the machining requirements, reducing manual intervention, and improving the automation and intelligence level of the production line. By implementing database security measures, the security of the tool database is ensured, preventing data leakage and illegal access, and protecting the core assets and competitive advantages of the enterprise. The construction and use of the tool database provide data support for the continuous improvement of the enterprise. By recording and analyzing the tool usage situation, the enterprise can discover problems and bottlenecks in the machining process and take targeted measures for optimization and improvement, promoting the continuous improvement of product quality and the continuous growth of production efficiency. In step S2, by traversing the tool database to extract the characteristic portrait information of the tool to be installed, not only the machining 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 continuous improvement of the enterprise.

[0082] S3. Conduct tool wear analysis on the process characteristic information to predict the tool wear amount caused by the tool to be installed in the next process.

[0083] The method for conducting tool wear analysis on the process characteristic information includes:

[0084] Extract cutting parameters directly related to tool wear from the process characteristic 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 characteristics of the workpiece material such as hardness, toughness, and thermal conductivity will also affect the friction, heat generation, and tool wear during the cutting process.

[0086] Utilize the existing tool wear database to find tool wear data under cutting conditions similar to the current process characteristic information.

[0087] Based on the information in the tool wear database and combined with the specific conditions of the current process, apply wear coefficients to estimate the tool wear in the next process;

[0088] In addition to cutting parameters and material properties, other factors affecting 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 for their impact on tool wear through empirical judgment or experimental data and given appropriate consideration in the prediction process;

[0089] Integrate the analysis results to judge the predicted wear of the tool to be installed in the next process;

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

[0091]

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

[0093] In this step, by predicting tool wear, it is possible to replace overly worn tools in a timely manner, avoiding a decline in machining accuracy and product quality caused by tool wear; it helps to maintain stable machining accuracy and improve the overall quality level of products; through reasonable tool management and replacement strategies, it is possible to reduce the situation of tools being scrapped prematurely due to excessive wear, thereby extending the service life of tools and reducing tool replacement costs; reducing the downtime for tool change caused by tool wear can improve the continuous operation ability of the production line, shorten the production cycle, and improve the overall production efficiency; by predicting and optimizing the tool picking path, it is possible to reduce the energy consumption of the tool picking mechanism and at the same time reduce the indirect costs generated by frequent tool changes; in addition, reasonable tool management can also reduce tool inventory costs and improve the utilization rate of funds; this step is an important part of the intelligent management of CNC machine tools and warehouse tool magazines. By introducing data analysis and prediction technologies, it improves the intelligent level of the manufacturing process and lays a foundation for the further development of intelligent manufacturing; it provides scientific and accurate tool wear prediction data for production managers, helping 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 dimensional specifications as the tool to be installed, use the tool wear amount to verify the wear of its tool characteristic portrait information, eliminate the tools that cannot independently complete the next process, and mark the remaining tools with the same dimensional specifications as the tool to be installed as first-order tools to be installed; the elimination condition is: the tool wear amount plus the real-time tool 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 completing the next process.

[0096] Compare the calculated total wear amount with the tool wear threshold; the tool wear threshold indicates that after the tool reaches this wear level, its performance will be significantly reduced and it is no longer suitable for continued use.

[0097] For tools with a total wear amount exceeding the wear threshold, they are regarded as unable to independently complete the next process and should be excluded from the candidate list.

[0098] The remaining tools, that is, those with an expected total wear amount not exceeding the wear threshold, are marked as first-order tools to be installed and are eligible as candidate tools for the next process.

[0099] The influencing factors for setting the tool wear threshold include:

[0100] Tool material characteristics: Tools made of different materials have different wear resistances; tools with high wear resistance can withstand more cutting loads, so their wear thresholds are 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 easily damaged during high-temperature cutting, and the wear threshold can be appropriately increased; chemical reactions between the tool and the workpiece material may also cause increased wear; therefore, the chemical stability of the tool is also a factor 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 tool wear; materials with good toughness will generate greater cutting forces and vibrations during cutting, thus 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 in the cutting area to rise, exacerbating tool wear.

[0102] Cutting parameters: Excessively high cutting speed will lead to an increase in cutting temperature and accelerate tool wear. Therefore, the influence of cutting speed needs to be considered when setting the wear threshold. The magnitude of the feed rate also directly affects tool wear. An excessively high feed rate will increase the cutting load and cutting force, thereby accelerating tool wear. The cutting depth has a significant impact on tool wear. A deeper cutting depth requires greater cutting force and more wear-resistant tools to withstand.

