Cutting load balancing control method and system based on real-time working condition data

Abstract

The invention relates to a cutting load balancing control method and system based on real-time working condition data, and belongs to the field of relevant research of intelligent manufacturing. Firstly, real-time working condition data of processing parameters and sensor data based on a standard protocol is constructed based on an OPC-UA server; secondly, time sequence data processing of real-time working condition data is achieved based on MongoDB, and the time sequence processing capacity of the data is improved; and then, load analysis and control parameter processing are carried out on time sequence data in the machining process through a fuzzy controller, and control over constant-load stable machining is achieved. The problems of load imbalance and low-efficiency control in product machining are solved, and constant-load stable and efficient machining is achieved.

Description

Technical Field

[0001] This invention belongs to the research field of intelligent manufacturing, and specifically relates to a cutting load balancing control method and system based on real-time working condition data, which involves real-time working condition data acquisition and cutting load balancing control in product processing. Background Technology

[0002] In machining the external features of composite material products, the problem of uneven cutting load caused by factors such as dynamic tool wear, uneven allowance distribution, and material anisotropy is currently mainly addressed by relying on human experience to judge the cutting load and manually adjusting parameters such as spindle speed and feed rate. This method can achieve high machining efficiency and quality by dynamically adjusting key parameters such as spindle speed and feed rate, but it still has certain limitations and shortcomings:

[0003] (1) Relatively weak in data collection and quantitative analysis capabilities. In manual control mode, there is a lack of efficient data collection systems and advanced quantitative analysis tools. It is impossible to collect various data in the machining process comprehensively and accurately, especially data closely related to the distribution of cutting load, making it difficult to accurately quantify the specific distribution of cutting load, limiting the feasibility of fine control, and making it impossible to achieve precise adjustment and optimization of cutting load in the machining process.

[0004] (2) The accuracy and timeliness of adaptive control of quality and efficiency are lacking. On the one hand, it is difficult to achieve sufficient accuracy in the adaptive control of quality and efficiency during the processing. In actual processing, due to the inability to accurately grasp the complex relationship between cutting load and processing quality and efficiency, only rough adjustments can be made, making it difficult to achieve precise control of processing quality and efficiency. On the other hand, there are also shortcomings in terms of timeliness. The qualitative control mode based on human experience is not quick enough to respond to complex and ever-changing processing conditions, making it difficult to make timely and effective adaptive adjustments to quality and efficiency during the processing. Summary of the Invention

[0005] The technical problem solved by this invention is to overcome the shortcomings of the prior art and propose a cutting load balancing control method and system based on real-time working condition data to achieve online control of cutting load.

[0006] The solution of this invention is: a cutting load balancing control method based on real-time working condition data, which includes the following steps:

[0007] S1. Set the initial value of the spindle feed rate of the CNC machine tool, and use the initial value of the spindle feed rate to process the product;

[0008] S2. Real-time acquisition of product processing condition data, adding a timestamp T to the product processing condition data to form a product processing condition time-series dataset; the processing condition data includes spindle feed rate fr and spindle current C;

[0009] S3. Construct a fuzzy adaptive controller, wherein the input variables of the fuzzy adaptive controller are: current deviation and current deviation change rate; and the output variable is: spindle feed rate.

[0010] S4. Extract the latest product processing condition data from the product processing condition time series dataset, and use a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced control of cutting load.

[0011] Preferably, the product processing condition data conforms to the OPC-UA standard protocol.

[0012] Preferably, the product processing time sequence data is stored in a MongoDB database.

[0013] Preferably, the processing procedure of the fuzzy adaptive controller is as follows:

[0014] First, the spindle current deviation and current change rate are converted from the basic universe of discourse to the fuzzy set universe of discourse to obtain the fuzzy values ​​of the spindle current deviation and current change rate.

[0015] Then, a fuzzy control strategy is formulated, and a fuzzy inference algorithm is used to obtain the fuzzy value of the spindle feed rate according to the spindle current deviation and the rate of change of current.

[0016] Finally, the fuzzy value of the spindle feed rate is defuzzified to obtain the control value of the spindle feed rate.

[0017] Preferably, the fuzzy domain of the current deviation and the fuzzy domain of the current deviation change rate are represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated by the English letters {NB, NM, NS, O, PS, PM, PB}. Zero indicates that the current is in a stable state, negative large indicates that the current decreases significantly, negative medium indicates that the current decreases moderately, negative small indicates that the current decreases slightly, positive large indicates that the current increases significantly, positive medium indicates that the current increases moderately, and positive small indicates that the current increases slightly.

