Horizontal five-axis turning and milling composite center with self-adaptive matching system

Abstract

The invention discloses a horizontal five-axis turning and milling composite center with a self-adaptive matching system. The horizontal five-axis turning and milling composite center comprises a tool turret, a boring tool mechanism, a tool changing area and a control system. A radio frequency identification reading device is arranged in a tool changing area of the tool turret and the boring tool mechanism, and an RFID chip tag is attached to the outer surface of each tool and used for storing tool serial numbers, specifications and length compensation parameters. And the control system realizes self-adaptive matching and sequential error correction of the cutter and the working procedure through radio frequency identification. The tool changing area is provided with a visual monitoring assembly which comprises a plurality of sets of industrial cameras, an adjustable light source and a structured light stripe illumination module and is used for detecting the clamping posture, the abrasion state and the type of a tool. The system has self-learning and digital twinning simulation functions, and can simulate the motion trail of the cutter and predict the interference risk before the cutter is changed. According to the method, integrated control over automatic tool recognition, posture correction, abrasion detection and path prediction is achieved, and the tool changing precision, the operation stability and the automation level of the horizontal five-axis turning and milling composite center are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of five-axis machining center technology, specifically a horizontal five-axis turning and milling composite center with an adaptive matching system. Background Technology

[0002] Horizontal five-axis milling and turning centers are high-end CNC machine tools that integrate multiple machining functions such as turning, milling, boring, and drilling. They are widely used in aerospace, energy equipment, and precision mold manufacturing. These machine tools typically achieve continuous multi-process machining through turrets and automatic tool changers to improve machining efficiency and accuracy. However, with the increasing demand for machining complex parts, the shortcomings of traditional automatic tool changers in tool recognition, attitude detection, and clamping safety control are gradually becoming apparent.

[0003] In existing technologies, turret or boring machine mechanisms mostly rely on a single mechanical code or position sensor for tool identification. For example, common structures use mechanical limit plates or magnetic induction blocks on the turret tool position to confirm the tool position, or rely on manual input via tool code cards. While these methods are simple in structure, they struggle to achieve dynamic tool identification and automatic matching, especially in multi-process mixed machining scenarios, where problems such as tool sequence disorder, inconsistent information, or loss of life data can easily occur. Once a tool identification error occurs, it can lead to tool change failure at best, and at worst, spindle collision and workpiece scrapping.

[0004] Furthermore, most existing vision inspection systems in horizontal milling and turning machines are single-camera 2D inspection systems, capable of detecting only the tool's outline or position coordinates, but unable to accurately identify the tool's 3D posture and clamping angle. When the tool experiences slight misalignment or loosening during robotic loading and unloading, the system struggles to detect and correct it in time, leading to accumulated clamping angle errors that ultimately affect machining accuracy and workpiece surface quality. Meanwhile, traditional wear detection methods still rely primarily on periodic manual inspections or single-point photoelectric detection, lacking real-time performance and adaptive capabilities, and unable to provide online identification and early warning of tool wear, chipping, or edge breakage.

[0005] In complex production environments, factors such as coolant atomization, light reflection, and high-temperature oil mist from machine tools significantly interfere with visual inspection, leading to unstable image recognition results. Although some models have attempted improvements using filters or enhanced illumination modules, it remains difficult to simultaneously meet the requirements of multi-angle imaging and high-contrast recognition, limiting system reliability. Furthermore, existing tool changing systems generally lack trajectory prediction and collision avoidance simulation capabilities for tool movement. Tool changing path planning relies heavily on manual settings or experience values, failing to identify interference risks in advance within complex spaces, increasing the uncertainty of machine tool operation and maintenance costs.

[0006] In view of this, we will study and improve upon the existing problems and provide a horizontal five-axis milling and turning center with an adaptive matching system to solve the current problems. The aim is to solve the problems and improve the practical value through this technology. Summary of the Invention

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

[0008] Therefore, the technical solution adopted in this invention is as follows: a horizontal five-axis milling and turning center with an adaptive matching system, including a turret, a boring mechanism, a tool changing area, and a control system. The system constructs a dual-identification fusion system by setting up RFID reading devices and visual monitoring components in the tool changing area of ​​the turret and boring mechanism; the control system incorporates an adaptive matching module, a data fusion module, and a digital twin simulation module to achieve intelligent control of the entire process of tool handling, identification, clamping, and wear detection, thereby improving tool changing accuracy, safety, and automation. Both the turret and boring mechanism's tool changing area are equipped with RFID reading devices, and each tool surface is affixed with an RFID chip tag for storing tool number, specifications, lifespan, and length compensation parameters. The control system automatically reads tool information through the RFID reading device and compares it with the machining task database to achieve adaptive matching and sequence correction between tools and processes. The system can identify misordered tools when multiple tool magazines are used in combination, and instruct the robot arm to automatically return or replace them based on the comparison results. By linking RFID information to a database, the uniqueness of tool identification and the correctness of tool changing sequence are ensured, effectively preventing incorrect tool installation and improving machining safety and management accuracy.

