A real-time monitoring method and system for a collision avoidance machine of a nine-link turning-milling combined machine tool

Abstract

The application discloses a kind of nine compound machine tool of linkage car milling machine real-time monitoring method and system, it is related to machine tool intelligent control technical field, method includes: based on the actual size of nine compound machine tool of linkage car milling machine three-dimensional modeling is carried out and motion simulation is executed, and compound machine tool motion simulation model is constructed;The simulation model is simulated and collision risk analysis is carried out in combination with machine tool working condition scene, and machine tool collision risk heat map is generated;According to the heat map, the machine tool anti-collision machine containing perception monitoring, risk prediction, decision control module is laid out, real-time monitoring, collision risk prediction and machine tool anti-collision control are carried out.The application solves the technical problem that existing machine tool control mode is difficult to adapt to the complex motion characteristics of multi-axis linkage equipment, cannot timely and accurately capture operation risk, achieves the precise control of the collision risk of multi-axis linkage equipment, and the safety and reliability of machine tool machining process are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control of machine tools, and in particular to a real-time monitoring method and system for a collision-preventing machine of a nine-axis linkage turning-milling combined machine tool. BACKGROUND

[0002] The control device of a nine-axis linkage turning-milling combined machine tool is the core of ensuring machining safety and efficiency, and its anti-collision performance is directly related to the service life of the equipment and production efficiency. Precise control of collision risks is of great importance. In the prior art, machine tool anti-collision control relies on mechanical limiting, single sensor monitoring and other technical means. These methods have played a role in simple working conditions. However, with the improvement of machining precision requirements, such traditional control technology applied to nine-axis linkage equipment has obvious limitations. Because the motion coupling of nine-axis linkage equipment is complex, traditional methods cannot capture dynamic risks in real time, resulting in one-sided monitoring data and delayed early warning, which makes it difficult to meet the needs of precise anti-collision and safety control in high-precision machining. SUMMARY

[0003] The present application provides a real-time monitoring method and system for a collision-preventing machine of a nine-axis linkage turning-milling combined machine tool, which solves the technical problem that existing machine tool control methods cannot adapt to the complex motion characteristics of multi-axis linkage equipment and cannot capture running risks in a timely and accurate manner.

[0004] In a first aspect, the present application provides a real-time monitoring method for a collision-preventing machine of a nine-axis linkage turning-milling combined machine tool, which comprises: performing three-dimensional modeling based on actual structural size data of the nine-axis linkage turning-milling combined machine tool, generating a three-dimensional model of the combined machine tool, performing motion simulation on the three-dimensional model of the combined machine tool according to the motion mechanism of the machine tool and the motion constraint relationship of the machine tool components, and constructing a motion simulation model of the combined machine tool; performing motion simulation and collision risk analysis on the motion simulation model of the combined machine tool according to the working condition scene of the machine tool, and generating a machine tool collision risk heat map; according to the machine tool collision risk heat map, arranging a machine tool anti-collision machine on the nine-axis linkage turning-milling combined machine tool, the machine tool anti-collision machine being composed of a machine tool perception monitoring module, a collision risk prediction module and an anti-collision decision control module; based on the machine tool perception monitoring module and the collision risk prediction module, performing real-time monitoring and collision risk prediction on the motion process of the nine-axis linkage turning-milling combined machine tool, determining machine tool collision risk prediction parameters, and performing machine tool anti-collision control based on the machine tool collision risk prediction parameters through the anti-collision decision control module.

[0005] In a second aspect of the present application, a real-time monitoring system for a collision avoidance machine of a nine-axis linkage turning-milling composite machine tool is provided, the system comprising: a machine tool model construction module, which performs three-dimensional modeling based on actual structural size data of the nine-axis linkage turning-milling composite machine tool, generates a three-dimensional model of the composite machine tool, performs motion simulation on the three-dimensional model of the composite machine tool according to the machine tool motion mechanism and the machine tool component motion constraint relationship, and constructs a motion simulation model of the composite machine tool; a risk heat map acquisition module, which performs motion simulation and collision risk analysis on the motion simulation model of the composite machine tool according to the machine tool working condition scene, and generates a machine tool collision risk heat map; a machine tool collision avoidance machine layout module, which lays out a machine tool collision avoidance machine on the nine-axis linkage turning-milling composite machine tool according to the machine tool collision risk heat map, the machine tool collision avoidance machine being composed of a machine tool perception monitoring module, a collision risk prediction module, and a collision avoidance decision control module; and a machine tool collision avoidance control execution module, which performs real-time monitoring and collision risk prediction on the motion process of the nine-axis linkage turning-milling composite machine tool based on the machine tool perception monitoring module and the collision risk prediction module, determines machine tool collision risk prediction parameters, and performs machine tool collision avoidance control based on the machine tool collision risk prediction parameters through the collision avoidance decision control module.

[0006] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] In the present application, the collision risk heat map is generated by modeling based on the actual structure of the machine tool and performing motion simulation, the collision avoidance device containing the perception, prediction, and decision modules is laid out accordingly, the motion state of the equipment is monitored in real time and the risk parameters are predicted, the machining path is adjusted in combination with the risk threshold and the equipment, the precise prevention and control of the collision risk of the multi-axis linkage equipment is achieved, and the safety and reliability of the machine tool processing process are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0009] Figure 1 is a flowchart of a real-time monitoring method for a collision avoidance machine of a nine-axis linkage turning-milling composite machine tool provided by the embodiments of the present application.

[0010] Figure 2 is a structural schematic diagram of a real-time monitoring system for a collision avoidance machine of a nine-axis linkage turning-milling composite machine tool provided by the embodiments of the present application.

[0011] Explanation of reference signs: machine tool model construction module 1, risk thermodynamic diagram acquisition module 2, machine tool anti-collision machine layout module 3, machine tool anti-collision control execution module 4. DETAILED DESCRIPTION

[0012] The application provides a real-time monitoring method and system for a nine-axis linkage turning-milling composite machine tool anti-collision machine, and solves the technical problem that the existing machine tool control mode is difficult to adapt to the complex motion characteristics of multi-axis linkage equipment and cannot timely and accurately capture the operation risk.

[0013] The technical solutions in the embodiments of the application will be clearly and completely described in connection with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0014] It should be noted that the terms "first", "second", and the like in the specification and the above drawings of the application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment one, as shown in a real-time monitoring method for a nine-axis linkage turning-milling composite machine tool anti-collision machine, wherein the method comprises: Figure 1

[0016] Based on the actual structural size data of the nine-axis linkage turning-milling composite machine tool, a three-dimensional model is constructed, a composite machine tool three-dimensional model is generated, the composite machine tool three-dimensional model is simulated according to the machine tool motion mechanism and the machine tool component motion constraint relationship, and a composite machine tool motion simulation model is constructed.

[0017] In the embodiments of the application, the nine-axis linkage turning-milling composite machine tool has nine-axis cooperative control capability, can simultaneously or continuously complete various machining processes such as turning and milling, and is a high-end numerical control machine tool for integrated machining of complex precision parts.