[0018] Preferably, the fuzzy control strategy R is represented by a fuzzy control table, as follows:

[0019]

[0020] In the table above, E is the fuzzy value of the current deviation, and EC is the fuzzy value of the rate of change of the current deviation.

[0021] Another technical solution of the present invention is: a cutting load balancing control system based on real-time working condition data, the system comprising a machine tool data acquisition module, a current sensor, an OPC-UA server, a MongoDB data access module, and an adaptive control module; wherein:

[0022] The machine tool data acquisition module is used to collect machining parameters from the CNC machine tool controller, including the spindle feed rate fr.

[0023] A current sensor is used to collect the spindle current c;

[0024] The OPC-UA server constructs a data model for the OPC-UA protocol, collects data from all nodes in the OPC-UA protocol data model architecture in a loop, and adds timestamps to complete the data encapsulation based on OPC-UA.

[0025] The OPC-UA client, in a data subscription mode, obtains machining condition data from the OPC-UA server and sends it to the MongoDB data access module. The machining condition data includes the spindle feed rate fr and the spindle current C.

[0026] The MongoDB data access module is used to store machining condition data, providing data support for achieving cutting load balancing.

[0027] The adaptive control module extracts the latest product processing condition data from the product processing condition time series dataset and uses a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced cutting load control.

[0028] Preferably, the OPCUA server, OPC-UA client, MongoDB data access module, and adaptive control module run on a host computer.

[0029] Preferably, the processing procedure of the fuzzy adaptive controller is as follows:

[0030] First, the spindle current deviation and current change rate are converted from the basic universe of discourse to the fuzzy set universe of discourse to obtain the fuzzy values ​​of the spindle current deviation and current change rate.

[0031] Then, a fuzzy control strategy is formulated, and a fuzzy inference algorithm is used to obtain the fuzzy value of the spindle feed rate according to the spindle current deviation and the rate of change of current.

[0032] Finally, the fuzzy value of the spindle feed rate is defuzzified to obtain the control value of the spindle feed rate.

[0033] Preferably, the fuzzy domain of the current deviation and the fuzzy domain of the current deviation change rate are represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated by English letters as {NB, NM, NS, O, PS, PM, PB}.

[0034] The fuzzy control strategy R is represented by a fuzzy control table, which is as follows:

[0035]

[0036] The advantages of this invention compared to the prior art are:

[0037] (1) This invention addresses the problem of needing to manually interpret cutting load conditions during product processing. By using OPC-UA and a time-series database, it enables automatic data acquisition and storage of real-time operating conditions. Compared with the current manual interpretation method, it can improve the accuracy and real-time performance of the data.

[0038] (2) This invention addresses the problem of uneven cutting load during product processing by using a fuzzy adaptive controller. The controller can automatically adjust the control parameters based on the changes and rate of change of the current data collected in real time, thereby achieving stable control of nonlinear and time-varying systems and achieving stable power output. Attached Figure Description

[0039] Figure 1 This invention provides a cutting load balancing control method based on real-time operating data.

[0040] Figure 2 This is a data model based on the OPC-UA protocol in an embodiment of the present invention;

[0041] Figure 3 This invention provides a data flow framework based on OPC-UA and MongoDB.

[0042] Figure 4 This invention provides a cutting load balancing control method based on a fuzzy controller.

[0043] Figure 5 This invention relates to a cutting load balancing control system based on real-time operating data. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] This invention provides a cutting load balancing control method based on real-time operating data. First, real-time operating data is acquired based on the standard OPC-UA protocol. Second, the acquired real-time operating data is processed using a time-series database, which utilizes sharding and indexing techniques to enhance real-time processing capabilities. Then, a fuzzy adaptive controller is used to analyze the real-time changes in current and control the spindle feed rate adjustment, thus achieving a cutting load balancing control strategy. Finally, a cutting load control system based on real-time operating data is developed. Through the connection between the OPC-UA server in the host computer and the machine tool controller, and utilizing the OPC-UA read / write capabilities, the cutting load balancing control is achieved.

[0047] like Figure 1 As shown, the specific steps of the cutting load balancing control method based on real-time working condition data provided by the present invention are as follows:

[0048] S1. Set the initial value of the spindle feed rate of the CNC machine tool, and use the initial value of the spindle feed rate to process the product;

[0049] S2. Establish a real-time operating condition data acquisition method to collect product processing operating condition data in real time, add a timestamp T to the product processing operating condition data, and form a product processing operating condition time series dataset; the processing operating condition data includes spindle feed rate fr and spindle current C.

