Automatic numerical control workshop transformation method and system

Abstract

The invention discloses an automatic numerical control workshop transformation method and system. The method comprises the following steps: acquiring a machining process chain of a plurality of machining parts according to an acquired part machining scheme; a to-be-machined part is determined, a machining process chain is selected according to the to-be-machined part, and a candidate machine tool data set meeting the part machining type is matched; screening the candidate machine tool data set in combination with the constructed production process database, and determining a first execution machine tool; based on the production process database, the machining state of a to-be-machined part is monitored in real time; if the machining state of the to-be-machined part is in the deviation state, a machine tool redistribution mechanism is triggered, and a second execution machine tool is determined; and the machining state of the to-be-machined part continues to be monitored till machining of the to-be-machined part is completed. Compared with the prior art, self-adaptive screening and dynamic redistribution of the machine tool can be realized according to a part machining scheme and a production process database, and the production efficiency and the production quality are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of manufacturing automation, more particularly, to an automatic numerical control workshop transformation method and system. BACKGROUND

[0002] With the acceleration of the intelligent transformation of manufacturing industry, as the core production unit, the automation level of numerical control workshop directly determines the production efficiency and product competitiveness of enterprises. At present, the number and types of numerical control machine tools in enterprises continue to increase, and the processing demand presents the complex characteristics of multi-variety and multi-process. The production organization and equipment control of numerical control workshop has become the core challenge of manufacturing automation upgrade. In the numerical control processing process, the part processing needs to match the specific process chain and machine tool performance, and the machine tool running state, processing precision and life cycle have individual differences. In addition, manual intervention is easy to lead to task issuing lag, poor equipment collaboration, and the requirements of process adaptability, equipment scheduling flexibility, abnormal response timeliness for workshop automation transformation are increasingly stringent. At present, the mainstream operation mode of numerical control workshop in the industry is still mainly single-bed manual control, and some transformation schemes only realize the local automation of single equipment.

[0003] In the prior art, although some schemes try to realize multi-machine cooperation through manual preset process or rely on basic data statistics to select processing equipment, there is still no automatic numerical control workshop with multi-cooperation and terminal overall control in the prior art. One person one machine leads to low equipment utilization, poor processing efficiency and high labor cost.

[0004] Therefore, how to provide an automatic numerical control workshop capable of multi-cooperation and terminal overall control has become a technical problem to be solved by those skilled in the art. SUMMARY

[0005] To solve the above technical problems, the present application provides an automatic numerical control workshop transformation method which can realize machine self-adaptive screening and dynamic reallocation according to part processing scheme and production process database, effectively improving production efficiency and production quality.

[0006] The first technical solution provided by the present application is as follows: The application provides an automatic numerical control workshop transformation method, comprising the following steps: acquiring a part processing scheme, acquiring a processing technology chain of a plurality of processing parts according to the part processing scheme; identifying and determining a part to be processed, selecting the processing technology chain according to the part to be processed, matching a candidate machine tool data set that meets the part processing type according to the processing technology chain; constructing a production process database, adaptively screening the candidate machine tool data set in combination with the production process database, and determining a first execution machine tool; sending the part to be processed to the first execution machine tool, and monitoring the processing state of the part to be processed in real time based on the production process database; if the processing state of the part to be processed is in a deviation state, an alarm is sent, a machine tool redistribution mechanism is triggered, idle second execution machine tool information is obtained, and a workpiece is moved to a second execution machine tool according to the idle second execution machine tool information; the processing state of the part to be processed is continuously monitored until the part to be processed is processed.

[0007] Further, in a preferred mode of the application, the step of identifying and determining the part to be processed comprises: analyzing the part to be processed; collecting processing indicators of the part to be processed, the processing indicators comprising precision requirement information, processing time and processing yield; wherein the processing indicators of the part to be processed come from user input.

[0008] Further, in a preferred mode of the application, adaptively screening the candidate machine tool data set in combination with the production process database comprises: taking the precision requirement information as a retrieval condition, retrieving the production process database, and matching the precision requirement with the candidate machine tool data set according to the retrieval result to obtain a first candidate machine tool subset; taking the processing time as a retrieval condition, retrieving the production process database, and matching the processing time with the candidate machine tool data set according to the retrieval result to obtain a second candidate machine tool subset; taking the processing yield as a retrieval condition, retrieving the production process database, and matching the processing yield with the candidate machine tool data set according to the retrieval result to determine the first execution machine tool.

