Horizontal five-axis turning-milling center with adaptive matching system

Abstract

The application discloses a horizontal five-axis turning-milling combined center with an adaptive matching system, comprising a tool tower, a boring tool mechanism, a tool changing area and a control system. The tool tower and the tool changing area of the boring tool mechanism are provided with a radio frequency identification reading device, and each tool is externally provided with an RFID chip label for storing tool number, specification and length compensation parameters; the control system realizes adaptive matching and sequential error correction of tools and processes through radio frequency identification. The tool changing area is provided with a visual monitoring assembly, comprising multiple industrial cameras, an adjustable light source and a structured light stripe illumination module, for detecting the clamping posture, wear state and type of the tool. The system has self-learning and digital twin simulation functions, can simulate tool motion trajectories and predict interference risks before tool changing. The application realizes integrated control of automatic tool identification, posture correction, wear detection and path prediction, and significantly improves the tool changing accuracy, running stability and automation level of the horizontal five-axis turning-milling combined center.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of five-axis machining centers, in particular to a horizontal five-axis turning-milling composite center with a self-adaptive matching system. BACKGROUND

[0002] The horizontal five-axis turning-milling composite center is a kind of high-end numerical control machine tool integrating turning, milling, boring and drilling and other multiple machining functions, which is widely used in the fields of aerospace, energy equipment and precision mold manufacturing. This kind of machine tool usually realizes multi-process continuous machining through the tool turret and automatic tool changer to improve the machining efficiency and precision. However, with the increasing demand for machining of complex parts, the shortcomings of the traditional automatic tool changing system in tool recognition, posture detection and clamping safety control gradually appear.

[0003] In the prior art, the tool turret or boring tool mechanism relies on a single mechanical code or position sensing device for tool recognition. For example, a common structure realizes tool position confirmation by setting mechanical limit pieces or magnetic sensing blocks on the tool turret tool position, or realizes manual input by tool code card. Although this kind of method is simple in structure, it is difficult to realize dynamic recognition and automatic matching of tools, especially in the multi-process mixed machining scene, it is easy to cause tool sequence disorder, inconsistent information or life data loss. Once tool recognition error occurs, it may cause tool changing failure, spindle collision, workpiece scrap and other serious consequences.

[0004] In addition, the common visual detection system in the existing horizontal turning-milling composite machine tool is a two-dimensional detection mode of single camera, which can only detect the shape contour or position coordinates of the tool, and cannot accurately identify the three-dimensional posture and clamping angle of the tool. When the tool is slightly skewed or loose during the taking and placing process of the mechanical hand, the system is difficult to detect and correct in time, resulting in accumulation of clamping angle error, and finally affecting the machining precision and workpiece surface quality. At the same time, the traditional wear detection method still mainly relies on manual periodic inspection or single-point photoelectric detection, which lacks real-time and adaptive ability, and cannot realize online identification and early warning of tool wear, blade collapse or blade edge damage.

[0005] In complex production environment, the interference of cooling liquid atomization, light reflection and machine tool high-temperature oil mist on visual detection is particularly significant, resulting in unstable image recognition result. Although some models try to use filters or enhanced lighting modules for improvement, it is still difficult to balance the needs of multi-angle imaging and high-contrast recognition, and the system reliability is limited. At the same time, the existing tool changing system generally lacks trajectory prediction and anti-collision simulation function for tool motion process, and the tool changing path planning relies on manual setting or experience value, which cannot identify the interference risk in advance in complex space, increasing the uncertainty and maintenance cost of machine tool operation.

[0006] Therefore, the existing problems are researched and improved, and a horizontal five-axis turning-milling composite center with an adaptive matching system is provided to solve the existing problems, so as to solve the problems and improve the practical value. SUMMARY

[0007] The present application aims to solve one of the technical problems in the prior art or related art.