[0018] ​Specifically, first, the bed body, spindle, tool turret, guide rail and all other key components of the nine-axis turning-milling combined machine tool and the overall assembly relationship are comprehensively data collected by means of laser measurement, three-dimensional scanning and the like, and the actual structure size data of each component, such as geometric size, relative position and mounting interface, are obtained. The collected raw data are screened, denoised and error-corrected, and abnormal data are eliminated and the data format is unified to ensure the accuracy and standardization of the data. The preprocessed actual structure size data are imported into a CAD three-dimensional modeling software, and the three-dimensional model of each component is constructed in turn according to the actual structure characteristics and assembly logic of each component of the machine tool, and the modeling size of each component strictly follows the collected actual data. After the modeling of each component is completed, the three-dimensional models of the components are accurately assembled according to the actual assembly relationship of the machine tool, and the connection constraint relationship between the components is defined to restore the overall structure layout of the machine tool. Finally, the overall model after assembly is compared and verified with the actual structure parameters and appearance form of the machine tool, and adjustments and optimizations are made for the size deviation or unreasonable assembly, and finally a three-dimensional model of the combined machine tool completely matching the actual structure of the nine-axis turning-milling combined machine tool is generated.

[0019] Next, when constructing the combined machine tool motion simulation model, first, the linear motion, rotary motion and linkage constraint types of the motion pairs are divided according to the motion mechanism of the machine tool, then the motion analysis and identification of the combined machine tool three-dimensional model are carried out according to the types to obtain the global motion pairs, and then the motion range limitation parameters are determined in combination with the motion constraint relationship of the machine tool components, and finally the motion simulation of the combined machine tool three-dimensional model is implemented based on the global motion pairs and the motion constraint parameters, and the construction of the model is completed. This step is described in detail in the subsequent content.

[0020] According to the working condition scene of the machine tool, the motion simulation model of the combined machine tool is simulated and analyzed for collision risk to generate a machine tool collision risk heat map.

[0021] Optionally, when generating the machine tool collision risk heat map, first, the simulation parameters such as processing type, process parameters and motion trajectory of the machine tool working condition scene are extracted, and then the parameters are loaded to simulate and simulate the motion of the combined machine tool motion simulation model to obtain machine tool collision simulation test data. Then, a collision risk quantitative index is constructed, and the collision simulation test data is quantitatively evaluated and assigned to determine the collision risk quantitative parameters. Finally, the combined machine tool three-dimensional model is meshed, the collision risk quantitative parameters are mapped to the meshed model and rendered into a heat map to complete the generation of the machine tool collision risk heat map. This step is described in detail in the subsequent content.

[0022] According to the machine tool collision risk heat map, a machine tool anti-collision machine is arranged on the nine-axis turning-milling combined machine tool, and the machine tool anti-collision machine is composed of a machine tool perception monitoring module, a collision risk prediction module and an anti-collision decision control module.

[0023] In an embodiment of the present application, when deploying the machine tool anti-collision machine, first define the machine tool anti-collision requirements and determine the monitoring performance indicators accordingly, deploy the perception network according to the indicators and the machine tool collision risk heat map to build the machine tool perception monitoring module. Then collect the historical collision data set of the composite machine tool, predict the collision risk through the data set, obtain the collision risk prediction module and the anti-collision decision control module, and finally integrate the above three modules for communication on the nine-link turning-milling composite machine tool to complete the deployment of the machine tool anti-collision machine. This step is described in detail in the subsequent content.

[0024] Based on the machine tool perception monitoring module and the collision risk prediction module, the movement process of the nine-link turning-milling composite machine tool is monitored and the collision risk is predicted in real time, the machine tool collision risk prediction parameters are determined, and the machine tool anti-collision control is performed based on the machine tool collision risk prediction parameters through the anti-collision decision control module.

[0025] Specifically, first, the various sensors in the machine tool perception monitoring module continuously collect real-time motion data of each risk component of the nine-link turning-milling composite machine tool according to the pre-set position parameters and specification requirements, including axis position coordinates, motion speed, acceleration, and adjacent component spacing information. The data collected by the sensors is transmitted to the data acquisition unit through the perception network, and after filtering, noise reduction and other preprocessing to remove interference signals, the data accuracy is ensured, and then it is sent to the collision risk prediction module in a fixed transmission cycle.

[0026] Then, the trained LSTM model is used to predict the collision risk and determine the machine tool collision risk prediction parameters. The collision risk prediction module receives the pre-processed real-time motion data, organizes it into time series data sequences according to the pre-set time window, and inputs it into the LSTM model trained to the pre-set accuracy. The model analyzes the motion trends and risk characteristics contained in the data sequence through the built-in feature extraction and mapping logic, and outputs the corresponding collision risk level and quantitative score. The risk level and quantitative score together constitute the machine tool collision risk prediction parameters.

[0027] Finally, the anti-collision decision control module receives the machine tool collision risk prediction parameters and compares them with the pre-set machine tool risk threshold in real time. If the prediction parameters are at a low risk level and do not exceed the threshold, the current machine tool operating state is maintained and continuous monitoring is performed; if the risk level is medium or exceeds the threshold, the deceleration adjustment strategy is triggered or the machine tool processing path correction unit is started, and the standby part processing path is switched to; if the risk level is high, an emergency stop command is immediately output, the machine tool is controlled to cut off the power source, and all moving parts are stopped to run, achieving precise anti-collision control in different risk scenarios.

[0028] Further, the method provided by the embodiment of the present application comprises:

[0029] The kinematic pair of the machine tool is classified based on the kinematic mechanism of the machine tool to obtain a machine tool kinematic pair type, the machine tool kinematic pair type including linear motion, rotary motion and linkage constraint; the three-dimensional model of the compound machine tool is identified for motion analysis according to the machine tool kinematic pair type to obtain a machine tool global kinematic pair; the three-dimensional model of the compound machine tool is limited for motion range according to the machine tool component motion constraint relationship to obtain machine tool component motion constraint parameters; the three-dimensional model of the compound machine tool is simulated for motion based on the machine tool global kinematic pair and the machine tool component motion constraint parameters to construct a compound machine tool motion simulation model.

[0030] In the embodiments of the application, the kinematic pair of the machine tool is a connecting structure for realizing relative motion between components of the machine tool, such as linear motion and rotary motion, for transmitting motion and power to ensure the realization of machine tool machining actions.

[0031] Specifically, first, the transmission principle of the nine-linkage turning-milling compound machine tool is comprehensively sorted out, the relative motion forms of the components in the machining process are observed, the motion forms of the components of the machine tool are divided into three categories of linear motion, rotary motion and linkage constraint according to the commonly used kinematic pair classification standard in the field of mechanical design, the core motion characteristics of different kinematic pairs are clarified, and finally the machine tool kinematic pair type is obtained.

[0032] Then, the feature recognition and labeling function of the three-dimensional modeling software is used to develop motion analysis and identification. The generated three-dimensional model of the compound machine tool is opened, the kinematic pair identification module is called in the commonly used modeling software such as UG and SolidWorks, the connecting parts of the components in the model are matched one by one according to the three types of kinematic pair types of linear motion, rotary motion and linkage constraint, each kinematic pair is given a unique identification through the labeling tool of the software, and the corresponding motion type attribute is associated, the system integrates all the identified kinematic pair information, and the machine tool global kinematic pair is obtained.