[0009] Further, in a preferred mode of the application, the step of constructing a production process database comprises: collecting historical machine tool performance parameters, rated life cycle and historical service life of a plurality of numerical control machine tools, and historical part performance requirements of the processing parts; The historical machine tool performance parameters include historical machining time and historical machining yield of each numerical control machine tool for machining each machining part, and the historical part performance demand includes historical accuracy requirement of the machining part. According to the rated service life and the historical service life of each numerical control machine tool, a machine tool service life deviation is obtained. Each numerical control machine tool is respectively combined with the historical machine tool performance parameters, the historical part performance demand, the rated service life, the historical service life and the machine tool service life deviation to establish a combined index, and a production process database is constructed.

[0010] Further, in a preferred manner of the present application, the step of iteratively optimizing the production process database comprises: According to the real-time monitoring result, part production data is recorded, the part production data is input into the production process database for database updating, and the iterative optimization of the production process database is completed until the iteration number reaches a preset number.

[0011] Further, in a preferred manner of the present application, the step of adaptively screening the candidate machine tool data set in combination with the production process database comprises: The historical machine tool performance parameters of each candidate machine tool are determined according to the production process database, and the historical machine tool performance parameters are divided into a plurality of group performance data sets, wherein the performance data set is a data set of each category of machining performance data. The performance requirements of the machining part are subjected to performance weight distribution, and a plurality of group performance data sets are screened according to the performance weight distribution result as a reference to determine a first execution machine tool.

[0012] Further, in a preferred manner of the present application, the step of performing performance weight distribution on the performance requirements of the machining part comprises: According to the production process database, the historical part performance demand of the machining part is extracted, and an initial performance weight is set according to the historical part performance demand. The machining process chain of the machining part is disassembled to obtain a rough machining stage and a finish machining stage, a main machining target is set for each of the rough machining stage and the finish machining stage, the initial performance weight is corrected according to the main machining target of each stage to obtain a stage performance weight. According to the production process database, equipment service life data of the candidate machine tool is obtained, and whether the corresponding candidate machine tool is in a healthy state is judged according to the service life data, if yes, the stage performance weight is maintained, and if not, the stage performance weight is adjusted to obtain a stage optimized weight. Screen the candidate machine tool dataset according to the stage optimization weight and the stage performance weight, and obtain a first execution machine tool.

[0013] Further, in a preferred mode of the present application, when the first execution machine tool is in a deviation state, triggering machine tool reallocation includes: According to the machining process chain, determine a process deviation node, and determine a subsequent machining machine tool and machining process according to the process deviation node; According to the production process database, search for the machining process, search for historical execution machine tools of the machining process, and obtain a plurality of to-be-matched machine tools; According to a plurality of to-be-matched machine tools, process matching is performed to determine a reallocation machine tool.

[0014] Further, in a preferred mode of the present application, the step of performing process matching according to a plurality of to-be-matched machine tools includes: Real-time acquisition of the working state of the to-be-matched machine tool, filtering of the to-be-matched machine tool according to the working state, and acquisition of an idle machine tool; According to the production process database, the machining yield of the idle machine tool is called, the machining yield is sorted, and a machining qualified sequence is obtained; According to the machining qualified sequence, the to-be-matched machine tool is matched, and process matching is completed.

[0015] The present application provides a second technical solution as follows: The present application also provides an automatic numerical control workshop reconstruction method system, which includes: A process chain acquisition module acquires a part machining scheme, and acquires a plurality of machining process chains of machining parts according to the part machining scheme; A candidate machine tool acquisition module identifies and determines a to-be-machined part, selects a candidate machine tool dataset satisfying the machining type of the to-be-machined part according to the machining process chain, and matches the candidate machine tool dataset according to the machining process chain; A database construction module constructs a production process database, and performs self-adaptive screening on the candidate machine tool dataset in combination with the production process database to determine a first execution machine tool; A machine tool reallocation module sends the to-be-machined part to the first execution machine tool, and monitors the machining state of the to-be-machined part in real time based on the production process database; If the machining state of the to-be-machined part is in a deviation state, an alarm is issued, a machine tool reallocation mechanism is triggered, idle second execution machine tool information is obtained, and the workpiece is moved to a second execution machine tool according to the idle second execution machine tool information; The machining state of the to-be-machined part is continuously monitored until the to-be-machined part is machined.

