A method for online detection of carbon layer thickness in cast wheels and an adaptive online grinding system

Abstract

This invention relates to the field of metal continuous casting equipment testing technology, and particularly to an online detection method for carbon layer thickness of casting wheels and an adaptive online grinding system. The method includes: using partitioned detection units corresponding one-to-one with each region of the casting wheel cavity to collect original detection data of the carbon layer in each region of the casting wheel cavity; collecting the rotational position data of the casting wheel spindle; hard-synchronizing and binding the original carbon layer detection data with the rotational position data to obtain carbon layer detection data with spatial coordinates at each point in the casting wheel cavity; and fusing the carbon layer detection data with spatial coordinates to output the effective working layer thickness data of the carbon layer at each point in the casting wheel cavity. This invention effectively solves the problems of existing technologies being unable to accurately obtain the true effective working layer thickness of the carbon layer with spatial coordinates at each point in the casting wheel cavity, and the difficulty in supporting precise recoating control of the carbon layer in casting wheels. It achieves precise control of the carbon layer thickness, improving production stability and billet forming quality.

Description

Technical Field

[0001] This invention relates to the field of metal continuous casting equipment testing technology, and in particular to an online detection method for carbon layer thickness of casting wheels and an adaptive online grinding system. Background Technology

[0002] As the core crystallization component in the continuous casting production of copper rods, the uniformity of the carbon layer thickness on the cavity surface of the casting wheel directly affects the quality of the cast billet and the stability of production. Precise carbon layer thickness data is a prerequisite for achieving adaptive and precise recoating. Existing carbon layer detection technology cannot provide accurate carbon layer thickness data that is precisely tied to the position of the casting wheel cavity and can distinguish between the effective working layer and the loose floating carbon layer. Due to the lack of effective data that accurately corresponds to the position and thickness, the recoating process cannot differentiate the actual wear state of different areas of the cavity and can only adopt a blind spraying mode with no difference around the circumference. This leads to a series of derivative problems such as insufficient recoating in worn areas, overcoating in low-wear areas, floating carbon accumulation, and material waste, making it difficult to meet the needs of high-precision production control. Summary of the Invention

[0003] This invention provides an online detection method for the carbon layer thickness of cast wheels, which can effectively solve the problems in the background art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for online detection of carbon layer thickness in cast wheels, the method comprising: A partitioned detection unit, which corresponds one-to-one with each region of the casting cavity, is used to collect the original detection data of the carbon layer in each region of the casting cavity. Collect the rotational position data of the casting wheel spindle; The original carbon layer detection data and the corner position data are hard-synchronized and bound to obtain carbon layer detection data with spatial coordinates at each point in the casting wheel cavity; The carbon layer detection data with spatial coordinates is fused and processed to output the effective working layer thickness data of the carbon layer at each point in the casting wheel cavity.

[0005] Furthermore, each region includes the bottom of the casting cavity, the sidewall, and the rounded corner, comprising: A first acquisition device is set up at the bottom of the groove, and low-frequency excitation is used to continuously detect the thickness of the carbon layer in the full circumference of the casting wheel cavity, which serves as the reference calibration channel for the detection system. A second acquisition device is set up corresponding to the side wall position, and a medium frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumferential direction of the casting wheel cavity; A third acquisition device is set up corresponding to the rounded corner position, and high-frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumference of the casting wheel cavity; The first acquisition device, the second acquisition device, and the third acquisition device are arranged in an array, and each is set with a lift-off distance from the target detection position.

[0006] Furthermore, the raw test data includes raw data on the thickness and density of the carbon layer in the cast wheel, including: Set the acquisition parameters of the partitioned detection unit to match the acquisition requirements of thickness data and density data respectively; The original thickness and density data of the carbon layer in the cast wheel are simultaneously collected through the partition detection unit. The collected raw thickness data and raw density data are correlated one-to-one according to the detection points to form complete raw detection data.

[0007] Furthermore, the partition detection unit is a coaxial dual-sensor detection unit, which integrates a laser detection module and an eddy current detection module.

[0008] Furthermore, the fusion process employs an adaptive weighted fusion algorithm, including: Collect current testing parameters, including ambient dust concentration, cavity temperature, and signal-to-noise ratio of the testing signal; Based on the collected operating parameters and preset benchmark thresholds, a matching weight allocation rule is established; According to the weight allocation rule, the fusion weight of the original data of the laser detection module and the eddy current detection module is dynamically allocated to match the detection requirements of complementary dual sensing characteristics; The raw detection data are weighted and fused according to the assigned weights to obtain preliminary detection data; The preliminary detection data is calibrated using a preset calibration coefficient to complete the fusion process.

[0009] Furthermore, the partition detection unit is equipped with an adaptive tracking mechanism, including: Start the casting wheel thermal deformation detection unit to collect the casting wheel's thermal deformation data in real time; The thermal deformation data is analyzed to determine the deformation amount and direction of each region of the casting wheel cavity. The analyzed thermal deformation data is transmitted to the adaptive tracking mechanism; The adaptive tracking mechanism adjusts the detection angle and lift-off distance of the corresponding zone detection unit in real time according to the deformation and direction of each region. Real-time feedback of adjusted detection posture data maintains stable detection posture of the partitioned detection unit.