[0033] Then, using measuring tools such as calipers and laser range finders, the actual travel limits of the components of the machine tool are measured on site, including the maximum moving distance of the guide rail, the maximum rotation angle of the spindle and the like. At the same time, the machine tool design manual and technical parameter table are consulted, the interference limiting conditions between the components are extracted, the measured data and the design parameters are compared and calibrated, and then the verified data are input into the constraint setting module of the three-dimensional modeling software to limit the motion degrees of freedom of the components in the three-dimensional model of the compound machine tool, and finally the machine tool component motion constraint parameters are obtained.

[0034] Finally, based on the machine tool global kinematic pair and the machine tool component motion constraint parameters, motion simulation is performed on the compound machine tool three-dimensional model to generate an initial machine tool motion simulation model. According to the actual part machining scene, a machine tool driving simulation environment is preset, and the initial model is simulated and verified according to the environment to obtain initial model simulation effect parameters. Finally, based on the initial model simulation effect parameters, the initial machine tool motion simulation model is improved and optimized to complete the construction of the compound machine tool motion simulation model. This step is described in detail in the subsequent content.

[0035] Further, the method provided by the embodiment of the application comprises:

[0036] Based on the machine tool global kinematic pair and the machine tool component motion constraint parameters, motion simulation is performed on the compound machine tool three-dimensional model to generate an initial machine tool motion simulation model. According to the actual part machining scene, a machine tool driving simulation environment is preset, and the initial model is simulated and verified according to the environment to obtain initial model simulation effect parameters. Finally, based on the initial model simulation effect parameters, the initial machine tool motion simulation model is improved and optimized to complete the construction of the compound machine tool motion simulation model.

[0037] Optionally, first, the acquired machine tool global kinematic pair data and machine tool component motion constraint parameters are imported into general motion simulation software such as ADAMS or ANSYS, a model import module is called in the simulation software to load the compound machine tool three-dimensional model, constraint parameters such as the motion type, transmission ratio of each kinematic pair, and the stroke limit and interference boundary of each component are entered one by one through a parameter configuration interface, the simulation step is set to a conventional interval of 0.01 seconds to 0.1 seconds, the kinematics simulation calculation function of the software is started, the motion process of each component of the machine tool under the constraint condition is simulated, and finally an initial machine tool motion simulation model is generated.

[0038] Then, the part machining scene data of the nine-axis machine tool in actual production is collected by a person skilled in the art, including typical machining types such as shaft parts and box parts, and corresponding process parameters such as cutting speed, feed amount, spindle speed, and standard machining motion trajectory. In the environment setting module of the simulation software, the driving source parameters are configured in sequence according to the parameter range of the actual machining scene, including spindle driving power, shaft feed driving torque, motion trajectory trigger logic, and the like, while simulating the load change in the machining process, and a machine tool driving simulation environment highly consistent with actual production is constructed.

[0039] Then, the preset machine tool driving simulation environment parameters are loaded to the initial machine tool motion simulation model, a simulation running program is started, and the initial machine tool motion simulation model completes a complete part machining simulation process under the environment. By using a data analysis tool of the simulation software, key parameters in the model running process are collected in real time, including motion accuracy errors of each motion pair, minimum safety distances between parts, motion response delay time and the like, and whether the initial machine tool motion simulation model has a virtual collision is recorded by using an interference detection function of the simulation software. The collected data are sorted and summarized to form initial model simulation effect parameters.

[0040] Finally, by comparing the simulation effect parameters of the initial machine tool motion simulation model with standard parameter ranges of actual machine tool machining, if it is found that the motion accuracy error exceeds the allowable value, the transmission stiffness parameter or the constraint damping coefficient of the corresponding motion pair is adjusted; if there is a virtual collision risk, the motion constraint range of the related part is corrected; if the motion response delay is too long, the adjustment logic of the driving parameter is optimized. After each adjustment, the simulation verification is restarted, and the above comparison and adjustment process is repeated until the simulation effect parameters of the initial machine tool motion simulation model all meet the actual machining requirements, and finally a precise and reliable composite machine tool motion simulation model is constructed.

[0041] Further, the method provided by the embodiment of the application comprises:

[0042] The simulation parameter extraction is performed on the machine tool working condition scene to obtain machine tool working condition simulation parameters, the machine tool working condition simulation parameters comprising a machining type, process parameters and a motion trajectory; the machine tool working condition simulation parameters are loaded to perform motion simulation and simulation on the composite machine tool motion simulation model to obtain machine tool collision simulation test data; a collision risk quantitative index is constructed, the machine tool collision simulation test data is quantitatively evaluated and assigned according to the collision risk quantitative index, and a machine tool collision risk quantitative parameter is determined; the composite machine tool three-dimensional model is meshed, and the machine tool collision risk quantitative parameter is mapped to the meshed composite machine tool three-dimensional model to perform a heat map rendering, thereby generating a machine tool collision risk heat map.

[0043] Specifically, first, typical working condition scenes of a nine-axis turning-milling composite machine tool in actual production are collected, which are divided into common categories such as shaft part machining and box part machining according to the machining type. By using a parameter export function of a machine tool numerical control system, process parameters in the corresponding working condition scenes are extracted, including a cutting speed, a feed rate, a spindle speed, a cutting depth and the like. At the same time, by using a trajectory recording module of the machine tool numerical control system, motion trajectory data of machine tool axes are obtained, which cover coordinate changes, motion time sequences and the like. The machining type, process parameters and motion trajectory data are sorted and summarized to form standardized machine tool working condition simulation parameters.

[0044] Then, the extracted machine tool working condition simulation parameters are imported into the built composite machine tool motion simulation model. In mainstream motion simulation software such as ADAMS and ANSYS, the corresponding motion logic of the machining type, the corresponding driving parameters of the process parameters, and the corresponding path instructions of the motion trajectory are entered one by one through the parameter configuration interface, the simulation duration is set to be consistent with the actual machining cycle, the simulation program is started, and the composite machine tool motion simulation model is used to simulate the machine tool machining motion process under the preset working condition. The real-time data acquisition function of the simulation software is used to record the spatial position coordinates, relative motion speed, and minimum distance between components during the simulation process. At the same time, the interference detection module of the simulation software is used to capture the relevant information of potential collision points, and the data is integrated to obtain machine tool collision simulation test data.

[0045] Then, the index construction and quantitative assignment are used to determine the machine tool collision risk quantitative parameters, and the specific steps are as follows:

[0046] Step a: Define the specific definition and value standard of the collision risk quantitative index. Referring to relevant standards such as GB / T25609-2010 “Machine Tool Safety Protection Device Safety Requirements” in the field of mechanical processing, the specific connotation of three core quantitative indexes is determined: first, the minimum safety distance between components, which refers to the shortest straight line distance between the surfaces of two adjacent components during the machine tool motion process; second, the relative motion speed, which refers to the velocity vector difference of adjacent components along the motion direction; third, the collision probability, which refers to the frequency of component collision under the same working condition based on historical collision data statistics.