[0016] This invention provides a method for upgrading an automated CNC workshop, comprising: S1 acquiring a part processing plan, and acquiring a number of processing technology chains for processing parts according to the part processing plan; S2 identifying and determining the part to be processed, selecting the processing technology chain according to the part to be processed, and matching a candidate machine tool dataset that meets the part processing type according to the processing technology chain; S3 constructing a production process database, and adaptively filtering the candidate machine tool dataset according to the production process database to determine a first execution machine tool; S4 sending the part to be processed to the first execution machine tool, and monitoring the processing status of the part to be processed in real time based on the production process database; if the processing status of the part to be processed is in a deviated state, issuing an alarm, triggering a machine tool reassignment mechanism, obtaining information on an idle second execution machine tool, and moving the workpiece to the second execution machine tool according to the information on the idle second execution machine tool; continuing to monitor the processing status of the part to be processed until the part to be processed is completed. Among these methods, acquiring part processing plans and obtaining processing technology chains for several processed parts can avoid the problem of existing technologies relying on fragmented manual process formulation or applying general processes, leading to a disconnect between the technology chains and the complex processing requirements of multiple parts and multiple processes. After identifying the parts to be processed, selecting the corresponding processing technology chain and matching the candidate machine tool dataset that meets the part processing type based on the technology chain can solve the problem of existing technologies relying solely on basic data statistics to select processing equipment without associating the specific process chain of the part with the machine tool performance. Constructing a production process database and using the database to adaptively filter the candidate machine tool dataset to determine the first executing machine tool can solve the problem of existing technologies ignoring individual differences such as machine tool operating status, processing accuracy, and life cycle, and relying mainly on single-machine manual control. This solves the risk of low equipment utilization caused by one person per machine. At the same time, by constructing data support for terminal control through the database, it breaks the limitations of partial automation of single equipment and provides a data-driven foundation for multi-machine collaboration. The machine tool reassignment mechanism can overcome the bottleneck of mainstream single-machine manual control and lack of dynamic equipment scheduling, avoid efficiency losses caused by processing interruptions, and improve equipment utilization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the steps of an automated CNC workshop transformation method provided in this embodiment of the invention; Figure 2This is a schematic diagram illustrating adaptive filtering of candidate machine tool datasets provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of machine tool redistribution provided in an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] It should be noted that when a component is referred to as being "fixed to" or "set on" another component, it can be directly on or indirectly set on the other component; when a component is referred to as being "connected to" another component, it can be directly connected to or indirectly connected to the other component.

[0021] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "first", "second", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" or "several" means two or more, unless otherwise explicitly specified.

[0023] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which this application can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size should still fall within the scope of the technical content disclosed in this application, provided that they do not affect the effects and purposes that this application can produce.

[0024] like Figure 1As shown in the embodiment of this application, an automated CNC workshop transformation method is provided, including: S1 obtaining a part processing plan, and obtaining a number of processing technology chains for processing parts according to the part processing plan; S2 identifying and determining the part to be processed, selecting a processing technology chain according to the part to be processed, and matching a candidate machine tool dataset that meets the part processing type according to the processing technology chain; S3 constructing a production process database, and adaptively filtering the candidate machine tool dataset according to the production process database to determine the first execution machine tool; S4 sending the part to be processed to the first execution machine tool, and monitoring the processing status of the part to be processed in real time based on the production process database; if the processing status of the part to be processed is in a deviated state, an alarm is issued, triggering a machine tool reassignment mechanism to obtain idle second execution machine tool information, and moving the workpiece to the second execution machine tool according to the idle second execution machine tool information; continuing to monitor the processing status of the part to be processed until the part to be processed is completed.

[0025] In this embodiment, firstly, a general parts processing plan is imported. This plan must include core information such as part design drawings, processing accuracy requirements, surface quality standards, and process sequence logic. Then, the material, structural features, and processing requirements of different processed parts in the plan are automatically identified, and the complete processing technology chain corresponding to each part is extracted. Next, the processed parts are identified and determined, and the extracted dedicated processing technology chain for each part is automatically matched. Based on the processing type of each process in the technology chain, candidate machine tool datasets matching the processing type are selected. A production process database is constructed, and adaptive filtering of the candidate machine tool datasets is performed based on the production process database, ultimately determining the first executing machine tool. Subsequently, the parts to be processed are transferred to the first executing machine tool through a workshop logistics conveyor system. The processing status of the parts to be processed is monitored in real time, and the processing is transmitted via the machine tool's built-in transmission... Sensors (such as temperature sensors, vibration sensors, and displacement sensors) collect processing data in real time and compare it with monitoring parameters to determine whether the processing status is normal. If the real-time collected data exceeds the monitoring parameter threshold, the processing status is immediately determined to be in a deviation state, and an audible and visual alarm is triggered simultaneously, displaying the deviation parameters and possible causes. The machine tool reassignment mechanism is then activated, querying the production process database for machine tool information that has the same processing capacity as the first executing machine tool and is currently idle. The part to be processed is then transferred from the first executing machine tool to the second executing machine tool. After the part is transferred to the second executing machine tool, the system updates the processing parameters synchronously and continues to monitor the processing status in real time until all processes of the part are completed. The terminal generates a full-process processing record, completing the processing loop. If the real-time collected data does not exceed the monitoring parameter threshold, the first executing machine tool is not replaced, and the part processing is completed normally.

