A closed-loop adjustment method and system for wheel hub weight machining

Abstract

A closed-loop adjustment method and system for wheel hub weight machining, the system includes a through-type weighing device, a vibration sensor, an edge gateway, an edge computer, a machine tool, and a cloud. Before entering the machine tool and after machining, the wheel hub enters the through-type weighing device to measure the weight of the wheel hub, which is automatically sent to the edge gateway via the MODBUS protocol and then to the machine tool. The compensation value is calculated in the cloud based on the wheel hub weight and sent to the edge computer. The compensation value of the machine tool tool is adjusted in real time through the OPCUA protocol and communication with the machine tool. During the machining process, a vibration sensor is used to detect the vibration of the spindle machining process to avoid damage to the machine due to excessive or insufficient adjustment, thereby realizing a closed-loop adjustment. In this way, the requirements for lightweight wheel hubs are met, and the machine tool can be automatically adjusted without human intervention. At the same time, it is ensured that the equipment will not be damaged due to the adjustment. This can not only ensure the safe operation of the equipment, but also save costs and achieve wheel hub weight reduction.

Description

Technical Field

[0001] The present application relates to the technical field of mechanical processing equipment, and in particular to a closed-loop adjustment method and system for wheel hub weight machining. Background Art

[0002] With the increasing popularity of new energy vehicles, the weight of battery boxes is increasing, and as a result, the overall weight of vehicles is also increasing. Therefore, continuous weight reduction through other automotive components is crucial to achieving lightweighting. Furthermore, the price of aluminum, the raw material for automotive wheels, is extremely volatile, with wide price fluctuations. When aluminum prices are high, reducing wheel weight can reduce costs. For high-volume manufacturers, reducing wheel weight can help them achieve cost leadership. As a machining process that can be adjusted in real time and has the most significant weight adjustment effect, real-time adjustment of tool compensation values ​​can directly affect wheel weight. Meeting weight requirements through advanced technologies such as the Internet of Things and machine learning has become a top priority in ensuring wheel weight. Furthermore, to ensure cycle times meet requirements and maintain machining efficiency, wheel weight measurement and rapid processing are crucial to avoid impacting subsequent processing. The average weight of an automotive wheel is 12 kg, with a mass tolerance of ±200 g, leaving ample room for adjustment. Quality and process requirements vary from product to product, necessitating targeted weight reduction adjustments. If the parameters are adjusted too much, it will affect the quality of the product and easily cause damage to the machine tool equipment. Summary of the Invention

[0003] To solve the above problems, the present invention aims to provide a closed-loop adjustment method and system for wheel hub weight machining.

[0004] According to one aspect of the present invention, a closed-loop adjustment method for wheel hub weight machining is provided, which is used to measure the wheel hub weight online during the machining process and adjust the machine tool tool compensation value in real time, wherein a cloud-edge-end integrated architecture including: machine tools, through-type weighing equipment, vibration sensors, edge gateways, edge computers and cloud is adopted, wherein the central cloud in the cloud carries the weight data and size of each wheel type and the weight adjustment model for query and calculation and can store the data of each adjustment, the edge end is the edge computer and the edge gateway installed on the through-type weighing equipment, which is used to obtain the data of the machine tool and the through-type weighing equipment and control the machine tool, and can use the model of the central cloud for calculation, the terminal device is the machine tool and the vibration sensor and the through-type weighing equipment, wherein the vibration sensor is installed on the spindle of the machine tool, and the machine tool calculates the location of the machine tool tool compensation value. The address is written into the ladder diagram of the machine tool, allowing the edge computer to directly adjust the parameters of the ladder diagram of the machine tool to adjust the tool compensation value. This method uses a set of wheel hub weight and tool compensation value in the cloud for machine learning clustering analysis modeling calculation to obtain the correlation between the wheel hub weight and the tool compensation value as a weight adjustment model. For the wheel hub blank to be processed, the tool compensation value of the machine tool is adjusted and sent to the edge computer to guide the weight reduction adjustment of the wheel hub blank in the machining process. During the processing, the vibration sensor is used to detect the vibration of the machine tool spindle during the processing process to realize the adjustment closed loop, which includes the following steps: Step 14: The vibration sensor transmits the detection value to the edge computer; Step 15: Record the root mean square vibration value of the machine tool spindle during each tool processing. Suppose there are n points in the processing time period of a certain tool, and the vibration value of each point is measured using the sensor Z-axial movement speed v i ,Right now:

[0005] ,

[0006] Step 16: When the RMS value of a tool after adjustment is greater than a predetermined value, it is determined that the tool is adjusted too much, and the adjusted compensation value is reset and step 15 is repeated.

[0007] According to another aspect of the present invention, a closed-loop adjustment system for hub weight machining is provided, which is used to implement the above-mentioned method, and includes: a data acquisition and data processing module: establishing variables including machining tool code, vibration value, i.e., sensor movement speed, tool compensation value, and hub weight, and deleting data that does not conform to the rules in the correspondence between tool compensation value and hub weight through data cleaning; a hub weight adjustment algorithm modeling module: establishing a hub weight adjustment algorithm model using cluster analysis in machine learning; a cluster analysis reliability assessment module: using cluster variance sum to assess the reliability of cluster analysis; a cloud storage module, uploading the algorithm model to the cloud and saving it as a weight adjustment model, while also saving the hub weight standard and product size; a vibration monitoring module: using a vibration sensor to detect the vibration frequency of the machine tool spindle, and when the vibration exceeds the limit, promptly adjusting the compensation value to reset and feeding back to the machine tool; a tool compensation value adjustment module: when the hub blank is overweight, first reading the current size of the hub blank to determine the tolerance limit for size adjustment, and then using the hub weight adjustment algorithm model to calculate and output a tool compensation value adjustment plan, and feeding back to the machine tool.

[0008] According to another aspect of the present invention, a computer program product is provided, characterized in that it comprises: computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the above method.

[0009] According to another aspect of the present invention, an electronic device is provided, comprising: a processor for executing multiple instructions; a memory for storing multiple instructions; wherein the multiple instructions are stored in the memory and loaded and executed by the processor to perform the above method.

[0010] According to yet another aspect of the present invention, a computer-readable storage medium is provided, which stores computer program instructions. When the computer program instructions are executed by a processor, the processor is caused to perform the above method.

[0011] Compared with the prior art, the beneficial effects of the patent of the present invention are as follows: a closed-loop adjustment system for wheel hub weight machining is provided, which includes an edge computer, a pass-through weighing device, a vibration sensor, an edge gateway, an edge computer, a machine tool and a cloud. The pass-through weighing device detects the weight of the wheel hub and then automatically sends it to the edge gateway via the Modbus protocol. The edge gateway then sends the data to the edge computer via the TCP / IP protocol and finally transmits it to the cloud. The edge computer collects the machining tool code and tool compensation value of the machine tool through OPC UA and issues the tool compensation value. The cloud performs modeling calculations to obtain the correlation between the wheel hub weight and the tool compensation value, and sends it to the edge computer via TCP / IP. The vibration sensor is adsorbed on the spindle position of the machine tool and collects the Z-direction movement speed of the spindle as the vibration value with a frequency of 2ms per point. The value is sent to the edge computer in real time via the Wi-Fi protocol. The edge computer calculates the root mean square of each tool based on big data to determine whether the vibration is too large, affecting the quality of the machine tool and causing equipment damage. The set of wheel hub weight and tool compensation values ​​is used for machine learning cluster analysis modeling to obtain their correlation and guide weight reduction adjustment. Through scientific modeling, the accuracy of wheel weight reduction is improved, facilitating lean production. The cost-effectiveness is even more significant in large-scale production. For example, if 10 million wheels are produced, saving 200g per wheel translates to a cost savings of 40 million yuan. Furthermore, through modeling, calculations and measurements are more scientific and reliable, reducing the need for arbitrary weight adjustments. Calculation results are sent directly to the machine tool via an edge computer, eliminating downtime caused by each adjustment. This makes the process more accurate and reliable, meeting the demands of the digital and intelligent era. Vibration sensors also monitor spindle vibration in real time, enabling closed-loop adjustments and making the entire process more reliable and stable. This aligns with the trend of lightweighting for new energy vehicles and supports intelligent manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] Figure 1 The following schematically illustrates the architecture of a closed-loop adjustment system for wheel hub weight machining according to the present application;