[0047] Step b: Set the grading score standard of each index. Each index is divided into several grades according to the corresponding collision risk level, among which the minimum safety distance between components is divided into risk levels from high to low according to the distance from near to far, the relative motion speed is divided into risk levels from high to low according to the speed from fast to slow, and the collision probability is divided into risk levels from high to low according to the probability from high to low. Each risk level is set with a unique and comparable score value, forming a unified grading score standard that the higher the risk level, the lower the score, and the lower the risk level, the higher the score, ensuring that the quantitative score of each index can be directly used for subsequent weighted calculation.

[0048] Step c: the index weight distribution is completed by the analytic hierarchy process. A hierarchical structure model is constructed, in which the target layer is the collision risk quantitative evaluation, the criterion layer is the three core indexes, and the scheme layer is different monitoring points. Five professional technicians in the field of mechanical processing and machine tool control are invited to compare the relative importance of each index according to the 1-9 scale method, and a judgment matrix is formed. For example, the minimum safety distance is more important than the relative motion speed, and the scale value is 3; the minimum safety distance is more important than the collision probability, and the scale value is 3; the relative motion speed and the collision probability are equally important, and the scale value is 1. The consistency of the judgment matrix is checked to ensure that the CR value is less than 0.1. After the test is passed, the characteristic vector of each index is calculated, and the specific weight distribution of the three indexes is determined based on the characteristic vector, so that the sum of the weights of each index is 1.

[0049] Step d: the machine tool collision risk quantitative parameter calculation is performed. From the machine tool collision simulation test data, the minimum safety distance measured value between components of each monitoring point, the relative motion speed calculation value, and the collision probability statistical value are extracted, and the single score of each index is determined according to the above grading standard. The specific weights of the three collision risk quantitative indexes are weighted and summed to calculate the specific score of each monitoring point. The score is the machine tool collision risk quantitative parameter.

[0050] Finally, in UG, SolidWorks and other three-dimensional modeling software, the three-dimensional model of the composite machine tool constructed in the foregoing steps is structured and meshed. According to the complexity of the machine tool structure, the model is divided into uniform meshes with a side length of 5-10 mm, ensuring that the grid density of the high-risk area meets the accuracy requirements. Through the data interface of the three-dimensional modeling software, the machine tool collision risk quantitative parameter is one-to-one corresponding to the grid node, the heat map rendering function of the software is called, the mapping relationship between the risk score and the color is set, the low risk score corresponds to the blue color, the medium risk corresponds to the yellow color, and the high risk corresponds to the red color. The three-dimensional model of the composite machine tool after meshing is rendered as a whole, and finally the machine tool collision risk heat map is generated, which directly presents the collision risk level of each area of the machine tool.

[0051] Through the ordered combination of the steps of working condition parameter acquisition, simulation, index quantification, and grid rendering, the visualization of the machine tool collision risk is realized, which provides a clear regional pointing basis for the subsequent precise arrangement of the anti-collision machine.

[0052] Further, the method provided in the embodiments of the present application comprises:

[0053] Define machine tool anti-collision requirements, determine monitoring performance indicators according to the machine tool anti-collision requirements, deploy a perception network on the nine-axis linkage turning-milling composite machine tool according to the monitoring performance indicators based on the machine tool collision risk heat map, and construct a machine tool perception monitoring module; collect a composite machine tool historical collision data set, predict the collision risk of the composite machine tool historical collision data set, obtain a collision risk prediction module and an anti-collision decision control module; communicate and integrate the machine tool perception monitoring module, the collision risk prediction module and the anti-collision decision control module on the nine-axis linkage turning-milling composite machine tool, and lay out a machine tool anti-collision machine.

[0054] Specifically, first, the general requirements for machine tool anti-collision are sorted out by consulting relevant safety standard documents and industry application cases of nine-axis linkage turning-milling composite machine tools. By collecting machine tool operation logs, fault records and collision case reports provided by machine tool using enterprises, common collision scenarios, collision parts and loss information caused by collision in actual production are obtained. Extract historical collision data of the past three years from the machine tool fault record system and production management log, including information such as processing type, process parameters and motion state when collision occurs. Statistical analysis is performed on the collected fault records and historical collision data, collision reasons are classified and summarized, and high-risk components such as main shaft, tool turret, workbench and key collision types such as component interference collision and collision caused by processing path deviation of the machine tool in nine-axis linkage operation and complex part processing scenarios are determined. Finally, the core machine tool anti-collision requirements covering the whole high-risk area, early warning of collision risk, rapid response to protective actions, avoidance of component damage and processing interruption are defined.

[0055] Next, based on the defined machine tool anti-collision requirements, referring to relevant standards such as GB / T 8196-2018 "Mechanical safety Protective devices Design and manufacture of stationary and movable protective devices General requirements", etc., the core anti-collision requirements are decomposed into quantifiable and executable monitoring performance indicators. For the requirement of covering the whole high-risk area, the monitoring coverage range indicator is determined, which requires the monitoring range to completely contain the motion trajectory of all high-risk components and the interaction area of adjacent components, with no monitoring blind area. Through the component coordinate data at the time of historical collision, the high-risk space range is calculated, and the range is extended by 5-10 mm as the boundary of the interaction area of adjacent components. For the requirement of early warning of collision risk, the risk warning response time indicator is determined. According to the maximum motion speed v of the machine tool and the minimum safety distance s, the minimum safety distance is 1.2 times the maximum size of the component, and the maximum motion speed is the linear speed or shaft fast-moving speed corresponding to the rated maximum speed of the machine tool. The response time threshold is derived according to t=s / v. For the requirement of rapid response of protective action, the monitoring accuracy indicator is determined, such as the requirement that the monitoring error of component position and motion speed should not exceed ±0.1 mm, to ensure the accuracy of risk judgment. For the requirement of avoiding component damage and processing interruption, the system reliability indicator is determined, such as the requirement that the monitoring system should have a continuous fault-free running time of not less than 800 hours under the test conditions of machine tool rated load, environmental temperature 15-35℃, humidity 40%-80% of the conventional industrial working conditions, and at the same time have a fault self-diagnosis function.

[0056] Then according to the machine tool collision risk heat map, the machine tool collision risk component set and the corresponding collision component risk level are determined, and the sensor layout analysis is performed on the risk component set and the risk level according to the monitoring performance indicators, to obtain the monitoring sensor specification type parameters and the monitoring sensor position parameters. Finally, based on these two types of parameters, the perception network is deployed on the nine-link turning-milling combined machine tool, and the construction of the machine tool perception monitoring module is completed. This step is described in detail in the subsequent content.

[0057] Then the collision risk identification and division of the combined machine tool historical collision data set are performed according to the preset time window, and the combined machine tool collision risk sample set is obtained. Then the time series network is used to train the risk prediction of the sample set, and the collision risk prediction module is obtained. Finally, the hierarchical anti-collision strategy is configured for the risk level information output by the prediction module, and the determination of the anti-collision decision control module is completed. This step is described in detail in the subsequent content.