[0008] To this end, the technical solution adopted by the present application is as follows: a horizontal five-axis turning-milling composite center with an adaptive matching system, comprising a turret, a boring tool mechanism, a tool changing area, and a control system. The system is provided with a radio frequency identification reading device and a visual monitoring component in the tool changing area of the turret and the boring tool mechanism to construct a dual recognition fusion system. The control system is internally provided with an adaptive matching module, a data fusion module, and a digital twin simulation module to realize intelligent control of the whole process of tool taking, placing, recognition, clamping, and wear detection, thereby improving the tool changing accuracy, safety, and automation level. The tool changing area of the turret and the boring tool mechanism is provided with a radio frequency identification reading device, and each tool surface is provided with an RFID chip label for storing tool number, specification, service life, and length compensation parameters. The control system automatically reads tool information through the radio frequency identification reading device and compares it with the processing task database to realize adaptive matching and sequence correction of the tool and the process. The system can identify out-of-sequence tools when multiple tool magazines are used together, and automatically return or replace the tools according to the comparison results. Through the association of RFID information and the database, the uniqueness of tool identity and the correctness of tool changing sequence are ensured, effectively preventing the wrong tool from being installed, and improving the processing safety and management accuracy.

[0009] In a preferred example, the tool changing area of the turret and the boring tool mechanism is provided with a visual monitoring component for monitoring the tool changing state of the main shaft and the auxiliary shaft, including an industrial camera and an adjustable light source for detecting the tool clamping posture, wear state, and type.

[0010] The control system determines based on the RFID identification results and the visual detection data fusion to realize dual verification and automatic selection of tool type, position, and state.

[0011] Specifically, the visual detection compensates for the blind area of the radio frequency identification to realize complementary fusion of tool recognition and state verification, ensuring accurate identification and safe action during tool changing.

[0012] In a preferred example, the visual monitoring component adopts a multi-camera time sequence fusion algorithm to reconstruct the three-dimensional posture of the tool through multiple images, thereby realizing automatic tool setting and clamping angle correction.

[0013] The control system performs multi-frame fusion and spatial fitting on the synchronous images collected by each camera to calculate the posture angle and eccentricity of the tool, and automatically instructs the manipulator to perform posture fine adjustment.

[0014] Specifically, the multi-view three-dimensional reconstruction is used to realize active correction of the clamping angle and eccentric error of the tool, thereby improving the tooling accuracy and the consistency of the repeated positioning.

[0015] In a preferred example, the visual monitoring assembly is configured with a structured light stripe illumination module for forming a light stripe scan on the tool edge, and the control system determines the tool wear, notch or edge damage degree through the light stripe deformation, and automatically switches to a backup tool when the wear exceeds the limit.

[0016] Specifically, non-contact optical measurement is used to realize real-time detection of the tool edge topography, thereby preventing the use of worn tools and ensuring the processing quality and equipment safety.

[0017] In a preferred example, the visual monitoring assembly adopts a multi-camera perception architecture, and the tool multi-angle image is synchronously collected through industrial cameras distributed in different directions; the control system identifies and predicts the motion trajectory, clamping dynamics and path deviation during the tool taking and placing process based on time series feature extraction and multi-frame fusion algorithm.

[0018] Specifically, the dynamic deviation correction of the tool changing path is realized through time sequence perception and motion prediction, thereby improving the stability and safety of the tool changing action.

[0019] In a preferred example, the visual monitoring assembly is provided with an adjustable lighting system, including a coaxial ring light source and a lateral strip light source, which can automatically adjust the brightness and color temperature according to the tool reflection characteristics. The system is also equipped with an oil mist filtering algorithm and a polarized filter to optimize the image quality in high oil mist or strong reflection environment.

[0020] Specifically, the robustness of visual recognition in complex environments is improved to ensure clear images and edge recognition accuracy.

[0021] In a preferred example, the control system uses a time sequence difference algorithm to detect the tool micro-displacement based on the continuous images collected by the visual monitoring assembly, thereby realizing dynamic compensation of the taking and placing error and misloading detection.