[0014] Figure 2 The flowchart of a closed-loop adjustment system for wheel hub weight machining of the present application is schematically shown;

[0015] Figure 3The vibration value calculation algorithm of the wheel weight machining closed-loop adjustment system is schematically shown. DETAILED DESCRIPTION

[0016] The terms "first", "second", "third" and "fourth" in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. Reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is understood explicitly and implicitly by those skilled in the art that the embodiments described herein can be combined with other embodiments. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0017] Example 1:

[0018] In this embodiment 1, a wheel hub weight machining closed-loop adjustment system is provided, such as Figure 1 As shown in the architecture diagram, it adopts an advanced cloud-edge-end integrated architecture, including: machine tools, pass-through weighing equipment, vibration sensors, edge gateways, edge computers, and the cloud.

[0019] The central cloud in the cloud carries the weight data and dimensions of each wheel type, as well as the weight adjustment model, for query and calculation, and can store the data of each adjustment for traceability. It has better stability and larger storage capacity, and is more efficient in building model calculations. The model can be quickly sent to the edge computer for calculation.

[0020] The edge end is an edge computer and an edge gateway, which can quickly obtain data from machine tools and flow-through weighing equipment, improve real-time performance, and continuously control machine tools. It can use the central cloud model for calculations. The edge gateway is installed on the flow-through weighing equipment. In order to improve data collection efficiency and accuracy and reduce costs, the weight data of the flow-through weighing equipment is collected using the Modbus protocol, and then forwarded to the edge computer for calculation using Internet technology and the TCP / IP protocol.

[0021] The terminal devices are machine tools, vibration sensors, and pass-through weighing devices. The vibration sensor is installed on the spindle of the machine tool to ensure true feedback of the actual vibration state of the machine tool. The machine tool writes the address of the machine tool tool compensation value into the machine tool ladder diagram, so that the edge computer can be used to directly adjust the parameters of the machine tool ladder diagram, thereby adjusting the tool compensation value, improving efficiency, and ensuring the security of machine tool data. Therefore, the terminal device can obtain the data required for system operation in real time and realize control.

[0022] In this way, based on the advanced cloud-edge-end integrated architecture, the three can cooperate with each other and penetrate each other to meet the required IoT deployment methods.

[0023] Example 2

[0024] This embodiment 2 provides a wheel hub weight machining closed-loop adjustment system, such as Figure 2 As shown in the figure, the system operation process is that the wheel hub product first enters the pass-through weighing equipment for measurement to obtain the weight of the wheel hub blank. The weight of the blank is first transmitted to the edge gateway via the Modbus protocol, and then transmitted to the edge computer via the TCP / IP protocol. The wheel hub blank then enters the machine tool for processing. The vibration sensor detects the vibration value of each tool during processing, and the movement speed of the sensor's Z axis is used as a substitute for the vibration. Because the vibration sensor is installed inside the machine tool, communication is inconvenient, and the Wi-Fi protocol is used to transmit the vibration value to the edge computer; the machine tool transmits the processing tool code during processing to the edge computer via the OPC UA protocol to calculate the vibration value before and after a certain tool is adjusted. The edge computer, in turn, controls the operation of the machine tool via the OPC UA protocol. The edge computer also calculates the root mean square of the vibration value and uses the adjustment model to calculate the tool compensation value; the central cloud receives the situation of each adjustment via the TCP / IP protocol, and transmits the model, standard weight of the wheel hub product, and wheel hub product size information via the TCP / IP protocol.