[0058] Subsequently, the commonly used Profinet communication protocol in industry is selected as the data interaction standard between modules. First, the signal output ends of each sensor in the machine tool perception monitoring module are connected to the corresponding interfaces of the data acquisition card, such as RS485 interface and Ethernet interface, through a shielded cable to establish a reliable connection. The data acquisition card is integrated with the machine tool main control unit through a PCIe slot. The hardware units of the collision risk prediction module and the anti-collision decision control module, such as embedded processors and storage modules, are fixedly installed in the electrical control cabinet of the machine tool. The two modules are connected to the machine tool main control unit through an Ethernet switch for wired communication. Redundant wiring is used to ensure communication stability.

[0059] Finally, according to the preset data interaction protocol, the communication addresses, data transmission baud rate, and data frame format of each module are configured in the control system software of the machine tool main control unit. The transmission period of real-time data such as position and speed collected by the machine tool perception monitoring module to the collision risk prediction module, the feedback time limit of risk parameters from the collision risk prediction module to the anti-collision decision control module, and the control instruction output logic of the anti-collision decision control module to the machine tool actuators such as servo drivers and emergency stop devices are determined. After completing the hardware connection and software configuration, the system is started for debugging and testing. The integrity and real-time performance of data transmission between modules are verified in sequence. By simulating different collision risk scenarios, it is confirmed that the perception data acquisition is accurate, the prediction parameter calculation is correct, and the control instruction execution is timely. It is ensured that the three modules form an organic whole for collaborative work, and finally the machine tool anti-collision machine is completed.

[0060] Further, the method provided by the embodiment of the present application comprises:

[0061] According to the machine tool collision risk heat map, a machine tool collision risk component set and corresponding collision component risk levels are determined. Sensor layout analysis is performed on the machine tool collision risk component set and corresponding collision component risk levels according to the monitoring performance indicators to obtain monitoring sensor specification type parameters and monitoring sensor position parameters. The nine-axis turning-milling combined machine tool is deployed based on the monitoring sensor specification type parameters and monitoring sensor position parameters to construct a machine tool perception monitoring module.

[0062] Specifically, first, by observing the color distribution characteristics of the machine tool collision risk heat map, the high-risk areas marked in red, the medium-risk areas marked in yellow, and the low-risk areas marked in blue in the heat map are matched with the specific components in the machine tool three-dimensional model. The machine tool entity components corresponding to each risk color area are identified one by one, and these components are summarized to form a machine tool collision risk component set. According to the color identification of the corresponding area, each component is assigned a high-risk, medium-risk, or low-risk collision component risk level.

[0063] Then, sensor layout analysis is performed, referring to the monitoring performance indexes determined in the foregoing steps, and according to the machine tool entity components of different collision component risk levels in the machine tool collision risk component set, the sensor layout analysis is performed to obtain monitoring sensor specification type parameters and monitoring sensor position parameters. For example, for high-risk level components, a laser displacement sensor or a grating ruler with a measurement accuracy of not less than ±0.1 mm and a response time of not more than 50 ms is selected as a monitoring sensor; for medium-risk level components, an infrared sensor with a measurement accuracy of not less than ±0.2 mm and a response time of not more than 80 ms is selected; and for low-risk level components, a proximity switch with a measurement accuracy of not less than ±0.5 mm and a response time of not more than 100 ms is selected. In the sensor position planning, the principles of covering the motion trajectory, no shielding interference, and being close to the key motion pair are followed, the sensor installation candidate positions are determined according to the structure size and motion range of each risk component at the key nodes of the component motion trajectory and the interaction area of adjacent components, the shielding positions are excluded through simulation of the sensor signal transmission path, and finally the specific installation coordinates of each sensor are determined to form the monitoring sensor position parameters.

[0064] Finally, according to the monitoring sensor position parameters, the sensors of corresponding specifications are fixed one by one at the preset installation positions of the nine-axis turning-milling combined machine tool in a bolt fixing or bracket mounting manner, so as to ensure that the sensors are firmly installed and the detection directions are aligned with the monitoring areas. The signal output ends of all the sensors are connected to the corresponding interfaces of the data acquisition card through shielded cables, a perception network is established according to a star-shaped topology structure, and the data acquisition card establishes communication with the machine tool main control unit through an industrial Ethernet. After the hardware connection is completed, the sensors are powered on and calibrated, the measurement accuracy and data transmission stability of the sensors are verified through input of standard position signals, it is ensured that all the sensors can normally collect and transmit real-time data such as position and speed of the machine tool components, and finally a complete machine tool perception monitoring module is formed.

[0065] Further, the method provided in the embodiments of the present application comprises:

[0066] According to the preset time window, the collision risk identification and division are performed on the combined machine tool historical collision data set to obtain a combined machine tool collision risk sample set; a time series network is used for risk prediction training on the combined machine tool collision risk sample set to obtain a collision risk prediction module; and each risk level information output by the collision risk prediction module is configured with a hierarchical anti-collision strategy to determine an anti-collision decision control module.

[0067] In the embodiments of the present application, the time series network is a model capable of capturing time-dependent characteristics of data and adapting to time series data processing, such as LSTM (Long Short Term Memory Network), which is commonly used to learn rules from historical time series data to achieve target prediction.

[0068] In one embodiment, first, through the log export function of the machine tool numerical control system, real-time data in the machine tool operation process in the past five years are extracted, including process parameters such as axis position coordinates, movement speed, spindle speed, and feed amount, as well as collision alarm signals and fault codes recorded by the system. At the same time, processing task information, part drawing parameters, and maintenance records in the machine tool production management system are collected to supplement associated data such as processing type and workpiece specifications when a collision occurs. The collected raw data is preprocessed, the data points obviously deviating from the normal range are deleted by using the outlier rejection algorithm, the missing data is filled by using the linear interpolation method, and all the data is standardized to map the numerical values to the 0-1 interval, finally forming a structured complex machine tool historical collision data set.

[0069] Then, according to the typical processing cycle of the nine-link turning-milling complex machine tool, the preset time window length is set to 1 second, and the complex machine tool historical collision data set is continuously intercepted with a sliding step of 0.5 seconds, each time window corresponding to a group of time series data sequence. Referring to the machine tool collision risk assessment standard, each group of time series data is identified for risk: if the data sequence contains a collision alarm signal or a fault code, it is marked as high risk and assigned a value of 3; if the distance between components in the data sequence is close to the safety threshold but does not trigger an alarm, it is marked as medium risk and assigned a value of 2; if all parameters in the data sequence are within the safe range, it is marked as low risk and assigned a value of 1. The labeled time series data sequence is divided into training set, validation set and test set in the ratio of 7:2:1 to form a complex machine tool collision risk sample set.