[0026] The following describes in detail the steps of an automated CNC workshop transformation method with specific embodiments.

[0027] Specifically, in a specific embodiment of the present invention, identifying and determining the part to be processed includes: Analyze the parts to be processed; Collect the processing parameters of the parts to be processed, including: accuracy requirements, processing time, and processing yield. The processing parameters of the parts to be processed are derived from user input.

[0028] In this embodiment, a part identification reading device, such as an industrial barcode scanner or RFID reader, is used to collect information about the parts to be processed. If the surface of the part to be processed has a unique identification label (such as a QR code or barcode), the label information is read by the barcode scanner, and the basic attributes of the part are automatically parsed, including the part name, material type, basic dimensions, and structural features. If the part to be processed has no identification label, a visual recognition camera is used to capture an image of the part's appearance. Through a preset part structure database, the edge feature matching and contour similarity calculation of the part to be processed are performed to identify the part type. Subsequently, the workshop process engineer or production scheduler inputs the processing parameters of the part to be processed, including accuracy requirements, processing time, and processing yield.

[0029] Specifically, such as Figure 2 As shown, in a specific embodiment of the present invention, the step of adaptively filtering the candidate machine tool dataset in conjunction with the production process database includes: Based on the accuracy requirement information as the search criteria, the production process database is searched, and the search results are matched with the candidate machine tool dataset according to the accuracy requirements to obtain the first candidate machine tool subset; Based on processing time as the search condition, the production process database is searched, and the processing time is matched with the candidate machine tool dataset according to the search results to obtain the second candidate machine tool subset; Based on the processing yield as the search criterion, the production process database is searched, and the processing yield is matched with the candidate machine tool dataset according to the search results to determine the first execution machine tool.

[0030] In this embodiment, firstly, accuracy requirement information (such as dimensional tolerances, geometric tolerances, and surface roughness of key processes) is retrieved as search criteria. The production process database is then searched, and the search results are compared with the candidate machine tool dataset. Machine tools that do not meet the accuracy requirements are eliminated, ultimately forming a first candidate machine tool subset. The accuracy matching degree of each machine tool in this subset needs to be labeled. Then, processing time requirements (including the upper limit of total processing time per piece and the upper limit of time for key processes) are extracted and used as search criteria. The production process database is then searched, and the search results are compared with the first candidate machine tool subset, eliminating those that do not meet the accuracy requirements. Machine tools whose estimated processing time exceeds the limit are selected as a second subset of candidate machine tools, with each machine tool's estimated processing time marked. Then, the processing yield (e.g., overall pass rate ≥ 98%, key process pass rate ≥ 99%) is retrieved and used as a search criterion. The production process database is searched, and the search results are compared with the second subset of candidate machine tools. If multiple machine tools in the second subset meet the yield requirements, the stability of the processing yield is further compared, prioritizing those with smaller standard deviations in recent periods. The machine tool with the best overall performance is ultimately determined as the first executing machine tool. If only one machine tool meets the requirements, it is directly designated as the first executing machine tool.

[0031] Specifically, in a specific embodiment of the present invention, the step of constructing a production process database includes: Collect historical machine tool performance parameters, rated life cycle and historical service life of several CNC machine tools, as well as historical part performance requirements of machined parts. Among them, the historical machine tool performance parameters include: the historical processing time and historical processing yield of each CNC machine tool for each machined part, and the historical part performance requirements include: the historical accuracy requirements of the machined parts; The machine tool life deviation is obtained based on the rated life cycle and historical service life of each CNC machine tool. Each CNC machine tool is linked to its historical machine tool performance parameters, historical part performance requirements, rated life cycle, historical service life, and machine tool life deviation to create a production process database.