[0022] Specifically, real-time monitoring and self-correction of the tool changing motion deviation are realized to avoid processing deviation caused by accumulated clamping errors.

[0023] In a preferred example, the visual monitoring assembly and the radio frequency identification reading device share a data bus, and the control system performs joint comparison of the visual recognition result and the radio frequency identification information through a multi-source data fusion model to comprehensively determine the tool identity, position and attitude.

[0024] Specifically, a dual-channel joint verification mechanism is established to ensure the safety and reliability of tool information identification.

[0025] In a preferred example, the control system has a self-learning function, which can automatically update the tool feature template based on historical image samples, and adaptively adjust the detection parameters according to different tool models.

[0026] Specifically, the system can dynamically optimize the detection model with the change of tool model and environment, and maintain long-term recognition accuracy and system adaptability.

[0027] In a preferred example, the visual monitoring component is provided with an image anomaly detection module for automatically identifying image distortion state when the monitoring signal is disturbed or blocked, and triggering the standby visual channel for redundant identification.

[0028] Specifically, through the multi-channel redundant design, the single camera failure is prevented, and the continuous operation stability of the system is improved.

[0029] In a preferred example, the visual monitoring component and the control system jointly build a digital twin tool changing model, which is used to simulate the tool motion trajectory and attitude change before tool changing, predict potential interference risks in advance, and perform avoidance correction.

[0030] Specifically, virtual verification and path optimization are realized before actual action, mechanical interference is prevented, and tool changing safety and system collaboration are improved.

[0031] The present application realizes the intelligent control of the whole process of tool recognition, state monitoring, error compensation and trajectory prediction through the collaborative design of "RFID + multi-vision fusion + adaptive compensation + digital twin prediction", and builds an intelligent tool changing system with multi-channel perception, data fusion judgment, self-learning optimization and dynamic error-proofing capability.

[0032] This scheme not only realizes the intelligent control of the whole process of tool recognition, state monitoring, error compensation and trajectory prediction, but also significantly improves the automation level, machining precision and operation safety of the horizontal five-axis turning-milling combined center in complex machining scenarios.

[0033] The beneficial effects obtained by the present application are:

[0034] 1. In the present application, the radio frequency identification device and the visual monitoring component are arranged in the tool turret and the boring tool mechanism tool changing area to form a dual-channel identification system, which realizes automatic identification, sequence error correction and state verification of tool information. The system can realize one-to-one correspondence between tool identity and process under the condition of mixed use of multiple tool magazines, greatly reduces manual intervention, and significantly improves tool changing accuracy and reliability.

[0035] 2. In the present application, the visual monitoring component adopts multi-camera time sequence fusion and structured light detection technology, which can realize three-dimensional attitude reconstruction, clamping angle correction and wear and damage identification of the tool. Through continuous monitoring and trajectory prediction of the tool taking and placing process, dynamic compensation of clamping error and wear warning are realized, so as to ensure the stability, safety and machining quality of the machine tool tool changing process.

[0036] 3. In this invention, the control system integrates an adaptive matching module, a self-learning algorithm, and a digital twin simulation model, realizing automatic tool length compensation, self-optimization of identification parameters, and virtual prediction of tool changing actions. The system can predict potential interference risks and perform avoidance corrections before tool changing, possessing self-learning and intelligent error prevention functions. This constructs a highly intelligent tool changing system integrating identification, detection, compensation, and prediction, significantly improving the automation level and overall machining efficiency of horizontal five-axis milling and turning centers. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the overall structure of one embodiment of the present invention;

[0038] Figure 2 This is a side view structural diagram of an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the control system structure according to an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of the control system flow structure according to an embodiment of the present invention.

[0041] Figure label:

[0042] 100. Turret; 200. Boring mechanism; 300. Milling machining center; 400. Spindle; 500. Sub-spindle. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0044] It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the invention.

[0045] The following describes, with reference to the accompanying drawings, some embodiments of the present invention, a horizontal five-axis milling and turning center with an adaptive matching system.