[0010] Furthermore, the hard synchronization binding is implemented through the FPGA main control unit, which receives and synchronously distributes the acquisition trigger signals of detection data and spatial position data.

[0011] Furthermore, based on the effective working layer thickness data at each point in the casting wheel cavity, a three-dimensional distribution model of the carbon layer thickness in the entire circumference is constructed. The three-dimensional distribution model identifies areas with insufficient carbon layer thickness and areas with excessive carbon layer thickness, generating basic data for differentiated recoating in different zones.

[0012] Furthermore, the effective working layer thickness data is archived and stored according to the rotation position of the casting wheel spindle to obtain a historical database of carbon layer thickness.

[0013] An adaptive online grinding system employs any of the above-mentioned online detection methods for carbon layer thickness of cast wheels, and applies additional carbon layers to each area of ​​the cast wheel cavity based on the effective working thickness data of the carbon layer at each point of the output cast wheel cavity.

[0014] The technical solution of this invention can achieve the following technical effects: It effectively solves the problem that existing technologies cannot accurately obtain the actual effective working layer thickness of the carbon layer with spatial coordinates at each point in the casting wheel cavity, and it is difficult to support the precise recoating and control of the carbon layer in the casting wheel. It achieves precise control of the carbon layer thickness, and improves production stability and casting billet forming quality.

[0015] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the online detection method for carbon layer thickness in cast wheels. Figure 2 This is a schematic diagram of the cross-section of the casting wheel; Figure 3 This is a flowchart illustrating the fusion process; Figure 4 This is a schematic diagram of the workflow of the adaptive tracking mechanism in the partition detection unit.

[0018] Attached reference numerals: 1. Side wall position; 2. Rounded corner position; 3. Groove bottom position. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: like Figure 1 As shown, this application provides an online detection method for the carbon layer thickness of cast wheels, the method comprising: S1: Using partitioned detection units that correspond one-to-one with each region of the casting wheel cavity, the original detection data of the carbon layer in each region of the casting wheel cavity are collected; Specifically, based on the cross-sectional structure of the casting wheel cavity and the requirements for full circumferential inspection coverage, the cavity is pre-divided into several inspection areas. For each of these areas, independent zone inspection units are deployed, ensuring a one-to-one correspondence between each zone inspection unit and each inspection area of ​​the cavity. This guarantees that the effective inspection range of each zone inspection unit completely covers its corresponding cavity area, with no blind spots. During the inspection process, the casting wheel rotates synchronously with the spindle, and each zone inspection unit continuously performs full circumferential inspection of the carbon layer in its corresponding cavity area, collecting raw inspection data of the carbon layer within that area. This completes the collection of raw inspection data of the carbon layer in all circumferential areas of the casting wheel cavity, providing a basic data source for subsequent data processing.

[0022] S2: Collect the rotational position data of the casting wheel spindle; Specifically, a position acquisition component is rigidly and synchronously connected to the spindle at the transmission end of the casting wheel spindle. This ensures that the rotation state of the position acquisition component is completely consistent with that of the casting wheel spindle, with no relative displacement or transmission deviation. After the inspection operation starts, the position acquisition component and the zoned inspection unit start and stop synchronously. As the casting wheel rotates continuously with the spindle, it continuously acquires real-time angular position data during the rotation of the casting wheel spindle. This fully records the angular position information corresponding to each moment during the full circumference rotation of the casting wheel spindle, thereby establishing a circumferential position reference for the entire circumference of the casting wheel cavity, providing a basic position data source for the spatial position matching of subsequent carbon layer inspection data.

[0023] S3: Hard-synchronize and bind the original carbon layer detection data with the corner position data to obtain carbon layer detection data with spatial coordinates at each point in the casting wheel cavity; Specifically, this step achieves hard synchronization binding through a hardware-level synchronization mechanism, unlike the software-level post-event timestamp matching method. This eliminates timing discrepancies between the two sets of data from the data acquisition source, ensuring the accuracy of position matching. A shared hardware triggering link is pre-built to establish a unified hardware timing benchmark. The synchronization output of this triggering link forms a hardware electrical connection with the acquisition control port of the partition detection unit and the acquisition control port of the position acquisition component, respectively. This ensures that the acquisition actions of the original carbon layer detection data and the corner position data share the same triggering timing system, guaranteeing the timing homogeneity of the two acquisition actions from the root. During the detection process, the same-source hardware triggering link continuously outputs synchronous trigger pulse signals at a preset acquisition frequency. Each set of trigger pulse signals is simultaneously sent to all partition detection units and position acquisition components, driving them to start data acquisition at exactly the same time, ensuring that the start time and sampling frequency of the acquisition of the original carbon layer detection data and the corner position data are completely matched. After each synchronous acquisition is completed, the original carbon layer detection data and the corner position data acquired in this acquisition are latched in the same time sequence through the hardware link, completing the one-to-one correspondence binding of the two sets of data at the hardware level, completely avoiding the timing offset error caused by data transmission and software caching.