[0103] Processing conditions: The use of coolant can effectively reduce the temperature in 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 also affects tool wear. Excessive vibration of the machine tool will accelerate tool wear. Therefore, 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 to be considered when setting the wear threshold. High-cost tools may require a higher wear threshold to make full use of their value. The tool replacement frequency directly affects production efficiency. An excessively low wear threshold may lead to frequent tool replacement and reduce production efficiency. Therefore, when setting the wear threshold, it is necessary to improve production efficiency as much as possible on the premise of ensuring processing quality.

[0105] In this step, by eliminating the tools that cannot independently complete the next process, it is ensured that only tools in good condition are used for processing, thus avoiding the decline in processing quality and premature scrapping of tools caused by excessive tool wear, 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 still maintain good performance after completing the next process. Although this step itself is not directly involved in energy consumption management, by reducing the number of downtime replacements caused by tool wear, it indirectly reduces the energy consumption in the production process. At the same time, by quickly and accurately finding the optimal tools to be installed, it reduces the moving distance and time of the tool picking mechanism, further improving production efficiency. This step introduces a prediction and verification mechanism for tool wear, making the control of tool retrieval in the storage tool magazine more intelligent. By real-time updating the tool feature portrait information, it can dynamically adjust the tool wear threshold and selection strategy to meet the needs of different processing tasks, enhancing the adaptability and flexibility of the system. By reasonably using tools, 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 enhance the overall economic benefits. This step plays a crucial role in the control of tool retrieval in the storage tool magazine, not only improving the processing quality and tool life, but also optimizing tool selection, reducing energy consumption, improving production efficiency, and enhancing the intelligence and adaptability of the system, ultimately enhancing 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-taking route corresponding to the first-order tool to be installed; the three-dimensional feature vector of the tool-taking route includes the travel distance in the X-axis, the travel distance in the Y-axis, and the travel distance in the Z-axis;

[0107] The method for obtaining the three-dimensional feature vector of the tool-taking route includes:

[0108] For each candidate tool marked as a first-order tool to be installed, calculate the path from the current tool-taking mechanism position to the position of the tool; this path involves movements in the three dimensions of 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 on the X-axis, Y-axis, and Z-axis;

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

[0110] The travel distance in the Y-axis represents the distance that the tool-taking 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 travel distance in the Z-axis represents the distance that the tool-taking 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, generate a three-dimensional feature vector of the tool-taking route for each first-order tool to be installed, and this vector contains three components: the travel distance in the X-axis, the travel distance in the Y-axis, and the travel distance in the Z-axis; this feature vector not only describes the total travel distance required for the tool-taking mechanism to reach the target tool, but also implies the direction changes and possible obstacle avoidance requirements during the movement process.

[0113] In this step, the three-dimensional route splitting refines the continuous tool fetching path into independent travel distances in each dimension, making the path planning more accurate; it helps reduce the extra movement and energy consumption caused by inaccurate path estimation; by obtaining the three-dimensional feature vectors of the tool fetching routes for each tool, the complexity and energy consumption of different tool fetching paths can be comprehensively evaluated, so as to select the optimal tool fetching order; it helps reduce the energy consumption of the tool fetching mechanism and improve the overall processing efficiency; the acquisition of the three-dimensional feature vectors takes into account the direction changes and obstacle avoidance requirements during the movement, enabling the system to flexibly adjust the tool fetching path in the face of different tool magazine layouts and working environments, ensuring the safety and smoothness of the tool fetching process; the three-dimensional feature vectors of the tool fetching routes, as input data, can be further used in the intelligent decision-making system to automatically select the optimal tool fetching path; it helps improve the intelligent level of the warehouse tool magazine management, reducing manual intervention and errors; by precisely planning the tool fetching path, the potential damage to the tools and workpieces caused by improper tool fetching process is reduced, thus helping to improve the processing quality and extend the service life of the tools; the three-dimensional route splitting in step S5 and the acquisition of the three-dimensional feature vectors of the tool fetching routes are the key links in optimizing the tool retrieval process of the warehouse tool magazine.

[0114] S6. Collect the translational drive energy consumption information of each dimension in the tool fetching mechanism and perform data conversion to obtain the translational drive energy consumption feature vector of the tool fetching mechanism.