[0032] In this embodiment of the invention, firstly, the PLC control systems of each CNC machine tool are connected via the OPC UA protocol to extract historical data on the processing of different parts by each machine tool, and the data is stored according to machine tool and part dimensions: The processing time for a single part on each machine tool is recorded, including the complete time from workpiece loading to unloading, accurate to the second; then, the pass rate of processing a certain part on each machine tool is calculated; next, the rated life cycle of each machine tool is extracted, provided by the machine tool manufacturer, and the historical practical life from the machine tool's initial use to its current actual operating time is obtained; then, the historical accuracy requirements for the processed parts are extracted, including dimensional tolerances, geometric tolerances, and surface quality requirements; for each CNC machine tool, the rated life cycle is used as the basis for... The life deviation of the computer tool is calculated based on its historical service life, as follows: rated life cycle - historical service life. Finally, a multi-dimensional combined index is established with the unique identifier of the machine tool and the unique identifier of the machined part as the core. In this embodiment, the steps are as follows: each index is associated with core data such as the unique identifier of the machine tool, the unique identifier of the machined part, historical machine tool performance parameters, historical part performance requirements, rated life cycle of the machine tool, historical service life of the machine tool, and life deviation of the machine tool. Then, an industrial-grade relational database is used to build a production process database. Compared with the existing isolated machine tool ledgers and part files, this helps to quickly locate data during screening and is used for rapid data matching.

[0033] Specifically, in a specific embodiment of the present invention, the step of iteratively optimizing the production process database includes: Based on real-time monitoring results, record part production data, input the part production data into the production process database for database updates, until the preset number of iterations is reached, thus completing the iterative optimization of the production process database.

[0034] In this embodiment of the invention, a real-time data interface directly connected to the CNC machine tool is established based on the OPC UA industrial communication protocol. Real-time data of part production and machine tool-related real-time data are simultaneously collected. The collected real-time data is bound to the corresponding machine tool and part, and directly updated to the production process database without replacing existing historical data. Simultaneously, the real-time lifespan data of the machine tool is updated, and the machine tool lifespan deviation is recalculated. For the updated data, the combined index of the machine tool and part is updated to ensure that historical and latest data can be simultaneously retrieved during subsequent filtering, balancing data stability and real-time performance. Compared to existing methods, incremental updates significantly reduce data writing time and retain historical data for analyzing production trends, providing data support for process optimization. Iterative updates based on a preset number of iterations avoid the problem of infinite database iteration and inability to be implemented, significantly reducing issues such as machine tool mismatch and processing errors caused by inaccurate data.

[0035] Specifically, in this embodiment of the invention, the step of adaptively filtering the candidate machine tool dataset in conjunction with the production process database includes: Based on the production process database, the historical machine tool performance parameters of each candidate machine tool are determined, and the historical machine tool performance parameters are divided into several sets of performance datasets. Each performance dataset is a dataset of processing performance data for each category. The performance requirements of the parts to be processed are assigned performance weights. Based on the performance weight assignment results, several sets of performance datasets are screened to determine the first execution machine tool.

[0036] In this embodiment, historical data on the processing of similar parts by each candidate machine tool is obtained through the production process database, including processing quality parameters, stability parameters, etc. The above parameters are classified, for example, according to three categories: processing efficiency, processing quality, and stability. Each group forms an independent performance dataset. Dynamic weight allocation is performed according to the performance requirements of the parts to be processed (such as the priority of accuracy, efficiency, and stability). The weight allocation result is used as a reference to determine the first execution machine tool.

[0037] Specifically, in a specific embodiment of the present invention, the step of allocating performance weights to the performance requirements of the parts to be processed includes: Based on the production process database, extract the historical performance requirements of the parts to be processed, and set the initial performance weights according to the historical performance requirements. The machining process chain of the part to be machined is broken down to obtain the roughing stage and the finishing stage. The main machining objectives are set for the roughing stage and the finishing stage respectively. The initial performance weights are corrected according to the main machining objectives of different stages to obtain the stage performance weights. The equipment life cycle data of candidate machine tools is obtained from the production process database. Based on the life cycle data, it is determined whether the corresponding candidate machine tool is in a healthy state. If it is in a healthy state, the stage performance weight is maintained. If not, the stage performance weight is adjusted to obtain the stage optimization weight. The candidate machine tool dataset is filtered based on the stage optimization weight and the stage performance weight to obtain the first execution machine tool.