[0024] Before starting the inspection operation, a coordinate system for the casting wheel cavity is established, and the coordinate mapping relationship between the casting wheel spindle rotation position and the detection points of the casting wheel cavity is pre-calibrated. The casting wheel cavity coordinate system uses the casting wheel spindle axis as the coordinate axis and the radial direction corresponding to the preset zero point of the absolute encoder as the circumferential angle zero position. Based on the design structural parameters of the casting wheel cavity, standard part calibration data, or pre-stored cavity geometric model, the reference position parameters of the groove bottom, two side wall positions, and two fillet positions on the cavity cross section are determined, and each reference position parameter is associated with the area identifier of the corresponding zone detection unit. For each zone detection unit, the installation angle offset of its detection center relative to the encoder circumferential angle zero position is pre-calibrated. The coordinate mapping relationship includes at least the encoder rotation angle conversion coefficient, encoder zero point correction parameters, rotation direction parameters, zone detection unit area identifier, installation angle offset, and the cross-sectional reference position parameters of the corresponding detection area. After completing hardware-level timing latching and binding, the corner position data bound to the original carbon layer detection data is converted into the circumferential spatial coordinates of the corresponding detection points in the casting wheel cavity. A unique spatial coordinate identifier is matched for each set of original carbon layer detection data, and finally, carbon layer detection data with spatial coordinates for each detection point in the entire circumference of the casting wheel cavity is formed.

[0025] It should be noted that hard synchronization binding of the raw carbon layer detection data with the corner position data means matching a unique spatial coordinate identifier to the laser detection data and eddy current detection data obtained at each synchronous acquisition moment. Hard synchronization binding does not change the detection values ​​of the laser detection data and eddy current detection data themselves, but uses the spatial coordinate identifier as the data index of the corresponding detection data, together with the detection data to form carbon layer detection data with spatial coordinates.

[0026] S4: Perform fusion processing on the carbon layer detection data with spatial coordinates, and output the effective working layer thickness data of the carbon layer at each point in the casting wheel cavity.

[0027] Specifically, a data fusion mechanism is established based on real-time operating conditions. Combining the real-time status of the testing environment, multi-source testing data is integrated and corrected. According to preset fusion rules, carbon layer testing data with spatial coordinates is integrated and processed. Dynamic corrections are made based on environmental disturbances during the testing process to mitigate data deviations caused by environmental interference and coordinate the response characteristics of various testing signals. Through standardized data integration calculations, the collected multi-source testing information is uniformly corrected and integrated, eliminating abnormal data interference. This ensures that the processed data objectively reflects the actual state of the carbon layer in each area of ​​the mold cavity. Finally, the effective working layer thickness data of the carbon layer at each testing point in the casting wheel cavity is stably output, providing reliable data for subsequent operating condition analysis and control.

[0028] The fusion process is a point-by-point fusion process based on spatial coordinate identifiers. For any spatial coordinate identifier, the laser detection data and eddy current detection data bound to that spatial coordinate identifier are extracted and identified as the data to be fused at the same detection point. The spatial coordinate identifier is used to complete the point matching, data indexing and position association of the fusion results of the dual-sensor detection data, and is not used as the detection value participating in the weighted calculation.

[0029] As a preferred embodiment of the above, such as Figure 2 As shown, each area includes the bottom of the casting mold cavity, the sidewalls, and the fillet areas, including: A first acquisition device is set at the bottom of the corresponding tank, and low-frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumference of the casting wheel cavity, which serves as the reference calibration channel for the detection system. A second acquisition device is set at the corresponding side wall position, and medium frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumference of the casting wheel cavity; A third acquisition device is set up at the corresponding rounded corner position, and high-frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumference of the casting wheel cavity; The first acquisition device, the second acquisition device, and the third acquisition device are arranged in an array, and each is set with a lift-off distance from the target detection position.

[0030] Specifically, considering the irregular structural features of the trapezoidal cross-section of the casting wheel cavity, the differences in surface curvature in different areas, the adhesion characteristics of the carbon layer spraying, and the wear patterns during operation, the casting wheel cavity cross-section is divided into five independent detection areas: the bottom of the groove, the two symmetrical sidewalls, and the two rounded corners where the bottom of the groove connects to the sidewalls. Each detection area is matched with a dedicated acquisition device, forming an array-type detection architecture adapted to the cavity cross-section structure.

[0031] A first acquisition device is installed at the bottom of the corresponding cavity groove. The bottom of the groove has a planar structure, and the carbon layer wears evenly in the circumferential direction with small thickness fluctuations. It is the most stable area of ​​the carbon layer in the cavity. Therefore, a low-frequency excitation is configured for the first acquisition device. The low-frequency excitation uses an AC excitation signal in the range of 100kHz-500kHz, preferably 200kHz-300kHz. This frequency band has a large excitation penetration depth, strong signal anti-interference ability and long-term stability, and can continuously and uninterruptedly detect the thickness of the carbon layer at the bottom of the casting wheel cavity in the entire circumferential direction. At the same time, the detection channel corresponding to the first acquisition device is set as the reference calibration channel of the entire detection system. During the detection process, the stable detection data of the entire circumferential direction collected by this channel is used as the reference to perform real-time zero-point calibration and system drift correction on the detection data of other acquisition devices, eliminating the overall detection deviation caused by temperature drift and mechanical vibration, and ensuring the long-term operational stability of the entire detection system.