[0046] Combination Figures 1-2 As shown, the present invention provides a horizontal five-axis milling and turning center with an adaptive matching system, including a turret 100, a boring mechanism 200, a tool changing area, and a control system. This machine tool adopts a horizontal layout structure, possessing both a high-rigidity spindle 400 drive and five-axis simultaneous machining capability, making it suitable for one-time clamping and forming of complex parts.

[0047] The tool turret 100 is arranged on the side of the main shaft 400, has multiple tool positions, and is used for mounting different types of turning, milling or drilling tools; the tool turret 100 is internally provided with a driving servo device, and accurate rotation and positioning of multiple tool positions can be realized. The boring tool mechanism 200 adopts a wheel disc type arrangement, multiple boring tools of different specifications are uniformly arranged on the outer periphery of the wheel disc, the wheel disc is driven to rotate by a indexing servo motor, so that high-precision machining of multiple hole diameters and internal cavities can be realized. The tool changing area is arranged between the tool turret 100 and the boring tool mechanism 200, and is a core area of tool transfer and identification. The tool changing area is provided with an automatic tool taking and placing manipulator, and is used for automatic transfer, clamping and storage operations between different tools.

[0048] The control system is a core coordination unit, and is integrated with a numerical control main controller, an RFID identification module, a visual monitoring module, a self-adaptive matching module, a data fusion module and a digital twin simulation module. Data interaction and coordinated control of the functional units are realized through an industrial network bus, so that a closed intelligent tool changing control system is formed.

[0049] In the system, the tool turret 100 and the tool changing area of the boring tool mechanism 200 are each provided with a radio frequency identification reading device, and an RFID chip label is attached to the outer surface of each tool. The label records basic data such as tool number, specification, service life, number of uses and length compensation parameters.

[0050] When the manipulator performs a tool taking or placing operation, the reading device can identify the label information in real time and upload the information to the control system. The control system compares the tool calling information of the corresponding process in the machining task database, automatically judges whether the tool meets the requirements of the process, and realizes self-adaptive matching of the tool and the process. When the tool type or sequence is detected to be abnormal, the system automatically triggers a sequence correction function, suspends the tool changing action and prompts manual confirmation or automatically calls a backup tool, so that the accuracy and safety of the tool changing process are ensured.

[0051] The linkage of the RFID system and the control system ensures the traceability of the tool data in the whole life cycle, so that the calling sequence, service life and state parameters of the tool in the continuous machining of multiple processes are monitored and dynamically updated in real time.

[0052] In this embodiment, the tool changing area is provided with a visual monitoring assembly for realizing omnibearing detection of the appearance, posture and state of the tool. The assembly is composed of multiple industrial cameras, an adjustable light system, a structured light stripe projection module, a light filtering device and an image processing unit.

[0053] Industrial cameras are distributed above, on the side and in front of the tool changing area, forming a ring-shaped arrangement structure to simultaneously collect tool images from different angles. The adjustable lighting system is composed of coaxial ring light and lateral strip light, which can automatically adjust the light intensity and color temperature according to the tool material, reflection characteristics and environmental brightness to achieve the best imaging effect. Polarizing filters and oil mist-proof optical sheets are set in front of the light source to suppress image interference caused by strong reflection of metal surface and cooling liquid atomization.

[0054] The structured light stripe projection module projects high-precision light stripes onto the tool surface, and obtains the geometric profile and wear state of the tool surface by analyzing the deformation of the light stripes. The control system analyzes the light stripe images, and when it finds that the light stripes are broken, shifted or discontinuous, it determines that the tool has wear, damage or attitude deviation. The system compares the detection results with the standard template of the tool, and automatically enables the backup tool when the preset threshold is exceeded, realizing intelligent early warning and replacement before processing.

[0055] In this embodiment, the visual monitoring assembly uses a multi-camera time sequence fusion algorithm to perform time sequence feature extraction and three-dimensional reconstruction using image data synchronously collected by multiple cameras. The system first realizes frame-level alignment of images from each camera through a time synchronization module, and then extracts key edge points and contour feature points of the tool using a feature matching algorithm. Through multi-frame data fusion, the system can obtain the three-dimensional attitude parameters of the tool, including spatial position and angle information.