[0070] Then, based on the open source TensorFlow framework, a time series network is built and trained to obtain a collision risk prediction module. First, an LSTM model is built, the input layer dimension of the model is set to be consistent with the number of data points in a single group of time series data sequence, assuming that each time window contains 50 data points, then 50Hz sampling frequency is collected within 1 second, the corresponding features include multiple dimensions such as axis position, movement speed, and process parameters. The hidden layer is set to 2 layers, each layer contains 128 neurons, the activation function is ReLU function, and the dropout rate is set to 0.2 through the Dropout layer to prevent overfitting. The output layer uses a Softmax activation function, and the output dimension is 3, corresponding to high, medium, and low collision risk levels respectively. During the training process, cross-entropy is used as the loss function, Adam optimizer is selected, the learning rate is initially set to 0.001, and every 50 rounds is attenuated to 0.8 of the original. The training set of the collision risk sample set is input into the model, the training rounds are set to 200 rounds, the model accuracy is verified after each training round with the validation set, and the training is stopped when the validation set accuracy does not improve for 10 consecutive rounds. Finally, the model performance is evaluated with the test set to ensure that the accuracy is not less than 95%, and the collision risk prediction module is finally obtained.

[0071] Finally, for the high, medium and low three collision risk levels output by the collision risk prediction module, corresponding anti-collision strategies are configured: the high collision risk level corresponds to the emergency stop strategy, the machine tool is controlled to immediately cut off the power source and stop all moving parts from running; the medium collision risk level corresponds to the speed adjustment strategy, the machine tool is controlled to reduce the running speed of each motion shaft by 50%, and the path planning correction program is started; the low collision risk level corresponds to the early warning prompt strategy, the early warning signal is sent through the sound and light alarm device of the machine tool operation panel, reminding the operator to pay attention to the running state. These strategies are solidified into control logic code and integrated into the embedded controller to form an anti-collision decision control module that can automatically output control instructions according to the risk level.

[0072] Through the methods of data collection and integration, sample division and identification, LSTM model training and strategy configuration, the precise construction of the collision risk prediction module and the anti-collision decision control module is realized, providing core technical support for real-time prevention and control of machine tool collision risk.

[0073] Further, the method provided by the embodiment of the application comprises:

[0074] The anti-collision decision control module further comprises a machine tool machining path correction unit, which is configured to perform anti-collision correction control on the current part machining path.

[0075] Optionally, the anti-collision decision control module comprises a machine tool machining path correction unit, which is configured to perform anti-collision correction control on the current part machining path. The machine tool machining path correction unit first plans a main part machining path and a backup part machining path for the nine-axis turning-milling composite machine tool according to part machining requirements, wherein the main part machining path is the current part machining path, and when the machine tool collision risk prediction parameter exceeds the preset machine tool risk threshold, the backup part machining path is used to replace the main part machining path to complete the anti-collision correction control, which will be described in detail in the subsequent content.

[0076] Further, the method provided by the embodiment of the application comprises:

[0077] The machine tool machining path correction unit plans machining paths for the nine-axis turning-milling composite machine tool according to part machining requirements to obtain a main part machining path and a backup part machining path, wherein the main part machining path is the current part machining path; when the machine tool collision risk prediction parameter exceeds the preset machine tool risk threshold, the backup part machining path is used to replace the main part machining path to perform anti-collision strategy control.

[0078] In one embodiment, first, import the three-dimensional CAD drawing of the part, parse the machining contour, key size, geometric tolerance and other geometric parameters through the CAM software, and at the same time, call the corresponding process procedure file of the part to determine the machining process, such as the sequence of milling and turning; the cutting parameter range, such as cutting speed, feed rate and cutting depth; the machining reference and the finished product quality requirement, and integrate to form a complete part machining requirement. Taking the machining efficiency optimization as the goal, the A* path planning algorithm is used to plan the main part machining path: the starting point of part machining, such as the workpiece origin, is set as the starting node of the A* algorithm, and the end point of machining, such as the final contour completion position, is set as the target node, the path length is taken as the cost function g(n), and the Manhattan distance of the node to the target node is taken as the heuristic function h(n), and the total cost function f(n)=g(n)+h(n). During the search process, combined with the motion constraint parameters of the machine tool components, including the stroke limit of each axis, the maximum motion speed, the linkage logic limit, the feasibility of each candidate node is checked, the nodes exceeding the motion constraint are removed, the node with the minimum total cost is preferentially selected to expand the search path, and finally the main part machining path with the shortest path, smooth process connection and meeting the motion constraint and machining requirement is generated, which is the current part machining path, and is stored in the path library of the machine tool numerical control system in the form of coordinate point sequence.

[0079] At the same time, based on the obtained same part machining requirement, the standby part machining path is planned according to the principle of the highest obstacle avoidance priority: adjust the heuristic function of the A* algorithm, add the collision risk weight coefficient to the Manhattan distance, give higher weight to the high-risk area nodes marked in the machine tool collision risk heat map, and make the algorithm preferentially avoid such nodes. During the search process, by offsetting the path nodes of the high-risk area, for example, offsetting the coordinate points near the high-risk area in the original path to the safe area by 5-10 mm, the motion axis linkage logic is optimized, that is, the time sequence order of multi-axis linkage is adjusted to avoid the high-risk components in the interaction area at the same time, and ensure that the planned path is not overlapped with the main part machining path and far away from the collision risk area. Check whether the path meets the part machining requirement and the motion constraint parameters of the machine tool components, correct the path nodes that do not meet the requirements, and finally obtain the standby part machining path with the best obstacle avoidance performance, which is also stored in the path library of the machine tool numerical control system in the form of coordinate point sequence.

[0080] Referring to the collision risk quantification index system, the preset machine tool risk threshold is set as the minimum quantification score corresponding to the medium risk level output by the collision risk prediction module, which is input by a person skilled in the art through a machine tool operation panel and solidified into the anti-collision decision control module. During machine tool processing, the anti-collision decision control module continuously receives the real-time collision risk prediction parameters output by the collision risk prediction module, and compares the parameters with the preset machine tool risk threshold through the built-in parameter comparison program. When it is detected that the prediction parameters exceed the preset machine tool risk threshold, the anti-collision decision control module immediately sends a path switching instruction to the machine tool numerical control system, the system stops the execution of the main part processing path through the path library calling interface, synchronously activates the standby part processing path, and drives each motion shaft to continue processing according to the coordinate sequence and speed parameter of the standby part processing path, so as to ensure that the processing process is not interrupted and the collision risk is avoided.

[0081] Through the cooperative application of the path planning algorithm and the real-time threshold comparison control, the rapid switching of the processing path in the collision risk scenario is realized, and the technical effects of considering the processing continuity and the anti-collision safety are achieved.

[0082] In summary, the real-time monitoring method of the nine-axis linkage turning-milling combined machine tool anti-collision machine provided by the embodiments of the application has the following technical effects:

[0083] The application generates a collision risk heat map through three-dimensional modeling and motion simulation of the nine-axis linkage turning-milling combined machine tool, and based on the heat map, an anti-collision machine containing a perception monitoring, risk prediction and anti-collision decision control module is laid out, the machine tool motion is monitored in real time and the collision risk is predicted, the anti-collision control is implemented in combination with the preset threshold and the path correction strategy, the precise prevention and control of the collision risk of the multi-axis linkage equipment is achieved, and the safety and reliability of the machine tool processing process are significantly improved.