[0032] Two sets of second acquisition devices are set at the symmetrical sidewalls on both sides of the cavity. The sidewalls are inclined structures with small curvature. The wear degree and thickness fluctuation of the carbon layer are between those at the bottom of the groove and the rounded corner. Therefore, the second acquisition devices are equipped with intermediate frequency excitation. The intermediate frequency excitation uses an AC excitation signal in the range of 500kHz-2MHz, preferably 800kHz-1.2MHz. This frequency band excitation takes into account both the detection penetration depth and the signal response sensitivity of the inclined structure. It can be adapted to the inclined direction of the sidewalls and continuously detect the thickness of the carbon layer on both sides of the circumference of the casting cavity, accurately capturing the circumferential thickness fluctuation of the carbon layer on the sidewalls.

[0033] Two sets of third acquisition devices are set at the two rounded corners where the bottom of the cavity groove connects to the two side walls. The rounded corners are curved surfaces with large curvature, and the carbon layer is prone to uneven thickness during spraying. The wear is most severe and the thickness fluctuation is the greatest during operation. Conventional excitation signals are prone to signal distortion caused by surface reflection. Therefore, high-frequency excitation is configured for the third acquisition devices. The high-frequency excitation uses an AC excitation signal in the range of 2MHz-5MHz, preferably 3MHz-4MHz. This frequency band has high spatial resolution, good fit to the curved surface, and fast signal response speed. It can accurately capture the small thickness changes of the thin carbon layer at the rounded corners and continuously detect the carbon layer thickness at the two rounded corners of the casting wheel cavity around the entire circumference, eliminating the detection blind spot of the irregular curved surface.

[0034] The aforementioned first acquisition device, two sets of second acquisition devices, and two sets of third acquisition devices are evenly distributed in an array along the circumferential direction of the casting cavity cross-section. The detection center axis of each acquisition device is perpendicular to the direction of the tangent normal of the cavity surface at its corresponding detection position, ensuring that the effective detection range of each acquisition device completely covers the corresponding detection area without detection overlap or blind spots. At the same time, based on the surface structure and excitation frequency characteristics of each detection position, an initial lift-off distance matching the target detection position is set for the first, second, and third acquisition devices. The overall lift-off distance is controlled within the range of 0.5mm-2mm, preferably 0.8mm-1.5mm. Among them, the first acquisition device with low-frequency excitation corresponds to a larger lift-off distance within the set range, and the third acquisition device with high-frequency excitation corresponds to a smaller lift-off distance within the set range, ensuring that the detection signal under different excitation frequencies is in the optimal response range, further improving the detection accuracy of the entire area.

[0035] As a preferred embodiment of the above, the original test data includes original data on the thickness and density of the carbon layer of the cast wheel, including: Set the acquisition parameters of the partitioned detection unit to match the acquisition requirements of thickness data and density data respectively; The original thickness and density data of the carbon layer in the cast wheel are collected simultaneously through the partitioned detection unit. The collected raw thickness data and raw density data are correlated one-to-one according to the detection points to form complete raw detection data.

[0036] Specifically, two sets of differentiated acquisition parameters are pre-configured for each zone detection unit to match the acquisition requirements of thickness data and density data, respectively. The parameters for thickness data acquisition have a sampling frequency of 5kHz-60kHz, preferably 10kHz-50kHz; the parameters for density data acquisition have a sampling frequency of 80kHz-220kHz, preferably 100kHz-200kHz. Simultaneously, based on the structural characteristics of each detection area in the cavity, suitable acquisition parameters are matched for the corresponding zone detection unit.

[0037] After the testing operation is started, each zone testing unit synchronously collects the original thickness and density data of the carbon layer of the casting wheel at the same testing point according to the preset acquisition parameters. With the continuous rotation of the casting wheel with the spindle, the two types of data are synchronously collected simultaneously at all testing points along the entire circumference of the cavity. The original thickness and density data collected at the same testing point are associated one-to-one with each point, matched with a unified point acquisition identifier, and integrated to form complete original testing data.

[0038] As a preferred embodiment of the above, the partition detection unit is a coaxial dual-sensor detection unit, which integrates a laser detection module and an eddy current detection module.

[0039] Specifically, each zone detection unit adopts a coaxial integrated dual-sensor architecture, coaxially integrating the laser detection module and the eddy current detection module. This ensures that the detection center axes of the two modules are completely aligned, and the detection focus area is aligned with the same detection point on the cavity surface, achieving synchronous dual-sensor detection at the same spatial location. The laser detection module and the eddy current detection module share the same acquisition trigger timing sequence, allowing them to start acquisition operations simultaneously and acquire carbon layer detection signals at their respective detection points, providing hardware support for the synchronous acquisition of two-dimensional raw data.

