Intelligent cutting machine collaborative control method and device for multi-process model

Abstract

The application provides a kind of intelligent cutting machine collaborative control method and device for multi-process model, comprising: establishing and storing digital process model library for different cutting materials and gem types in cloud server;Match and issue corresponding target process model from digital process model library to intelligent terminal controller;Determine motion control instruction;Determine illumination control instruction;Determine control instruction sequence according to motion control instruction and illumination control instruction;Send control instruction sequence to cutting machine via isolation communication link to control cutting machine to execute cutting operation corresponding to target process model.The application can realize quick switching of process, adaptive cooperation of motion and illumination, and ensure stable and reliable operation in strong interference industrial environment, thereby improving the overall intelligent level of precision machining and product quality.

Description

Technical Field

[0001] This application relates to the field of cutting machine control technology, and more specifically, to an intelligent collaborative control method and device for cutting machines oriented towards multiple process models. Background Technology

[0002] In the field of high-precision cutting of hard and brittle materials such as gemstones and metals, CNC cutting machines, laser cutting machines, or wire cutting machines are mainly used. Among them, CNC cutting machines are widely used in gemstone processing and precision parts manufacturing because they can precisely control the tool path and cutting depth. These machines typically include a multi-axis motion system, a spindle drive unit, a cooling system, and a basic lighting system.

[0003] However, existing cutting machine control systems suffer from the following major bottlenecks in practical applications: First, process switching is cumbersome and relies heavily on manual experience. For workpieces of different materials and designs, the optimal cutting parameters (such as path, spindle speed, feed rate, tool type, etc.) are usually stored in the form of operator experience or scattered paper records. When the processing object changes from diamond to sapphire, or from a regular shape to an irregular design, the operator needs to manually find and set a large number of parameters again, which is inefficient and prone to errors.

[0004] Secondly, the control system lacks coordination. Traditional solutions primarily focus on controlling the tool's motion trajectory, treating auxiliary systems such as lighting and cooling as independent units. The lighting system, in particular, typically uses light sources with fixed brightness and angles, failing to adapt to the optical characteristics of the processed material (such as highly reflective metals and transparent gemstones) and the real-time position of the tool. This results in operators or visual monitoring systems struggling to obtain clear images of the work area during critical cutting stages, impacting process monitoring and quality assessment.

[0005] Third, the anti-interference capability is weak. As cutting equipment develops towards miniaturization and high speed, the electrical noise and mechanical vibration generated by its spindle motor and motion mechanism are becoming increasingly significant. When using a centralized or wired direct connection control scheme, the control signal is easily affected by these interferences, which may lead to command delays, unstable communication, or abnormal vibration of the servo motor, directly affecting the processing accuracy and surface quality, and also limiting the flexibility of the control terminal layout.

[0006] Therefore, an innovative control scheme is urgently needed to achieve rapid process switching, adaptive coordination of motion and lighting, and ensure stable and reliable operation in industrial environments with strong interference, thereby improving the overall intelligence level and product quality of precision machining. Summary of the Invention

[0007] The present invention aims to overcome the shortcomings of the prior art and provide a method and device for intelligent cutting machine collaborative control for multiple process models, so as to solve the technical problems of cumbersome cutting process switching, poor multi-system coordination and weak anti-interference ability in the prior art.

[0008] In a first aspect, the present invention provides a collaborative control method for an intelligent cutting machine oriented towards multiple process models, the method comprising: Establish and store a digital process model library for different cutting materials and gem types on a cloud server; In response to the processing request from the intelligent terminal controller for the target workpiece, the corresponding target process model is matched from the digital process model library and sent to the intelligent terminal controller; The received target process model is analyzed to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters; Motion control commands are determined based on the cutting trajectory data and the tool compensation parameters; Real-time acquisition of feedback data from the vision sensor; determination of lighting control commands based on the lighting configuration parameters, the tool compensation parameters, and the feedback data. Determine the control command sequence based on the motion control command and the lighting control command; The control command sequence is sent to the cutting machine via an isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

[0009] Preferably, the step of parsing the received target process model to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters includes: Simultaneously calculate the geometric path information and material optical property information in the target process model; Based on the geometric path information, the cutting trajectory data and the tool posture data associated with the cutting trajectory data are determined; Based on the material optical property information and the tool posture data, the lighting configuration parameters are determined to adapt to different visual characteristics at different cutting stages.