[0050] The input of the real-time working condition data acquisition method is CNC system data and sensor data during product processing, and the output is the acquired data D based on the OPC-UA protocol data model;

[0051] The data acquisition method described is based on the OPC-UA protocol. By encapsulating the Open62541, OPC-UA-based data encapsulation is completed. Specific functions include: creating an OPC-UA server; connecting / disconnecting from the OPC-UA server; browsing all nodes in the OPC-UA protocol architecture; and subscribing to OPC-UA data variables. The main functions based on OPC-UA are shown in Table 1 below.

[0052] Table 1 Main Functions of OPCUA

[0053]

[0054]

[0055] Once the OPC-UA server connects, it can obtain the data D that needs to be collected during processing and construct a timestamped data DB based on the OPC-UA protocol. For example... Figure 2 As shown, this is a data model based on the OPC-UA protocol. OPC-UA is an object-oriented modeling method. The data model consists of nodes and references. Different nodes can be represented as variables, methods, data types, etc., and have basic attributes. Nodes can be connected through references, and different reference types give different relationships between nodes.

[0056] The known real-time acquired data (DB) is stored and retrieved using a time-series database to provide data support for realizing cutting loads;

[0057] The access and processing of time-series database data is based on real-time operational data processing of MongoDB time-series database:

[0058] (1) Data is collected in real time based on the OPC-UA server subscription mode;

[0059] (2) Construct a data loop function for the collected data;

[0060] (3) Use the OPC-UA client to obtain OPC-UA server data in subscription mode;

[0061] (4) Connect to the MongoDB database;

[0062] (5) Create a MongoDB data collection.

[0063] S3. Construct a fuzzy adaptive controller, wherein the input variables of the fuzzy adaptive controller are: current deviation and current deviation change rate; and the output variable is: spindle feed rate.

[0064] The fuzzy adaptive controller developed in this invention is a Mamdini controller composed of two input variables and a single output variable. For example... Figure 3 As shown, the input variables are: current deviation and current deviation change rate; the output variable is: spindle feed rate.

[0065] The processing procedure of the fuzzy adaptive controller is as follows:

[0066] First, the spindle current deviation and current change rate are converted from the basic universe of discourse to the fuzzy set universe of discourse to obtain the fuzzy values ​​of the spindle current deviation and current change rate.

[0067] Then, a fuzzy control strategy is formulated, and a fuzzy inference algorithm is used to calculate the fuzzy value of the spindle feed rate based on the spindle current deviation and the rate of change of current:

[0068] U=(ExEC)oR

[0069] In the formula, R is called the fuzzy control strategy, E is the fuzzy value of the current deviation, and EC is the fuzzy value of the rate of change of the current deviation.

[0070] Finally, the fuzzy value of the spindle feed rate is defuzzified to obtain the control value of the spindle feed rate.

[0071] The control rules of the fuzzy controller consist of three parts:

[0072] (1) Selecting an appropriate vocabulary to describe input and output variables: The fuzzy logic controller defines its control rules by constructing a series of fuzzy conditional statements. These rules are usually based on a general understanding of the state of things, dividing the state into three levels: large, medium, and small, and combining the concepts of positive and negative directions to form a variety of control strategies. In this way, the fuzzy logic controller can more accurately describe and control the state of things, achieving a more precise control effect. That is, the fuzzy domain of current deviation and the fuzzy domain of the rate of change of current deviation are:

[0073] {Negative large, negative medium, negative small, zero, positive small, positive medium, positive large}

[0074] It is common practice to use abbreviations of English letters to express this:

[0075] {NB, NM, NS, O, PS, PM, PB}

[0076] In conjunction with this invention, for the background of current change control feed, zero indicates that the current is in a stable state, negative large indicates that the current decreases significantly, negative medium indicates that the current decreases moderately, negative small indicates that the current decreases slightly, positive large indicates that the current increases significantly, positive medium indicates that the current increases moderately, and positive small indicates that the current increases slightly.

[0077] (2) Formulate corresponding fuzzy defuzzification and fuzzy control strategies: After obtaining the fuzzy set of deviation E and the fuzzy set of deviation change rate EC, the fuzzy set of control quantity U can be obtained by fuzzy inference algorithm:

[0078] U=(ExEC)oR

[0079] In the formula, R is called the fuzzy control strategy, E is the current deviation, EC is the rate of change of current deviation, and U is the control output quantity, which is the feed multiplier in this invention.