[0038] In this embodiment, historical performance requirements for the part to be processed are retrieved from the production process database, including the precision requirements, efficiency requirements, and yield requirements during past processing. Based on the frequency of these historical requirements and their impact on finished product quality, initial performance weights are comprehensively determined (e.g., precision, efficiency, and yield accounting for 50%, 30%, and 20%, respectively). Subsequently, the processing chain of the part to be processed is broken down, clearly distinguishing between the roughing stage (e.g., removing excess material from the blank and preliminary forming processes) and the finishing stage (e.g., precision dimensional machining and surface treatment processes). Main processing objectives are set for the core goals of the two stages: the roughing stage focuses on efficient material removal and ensuring uniform material removal in subsequent processes, while the finishing stage focuses on meeting final precision requirements and ensuring acceptable surface quality. The initial weights are then adjusted based on the main processing objectives: Roughing stage... The process first prioritizes efficiency and stability in the initial stage, while reducing the weight of accuracy and yield. Then, in the finishing stage, the weight of accuracy and yield is increased, while efficiency is reduced. This step aligns the weights with the core requirements of each stage, avoiding a one-size-fits-all approach. Next, the lifecycle data of candidate machine tools is extracted from the production process database, including rated lifecycle, historical lifespan, and lifecycle deviation. This data is used to assess the machine tool's health: the magnitude of the lifecycle deviation determines the machine tool's health; if healthy, the stage performance weight is maintained; if unhealthy, the stability weight is increased, and the efficiency weight is reduced to form the stage optimization weight. This step mitigates the processing risks of unhealthy machine tools through weight adjustment. Finally, the performance data of candidate machine tools (such as accuracy compliance rate, processing efficiency, and yield stability) are comprehensively screened based on the stage performance weight and stage optimization weight to select the first executing machine tool. Specifically, such as Figure 3 As shown, in a specific embodiment of the present invention, the step of triggering machine tool reallocation when the first executing machine tool is in a deviated state includes: S41 determines the process deviation node based on the machining process chain, and determines the subsequent machining machine tool and machining process based on the process deviation node; S42 searches the production process database for machining operations, searches for historical machine tools that have performed the machining operations, and obtains several machine tools to be matched. S43 performs process matching based on several machine tools to be matched, and determines the machine tools to be redistributed.

[0039] In this embodiment of the invention, the first executing machine tool is monitored in real time. When the first executing machine tool triggers a deviation status alarm, the machining process chain of the part is immediately retrieved. By comparing the current machining progress with the process chain nodes, the deviation node of the process is accurately located. Based on the deviation node, the incomplete part of the process chain is extracted to determine the subsequent machining process to be executed and the corresponding machine tool type requirements. At the same time, the machining parameters of the completed process are recorded as the benchmark data for the subsequent process. Subsequently, according to the production process database, machine tools that have successfully executed the same process in the past are selected to form a list of machine tools to be matched, and the historical process adaptation parameters of each machine tool are marked. For the set of machine tools to be matched.

[0040] Specifically, in embodiments of the present invention, the step of performing process matching based on a plurality of machine tools to be matched includes: The system obtains the working status of the machine tools to be matched in real time, filters the machine tools to be matched based on their working status, and obtains idle machine tools. The processing yield of idle machine tools is retrieved from the production process database, the processing yield is sorted, and a processing qualified sequence is obtained. Match the machine tool to be matched according to the qualified processing sequence to complete the process matching.

[0041] In this embodiment, the working status data of each machine tool to be matched is collected, including: whether it is currently in processing state, load rate, and remaining time of the current task. This process is completed in real time through real-time communication between the terminal control system and the machine tool PLC control system, effectively avoiding process waiting caused by misjudging non-idle machine tools, ensuring the timeliness of subsequent process connection, and reducing judgment errors caused by manual intervention. Idle machine tools with a load rate of 0% and no current task are retrieved. Then, the processing yield data of idle machine tools that have historically performed the same process are retrieved from the production process database, specifically including: batch pass rate, key dimension compliance rate, and surface quality pass rate of the process in the past 3 months. The batch pass rate is the core indicator, with a weight of 70%. Combined with the key dimension compliance rate, a weight of 20%, and the surface quality pass rate, a weight of 10%, the comprehensive yield score is calculated and sorted from high to low to form a processing qualified sequence. Based on the processing qualified sequence, the machine tools to be matched are matched to complete the process matching.