[0010] Preferably, determining the lighting control command based on the lighting configuration parameters, the tool compensation parameters, and the feedback data includes: The real-time spatial position and real-time tool attitude of the tool are determined based on the tool compensation parameters. Determine the reference lighting mode defined by the lighting configuration parameters; Based on the real-time spatial position, the tool posture, and the reference lighting mode, and using the real-time characteristics of the workpiece surface reflected in the feedback data, the reference lighting mode is dynamically compensated to determine the lighting control command that is adapted to the current cutting state.

[0011] Preferably, sending the control command sequence to the cutting machine via an isolated communication link includes: The control command sequence is converted into a differential signal by the first signal isolation chip built into the intelligent terminal controller and output through the communication port; The differential signal is received by the second signal isolation chip on the interface board built into the cutting machine, and the motion control command and the lighting control command are restored to drive the corresponding actuators of the cutting machine.

[0012] Preferably, the method further includes: On the human-machine interface of the intelligent terminal controller, a preview and limited adjustment interface is provided for the parameters determined by the target process model; It receives adjustment commands input by the user through the speed control knob and mode button, and updates the corresponding parameters in the motion control command or the lighting control command in real time according to the adjustment commands.

[0013] Preferably, the smart terminal controller has a built-in energy storage power supply, and the smart terminal controller is connected to the cloud server through a wireless communication module.

[0014] Preferably, the method further includes: During the cutting operation performed by the cutting machine, actual operating data is collected; The actual operating data is compared with the expected parameters in the target process model to determine the comparison results; Based on the comparison results, the corresponding model parameters in the digital process model library are optimized and iterated.

[0015] Secondly, the present invention provides an intelligent cutting machine collaborative control device for multi-process models, comprising: The digital process model library module is configured to create and store digital process model libraries for different cutting materials and gemstone types on a cloud server; The matching module is configured to, in response to a processing request from the smart terminal controller for a target workpiece, match and send the corresponding target process model from the digital process model library to the smart terminal controller. The parsing module is configured to parse the received target process model to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters; The motion control command determination module is configured to determine motion control commands based on the cutting trajectory data and the tool compensation parameters. The lighting control command determination module is configured to acquire feedback data from the vision sensor in real time and determine the lighting control command based on the lighting configuration parameters, the tool compensation parameters, and the feedback data. A control command sequence determination module is configured to determine a control command sequence based on the motion control command and the lighting control command; The control module is configured to send the control command sequence to the cutting machine via an isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

[0016] Thirdly, the present invention provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0017] Fourthly, the present invention provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0018] This invention provides a collaborative control method and device for intelligent cutting machines oriented towards multiple process models. It encapsulates cutting processes for different materials into a cloud-based model library, allowing operators to call up optimal parameters with a single click. This significantly reduces the time and technical barriers to process switching and promotes the standardization and accumulation of process knowledge. By synchronously calculating motion trajectories and lighting strategies from the same process model and introducing visual feedback for dynamic compensation, it ensures that the cutting area receives optimal lighting conditions at any processing time, significantly improving the visual monitoring quality and thermal management effect of the processing. By employing signal isolation chips and wireless communication technology, the intelligent terminal controller is physically isolated from the cutting machine body at the power supply and communication levels, effectively resisting the impact of strong interference in the industrial environment on the control core and ensuring the stability of control signals and the reliability of system operation. The portable controller with built-in energy storage allows operators to move flexibly, while the parameter preview and fine-tuning functions of the human-machine interface retain necessary manual intervention capabilities on the basis of automation, giving the entire system the dual advantages of intelligence, efficiency, and ease of operation.

[0019] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, 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.