[0025] Example 3

[0026] In this embodiment 3, a wheel hub weight machining closed-loop adjustment system is provided. The vibration value calculation algorithm of the system is shown in FIG. Figure 3 shown. Figure 3This is the vibration value of the same tool when machining the same program with different tool compensation values. This vibration value intuitively reflects the machining status. Vibration is higher when machining depth is high, and lower when machining depth is low. A good value for a normal tool (good) is significantly lower than a bad value when tool compensation is adjusted too high. Regardless of whether the machining depth is high or low, this shows that vibration can reflect changes in tool compensation values, and monitoring vibration values ​​can help prevent excessive tool compensation adjustments. To ensure closed-loop adjustment of the system, machine tool vibration values ​​are also tested after weight reduction adjustments to avoid excessive tool compensation adjustments that could cause equipment damage or tool collisions. A vibration sensor installed on the machine tool's spindle provides the most direct feedback on the machine's vibration status. The vibration value is measured every 2 milliseconds, reflecting the sensor's Z-axis speed. When the machine tool begins machining and switches to a specific tool, the vibration value at that moment reflects the machine's vibration status. The root mean square (RMS) vibration value for that tool during this machining period is calculated and used as the characteristic value for the entire machining segment. This characteristic value must be calculated when processing each wheel type and each tool to verify the vibration state during wheel hub processing, make timely adjustments, implement a closed loop, and avoid equipment damage caused by adjusting the tool compensation value.

[0027] According to one aspect of the present invention, a method for closed-loop adjustment of hub weight machining is provided, which is used to measure the hub weight online during the machining process, and adjust the machine tool tool compensation value in real time to implement closed-loop management, wherein the hub is weighed on a pass-through weighing device before entering the machine tool and after machining is completed, and the weight of the hub is measured, which is automatically sent to the edge gateway through the MODBUS protocol and then to the edge computer. The compensation value is calculated in the cloud according to the hub weight and sent to the edge computer, and the compensation value of the machine tool tool is adjusted in real time through the OPCUA protocol and communication with the machine tool. During the machining process, a vibration sensor is used to detect the vibration of the machine tool spindle during the machining process to avoid damage to the machine due to excessive or insufficient adjustment, thereby realizing closed-loop adjustment. The process of closed-loop adjustment is as follows:

[0028] Step 1: Collect information about the machine tool processing process and generate a data set R using the tool number as the index;

[0029] Step 2: Extract the weight change after each adjustment. Based on all records of wheel hub weight change and tool compensation change values ​​described in each piece of information, delete the data that does not meet the predetermined rules through data reduction.

[0030] Step 3: Repeat step 2 until the entire data set R=([W1,W2,…,W n ],[O1,O2,…,O n ],[T1,T2,…,T m ]) matching is completed, where W nis the weight change of the product, O n is the tool compensation change value, T m is the tool number;

[0031] Step 4: In order to ensure the accuracy of the machine learning model, make the model more concise, and achieve efficient and fast calculation, establish a feature vector set and convert the data set R into a vector set X, that is, X i =W i / O i ;

[0032] Step 5: n vectors X i (i=1,2,…,n) are divided into k clusters T i (i=1, 2, ..., k), k represents the class, and the cluster center of each cluster is obtained so that the sum of the variance within the cluster is minimized, that is,

[0033]

[0034] ;

[0035] Step 6: In n vectors X i After all the sample points in are divided, the centroid of each cluster is recalculated according to the division situation, and then the distance from each sample point to the centroid of each cluster is iteratively calculated to re-divide all the sample points;