[0040] The coaxial integrated structure eliminates the spatial deviation of detection points that exists in discrete sensing units, ensuring that the collected multi-source data accurately corresponds to the same detection position in the cavity without the need for additional point deviation correction. At the same time, it significantly reduces the installation volume of the detection unit, making it suitable for the narrow installation scenarios of casting wheel cavities and the detection needs of irregular cross-sections.

[0041] It should be noted that the first, second, and third acquisition devices in the above embodiments are functionally divided according to their corresponding casting cavity detection areas and installation positions. All three are specific implementations of a zoned detection unit, and do not employ sensors based on a single detection principle. Furthermore, each of the first, second, and third acquisition devices includes a coaxially arranged laser detection module and eddy current detection module. The detection center axes of the laser detection module and the eddy current detection module coincide, and they are aligned with the same detection point in the corresponding detection area to simultaneously acquire multi-source raw detection data for that point. Correspondingly, the laser detection module provides raw thickness data, and the eddy current detection module provides raw density data.

[0042] As a preferred embodiment of the above, such as Figure 3 As shown, the fusion processing employs an adaptive weighted fusion algorithm, including: Collect current testing parameters, including ambient dust concentration, cavity temperature, and signal-to-noise ratio of the testing signal; Based on the collected operating parameters and preset benchmark thresholds, a matching weight allocation rule is established; According to the weighting rules, the fusion weights of the original data from the laser detection module and the eddy current detection module are dynamically allocated to match the detection requirements of complementary dual-sensor characteristics. The raw detection data are weighted and fused according to the assigned weights to obtain preliminary detection data; The preliminary test data is calibrated using preset calibration coefficients to complete the fusion process.

[0043] Specifically, during the testing process, current testing parameters are collected simultaneously, including the dust concentration in the testing environment, the real-time temperature of the casting cavity, and the real-time signal-to-noise ratio of the output signals from the laser testing module and the eddy current testing module, providing a real-time basis for dynamic weight allocation.

[0044] Pre-set the baseline threshold range for operating parameters, with the dust concentration baseline threshold set at 30 mg / m³. 3 -80mg / m 3 Preferably 50 mg / m 3 The cavity temperature reference threshold is set to 80-150℃, preferably 100℃; the signal-to-noise ratio reference threshold is set to 20dB-40dB, preferably 30dB. The real-time acquired operating parameters are compared with the preset reference thresholds, and the corresponding weight allocation rules are matched.

[0045] When matching the weight allocation rules, if the collected operating parameters meet the preset benchmark conditions, i.e., the dust concentration is not higher than the benchmark threshold, the cavity temperature is not higher than the benchmark threshold, and the signal-to-noise ratio is not lower than the benchmark threshold, the fusion weight of the original data of the laser detection module is increased, with the weight ratio set to 60%-80%, preferably 70%, and the corresponding fusion weight of the original data of the eddy current detection module is set to 20%-40%, preferably 30%, giving priority to the high carbon layer layer recognition accuracy of the laser detection module; if at least one of the following occurs, the real-time dust concentration is higher than the dust concentration benchmark threshold, the real-time cavity temperature is higher than the cavity temperature benchmark threshold, or the real-time signal-to-noise ratio of the laser detection module output signal is lower than the signal-to-noise ratio benchmark threshold, it is determined that the current detection condition exceeds the preset benchmark conditions, and the fusion weight of the detection data of the eddy current detection module is increased, while the fusion weight of the detection data of the laser detection module is decreased, with the weight ratio set to 60%-90%, preferably 75%, and the corresponding fusion weight of the original data of the laser detection module is set to 10%-40%, preferably 25%, giving priority to the strong anti-interference capability of the eddy current detection module and achieving adaptive matching of complementary dual sensing characteristics.

[0046] The system calibration coefficients are obtained in advance through calibration tests. The preset calibration coefficients are used to calibrate the preliminary detection data to eliminate the inherent deviation of the system, complete the entire fusion processing flow, and finally output the effective working layer thickness data of the carbon layer at the corresponding detection point.

[0047] As a preferred embodiment, the current detection condition can be divided into normal condition, first-level abnormal condition, second-level abnormal condition, and third-level abnormal condition based on the number of operating parameters exceeding the corresponding benchmark threshold. When all three operating parameters meet the corresponding benchmark conditions, it is determined to be a normal condition, and the fusion weight of the laser detection module detection data is set to 70%, and the fusion weight of the eddy current detection module detection data is set to 30%. When one operating parameter exceeds the corresponding benchmark condition, it is determined to be a first-level abnormal condition, and the fusion weight of the laser detection module detection data is set to 40%, and the fusion weight of the eddy current detection module detection data is set to 60%. When two operating parameters exceed the corresponding benchmark conditions, it is determined to be a second-level abnormal condition, and the fusion weight of the laser detection module detection data is set to 25%, and the fusion weight of the eddy current detection module detection data is set to 75%. When all three operating parameters exceed the corresponding benchmark conditions, it is determined to be a third-level abnormal condition, and the fusion weight of the laser detection module detection data is set to 10%, and the fusion weight of the eddy current detection module detection data is set to 90%.