[0042] This application also provides an automated CNC workshop transformation system, the system comprising: The process chain acquisition module obtains the part processing plan and acquires the processing process chain of several processed parts according to the part processing plan. The candidate machine tool acquisition module identifies and determines the part to be processed, selects the processing technology chain based on the part to be processed, and matches the candidate machine tool dataset that meets the processing type of the part based on the processing technology chain. The database construction module builds a production process database and uses the production process database to adaptively filter the candidate machine tool dataset to determine the first execution machine tool. The machine tool redistribution module sends the parts to be processed to the first execution machine tool and monitors the processing status of the parts in real time based on the production process database. If the processing state of the part to be processed is in a deviated state, an alarm is issued, triggering the machine tool reassignment mechanism to obtain the information of the idle second execution machine tool, and the workpiece is moved to the second execution machine tool according to the information of the idle second execution machine tool; Continue to monitor the processing status of the parts to be processed until the processing is completed.

[0043] As described above, the present invention provides an automated CNC workshop transformation method, comprising: S1 acquiring a part processing plan, and acquiring a number of processing technology chains for processing parts according to the part processing plan; S2 identifying and determining the part to be processed, selecting a processing technology chain according to the part to be processed, and matching a candidate machine tool dataset that meets the part processing type according to the processing technology chain; S3 constructing a production process database, and adaptively filtering the candidate machine tool dataset according to the production process database to determine the first execution machine tool; S4 sending the part to be processed to the first execution machine tool, and monitoring the processing status of the part to be processed in real time based on the production process database; if the processing status of the part to be processed is in a deviated state, an alarm is issued, triggering a machine tool reassignment mechanism to obtain information on an idle second execution machine tool, and moving the workpiece to the second execution machine tool according to the information on the idle second execution machine tool; continuing to monitor the processing status of the part to be processed until the part to be processed is completed. Among these methods, acquiring part processing plans and obtaining processing technology chains for several processed parts can avoid the problem of existing technologies relying on fragmented manual process formulation or applying general processes, leading to a disconnect between the technology chains and the complex processing requirements of multiple parts and multiple processes. After identifying the parts to be processed, selecting the corresponding processing technology chain and matching the candidate machine tool dataset that meets the part processing type based on the technology chain can solve the problem of existing technologies relying solely on basic data statistics to select processing equipment without associating the specific process chain of the part with the machine tool performance. Constructing a production process database and using the database to adaptively filter the candidate machine tool dataset to determine the first executing machine tool can solve the problem of existing technologies ignoring individual differences such as machine tool operating status, processing accuracy, and life cycle, and relying mainly on single-machine manual control. This solves the risk of low equipment utilization caused by one person per machine. At the same time, by constructing data support for terminal control through the database, it breaks the limitations of partial automation of single equipment and provides a data-driven foundation for multi-machine collaboration. The machine tool reassignment mechanism can overcome the bottleneck of mainstream single-machine manual control and lack of dynamic equipment scheduling, avoid efficiency losses caused by processing interruptions, and improve equipment utilization.

[0044] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for transforming an automated CNC workshop, characterized in that, include: S1 obtains a part processing plan, and obtains a processing technology chain for several processed parts according to the part processing plan; S2 identifies and determines the part to be processed, selects the processing technology chain based on the part to be processed, and matches a candidate machine tool dataset that meets the part processing type based on the processing technology chain; S3 constructs a production process database, and adaptively filters the candidate machine tool dataset based on the production process database to determine the first execution machine tool; S4 sends the part to be processed to the first machine tool and monitors the processing status of the part in real time based on the production process database; If the processing state of the part to be processed is in a deviated state, an alarm is issued, triggering the machine tool reassignment mechanism to obtain the idle second execution machine tool information, and the workpiece is moved to the second execution machine tool according to the idle second execution machine tool information; Continue to monitor the processing status of the part to be processed until the processing of the part is completed.

2. The method for transforming an automated CNC workshop according to claim 1, characterized in that, The process of identifying and determining the part to be processed includes: Analyze the part to be processed; The processing parameters of the parts to be processed are collected, including: accuracy requirements, processing time, and processing yield. The processing parameters of the parts to be processed are derived from user input.

3. The method for transforming an automated CNC workshop according to claim 2, characterized in that, Adaptive filtering of the candidate machine tool dataset based on the production process database includes: Based on the accuracy requirement information as the retrieval criteria, the production process database is searched, and the accuracy requirements are matched with the candidate machine tool dataset according to the retrieval results to obtain a first candidate machine tool subset; Based on the processing time as the search condition, the production process database is searched, and the processing time is matched with the candidate machine tool dataset according to the search results to obtain a second candidate machine tool subset; Based on the processing yield as the search criterion, the production process database is searched, and the processing yield is matched with the candidate machine tool dataset according to the search results to determine the first execution machine tool.