[0009] In a preferred example, the tool changing area of ​​the turret and boring mechanism is equipped with a vision monitoring component for monitoring the tool changing status of the spindle and sub-spindle surfaces, including an industrial camera and an adjustable light source, for detecting the tool clamping posture, wear condition and type.

[0010] The control system uses the fusion of RFID identification results and visual inspection data to make judgments, thereby achieving dual verification and automatic selection of tool type, position and status.

[0011] Specifically, visual inspection compensates for the blind spots of radio frequency identification, achieving complementary integration of tool identification and status verification, ensuring accurate identification and safe operation during tool changing.

[0012] In a preferred example, the vision monitoring component employs a multi-camera temporal fusion algorithm to reconstruct the tool's three-dimensional posture from multiple frames of images, thereby achieving automatic tool setting and clamping angle correction.

[0013] The control system performs multi-frame fusion and spatial fitting on the synchronous images acquired by each camera, calculates the tool's attitude angle and eccentricity, and automatically instructs the robot arm to perform attitude fine-tuning.

[0014] Specifically, multi-view 3D reconstruction is used to actively correct tool clamping angle and eccentricity error, thereby improving tool setting accuracy and repeatability consistency.

[0015] In a preferred example, the vision monitoring component is equipped with a structured light stripe illumination module for forming light stripe scanning on the tool cutting edge. The control system determines the degree of tool wear, notching, or cutting edge damage by the amount of light stripe deformation, and automatically switches to a spare tool when the wear exceeds the limit.

[0016] Specifically, non-contact optical measurement is used to achieve real-time detection of the cutting edge morphology of tools, preventing worn tools from continuing to be used and ensuring machining quality and equipment safety.

[0017] In a preferred example, the visual monitoring component adopts a multi-camera perception architecture to achieve synchronous acquisition of multi-angle images of the tool through industrial cameras distributed in different directions; the control system identifies and predicts the motion trajectory, clamping dynamics and path deviation during the tool picking and placing process based on time series feature extraction and multi-frame fusion algorithms.

[0018] Specifically, dynamic correction of the tool change path is achieved through time-series perception and motion prediction, thereby improving the smoothness and safety of the tool change action.

[0019] In a preferred example, the visual monitoring component is equipped with an adjustable lighting system, including a coaxial ring light source and side strip light sources, which can automatically adjust the brightness and color temperature according to the reflective characteristics of the cutting tool. The system is also equipped with an oil mist filtering algorithm and a polarizing filter to optimize image quality in environments with high oil mist or strong reflectivity.

[0020] Specifically, it enhances the robustness of visual recognition in complex environments, ensuring image clarity and edge recognition accuracy.

[0021] In a preferred example, the control system uses a time-difference algorithm to detect the micro-displacement of the tool based on continuous images acquired by the vision monitoring component, thereby achieving dynamic compensation for pick-up and placement errors and detection of mis-installation.

[0022] Specifically, it enables real-time monitoring and self-correction of tool change motion deviations, avoiding machining offsets caused by the accumulation of clamping errors.

[0023] In a preferred example, the visual monitoring component and the RFID identification module share a data bus. The control system uses a multi-source data fusion model to jointly compare the visual recognition results with the radio frequency identification information to comprehensively determine the tool's identity, position, and attitude.

[0024] Specifically, a dual-channel joint verification mechanism is established to ensure the security 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 according to changes in tool type and environment, maintaining long-term identification accuracy and system adaptability.

[0027] In a preferred example, the visual monitoring component includes an image anomaly detection module for automatically identifying image distortion when the monitoring signal is interfered with or obstructed, and triggering a backup visual channel for redundant identification.

[0028] Specifically, multi-channel redundancy design prevents single-camera failure and improves the stability of continuous system operation.

[0029] In a preferred example, the vision monitoring component and the control system work together to build a digital twin tool change model to simulate the tool's motion trajectory and attitude changes before the tool change, predict potential interference risks in advance, and perform avoidance corrections.