[0021] Figure 1 A schematic diagram of a collaborative control method for an intelligent cutting machine oriented towards a multi-process model, provided by an embodiment of the present invention; Figure 2 A schematic diagram of another intelligent cutting machine collaborative control method for multi-process models provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the composition of an intelligent cutting machine collaborative control device for multi-process models, provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0023] In the field of high-precision cutting of hard and brittle materials such as gemstones and metals, CNC cutting machines, laser cutting machines, or wire cutting machines are mainly used. Among them, CNC cutting machines are widely used in gemstone processing and precision parts manufacturing because they can precisely control the tool path and cutting depth. These machines typically include a multi-axis motion system, a spindle drive unit, a cooling system, and a basic lighting system.

[0024] However, existing cutting machine control systems suffer from the following major bottlenecks in practical applications: First, process switching is cumbersome and relies heavily on manual experience. For workpieces of different materials and designs, the optimal cutting parameters (such as path, spindle speed, feed rate, tool type, etc.) are usually stored in the form of operator experience or scattered paper records. When the processing object changes from diamond to sapphire, or from a regular shape to an irregular design, the operator needs to manually find and set a large number of parameters again, which is inefficient and prone to errors.

[0025] Secondly, the control system lacks coordination. Traditional solutions primarily focus on controlling the tool's motion trajectory, treating auxiliary systems such as lighting and cooling as independent units. The lighting system, in particular, typically uses light sources with fixed brightness and angles, failing to adapt to the optical characteristics of the processed material (such as highly reflective metals and transparent gemstones) and the real-time position of the tool. This results in operators or visual monitoring systems struggling to obtain clear images of the work area during critical cutting stages, impacting process monitoring and quality assessment.

[0026] Third, the anti-interference capability is weak. As cutting equipment develops towards miniaturization and high speed, the electrical noise and mechanical vibration generated by its spindle motor and motion mechanism are becoming increasingly significant. When using a centralized or wired direct connection control scheme, the control signal is easily affected by these interferences, which may lead to command delays, unstable communication, or abnormal vibration of the servo motor, directly affecting the processing accuracy and surface quality, and also limiting the flexibility of the control terminal layout.

[0027] Therefore, an innovative control scheme is urgently needed to achieve rapid process switching, adaptive coordination of motion and lighting, and ensure stable and reliable operation in industrial environments with strong interference, thereby improving the overall intelligence level and product quality of precision machining.

[0028] In view of this, the present invention provides a collaborative control method for intelligent cutting machines oriented towards multi-process models. See also Figure 1 The image shows a specific embodiment of a collaborative control method for an intelligent cutting machine oriented towards a multi-process model provided by the present invention. In this embodiment, the collaborative control method for an intelligent cutting machine oriented towards a multi-process model includes:

[0029] Step 101: Establish and store a digital process model library for different cutting materials and gemstone types on a cloud server; Specifically, in this embodiment, the cloud server serves as a process data center, used to build and maintain a structured process knowledge base, namely a digital process model library. This model library contains predefined process models for various workpiece materials (such as ruby, sapphire, titanium alloys, silicon carbide, etc.) and typical geometric features (such as circular, square, and irregular shapes). Each digital process model is a data structure containing multi-dimensional parameters. Its core components include: geometric path information, describing the ideal three-dimensional spatial trajectory coordinate sequence that the tool tip should follow; tool parameters, specifying the recommended tool type, size, and theoretical compensation value; material optical property information, quantitatively describing the material's reflectivity, transmittance, and other characteristics to specific wavelengths of light; lighting configuration parameters, defining the recommended light source brightness, color temperature, and illumination angle range for different stages such as roughing and finishing; and process constraints, such as safe machining areas and coolant start / stop logic. Process engineers can remotely create, verify, and update these models through dedicated client software, achieving standardized management and version control of process knowledge.

[0030] Step 102: In response to the processing request of the intelligent terminal controller for the target workpiece, match and send the corresponding target process model from the digital process model library to the intelligent terminal controller. Furthermore, by establishing a task-driven model invocation mechanism, intelligent matching and rapid deployment of process solutions are achieved. The matching process can employ similarity calculations based on labels and feature vectors to ensure a high degree of match between the selected model and the processing requirements. The operator selects the target workpiece identifier through the human-machine interface of the intelligent terminal controller. The controller then generates a processing request and sends it to the cloud server via the wireless communication network. The matching module on the cloud server searches and matches the model in the model library based on the workpiece identifier, selects the model with the highest fit as the target process model, and sends its complete data packet to the intelligent terminal controller that initiated the request. The controller temporarily stores this data packet in its local memory in preparation for subsequent parsing.