[0048] The fusion weights corresponding to each operating condition level are pre-determined through calibration tests. These tests use standard samples with known effective working layer thicknesses and acquire the detection errors of the laser detection module and eddy current detection module under different dust concentrations, cavity temperatures, and signal-to-noise ratios. The fusion weights are determined based on the magnitude of the detection errors of the two types of detection data under corresponding operating conditions, forming a pre-defined mapping table between operating condition levels and fusion weights. Detection modules with smaller detection errors correspond to larger fusion weights.

[0049] As a preferred embodiment of the above, such as Figure 4 As shown, the partition detection unit is equipped with an adaptive tracking mechanism, including: Start the casting wheel thermal deformation detection unit to collect the casting wheel's thermal deformation data in real time; The thermal deformation data is analyzed to determine the deformation amount and direction of each region of the casting wheel cavity. The analyzed thermal deformation data is transmitted to the adaptive tracking mechanism; The adaptive tracking mechanism adjusts the detection angle and lift-off distance of the corresponding zone detection unit in real time according to the deformation and direction of each region. Real-time feedback of adjusted detection posture data maintains stable detection posture of the partitioned detection unit.

[0050] Specifically, the collected thermal deformation data is analyzed in real time. Combined with the regional division rules of the casting wheel cavity, the real-time deformation amount and deformation direction of each corresponding detection area of ​​the cavity are obtained, and the spatial position offset of the cavity surface in each area is clarified, providing a precise basis for the adjustment of the detection posture.

[0051] The analyzed regional thermal deformation data is synchronously transmitted to the adaptive tracking mechanism of each zone detection unit through a real-time communication link. The data transmission delay is controlled within 10ms, preferably within 5ms, to match the detection rhythm of the continuous rotation of the casting wheel.

[0052] After receiving thermal deformation data, the adaptive tracking mechanism adjusts the spatial pose of the corresponding zone detection unit in real time through the servo drive component according to the deformation amount and direction of the corresponding cavity area: for radial deformation of the cavity, the feed stroke of the detection unit is adjusted and the detection lift-off distance is corrected; for axial and angular deformation of the cavity, the deflection and pitch angles of the detection unit are adjusted and the direction of the detection center axis is corrected to ensure that the detection center of the detection unit always points perpendicularly to the surface normal direction of the corresponding cavity area.

[0053] After adjustment, the adaptive tracking mechanism provides real-time feedback on the current pose data of the detection unit, forming a closed-loop control of thermal deformation monitoring, pose adjustment and status feedback. This continuously maintains the stability of the detection posture of the zoned detection unit, eliminates the detection deviation caused by the thermal deformation of the casting wheel, and ensures the accuracy and stability of the entire detection process.

[0054] As a preferred embodiment of the above, hard synchronization binding is implemented through the FPGA main control unit, which receives and synchronously distributes the acquisition trigger signals of detection data and spatial position data.

[0055] Specifically, a hardware-level synchronous control architecture is built with the FPGA main control unit as the core. The multiple parallel IO ports of the FPGA main control unit are connected to each partition detection unit and the absolute encoder matched with the spindle via wired signals, thus building a hardware synchronous transmission link with the same trigger source throughout the entire link.

[0056] A high-precision synchronous clock module is pre-built inside the FPGA main control unit to generate a unified system reference clock. Before the detection operation starts, the synchronous acquisition trigger frequency is set through the FPGA main control unit. Based on the system reference clock and the set trigger frequency, a synchronous acquisition trigger signal with a stable period and accurate edges is generated, providing a unique timing reference for the acquisition of raw carbon layer detection data and corner spatial position data, eliminating the acquisition deviation caused by different timing sources from the signal source.

[0057] During the testing process, the FPGA main control unit will distribute each set of synchronous acquisition trigger signals generated to all partition detection units and absolute encoders in a synchronous manner through parallel hardware links. The synchronous deviation of the trigger signal distribution is controlled within 1μs, ensuring the timing consistency of the trigger actions throughout the entire link.

[0058] After receiving the synchronous acquisition trigger signal, the partition detection unit and the absolute encoder start the acquisition operation synchronously at the same time. The partition detection unit acquires the original detection data of the carbon layer in the corresponding cavity area, and the absolute encoder acquires the angular spatial position data of the casting wheel spindle, ensuring that the acquisition start time and sampling frequency of the two types of data are completely matched and there is no acquisition timing misalignment.

[0059] Each time a synchronous acquisition action is completed, the FPGA main control unit synchronously receives the raw detection data and spatial position data acquired corresponding to the trigger signal. Within the same reference clock cycle, it completes the hardware latching and one-to-one association binding of the two sets of data, matches a unique spatial coordinate identifier to the bound dataset, and generates detection data with a unique coordinate identifier. This completely avoids timing offset errors caused by data transmission, software caching, and operating system scheduling at the hardware level.

[0060] After hardware-level binding is completed, the FPGA main control unit will transmit the detection data with unique coordinate identifiers to the subsequent data processing unit in real time according to the preset communication protocol. This provides hardware-level protection for the accurate matching of detection data and cavity points, and avoids the problem of circumferential misalignment of detection points during the continuous rotation of the casting wheel.