[0084] Embodiment two, as Figure 2 shown, based on the same inventive concept as the foregoing embodiment one, the embodiment of the application provides a real-time monitoring system of a nine-axis linkage turning-milling combined machine tool anti-collision machine, which comprises:

[0085] A machine tool model construction module 1, which performs three-dimensional modeling based on the actual structure size data of the nine-axis linkage turning-milling combined machine tool, generates a three-dimensional model of the combined machine tool, performs motion simulation on the three-dimensional model of the combined machine tool according to the machine tool motion mechanism and the motion constraint relationship of the machine tool components, and constructs a motion simulation model of the combined machine tool.

[0086] A risk heat map acquisition module 2, which is used for performing motion simulation and collision risk analysis on the motion simulation model of the combined machine tool according to the machine tool working condition scene, and generating a machine tool collision risk heat map.

[0087] A machine tool anti-collision machine deployment module 3 is configured to deploy a machine tool anti-collision machine on the nine-axis turning-milling compound machine tool according to the machine tool collision risk heat map, and the machine tool anti-collision machine is composed of a machine tool perception monitoring module, a collision risk prediction module, and an anti-collision decision control module.

[0088] A machine tool anti-collision control execution module 4 is configured to monitor and predict the collision risk of the nine-axis turning-milling compound machine tool in real time based on the machine tool perception monitoring module and the collision risk prediction module, determine machine tool collision risk prediction parameters, and perform machine tool anti-collision control based on the machine tool collision risk prediction parameters through the anti-collision decision control module.

[0089] Further, the machine tool model construction module 1 is configured to perform the following steps:

[0090] Based on the machine tool motion mechanism, the kinematic pairs are classified to obtain machine tool kinematic pair types, including linear motion, rotary motion, and linkage constraints; the compound machine tool three-dimensional model is motionally analyzed and identified according to the machine tool kinematic pair types to obtain machine tool global kinematic pairs; the compound machine tool three-dimensional model is motionally limited according to the machine tool component motion constraint relationship to obtain machine tool component motion constraint parameters; and the compound machine tool three-dimensional model is motionally simulated based on the machine tool global kinematic pairs and the machine tool component motion constraint parameters to construct a compound machine tool motion simulation model.

[0091] Further, the machine tool model construction module 1 is configured to perform the following steps:

[0092] The compound machine tool three-dimensional model is motionally simulated based on the machine tool global kinematic pairs and the machine tool component motion constraint parameters to generate an initial machine tool motion simulation model; a machine tool driving simulation environment is preset according to an actual part machining scene; the initial machine tool motion simulation model is simulated and verified according to the machine tool driving simulation environment to obtain initial model simulation effect parameters; and the initial machine tool motion simulation model is improved and optimized based on the initial model simulation effect parameters to construct a compound machine tool motion simulation model.

[0093] Further, the risk heat map acquisition module 2 is configured to perform the following steps:

[0094] The machine tool working condition scene is simulated to obtain machine tool working condition simulation parameters, including processing type, process parameters and motion trajectory; the machine tool working condition simulation parameters are loaded to perform motion simulation on the compound machine tool motion simulation model to obtain machine tool collision simulation test data; a collision risk quantization index is constructed, and the machine tool collision simulation test data is quantitatively evaluated according to the collision risk quantization index to determine machine tool collision risk quantization parameters; the compound machine tool three-dimensional model is meshed, and the machine tool collision risk quantization parameters are mapped to the meshed compound machine tool three-dimensional model for heat map rendering to generate a machine tool collision risk heat map.

[0095] Further, the machine tool anti-collision machine layout module 3 is used to perform the following steps:

[0096] The machine tool anti-collision requirement is defined, and the monitoring performance index is determined according to the machine tool anti-collision requirement; the nine-axis turning and milling compound machine tool is deployed based on the machine tool collision risk heat map according to the monitoring performance index to construct a machine tool perception monitoring module; a compound machine tool historical collision data set is collected, and a collision risk prediction module and an anti-collision decision control module are obtained by predicting the collision risk of the compound machine tool historical collision data set; the machine tool perception monitoring module, the collision risk prediction module and the anti-collision decision control module are integrated and communicated on the nine-axis turning and milling compound machine tool to layout the machine tool anti-collision machine.

[0097] Further, the machine tool anti-collision machine layout module 3 is used to perform the following steps:

[0098] According to the machine tool collision risk heat map, a machine tool collision risk component set and a corresponding collision component risk level are determined; sensor layout analysis is performed on the machine tool collision risk component set and the corresponding collision component risk level according to the monitoring performance index to obtain monitoring sensor specification type parameters and monitoring sensor position parameters; the nine-axis turning and milling compound machine tool is deployed based on the monitoring sensor specification type parameters and the monitoring sensor position parameters to construct a machine tool perception monitoring module.

[0099] Further, the machine tool anti-collision machine layout module 3 is used to perform the following steps:

[0100] According to the machine tool collision risk heat map, a machine tool collision risk component set and a corresponding collision component risk level are determined; sensor layout analysis is performed on the machine tool collision risk component set and the corresponding collision component risk level according to the monitoring performance index to obtain monitoring sensor specification type parameters and monitoring sensor position parameters; the nine-axis turning and milling compound machine tool is deployed based on the monitoring sensor specification type parameters and the monitoring sensor position parameters to construct a machine tool perception monitoring module.

[0101] Further, the machine tool anti-collision machine arrangement module 3 is used to execute the following steps:

[0102] The anti-collision decision control module further comprises a machine tool machining path correction unit, which is used for anti-collision correction control on the current part machining path.

[0103] Further, the machine tool anti-collision machine arrangement module 3 is used to execute the following steps:

[0104] The machine tool machining path correction unit plans a machining path for the nine-axis turning-milling composite machine tool according to part machining requirements to obtain a main part machining path and a backup part machining path, wherein the main part machining path is the current part machining path; when the machine tool collision risk prediction parameter exceeds the preset machine tool risk threshold, the backup part machining path is used to replace the main part machining path for anti-collision strategy control.

[0105] The nine-axis turning-milling composite machine tool anti-collision machine real-time monitoring system provided in the embodiment of the application can execute the nine-axis turning-milling composite machine tool anti-collision machine real-time monitoring method provided in any embodiment of the application, has the corresponding function modules and beneficial effects of the execution method.

[0106] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for the convenience of mutual differentiation, and do not limit the protection scope of the present application.