[0036] Step 7: Continue iterating steps 5 and 6 until the center of mass no longer changes significantly, which is called convergence. For example, if the change is no more than 0.01, the function has converged. After classifying the sample points, a model of the wheel weight and tool compensation value is formed (corresponding to the "wheel weight adjustment algorithm model," that is, the algorithm model of the wheel weight and tool compensation value adjustment variables), and the correlation between the two is clarified;

[0037] Step 8: After changing the wheel type, query and record the standard weight PW of the wheel type through the cloud s , the upper limit weight PW of this wheel type u and lower limit weight PW d ;

[0038] Step 9: Before the wheel blank enters the machine tool, use a through-type weighing device to measure the weight PW 1a After processing, the weight is measured by a through-type weighing device and is PW 1b ,The two data are transmitted to the edge gateway via the Modbus protocol, and the edge gateway transmits the data to the edge computer for recording via the TCP / IP protocol;

[0039] Step 10: If PW d <PW 1b <PWs And PW 1b -PW d >50g, indicating that the wheel type still has sufficient adjustment margin and can be adjusted to reduce weight, which will not easily cause the product to be too light and fail to meet the requirements;

[0040] Step 11: After determining that the wheel type can be adjusted to reduce weight, first check the product's dimensions to confirm which size-related tools can be adjusted and the tolerance limit of the tool compensation value;

[0041] Step 12: After determining the tolerance limit, the edge computer sends data adjustment instructions to the machine tool via the OPC UA protocol to adjust the tool compensation value of the machine tool to achieve product weight reduction;

[0042] Step 13: Measure the product weight after adjustment to confirm that the wheel hub weight is reduced to PW d +50g;

[0043] Step 14: The machine tool spindle vibrates most noticeably during machining, so a vibration sensor is installed on the machine tool spindle. The vibration sensor transmits the detection value to the edge computer via the Wi-Fi protocol.

[0044] Step 15: Record the root mean square vibration value of the machine tool spindle during each tool processing. Assume that there are n points in the processing time period of a certain tool, and the vibration value of each point is calculated using the sensor Z-axis moving speed v i ,Right now:

[0045]

[0046] Step 16: If the RMS value of a tool after adjustment is greater than the preset value, for example, 150% of the RMS value before adjustment, the tool is considered over-adjusted and the excessive vibration may cause damage to the machine tool. In this case, reset the adjusted compensation value and repeat step 15.

[0047] According to another aspect of the present invention, a wheel hub weight machining closed-loop adjustment system is provided, comprising:

[0048] Data acquisition and data processing module: establish variables including machining tool code, vibration value (i.e. sensor movement speed), tool compensation value, wheel weight, etc., and delete data that does not conform to the rules in the corresponding relationship between tool compensation value and wheel weight through data cleaning;

[0049] Wheel weight adjustment algorithm modeling module: uses cluster analysis in machine learning to establish a wheel weight adjustment algorithm model;

[0050] Cluster analysis reliability assessment module: Use cluster variance sum to assess the reliability of cluster analysis;

[0051] The cloud storage module uploads the algorithm model to the cloud for storage, while also saving the wheel hub weight standard and product dimensions;

[0052] Vibration monitoring module: uses a vibration sensor to detect the vibration frequency of the machine tool spindle. When the vibration exceeds the limit, it adjusts the compensation value and resets it in time, and feeds back to the machine tool;

[0053] Tool compensation value adjustment module: When the product is overweight, the current size of the product is read first to determine the tolerance limit of the size adjustment, and then the wheel weight adjustment algorithm model is used to calculate and output the tool compensation value adjustment plan, which is fed back to the machine tool.

[0054] According to another aspect of the present invention, an electronic device is provided, comprising: a processor for executing multiple instructions; a memory for storing multiple instructions; wherein the multiple instructions are stored in the memory and loaded and executed by the processor to perform the above method.

[0055] According to another aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor is caused to perform the steps in the above method.