[0061] As a preferred embodiment of the above, a three-dimensional distribution model of the carbon layer thickness in the entire circumference is constructed based on the effective working layer thickness data of each point in the casting wheel cavity. The three-dimensional distribution model identifies areas with insufficient carbon layer thickness and areas with excessive carbon layer thickness, and generates basic data for differentiated recoating in different zones.

[0062] Specifically, based on the effective working layer thickness data with spatial coordinates at each point along the entire circumference of the casting wheel cavity, combined with the cross-sectional structural parameters of the casting wheel cavity and the circumferential position parameters corresponding to the spindle rotation angle, a three-dimensional distribution model of the carbon layer thickness along the entire circumference of the casting wheel cavity is constructed. This three-dimensional distribution model fully maps the spatial distribution state of the carbon layer thickness in the entire circumference and cross-sectional area of ​​the cavity, accurately presenting the carbon layer thickness differences in different areas, the continuous circumferential variation trend, and the local thickness anomaly points.

[0063] The effective working layer thickness data at each point within the 3D distribution model is compared with a preset standard thickness range. This automatically identifies areas with insufficient carbon layer thickness (below the lower limit of the range) and areas with excessively thick carbon layer thickness (above the upper limit of the range). Simultaneously, the spatial location, coverage area, and thickness deviation value of these abnormal areas are precisely marked. Based on the identified and marked abnormal thickness areas, and combined with the corresponding cavity location, thickness deviation magnitude, and circumferential distribution range, differentiated recoating baseline data for each area is generated. This data includes the spatial coordinates of the recoating area, recoating feed rate, recoating path parameters, and spraying time control parameters, providing precise data support for subsequent adaptive adjustment of the carbon layer thickness.

[0064] It should be noted that the three-dimensional distribution model is a spatial distribution model of carbon layer thickness formed by mapping the spatial coordinates of each detection point in the casting wheel cavity to the corresponding effective working layer thickness of the carbon layer, using a pre-stored reference geometric model of the casting wheel cavity as a carrier. The spatial coordinates include at least the cavity area identifier and the circumferential angle coordinates corresponding to the casting wheel spindle rotation angle, and may also include cavity cross-sectional position parameters, axial position coordinates, or three-dimensional rectangular coordinates.

[0065] As a preferred embodiment of the above, the effective working layer thickness data is archived and stored according to the rotation position of the casting wheel spindle to obtain a historical database of carbon layer thickness.

[0066] Specifically, the effective working layer thickness data of each point in the casting wheel cavity, with spatial coordinates, output from the detection process, is archived and organized in intervals according to the associated casting wheel spindle rotation angle. Using the spindle's 0°-360° full circumferential rotation angle as a baseline, continuous circumferential archiving intervals are divided according to a preset rotation angle resolution of 0.01°-0.1°. The effective working layer thickness data within the same rotation angle interval are then grouped under the corresponding archiving entry, ensuring precise correspondence between the data and the circumferential position of the cavity.

[0067] The thickness data archived at the corner positions, along with the corresponding inspection time and equipment operating parameters, will be classified and stored in a time-series manner according to production batch and casting wheel running time. Archived thickness data under different inspection cycles and operating conditions will be continuously collected to form a complete historical database of carbon layer thickness, providing full-cycle data support for subsequent carbon layer wear pattern analysis and process optimization.

[0068] Example 2: The present invention also provides an adaptive online grinding system, comprising: applying a carbon layer to each area of ​​the casting wheel cavity based on the effective working thickness data of the carbon layer at each point of the output casting wheel cavity.

[0069] Specifically, this adaptive online recoating system uses the effective working layer thickness data of the carbon layer at each point in the casting wheel cavity as the core control basis, and the absolute encoder matched with the casting wheel spindle as the spatial positioning core. It forms a complete closed-loop control link with the zone detection unit, FPGA main control unit, and recoating execution mechanism to achieve precise zoned recoating operation in the entire cavity area. The specific implementation process is as follows: The absolute encoder is pre-fixed coaxially to the transmission end of the casting wheel spindle to ensure that the encoder and the casting wheel spindle are rigidly connected and have no relative displacement. It can rotate synchronously with the casting wheel to provide a completely synchronized rotation reference for spatial position acquisition.

[0070] Before starting the equipment, the absolute encoder is calibrated, the zero point position of the encoder is set, and the actual structural parameters of the cast wheel cavity are matched. The conversion coefficient between the circumferential angle coordinates and the axial position coordinates is preset to complete the initial calibration of the encoder and ensure the accuracy of the coordinate conversion.

[0071] During the inspection and touch-up coating process, the absolute encoder is activated to continuously collect the rotation angle data of the casting wheel spindle in real time. According to the preset conversion factor, the real-time collected rotation angle data is converted into the circumferential angle coordinates of the corresponding inspection points in the casting wheel cavity. Simultaneously, the axial position data corresponding to each point in the casting wheel cavity is collected. The circumferential angle coordinates and axial position data of the same inspection point are associated one by one to form complete spatial position data, providing accurate point coordinate basis for the touch-up coating operation.