[0031] When the intelligent terminal controller establishes a communication connection with the cutting machine, the system's built-in "automatic matching" mode can automatically identify and match the host device. The operator only needs to turn on the power to the host and controller, and the system can automatically complete the communication handshake and parameter synchronization without the need for manual configuration of communication addresses or protocol parameters, greatly simplifying the equipment pairing process.

[0032] The intelligent terminal controller in this embodiment is a multifunctional hardware device integrating a high-performance processor, memory, wireless communication module, human-machine interface, energy storage power supply, and industrial-grade signal isolation circuit. It serves as an intelligent control hardware platform between the cloud-based process model library and the cutting machine body, internally running a software system including a communication module, a parsing module, and a control module. It possesses the following core features and functions:

[0033] Autonomous computing and parsing capabilities: Built-in dedicated parsing algorithms enable localized processing and instruction generation of process models distributed from the cloud.

[0034] Independent communication and power supply: It interacts with the cloud through a wireless communication module, and the built-in energy storage power supply achieves power isolation from the cutting machine body.

[0035] Human-computer interaction platform: Equipped with a touch screen, physical buttons and knobs, supporting task selection, parameter preview and manual fine-tuning.

[0036] Signal isolation transmitter: Built-in first signal isolation chip to ensure that the control commands sent to the cutting machine have high anti-interference ability.

[0037] The intelligent terminal controller used in this embodiment adopts a one-piece molded body structure with overall dimensions of 156×81×23mm, possessing good structural strength and portability. The front of the intelligent terminal controller is equipped with a 4-inch high-definition color LCD screen, used to display cutting speed, current process mode, tool status, and system prompts in real time, providing operators with intuitive data feedback.

[0038] The intelligent terminal controller's operation panel integrates five finger-operated buttons, featuring clear tactile feedback and quick rebound, supporting functions such as mode switching and parameter adjustment. Additionally, a 360° continuously variable speed knob is located on the side of the controller, offering a smooth, tactile feel with tactile markings, translating user rotation into precise speed adjustment signals, providing intuitive control feedback with every operation.

[0039] The intelligent terminal controller supports two Type-C interfaces, one for program burning and the other for communication control, and comes with a Type-C data cable. The controller supports OTA remote upgrades, allowing it to receive and install system updates via a wireless network, enabling continuous functional expansion and optimization.

[0040] Step 103: Analyze the received target process model to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters; Furthermore, this step uses a parsing module to convert abstract process parameters into specific execution parameters. Geometric path information is processed through kinematic calculations to generate executable trajectory data, and material properties combined with tool posture generate a lighting strategy, achieving an organic integration of machining process parameters. Specifically, the parsing process includes: the parsing module first simultaneously calculates the geometric path information and material optical property information in the target process model. Geometric path information defines the theoretical spatial trajectory of workpiece cutting, while material optical property information describes the material's optical properties such as reflection and transmission of light. Simultaneous calculation means that these two types of information maintain a temporal and spatial correspondence during the calculation process, rather than being processed independently.

[0041] Specifically, this includes trajectory data generation based on geometric path information: the controller, combined with the built-in kinematic model of the cutting machine, performs coordinate transformation on the geometric path information. This process includes: transforming the workpiece coordinate system in the cutting machine kinematic model to the machine tool coordinate system, decomposing the theoretical path into components of each motion axis, and generating a continuous sequence of trajectory points with time labels, i.e., cutting trajectory data, through an interpolation algorithm. This data sequence clearly defines the target position of the tool in three-dimensional space at each moment.

[0042] Determining tool attitude data: While generating cutting trajectory data, the analysis module calculates tool attitude data matching each trajectory point in real time based on the curvature changes of the geometric path, machining process requirements, and tool geometric parameters. This tool attitude data includes the tilt angle and yaw angle of the tool axis relative to the workpiece surface, which directly affect the cutting angle and machining quality.