[0056] In addition, the embodiments of the present disclosure may also be a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to perform the steps of the method of the above-mentioned embodiments of this specification. The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present disclosure, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0057] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A closed-loop adjustment method for wheel hub weight machining, used in wheel hub machining process, to measure wheel hub weight online and adjust machine tool tool compensation value in real time, characterized in that: Adoption includes: The cloud-edge-end integrated architecture of machine tools, pass-through weighing equipment, vibration sensors, edge gateways, edge computers and the cloud, in which the central cloud on the cloud carries the weight data and size of each wheel type and the weight adjustment model for query and calculation and can store the data of each adjustment. The edge end is the edge computer and the edge gateway installed on the pass-through weighing equipment, which are used to obtain data from the machine tool and the pass-through weighing equipment and control the machine tool, and can use the weight adjustment model of the central cloud for calculation. The terminal equipment is the machine tool, vibration sensor and pass-through weighing equipment, in which the vibration sensor is installed on the spindle of the machine tool. The machine tool writes the address of the machine tool tool compensation value into the ladder diagram of the machine tool, allowing the edge computer to directly adjust the parameters of the machine tool ladder diagram to adjust the tool compensation value. This method uses a collection of wheel hub weight and tool compensation values ​​to perform machine learning cluster analysis modeling calculations in the cloud, obtains the correlation between wheel hub weight and tool compensation value as a weight adjustment model, adjusts the tool compensation value of the machine tool for the wheel hub blank to be processed, and sends it to the edge computer to guide the weight reduction adjustment of the wheel hub blank during the machining process. During the machining process, a vibration sensor is used to detect the vibration of the machine tool spindle during the machining process to achieve an adjustment closed loop, which includes the following steps: Step 14: The vibration sensor transmits the detection value to the edge computer; Step 15: Record the root mean square vibration value of the machine tool spindle during each tool processing. Assume that there are n points in the processing time period of a certain tool. The vibration value of each point is calculated using the sensor Z-axis moving speed v i ,Right now: ， Step 16: When the RMS value of a tool after adjustment is greater than a predetermined value, it is determined that the tool is adjusted too much, and the adjusted compensation value is reset and step 15 is repeated.

2. The closed-loop adjustment method for wheel hub weight machining according to claim 1, characterized in that: The process of adjusting the closed loop includes the following steps: Step 1: Collect information about the machine tool processing process and generate a data set R using the tool number as the index; Step 2: Extract the weight change after each adjustment. Based on all records of wheel hub weight change and tool compensation change values ​​described in each piece of information, delete the data that does not meet the predetermined rules through data reduction. Step 3: Repeat step 2 until the entire data set R=([W1,W2,…,W n ], [O1,O2,…,O n ], [T1,T2,…,T m ]) matching is completed, where W n is the weight change of the product, O n is the tool compensation change value, T m is the tool number; Step 4: Create a feature vector set and convert the data set R into a vector set X, that is, X i =W i / O i ; Step 5: n vectors X i (i=1,2,…,n) are divided into k clusters T i (i=1, 2, ..., k), k represents the class, and the cluster center of each cluster is obtained so that the sum of the variance within the cluster is minimized, x is the cluster T i A single sample point within, that is: ， ； Step 6: In n vectors X i After all the sample points in are divided, the centroid of each cluster is recalculated according to the division situation, and then the distance from each sample point to the centroid of each cluster is iteratively calculated to re-divide all the sample points; Step 7: Continuously iterate step 5 and step 6 until convergence. After completing the classification of the sample points, a hub weight adjustment algorithm model of the hub weight and the tool compensation value is formed as the weight adjustment model.