[0080] The fuzzy control strategy R is represented by a fuzzy control table, which is as follows:

[0081] Table 2 Fuzzy Control Table

[0082]

[0083] (3) Determine the parameters of the fuzzy controller: The universe of discourse for both the input and output variables must be determined before designing the specific fuzzy controller. For example, the range of variation for the spindle current deviation and current rate of change as input variables, and the range of variation for the feed rate as output variables. Simultaneously, the proportional gain and quantization factor of the fuzzy controller must be appropriately selected. These two factors have a significant impact on the control performance of the fuzzy controller.

[0084] Assuming the fundamental universe of discourse for the bias is set to [-x, +x], and the fuzzy set universe of discourse for the bias is {-n, -n+1, ​​..., 0, ..., n-1, n}, then the quantization factor k of the bias can be determined using the following formula:

[0085] k = n / x

[0086] Similarly, the proportional factor of the output control quantity and the quantization factor of the deviation change can also be determined using the method described above.

[0087] Assuming the actual current control value is 15A with a range of [12, 18], the actual feed rate control range is [-20, 20], the fundamental universe of discourse for deviation E is [-3, +3], the fundamental universe of discourse for the rate of change of deviation EC is [-5, +5], and the fuzzy set universe of discourse is [-6, +6]. Then:

[0088] Deviation quantification factor ke = 6 / 3 = 2, deviation change rate quantification factor kec = 6 / 5 = 1.2. Proportion factor ku = 20 / 6 = 6.67;

[0089] In this embodiment of the invention, the range of the basic universe of discourse for deviation can be widened to [-3, +3], the basic universe of discourse for the rate of change of deviation EC is [-5, +5], the range of the basic universe of discourse for the feed rate is [-20, 20], and the fuzzy set universes of discourse for both deviation E and the rate of change of deviation are [-6, +6].

[0090] (4) Blurring and deblurring processing

[0091] The fuzzification of inputs E and EC is achieved using the aforementioned fuzzy strategy and fuzzy control table R. This yields the fuzzy value uf of the output control variable U.

[0092] The control value of the feed rate after defuzzification is Uk = uf * ku.

[0093] S4. Extract the latest product processing condition data from the product processing condition time series dataset, and use a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced control of cutting load.

[0094] The system is divided into two main parts: a hardware system for acquiring operating data and a software system for load analysis and control.

[0095] The hardware system includes a machine tool data acquisition module and a current acquisition module; the load analysis and control software system includes an OPC-UA server, an OPC-UA client, a MongoDB data access module, and an adaptive control module, all integrated into a host computer. The hardware system is the environment that generates real-time operating condition data (D) during machining. The load analysis and control software system acquires this data in real-time through a data gateway and the OPC-UA server, uses MongoDB to construct a time-series dataset (DB) from the real-time operating condition data, and implements load analysis and adaptive control based on the time-series data and a fuzzy controller through adaptive control software.

[0096] The machine tool data acquisition module is used to collect machining parameters from the CNC machine tool controller, including the spindle feed rate fr.

[0097] A current sensor is used to collect the spindle current c;

[0098] The OPC-UA server constructs a data model for the OPC-UA protocol, collects data from all nodes in the OPC-UA protocol data model architecture in a loop, and adds timestamps to complete the data encapsulation based on OPC-UA.

[0099] The OPC-UA client, in a data subscription mode, obtains machining condition data from the OPC-UA server and sends it to the MongoDB data access module. The machining condition data includes the spindle feed rate fr and the spindle current C.

[0100] The MongoDB data access module is used to store machining condition data, providing data support for achieving cutting load balancing.

[0101] The adaptive control module extracts the latest product processing condition data from the product processing condition time series dataset and uses a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced cutting load control.

[0102] In summary, the cutting load balancing control method based on real-time operating data proposed in this invention first constructs real-time operating data based on standard protocols for machining parameters and sensor data using an OPC-UA server; secondly, it uses MongoDB to perform time-series data processing of the real-time operating data, improving the data's time-series processing capabilities; and thirdly, it uses a fuzzy controller to perform load analysis and control parameter processing on the time-series data during machining, achieving stable machining control under constant load. This invention solves the problems of unbalanced load and inefficient control in product machining, achieving stable and efficient machining under constant load.

[0103] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make possible changes and modifications to the technical solutions of the present invention by utilizing the methods and techniques disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall fall within the protection scope of the technical solutions of the present invention.