[0072] After the system obtains the effective working layer thickness data of the carbon layer with corresponding spatial coordinates through the FPGA main control unit, it compares the effective working layer thickness data of each point in the cavity with the preset standard thickness range to accurately identify the areas that are insufficient in thickness and need to be recoated. At the same time, it generates the corresponding recoating control parameters for the area to be recoated by combining the spatial position and thickness deviation of the area to be recoated, including the coordinate range of the recoating area, the spraying feed rate, the spraying time, and the spraying path planning parameters.

[0073] Based on the generated coating control parameters and the real-time feedback of the casting wheel spindle circumferential position data from the absolute encoder, the coating actuator precisely matches the spatial position of the area to be coated in the cavity. During the continuous rotation of the casting wheel, it performs differentiated coating operations on the areas of insufficient thickness in the cavity. After the coating is completed, the coated area is simultaneously re-inspected by the partition detection unit to obtain the effective working layer thickness data of the carbon layer after coating, forming a closed-loop control of detection, regulation and re-inspection to ensure that the carbon layer thickness in the entire circumference of the cavity is stably within the preset standard range.

[0074] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for online detection of carbon layer thickness in cast wheels, characterized in that, The method includes: A partitioned detection unit, which corresponds one-to-one with each region of the casting cavity, is used to collect the original detection data of the carbon layer in each region of the casting cavity. The partition detection unit is a coaxial dual-sensor detection unit, which integrates a laser detection module and an eddy current detection module. Collect the rotational position data of the casting wheel spindle; The original carbon layer detection data and the corner position data are hard-synchronized and bound to obtain carbon layer detection data with spatial coordinates at each point in the casting wheel cavity; The carbon layer detection data with spatial coordinates is fused and processed to output the effective working layer thickness data of the carbon layer at each point in the casting wheel cavity.

2. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The aforementioned regions include the bottom, sidewall, and rounded corner areas of the casting cavity, including: A first acquisition device is set up at the bottom of the groove, and low-frequency excitation is used to continuously detect the thickness of the carbon layer in the full circumference of the casting wheel cavity, which serves as the reference calibration channel for the detection system. A second acquisition device is set up corresponding to the side wall position, and a medium frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumferential direction of the casting wheel cavity; A third acquisition device is set up corresponding to the rounded corner position, and high-frequency excitation is used to continuously detect the thickness of the carbon layer in the entire circumference of the casting wheel cavity; The first acquisition device, the second acquisition device, and the third acquisition device are arranged in an array, and each is set with a lift-off distance from the target detection position.

3. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The original test data includes the original thickness data and density data of the carbon layer in the cast wheel, including: Set the acquisition parameters of the partitioned detection unit to match the acquisition requirements of thickness data and density data respectively; The original thickness and density data of the carbon layer in the cast wheel are simultaneously collected through the partition detection unit. The collected raw thickness data and raw density data are correlated one-to-one according to the detection points to form complete raw detection data.

4. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The fusion process employs an adaptive weighted fusion algorithm, including: Collect current testing parameters, including ambient dust concentration, cavity temperature, and signal-to-noise ratio of the testing signal; Based on the collected operating parameters and preset benchmark thresholds, a matching weight allocation rule is established; According to the weight allocation rule, the fusion weight of the original data of the laser detection module and the eddy current detection module is dynamically allocated to match the detection requirements of complementary dual sensing characteristics; The raw detection data are weighted and fused according to the assigned weights to obtain preliminary detection data; The preliminary detection data is calibrated using a preset calibration coefficient to complete the fusion process.

5. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The partition detection unit is equipped with an adaptive tracking mechanism, including: Start the casting wheel thermal deformation detection unit to collect the casting wheel's thermal deformation data in real time; The thermal deformation data is analyzed to determine the deformation amount and direction of each region of the casting wheel cavity. The analyzed thermal deformation data is transmitted to the adaptive tracking mechanism; The adaptive tracking mechanism adjusts the detection angle and lift-off distance of the corresponding zone detection unit in real time according to the deformation amount and direction of each region; Real-time feedback of adjusted detection posture data maintains stable detection posture of the partitioned detection unit.

6. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The hard synchronization binding is implemented through the FPGA main control unit, which receives and synchronously distributes the acquisition trigger signals of detection data and spatial position data.

7. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, Based on the effective working layer thickness data at each point in the casting wheel cavity, a three-dimensional distribution model of the carbon layer thickness in the entire circumference is constructed. The three-dimensional distribution model identifies areas with insufficient carbon layer thickness and areas with excessive carbon layer thickness, and generates basic data for differentiated recoating in different zones.

8. The online detection method for carbon layer thickness of cast wheels according to claim 1, characterized in that, The effective working layer thickness data is archived and stored according to the rotation position of the casting wheel spindle to obtain a historical database of carbon layer thickness.

9. An adaptive online polishing system, characterized in that, Using any one of the online carbon layer thickness detection methods for cast wheels as described in claims 1-8, carbon layers are applied to each area of ​​the cast wheel cavity based on the effective working thickness data of the carbon layer at each point of the output cast wheel cavity.

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