4. The method for transforming an automated CNC workshop according to claim 1, characterized in that, The steps to build a production process database include: Collect historical machine tool performance parameters, rated life cycle and historical service life of several CNC machine tools, as well as historical part performance requirements of the machined parts. The historical machine tool performance parameters include: the historical processing time and historical processing yield of each CNC machine tool for processing each of the processed parts; the historical part performance requirements include: the historical accuracy requirements of the processed parts. The machine tool life deviation is obtained based on the rated life cycle and historical service life of each CNC machine tool. A production process database is constructed by creating combined indexes for each CNC machine tool with its historical machine tool performance parameters, historical part performance requirements, rated life cycle, historical service life, and machine tool life deviation.

5. The method for transforming an automated CNC workshop according to claim 4, characterized in that, The step involves iteratively optimizing the production process database, including: Based on real-time monitoring results, part production data is recorded and input into the production process database for database updates until the number of iterations reaches a preset number, thus completing the iterative optimization of the production process database.

6. The method for transforming an automated CNC workshop according to claim 1, characterized in that, The step involves adaptively filtering the candidate machine tool dataset using the production process database, including: Based on the production process database, the historical machine tool performance parameters of each candidate machine tool are determined, and the historical machine tool performance parameters are divided into several sets of performance datasets, wherein each performance dataset is a dataset of processing performance data for each category. The performance requirements of the parts to be processed are assigned performance weights, and the results of the performance weight assignment are used as a reference to filter several sets of performance datasets to determine the first execution machine tool.

7. The method for transforming an automated CNC workshop according to claim 6, characterized in that, The step involves allocating performance weights to the performance requirements of the parts to be processed, including: Based on the production process database, extract the historical performance requirements of the parts to be processed, and set initial performance weights based on the historical performance requirements. The processing chain of the part to be processed is broken down to obtain the roughing stage and the finishing stage. The main processing targets are set for the roughing stage and the finishing stage respectively. The initial performance weights are corrected according to the main processing targets of different stages to obtain the stage performance weights. The equipment life cycle data of the candidate machine tool is obtained from the production process database, and the corresponding candidate machine tool is determined to be in a healthy state based on the life cycle data. If it is in a healthy state, the stage performance weight is maintained; otherwise, the stage performance weight is adjusted to obtain the stage optimization weight. The candidate machine tool dataset is filtered based on the stage optimization weight and the stage performance weight to obtain the first execution machine tool.

8. The method for transforming an automated CNC workshop according to claim 1, characterized in that, When the first executing machine tool is in a deviated state, triggering machine tool reallocation includes: Based on the machining process chain, determine the process deviation node, and based on the process deviation node, determine the subsequent machining machine tool and machining process; The processing steps are retrieved from the production process database, and the historical execution machine tools of the processing steps are searched to obtain a number of machine tools to be matched. Based on several machine tools to be matched, process matching is performed to determine the reassigned machine tools.

9. The method for transforming an automated CNC workshop according to claim 7, characterized in that, The step involves matching processes based on several of the machine tools to be matched, including: The working status of the machine tool to be matched is obtained in real time, and the machine tools to be matched are filtered according to the working status to obtain idle machine tools; The processing yield of the idle machine tools is retrieved from the production process database, the processing yield is sorted, and a processing qualified sequence is obtained. The machine tool to be matched is matched according to the qualified processing sequence to complete the process matching.

10. An automated CNC workshop transformation system, characterized in that, The system includes: The process chain acquisition module obtains the part processing plan and acquires the processing process chain of several processed parts according to the part processing plan. The candidate machine tool acquisition module identifies and determines the part to be processed, selects the processing technology chain based on the part to be processed, and matches the candidate machine tool dataset that meets the part processing type based on the processing technology chain. The database construction module constructs a production process database and adaptively filters the candidate machine tool dataset based on the production process database to determine the first execution machine tool. The machine tool reassignment module sends the workpiece to be processed to the first execution machine tool and monitors the processing status of the workpiece in real time based on the production process database. If the processing state of the part to be processed is in a deviated state, an alarm is issued, triggering the machine tool reassignment mechanism to obtain the idle second execution machine tool information, and the workpiece is moved to the second execution machine tool according to the idle second execution machine tool information; Continue to monitor the processing status of the part to be processed until the processing of the part is completed.

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