[0107] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement and improvement made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps described in the present application can be executed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A real-time monitoring method for a collision avoidance machine of a nine-axis car-milling compound machine tool, characterized in that, The method comprises: Based on the actual structure size data of the nine-linkage turning-milling composite machine tool, a three-dimensional model of the composite machine tool is generated, the three-dimensional model of the composite machine tool is simulated according to the machine tool motion mechanism and the machine tool component motion constraint relationship, and a composite machine tool motion simulation model is constructed; According to the machine tool working condition scene, the composite machine tool motion simulation model is simulated and the collision risk is analyzed, and a machine tool collision risk heat map is generated; According to the machine tool collision risk heat map, a machine tool anti-collision machine is arranged on the nine-linkage turning-milling composite machine tool, and the machine tool anti-collision machine is composed of a machine tool perception monitoring module, a collision risk prediction module and an anti-collision decision control module; Based on the machine tool perception monitoring module and the collision risk prediction module, the motion process of the nine-linkage turning-milling composite machine tool is monitored and the collision risk is predicted in real time, the machine tool collision risk prediction parameters are determined, and the machine tool anti-collision control is performed based on the machine tool collision risk prediction parameters through the anti-collision decision control module; Wherein, the composite machine tool motion simulation model comprises: Based on the machine tool motion mechanism, the kinematic pairs are classified to obtain the machine tool kinematic pair type, and the machine tool kinematic pair type includes linear motion, rotary motion and linkage constraint; According to the machine tool kinematic pair type, the composite machine tool three-dimensional model is motion analysis and identification, and the machine tool global kinematic pair is obtained; According to the machine tool component motion constraint relationship, the motion range of the composite machine tool three-dimensional model is limited, and the machine tool component motion constraint parameter is obtained; Based on the machine tool global kinematic pair and the machine tool component motion constraint parameter, the motion simulation of the composite machine tool three-dimensional model is performed, and the composite machine tool motion simulation model is constructed; Wherein, based on the machine tool global kinematic pair and the machine tool component motion constraint parameter, the motion simulation of the composite machine tool three-dimensional model is performed, and the initial machine tool motion simulation model is constructed; According to the actual part processing scene, the machine tool driving simulation environment is preset; According to the machine tool driving simulation environment, the initial machine tool motion simulation model is simulated and verified, and the initial model simulation effect parameter is obtained; Based on the initial model simulation effect parameter, the initial machine tool motion simulation model is improved and optimized, and the composite machine tool motion simulation model is constructed. The machine tool collision risk heat map is generated, which comprises:

2. The real-time monitoring method of the anti-collision machine of the nine-link turning-milling combined machine tool according to claim 1, characterized in that, The simulation parameters of the machine tool working condition scene are extracted to obtain the machine tool working condition simulation parameters, and the machine tool working condition simulation parameters include the processing type, the process parameters and the motion trajectory; The machine tool working condition simulation parameters are loaded to simulate and simulate the composite machine tool motion simulation model, and the machine tool collision simulation test data is obtained; A collision risk quantitative index is constructed, the machine tool collision simulation test data is quantitatively evaluated and assigned according to the collision risk quantitative index, and the machine tool collision risk quantitative parameter is determined; ​ Grid division is performed on the compound machine tool three-dimensional model, and the machine tool collision risk quantization parameter is mapped to the grid divided compound machine tool three-dimensional model for heat map rendering, to generate a machine tool collision risk heat map.

3. The real-time monitoring method of a collision avoidance machine of a nine-axis turning-milling combined machine tool according to claim 1, characterized in that, The anti-collision machine for the machine tool is deployed, and the anti-collision machine for the machine tool comprises: Define the anti-collision requirements of the machine tool, and determine the monitoring performance index according to the anti-collision requirements of the machine tool. According to the monitoring performance index, the nine-axis turning-milling compound machine tool is deployed based on the machine tool collision risk heat map to construct a machine tool perception monitoring module. Collect a compound machine tool historical collision data set, and perform collision risk prediction on the compound machine tool historical collision data set to obtain a collision risk prediction module and an anti-collision decision control module. The machine tool perception monitoring module, the collision risk prediction module and the anti-collision decision control module are integrated in communication on the nine-axis turning-milling compound machine tool to deploy the anti-collision machine for the machine tool.

4. The real-time monitoring method of the anti-collision machine of the nine-link turning-milling combined machine tool according to claim 3, characterized in that, The machine tool perception monitoring module is constructed, comprising: According to the machine tool collision risk heat map, a machine tool collision risk component set and a corresponding collision component risk level are determined. According to the monitoring performance index, the machine tool collision risk component set and the corresponding collision component risk level are analyzed for sensor layout to obtain monitoring sensor specification type parameters and monitoring sensor position parameters. According to the monitoring sensor specification type parameters and the monitoring sensor position parameters, the nine-axis turning-milling compound machine tool is deployed for perception network to construct the machine tool perception monitoring module.

5. The real-time monitoring method of a collision avoidance machine of a nine-axis turning-milling-combined machine tool according to claim 3, wherein, The collision risk prediction module and the anti-collision decision control module are obtained, comprising: According to a preset time window, the compound machine tool historical collision data set is identified and divided for collision risk to obtain a compound machine tool collision risk sample set. A time series network is used to perform risk prediction training on the compound machine tool collision risk sample set to obtain a collision risk prediction module. The collision risk prediction module outputs each risk level information for hierarchical anti-collision strategy configuration to determine an anti-collision decision control module.

6. The real-time monitoring method of a collision avoidance machine of a nine-axis turning-milling-combined machine tool according to claim 1, wherein, The anti-collision decision control module further comprises a machine tool machining path correction unit for anti-collision correction control of the current part machining path.

7. The real-time monitoring method of a collision avoidance machine of a nine-axis turning-milling-combined machine tool according to claim 6, wherein, The machine tool machining path correction unit is used for anti-collision correction control of the current part machining path, comprising: The machine tool machining path correction unit plans a machining path for the nine-axis turning-milling compound machine tool according to part machining requirements to obtain a main part machining path and a backup part machining path, wherein the main part machining path is the current part machining path. When the machine tool collision risk prediction parameter exceeds the preset machine tool risk threshold, the backup part machining path is used to replace the main part machining path for anti-collision strategy control.

8. A real-time monitoring system for a collision avoidance machine of a nine-axis turning-milling-combined machine tool, characterized in that, A real-time monitoring method for a nine-axis turning-milling compound machine tool anti-collision machine according to any one of claims 1-7, the system comprising: A machine tool model construction module generates a compound machine tool three-dimensional model based on actual structure size data of the nine-axis turning-milling compound machine tool, and constructs a compound machine tool motion simulation model by performing motion simulation on the compound machine tool three-dimensional model according to machine tool motion mechanism and machine tool component motion constraint relationship. a risk heat map acquisition module, configured to perform motion simulation and collision risk analysis on the compound machine tool motion simulation model according to the machine tool working condition scene, and generate a machine tool collision risk heat map; a machine tool anti-collision machine arrangement module, configured to arrange a machine tool anti-collision machine on the nine-axis turning-milling compound machine tool according to the machine tool collision risk heat map, the machine tool anti-collision machine being composed of a machine tool perception monitoring module, a collision risk prediction module, and an anti-collision decision control module; a machine tool anti-collision control execution module, configured to perform real-time monitoring and collision risk prediction on the motion process of the nine-axis turning-milling compound machine tool based on the machine tool perception monitoring module and the collision risk prediction module, determine machine tool collision risk prediction parameters, and perform machine tool anti-collision control based on the machine tool collision risk prediction parameters through the anti-collision decision control module.

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