Claims

1. A cutting load balancing control method based on real-time operating data, characterized in that... Includes the following steps: S1. Set the initial value of the spindle feed rate of the CNC machine tool, and use the initial value of the spindle feed rate to process the product; S2. Real-time acquisition of product processing condition data, adding a timestamp T to the product processing condition data to form a product processing condition time-series dataset; the processing condition data includes spindle feed rate fr and spindle current C; S3. Construct a fuzzy adaptive controller, wherein the input variables of the fuzzy adaptive controller are: current deviation and current deviation change rate; and the output variable is: spindle feed rate. S4. Extract the latest product processing condition data from the product processing condition time series dataset, and use a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced control of cutting load.

2. The cutting load balancing control method based on real-time operating data according to claim 1, characterized in that, The product processing condition data conforms to the OPC-UA standard protocol.

3. The cutting load balancing control method based on real-time operating data according to claim 1, characterized in that, The product processing time-series data is stored in a MongoDB database.

4. The cutting load balancing control method based on real-time operating data according to claim 1, characterized in that, The processing procedure of the fuzzy adaptive controller is as follows: First, the spindle current deviation and current change rate are converted from the basic universe of discourse to the fuzzy set universe of discourse to obtain the fuzzy values ​​of the spindle current deviation and current change rate. Then, a fuzzy control strategy is formulated, and a fuzzy inference algorithm is used to obtain the fuzzy value of the spindle feed rate according to the spindle current deviation and the rate of change of current. Finally, the fuzzy value of the spindle feed rate is defuzzified to obtain the control value of the spindle feed rate.

5. The cutting load balancing control method based on real-time operating data according to claim 4, characterized in that, The fuzzy domains of current deviation and current deviation change rate are represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated by the English letters {NB, NM, NS, O, PS, PM, PB}. Zero indicates that the current is in a stable state, negative large indicates that the current decreases significantly, negative medium indicates that the current decreases moderately, negative small indicates that the current decreases slightly, positive large indicates that the current increases significantly, positive medium indicates that the current increases moderately, and positive small indicates that the current increases slightly.

6. The cutting load balancing control method based on real-time operating data according to claim 5, characterized in that, The fuzzy control strategy R is represented by a fuzzy control table, which is as follows: In the table above, E is the fuzzy value of the current deviation, and EC is the fuzzy value of the rate of change of the current deviation.

7. A cutting load balancing control system based on real-time operating condition data, characterized in that... This includes a machine tool data acquisition module, a current sensor, an OPC-UA server, a MongoDB data access module, and an adaptive control module; among which: The machine tool data acquisition module is used to collect machining parameters from the CNC machine tool controller, including the spindle feed rate fr. A current sensor is used to collect the spindle current c; The OPC-UA server constructs a data model for the OPC-UA protocol, collects data from all nodes in the OPC-UA protocol data model architecture in a loop, and adds timestamps to complete the data encapsulation based on OPC-UA. The OPC-UA client, in a data subscription mode, obtains machining condition data from the OPC-UA server and sends it to the MongoDB data access module. The machining condition data includes the spindle feed rate fr and the spindle current C. The MongoDB data access module is used to store machining condition data, providing data support for achieving cutting load balancing. The adaptive control module extracts the latest product processing condition data from the product processing condition time series dataset and uses a fuzzy adaptive controller to adjust the spindle feed rate in real time to achieve balanced cutting load control.

8. A cutting load balancing control system based on real-time operating data according to claim 7, characterized in that, The OPCUA server, OPC-UA client, MongoDB data access module, and adaptive control module run on the host computer.

9. A cutting load balancing control system based on real-time operating data according to claim 7, characterized in that, The processing procedure of the fuzzy adaptive controller is as follows: First, the spindle current deviation and current change rate are converted from the basic universe of discourse to the fuzzy set universe of discourse to obtain the fuzzy values ​​of the spindle current deviation and current change rate. Then, a fuzzy control strategy is formulated, and a fuzzy inference algorithm is used to obtain the fuzzy value of the spindle feed rate according to the spindle current deviation and the rate of change of current. Finally, the fuzzy value of the spindle feed rate is defuzzified to obtain the control value of the spindle feed rate.

10. A cutting load balancing control system based on real-time operating data according to claim 7, characterized in that, The fuzzy domain of the current deviation and the fuzzy domain of the rate of change of the current deviation are represented by {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, which are abbreviated by the English letters {NB, NM, NS, O, PS, PM, PB}. The fuzzy control strategy R is represented by a fuzzy control table, which is as follows:

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