[0030] Specifically, virtual verification and path optimization are performed before actual operation to prevent mechanical interference and improve tool changing safety and system synergy.

[0031] This invention constructs an intelligent tool changing system with multi-channel perception, data fusion judgment, self-learning optimization and dynamic error prevention capabilities through the collaborative design of "radio frequency identification + multi-vision fusion + adaptive compensation + digital twin prediction".

[0032] This solution not only achieves intelligent control of the entire process, including tool recognition, status monitoring, error compensation, and trajectory prediction, but also significantly improves the automation level, machining accuracy, and operational safety of the horizontal five-axis milling and turning center in complex machining scenarios.

[0033] The beneficial effects achieved by this invention are as follows: 1. In this invention, by setting up radio frequency identification (RFID) devices and visual monitoring components in the tool changing areas of the turret and boring mechanism, a dual-channel identification system is formed, realizing automatic identification, sequence correction, and status verification of tool information. The system can achieve a one-to-one correspondence between tool identity and process under mixed use of multiple tool magazines, significantly reducing manual intervention and substantially improving tool changing accuracy and reliability.

[0034] 2. In this invention, the visual monitoring component employs multi-camera temporal fusion and structured light detection technology to achieve tool 3D posture reconstruction, clamping angle correction, and wear and breakage identification. Through continuous monitoring and trajectory prediction of the tool handling process, dynamic compensation for clamping errors and wear warning are achieved, thereby ensuring the stability, safety, and machining quality of the machine tool tool changing process.

[0035] 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

[0036] Figure 1 This is a schematic diagram of the overall structure of one embodiment of the present invention; Figure 2 This is a side view structural diagram of an embodiment of the present invention; Figure 3 This is a schematic diagram of the control system structure according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the control system flow structure according to an embodiment of the present invention.

[0037] Figure label: 100. Turret; 200. Boring mechanism; 300. Milling machining center; 400. Spindle; 500. Sub-spindle. Detailed Implementation

[0038] 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.

[0039] It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the invention.

[0040] 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.

[0041] 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.

[0042] The turret 100 is located on the side of the spindle 400 and has multiple tool positions for mounting different types of turning, milling, or drilling tools. The turret 100 is equipped with a drive servo device, enabling precise rotation and positioning of the multiple tool positions. The boring mechanism 200 uses a wheel-type arrangement, with multiple boring tools of different specifications evenly arranged on its outer circumference. A servo motor drives the wheel to rotate, enabling high-precision machining of various hole diameters and internal cavities. The tool changing area is located between the turret 100 and the boring mechanism 200. This is the core area for tool transfer and identification. An automatic tool loading and unloading robot is installed in this area for automatic transfer, clamping, and storage of different tools.

[0043] The control system, as the core coordination unit, integrates a CNC main controller, an RFID identification module, a vision monitoring module, an adaptive matching module, a data fusion module, and a digital twin simulation module. It realizes data interaction and coordinated control of each functional unit through an industrial network bus, forming a closed intelligent tool changing control system.

[0044] In the system of this invention, both the turret 100 and the tool changing area of ​​the boring mechanism 200 are equipped with radio frequency identification (RFID) reading devices, and each tool has an RFID chip tag attached to its outer surface. This tag records basic data such as tool number, specifications, lifespan, number of uses, and length compensation parameters.

[0045] When the robotic arm 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, automatically determining whether the tool matches the process requirements, thereby achieving adaptive matching between the tool and the process. When an abnormality in tool type or sequence is detected, the system will automatically trigger the sequence correction function, pausing the tool change operation and prompting manual confirmation or automatically calling a spare tool to ensure the accuracy and safety of the tool change process.

[0046] The linkage between the RFID system and the control system ensures the traceability of tool data throughout its entire life cycle, enabling real-time monitoring and dynamic updates of the tool's call sequence, service life, and status parameters during multi-process continuous machining.

[0047] In this embodiment, the tool changing area is equipped with a visual monitoring component for comprehensive detection of the tool's appearance, posture, and status. This component consists of multiple industrial cameras, an adjustable lighting system, a structured light stripe projection module, a filtering device, and an image processing unit.

[0048] Industrial cameras are positioned above, to the sides, and in front of the tool changing area, forming a surrounding arrangement to simultaneously capture tool images from different angles. The adjustable lighting system consists of a coaxial ring light source and side-mounted strip light sources, automatically adjusting light intensity and color temperature based on tool material, reflectivity, and ambient brightness to achieve optimal imaging. A polarizing filter and an anti-fogging optics are placed in front of the light source to suppress image interference caused by strong reflections from metal surfaces and coolant atomization.