[0056] During tool mounting or tool alignment, the control system compares the actual attitude with the standard attitude template in the database, and generates correction instructions automatically when it detects a clamping angle deviation, driving the robot to adjust the tool clamping attitude, thereby realizing fully automatic tool alignment and angle correction.

[0057] At the same time, the control system performs time difference analysis on the continuous image sequence during tool picking and placing, extracts the motion trajectory of the tool end, and automatically performs path correction and dynamic compensation when it detects nonlinear deviation or micro-jitter in the trajectory, to improve the stability and safety of the tool changing action.

[0058] In the multi-camera time sequence fusion algorithm, multiple cameras are used to continuously collect image frames from different angles in a fixed or moving scene, and the attitude, position, motion trajectory and state change of the target (such as the tool) in the three-dimensional space are recovered or estimated through space-time data fusion. The key is to fuse the information of "multi-view" + "multi-time frame", which is more accurate and robust than single-frame and single-camera recognition.

[0059] Specifically, 3-5 cameras are arranged in the tool changing area (e.g. one above, one on the left and one on the right, and one in front) to cover the tool picking and placing, mounting, and clamping points.

[0060] Simultaneously acquire continuous frames after tool picking, tool loading, and clamping, use a time-series fusion algorithm to estimate the tool posture (including angular deviation, radial deviation, and tool holder tilt) and compare it with a standard template;

[0061] When the system detects that the posture deviation exceeds the preset tolerance, it automatically triggers the robot arm to make fine adjustments or re-clamp, thereby achieving "automatic tool setting and clamping angle correction".

[0062] By using a structured light module in conjunction with a camera, the wear and breakage of the cutting edge of a tool can be detected and fused into the temporal state for judgment.

[0063] The visual recognition results are combined with the RFID recognition results (tool identity + tool posture + motion trajectory) to determine whether to continue processing or call up a spare tool.

[0064] In this embodiment, the structured light module detects minute deformations in the cutting edge region of the tool using a stripe scanning method, and determines tool wear, chipping, or nicks based on the amount of light stripe deformation. The control system analyzes the detected light stripe morphology and calculates the wear depth and positional deviation. When the wear level exceeds a preset threshold, the system automatically triggers a tool replacement command and records the wear data.

[0065] This detection method enables non-contact detection of the tool's microstructure and allows for rapid detection before, after, and during machining intervals, enabling dynamic monitoring and precision control of the tool's condition.

[0066] In this embodiment, to ensure the reliability of the system's detection in complex industrial environments, the visual monitoring component of this invention features an adaptive imaging control mechanism. During the detection process, the system automatically adjusts exposure parameters and light intensity based on the tool surface reflectivity, coolant atomization level, and ambient light intensity. The adjustable illumination system works in conjunction with a polarizing filter to maintain stable edge sharpness and contrast under various conditions.

[0067] The control system incorporates an oil mist filtering algorithm that analyzes the image's grayscale histogram distribution in real time and performs frequency domain enhancement processing to eliminate high-frequency noise and fogging scattering. Even in environments with strong light, high temperature, and high reflectivity, the system can obtain clear and stable tool images, thereby ensuring the accuracy of subsequent identification and positioning.

[0068] In this embodiment, the control system uses a multi-source data fusion model to jointly calculate the RFID identification results and visual recognition features to comprehensively determine the tool's identity, position, and attitude. When the two identification information match, the system confirms the tool change process is legitimate; if there is an inconsistency or identification anomaly, the system immediately triggers a safety rejection logic, suspends the tool change action, and issues an alarm signal.

[0069] This fusion mechanism enables tool identification to have dual verification capabilities, avoiding clamping errors or workpiece scrapping caused by a single identification error, and improving the reliability of the entire tool changing process.