[0115] The method for obtaining the translational drive energy consumption feature vector includes:

[0116] Use sensors to real-time monitor the translational drive energy consumption data when the tool fetching mechanism moves in each axis.

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

[0118] Extract the data after preprocessing to extract the features that can reflect the translational drive energy consumption of the tool fetching 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 represents the average energy consumption of each movement in each dimension, reflecting the average energy consumption during the movement.

[0120] The 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 represents the energy consumption change rate between adjacent movements, reflecting the fluctuation of energy consumption.

[0122] The total energy consumption represents the total energy consumption during the entire movement from the starting point to the ending point.

[0123] The energy consumption characteristics of each dimension extracted are combined into a feature vector, namely the translation drive energy consumption feature vector; the translation drive energy consumption feature vector contains the energy consumption information of the tool taking mechanism during translation drive in different dimensions.

[0124] In this step, through real-time monitoring and precise measurement, the energy consumption data of the tool taking mechanism during translation drive in different dimensions can be accurately obtained, providing a solid foundation for subsequent energy consumption analysis and optimization; the data preprocessing link effectively removes problems such as noise, outliers, and different dimensions, improving the accuracy and comparability of the data and ensuring the reliability of subsequent analysis; the extracted features such as average energy consumption, maximum energy consumption, energy consumption change rate, and total energy consumption comprehensively reflect the energy consumption characteristics of the tool taking mechanism during translation drive 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 taking path; the construction of the translation drive energy consumption feature vector provides a key input for the comprehensive calculation model of the tool taking path; by comprehensively considering the energy consumption costs of different tool taking paths, the model can select the path with the lowest energy consumption and the highest efficiency, thus realizing the optimization of the tool taking path and reducing the overall energy consumption; the optimized tool taking path can reduce the energy consumption and moving time of the tool taking mechanism, improving the efficiency and accuracy of tool retrieval; it not only helps to improve the overall production efficiency of the production line, but also can reduce 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 warehouse 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 warehouse tool magazine and reduce manual intervention and error rate.

[0125] S7. Input the three-dimensional feature vector of the tool taking route and the translation drive energy consumption feature vector into the comprehensive calculation model of the tool taking path to obtain the comprehensive index of the tool taking path; and replace the first-order tool to be installed with the smallest comprehensive energy consumption index of the tool taking path as the optimal tool to be installed.

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

[0127] Input layer: used to receive the three-dimensional feature vector of the tool taking route and the translation drive energy consumption feature vector and input them into the model; the three-dimensional feature vector of the tool taking route includes the travel distance along the X-axis, the travel distance along the Y-axis, and the travel distance along the Z-axis, reflecting the geometric characteristics of the tool taking path; the translation drive energy consumption feature vector contains the energy consumption information of the tool taking mechanism during translation drive in each dimension and 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 degrees of emphasis on path length and energy consumption in different production scenarios; according to the three-dimensional feature vector of the tool-taking route, the total length cost of the path is calculated by weighted summation of the travel distances of each axis; among them, the weights of each axis can be adjusted according to the actual motion performance of the machine tool; using the translational drive energy consumption feature vector, combined with the specific energy consumption data of each segment in the tool-taking path, the total energy consumption cost of this path is calculated; it is necessary to map the energy consumption data to specific path segments; the path length cost and energy consumption cost are weighted and summed according to the assigned weights to obtain the comprehensive index of the tool-taking path, which is used to quantitatively evaluate the advantages and disadvantages of different tool-taking paths;

[0129] Optimization layer: According to specific application scenarios and requirements, select a suitable optimization algorithm to search for the optimal tool-taking path; the goal of the optimization algorithm is to find the path with the minimum comprehensive index among all possible tool-taking paths; during the optimization process, constraints such as the motion limitations of the machine tool, the accessibility of the tool, and the urgency of the processing task need to be considered to ensure that the selected path is actually feasible;

[0130] Output layer: Output the tool-taking path with the minimum comprehensive index, including specific path coordinates, travel order, and corresponding energy consumption prediction; at the same time, output the comprehensive energy consumption index of this path for production management personnel to reference and evaluate;

[0131] The mathematical calculation formula of the comprehensive calculation model of the tool-taking path is as follows:

[0132]