[0049] The structured light stripe projection module projects high-precision light patterns onto the tool surface, and obtains the geometric contour and wear condition of the tool surface by analyzing the deformation morphology of the light patterns. The control system analyzes the light pattern image, and when it detects light pattern breaks, offsets, or discontinuities, it determines that the tool has wear, damage, or posture deviation. The system compares the detection results with the tool standard template, and automatically activates a backup tool when the threshold is exceeded, realizing intelligent early warning and replacement before machining.

[0050] In this embodiment, the visual monitoring component employs a multi-camera temporal fusion algorithm, utilizing image data synchronously acquired by multiple cameras for temporal feature extraction and 3D reconstruction. The system first achieves frame-level alignment of images from each camera through a time synchronization module, and then uses a feature matching algorithm to extract key edge points and contour feature points of the tool. Through multi-frame data fusion, the system can obtain the tool's 3D attitude parameters, including spatial position and angle information.

[0051] During tool loading or tool setting, the control system compares the measured posture with the standard posture template in the database. When a deviation in the clamping angle is detected, a correction command is automatically generated to drive the robot arm to adjust the tool clamping posture, thereby achieving fully automatic tool setting and angle correction.

[0052] Meanwhile, the control system performs time difference analysis on the continuous image sequence during the tool picking and placing process to extract the motion trajectory of the tool end. When nonlinear deviation or micro-jitter is detected in the trajectory, the system automatically performs path correction and dynamic compensation to improve the smoothness and safety of the tool changing action.

[0053] In multi-camera temporal fusion algorithms, multiple cameras continuously acquire image frames from different perspectives within a fixed or moving scene. Spatial-temporal data fusion is then used to recover or estimate the pose, position, trajectory, and state changes of a target (such as a tool) in three-dimensional space. The key lies in fusing information from "multi-viewpoints" and "multi-temporal frames," resulting in greater accuracy and robustness compared to single-frame or single-camera recognition.

[0054] Specifically, 3-5 cameras are set up in the tool changing area (e.g., one above, one on each side, and one in front) to cover the tool picking, clamping, and holding points; 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; 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". 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. 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.

[0055] 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.

[0056] 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.

[0057] In this embodiment, to ensure the reliability of the system's detection in complex industrial environments, the visual monitoring component of this invention has an adaptive imaging control mechanism. During the detection process, the system automatically adjusts the exposure parameters and light intensity based on the reflectivity of the tool surface, the degree of coolant atomization, and the ambient light intensity. The adjustable illumination system works in conjunction with a polarizing filter to maintain stable edge sharpness and contrast under different environments.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] During long-term operation, the control system continuously records image samples, recognition results, and compensation data 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.

[0064] 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.

[0065] 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.

[0066] With the redundant design of multiple visual channels, even if a single camera is 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.

[0067] 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.

[0068] 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.

[0069] The working process of this invention includes the following steps: 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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 spindle (400) and a counterspindle (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 (RFID) 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 RFID reader 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 is based on the fusion of RFID identification results and visual inspection data to achieve dual verification and automatic selection of tool type, position and status; the visual monitoring component adopts a multi-camera temporal fusion algorithm to reconstruct the three-dimensional posture of the tool through multiple frames of images to achieve automatic tool setting and clamping angle correction; the visual monitoring component is equipped with a structured light stripe illumination module to detect tool wear, cutting edge damage or posture deviation, and the control system automatically switches to a spare tool according to the detection results. 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 data into the CNC machine tool control unit in real time, thereby realizing tool identification, posture correction, wear detection and adaptive matching control.

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 sensing 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 tool's motion trajectory, clamping dynamics, and path deviation during the tool picking and placing process, thereby improving the stability and safety of the tool changing action.

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 visual monitoring component is equipped with an adjustable lighting system, including a coaxial ring light source and a side strip light source, which is used to automatically adjust the 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, which is used to automatically optimize the imaging quality and improve the stability of tool edge recognition in high oil mist, reflective or high temperature environments.

6. 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.

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 and the RFID identification module share the same data bus. The control system compares the visual recognition results with the radio frequency identification information through a multi-source data fusion model to achieve a comprehensive determination of the tool's identity, position, and attitude.

8. 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.

9. 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.

10. A horizontal five-axis milling and turning center with an adaptive matching system according to claim 1, characterized in that, The visual monitoring component and the control system jointly construct a digital twin tool changing model, which is used to simulate the tool movement trajectory and attitude changes before tool changing, thereby predicting possible interference risks in advance and making avoidance corrections.

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