[0070] In this embodiment, the control system of the present invention is equipped with an adaptive matching module, which can automatically calculate the tool length deviation based on visual inspection data and RFID reading parameters. The system obtains the actual tool length through visual inspection and compares it with the standard length data stored in the RFID to calculate the tool length difference. Subsequently, the system automatically writes the compensation value into the tool length register of the CNC machine tool control unit in real time, thereby realizing automatic correction of the machining coordinates.

[0071] Through this closed-loop compensation mechanism, the machine tool can automatically complete tool setting and length correction without manual measurement, ensuring the long-term stability of machining accuracy.

[0072] During long-term operation, the control system continuously records image samples, recognition results, and compensation values ​​for each tool change, establishing a tool recognition feature database. The system utilizes a self-learning algorithm to extract features and perform cluster analysis on the samples, automatically updating the tool template parameters.

[0073] When a new tool model is introduced into use, the system can quickly generate a matching template from a small number of samples, automatically adjust the recognition parameters and thresholds, and achieve cross-model adaptive detection. This mechanism reduces manual intervention and significantly improves the system's scalability and adaptability.

[0074] In this embodiment, to prevent monitoring signal loss or environmental interference, the present invention includes an image anomaly detection module in the visual monitoring component. This module analyzes the image brightness distribution and contrast gradient in real time, and automatically determines the abnormal state when image distortion or occlusion is detected, and activates a backup camera or backup visual channel for redundant identification.

[0075] With the redundant design of multiple visual channels, even if a single camera becomes contaminated, out of focus, or obstructed, it will not affect the overall recognition function of the system, thereby ensuring the continuity and safety of the tool changing process.

[0076] In this embodiment, the control system and the vision monitoring components jointly establish a digital twin tool changing model. The model includes the geometric parameters and kinematic models of the turret 100, the boring mechanism 200, and the robotic arm. Before the tool changing action, the system simulates the tool movement trajectory based on real-time attitude data, predicts the clamping path and attitude changes, and identifies potential interference areas in advance.

[0077] If simulation results indicate a collision risk, the system will automatically optimize the robot's trajectory and tool change speed curve to perform avoidance corrections. This predictive control method effectively avoids mechanical collisions caused by path errors or differences in tool shape, ensuring the safe operation of the machine tool.

[0078] The working process of this invention includes the following steps:

[0079] The control system loads the machining task database and generates a tool call table; the RFID reader identifies the current tool and uploads tool information; the vision monitoring component detects the tool's appearance, posture, and wear status; the control system performs a fusion judgment of RFID and vision data; if the judgment results are consistent, the robotic arm performs the tool pick-up and drop-off operation; the system tracks the tool movement in real time and performs error compensation during the tool change process; after the tool change is completed, the control system automatically corrects the tool length compensation value based on the detection data; when wear or damage is detected, a spare tool is automatically activated; before the tool change, the digital twin module performs simulation verification and optimizes the trajectory; after the tool change is completed, the system updates the tool life data and records it to the database.

[0080] Through the above process, the present invention realizes intelligent control of the entire process from tool identification, status detection, attitude correction to error compensation, wear prediction and safety simulation.

[0081] In this embodiment, by combining RFID and visual monitoring technologies, the present invention achieves automatic identification and status verification of the tool; by using multi-camera fusion and structured light scanning, it achieves three-dimensional detection of tool posture, wear and breakage; by using time-series differential algorithms and dynamic compensation logic, it achieves self-correction of clamping deviation; and by using digital twin simulation, it achieves predictive avoidance control before tool change.

[0082] This system enables horizontal five-axis milling and turning centers to have self-identification, self-calibration, self-learning, and self-error prevention functions. It can achieve high-precision and high-reliability automatic tool changing operations in complex machining environments, significantly improving the intelligence level of the equipment and production efficiency.