[0133] Among them, I represents the comprehensive index of the tool-taking path, d x represents the travel distance on the X-axis, d y represents the travel distance on the Y-axis, d z represents 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 fetching path, the model can calculate the optimal path with the minimum comprehensive index, reduce the unnecessary movement of the tool fetching mechanism, thus shortening the tool change time and improving the overall production efficiency. When evaluating the path, the model not only considers the path length but also combines the energy consumption characteristic vector of the translation drive, making energy consumption one of the important factors in path selection. Selecting the path with the lowest energy consumption helps reduce the energy consumption cost during the production process, which is in line with the concept of green manufacturing. By considering the real-time wear condition of the tool and avoiding using overly worn tools when selecting the tool fetching path, the model helps protect the tool, extend its service life, and reduce the downtime and cost caused by tool damage. Using tools in good condition for processing can reduce the machining errors caused by tool wear, improve the machining accuracy and product quality, and meet the requirements of precision machining tasks. The weight allocation in the model can be adjusted according to different production scenarios and requirements, enabling the system to adapt to different production environments and task requirements, and enhancing the flexibility and adaptability of the system. By optimizing the tool fetching path, the model helps achieve the rational allocation and efficient utilization of production resources such as machine tools and tools, reduce resource waste, and improve the overall production efficiency. The tool fetching path with the minimum comprehensive index and its comprehensive energy consumption index output by the model provide an important decision-making basis for production managers. The tool fetching path comprehensive calculation model in step S7 significantly improves production efficiency, reduces energy consumption cost, 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 fetching path, providing strong support for the intelligent upgrading of modern manufacturing.

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

[0136] A machining process data parsing module, which obtains machining process 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 machining process data information; the process characteristic information includes tool size specifications, cutting parameters, tool materials, workpiece materials, and cooling conditions; the tool position number is composed of three coordinates X, Y, and Z;

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

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

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

[0140] The 3D route planning module conducts 3D route splitting for each first-order tool to be installed to obtain the 3D feature vector of the tool-taking route corresponding to the first-order tool to be installed; the 3D feature vector of the tool-taking route includes the travel distance along the X-axis, the travel distance along the Y-axis, and the travel distance along the Z-axis.

[0141] The energy consumption information conversion module collects the translational drive energy consumption information of each dimension in the tool-taking mechanism and conducts data conversion to obtain the translational drive energy consumption feature vector of the tool-taking mechanism.

[0142] The comprehensive optimization decision-making module inputs the 3D feature vector of the tool-taking route and the translational drive energy consumption feature vector into the comprehensive calculation model of the tool-taking path to obtain the comprehensive index of the tool-taking path; and replaces the first-order tool to be installed with the minimum comprehensive energy consumption index of the tool-taking path as the optimal tool to be installed.

[0143] Through the tool feature portrait query module and the tool wear analysis module, the system can grasp the tool wear situation in real time and conduct screening according to the tool wear amount in the tool screening module, avoiding the use of overly worn tools for processing, thereby improving the processing quality and extending the service life of the tools.

[0144] The entire system realizes the intelligent decision-making process from processing process data analysis to optimal tool selection, reduces manual intervention, and improves production efficiency and automation level; at the same time, through the 3D route planning module and the energy consumption information conversion module, the system can automatically plan the optimal tool-taking path, reducing energy consumption and tool-taking time.

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

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

[0147] In summary, the tool retrieval control system of this storage tool magazine has significant advantages in improving machining quality, optimizing tool management, reducing energy consumption, and increasing 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 means.

[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 on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it realizes each process of the method embodiment for controlling the output data above, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0149] It can be understood that the structure schematically shown in the embodiments 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 certain components, or split certain components, or have different component arrangements. The components shown in the figure can be implemented in hardware, software, or a combination of software and hardware.

[0150] The embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor is enabled to execute 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 codes for implementing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device is made to read and execute the program codes stored in the storage medium.

[0152] In this case, the program codes read from the storage medium itself can implement the functions of any one of the above embodiments, so the program codes and the storage medium storing the program codes constitute a part of the present invention.

[0153] Examples of the storage medium for providing the program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program codes can be downloaded from a server computer via a communication network.

[0154] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope 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 energy consumption feature vector of the translation drive 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; 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 Indicates the energy consumption when moving along the Z axis; 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.

2. 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.

3. The tool retrieval control method of the storage tool 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.

4. The tool retrieval control method of the storage tool 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.

5. 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.

6. 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 running 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 5 are implemented.

7. A tool retrieval control system for a storage tool magazine, characterized in that: The system is applied to the tool retrieval control method of the storage tool magazine according to claim 1, and 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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