3. The closed-loop adjustment method for wheel hub weight machining according to claim 2, characterized in that: The following steps are also included: Step 8: Query and record the standard weight PW of the wheel to be processed through the cloud s , the upper limit weight PW of this wheel type u and lower limit weight PW d ; Step 9: Before the wheel blank enters the machine tool, use a through-type weighing device to measure the weight PW 1a After processing, the weight is measured by a through-type weighing device and is PW 1b ,transmit these two data to the edge gateway, which transmits the data to the edge computer for record; Step 10: If PW d <PW 1b <PW s And PW 1b -PW d >50g, it is determined that the wheel type can be adjusted to reduce weight; Step 11: After determining that the wheel type can be adjusted to reduce weight, check the product's dimensions, confirm the tools associated with the adjustable dimensions, and the tolerance limits of the tool compensation values ​​that can be adjusted; Step 12: After determining the tolerance limit, the edge computer sends a data adjustment command to the machine tool to adjust the tool compensation value of the machine tool to achieve product weight reduction; Step 13: Measure the product weight after adjustment to confirm that the wheel hub weight is reduced to PW d +50g.

4. The closed-loop adjustment method for wheel hub weight machining according to claim 1, characterized in that: The weight data of the pass-through weighing equipment is collected using the Modbus protocol and transmitted to the edge gateway via the Modbus protocol. The edge gateway transmits the data to the edge computer via the TCP / IP protocol. The vibration sensor uses the Wi-Fi protocol to transmit the vibration value to the edge computer. The machine tool transmits the machining tool code during processing to the edge computer via the OPC UA protocol. The edge computer controls the operation of the machine tool via the OPC UA protocol. The central cloud receives the status of each adjustment via the TCP / IP protocol, and transmits the model, standard weight of the wheel product and the size of the wheel product via the TCP / IP protocol.

5. The closed-loop adjustment method for wheel hub weight machining according to claim 1, characterized in that: The vibration sensor is adsorbed on the spindle position of the machine tool and collects the moving speed of the spindle in the Z direction as the vibration value. The predetermined value is 150% of the RMS value of the tool before adjustment.

6. The closed-loop adjustment method for wheel hub weight machining according to claim 5, characterized in that: Before the wheel hub enters the machine tool and after processing is completed, it must be weighed on a pass-through weighing device to measure the weight of the wheel hub and automatically send it to the edge gateway through the Modbus protocol and then to the edge computer. The compensation value is calculated in the cloud based on the wheel hub weight and sent to the edge computer. It also communicates with the machine tool through the OPC UA protocol to adjust the compensation value of the machine tool tool in real time.

7. A wheel hub weight machining closed-loop adjustment system, characterized in that: The method for implementing claim 3, comprising: Data acquisition and data processing module: establish variables including machining tool code, vibration value (i.e. sensor moving speed), tool compensation value, and wheel weight, and delete data that does not conform to the rules in the corresponding relationship between tool compensation value and wheel weight through data cleaning; Wheel weight adjustment algorithm modeling module: uses cluster analysis in machine learning to establish a wheel weight adjustment algorithm model; Cluster analysis reliability assessment module: Use cluster variance sum to assess the reliability of cluster analysis; The cloud storage module uploads the wheel weight adjustment algorithm model to the cloud and saves it as a weight adjustment model, while also saving the wheel weight standard and product dimensions; Vibration monitoring module: uses a vibration sensor to detect the vibration frequency of the machine tool spindle. When the vibration exceeds the limit, it adjusts the compensation value and resets it in time, and feeds back to the machine tool; Tool compensation value adjustment module: When the wheel hub blank is overweight, the current size of the wheel hub blank is read first to determine the tolerance limit of the size adjustment, and then the wheel hub weight adjustment algorithm model is used to calculate and output the tool compensation value adjustment plan, which is fed back to the machine tool.

8. A computer program product, characterized in that include: Computer program instructions, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A processor for executing a plurality of instructions; a memory for storing a plurality of instructions; wherein the plurality of instructions are used to be stored in the memory and loaded and executed by the processor to perform the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that Computer program instructions are stored, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

Images