[0083] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0084] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A horizontal five-axis milling and turning center with an adaptive matching system, comprising a turret (100), a boring mechanism (200), a milling center (300), a control system, and a main spindle (400) and a sub-spindle (500) arranged in the milling center (300), characterized in that, The surfaces of the turret (100) and the boring mechanism (200) are provided with tool changing areas, and each tool changing area is provided with a radio frequency identification reading device. Each tool has an RFID chip tag attached to its outer surface to store the tool number, specifications, lifespan and length compensation parameters. The control system identifies tool information through the radio frequency identification reading device and compares it with the machining task database to achieve adaptive matching and sequence correction between tools and processes. The tool changing area of ​​the turret (100) and boring mechanism (200) is equipped with a vision monitoring component to monitor the tool changing status on the surfaces of the spindle (400) and the sub-spindle (500). The vision monitoring component includes an industrial camera and an adjustable light source, which is used to visually detect and identify the clamping posture, wear status and type of the tool. The control system, based on the fusion of RFID identification results and visual inspection data, achieves dual verification and automatic selection of tool type, position, and status. The visual monitoring component employs a multi-camera temporal fusion algorithm to reconstruct the tool's three-dimensional posture from multiple frames of images, enabling automatic tool setting and clamping angle correction. The visual monitoring component is equipped with a structured light stripe illumination module to detect tool wear, edge damage, or posture deviation; the control system automatically switches to a backup tool based on the detection results. The visual monitoring component features an adjustable lighting system, including a coaxial ring light source and lateral strip light sources, to automatically adjust brightness and color temperature under different reflective materials, thereby optimizing the accuracy of tool image edge recognition. The visual monitoring component is equipped with an oil mist filtering algorithm and a polarizing filter to automatically optimize imaging quality in high oil mist, reflective, or high-temperature environments. The control system includes an adaptive matching module, which automatically calculates the tool length compensation value based on the tool RFID data and visual inspection parameters, and writes the compensation value into the CNC machine tool control unit in real time, thereby realizing tool identification, posture correction, wear detection, and adaptive matching control. The visual monitoring component and the RFID reading device share the same data bus. The control system compares the visual recognition results with the RFID information through a multi-source data fusion model to achieve a comprehensive determination of tool identity, position, and posture. The visual monitoring component and the control system jointly construct a digital twin tool changing model, which simulates the tool's motion trajectory and posture changes before tool changing, thereby predicting possible interference risks in advance and making avoidance corrections. When the robot performs tool picking or placing operations, the reading device can identify the tag information in real time and upload it to the control system. The control system compares the tool call information of the corresponding process in the machining task database and automatically determines whether the tool matches the process requirements, thereby achieving adaptive matching between the tool and the process.

2. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The visual monitoring component includes multiple sets of industrial cameras distributed above, to the side and in front of the tool changing area. The industrial cameras collaboratively acquire multi-angle images of the tool. The control system generates three-dimensional posture information of the tool based on a time-series image fusion algorithm, which is used to realize the spatial positioning and clamping angle correction of the tool.

3. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The visual monitoring component adopts a multi-camera perception architecture, including multiple sets of industrial cameras distributed in different positions in the tool changing area, which can simultaneously acquire multi-angle images of the tool; the control system uses time series feature extraction and multi-frame fusion algorithms to identify, predict and correct the motion trajectory, clamping dynamics and path deviation of the tool during the picking and placing process.

4. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The visual monitoring component is equipped with a structured light projection module, which is used to form light patterns on the surface of the cutting edge of the tool. The control system judges the degree of tool wear, notches or edge damage by the amount of light pattern deformation.

5. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The control system uses continuous images acquired by the visual monitoring component and a time-difference algorithm to detect the micro-displacement of the tool, thereby achieving dynamic compensation for tool handling errors and detection of mis-assembly.

6. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The control system has a self-learning function, which can automatically update the tool feature template based on historical image samples and adaptively adjust the detection parameters according to different tool models.

7. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The visual monitoring component is equipped with an image anomaly detection module, which is used to automatically determine the image distortion state when the monitoring signal is interfered with or blocked, and trigger the backup visual channel for redundant identification.

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