[0043] Based on the obtained tool posture data, the analysis module combines material optical property information to further determine the lighting configuration parameters. This process achieves optical adaptation of the machining process. Specifically, this includes material optical property analysis: based on the material's optical property information, key optical characteristics such as surface roughness, reflectivity spectrum, and transparency are identified. For example, highly reflective metallic materials are prone to glare, while the optical manifestation of the internal structure of transparent gemstone materials needs to be considered.

[0044] The Relationship Between Tool Posture and Illumination Requirements: Tool posture data determines the spatial orientation of the cutting edge relative to the workpiece surface and the potential location of chips. Under different tool postures, the effective observation area and the requirements for avoiding shadows or reflections change accordingly. This allows for the determination of a phased lighting strategy.

[0045] Phased lighting strategy development: Integrating material optical properties and tool orientation, the analysis module generates differentiated lighting configuration parameters for different cutting stages (e.g., roughing, semi-finishing, finishing). These parameters include: light source type (e.g., specific wavelengths of LED arrays), brightness level, color temperature, illumination angle (e.g., direct, side, backlighting), and their combinations. The goal is to ensure that at each machining moment, the tool-workpiece contact area and chip formation area receive optimal lighting conditions suitable for quality assessment by visual monitoring or machine vision systems. For example, when finishing the internal facets of a transparent gemstone, low-angle side cold light might be used to highlight the facet edges; while when roughing metal, diffused light might be used to reduce surface reflection interference.

[0046] Finally, the analysis module outputs three sets of core parameters: Cutting trajectory data: a time-sequential spatial position sequence used for motion control. Tool compensation parameters: including tool posture data and possible radius compensation, length compensation, etc., used to correct deviations between the actual and theoretical tool geometry. Illumination configuration parameters: a structured lighting scheme that defines the baseline light source control settings for each machining stage.

[0047] Step 104: Determine motion control commands based on cutting trajectory data and tool compensation parameters; Further, this step converts the trajectory data into a sequence of timing instructions executable by the servo system. By applying tool compensation parameters in real time, factors such as tool wear and thermal deformation are compensated for, ensuring the stability of machining accuracy. In this step, the motion control command determination module generates motion control commands recognizable by the underlying drive system based on the cutting trajectory data and tool compensation parameters generated in step 103. These commands employ a position-speed-time pattern or curve acceleration / deceleration planning to precisely control the position, speed, and acceleration of each motion axis of the cutting machine, and achieve real-time adjustment of the tool posture. In some cases, the intelligent terminal controller can have two preset speed adjustment modes built-in for the operator to choose from: the standard mode supports precise speed adjustment in 200 r / min increments, suitable for conventional cutting scenarios; the fine mode supports fine adjustment in 50 r / min increments, suitable for precision machining scenarios with high speed accuracy requirements. The two modes can be quickly switched via panel buttons, with a speed range covering 0-5000 r / min to meet the speed requirements of different materials and different process stages.

[0048] Step 105: Acquire feedback data from the vision sensor in real time, and determine the lighting control command based on the lighting configuration parameters, tool compensation parameters, and feedback data; Furthermore, this step enables dynamic adaptive adjustment of lighting control. Visual sensors (such as industrial cameras) deployed near the machining area continuously acquire images of the workpiece surface, and the image processing unit extracts feedback data, such as quantitative characteristics like local area contrast, brightness uniformity, texture clarity, and the presence of overexposure. The lighting control command determination module receives three inputs: the lighting configuration parameters determined in step 103, the tool compensation parameters used in step 104, and the aforementioned visual feedback data. Its workflow is as follows: First, the real-time spatial position and real-time tool posture of the tool are determined based on the tool compensation parameters. Here, "real-time spatial position" and "real-time tool posture" are not directly taken from the static model data parsed in step 103, but rather variables reflecting the current instantaneous true state of the tool obtained after the model data has been dynamically corrected by the "tool compensation parameters." Real-time spatial position: refers to the current instantaneous three-dimensional coordinates of the tool's key points (such as the tool tip) in the machine tool coordinate system or workpiece coordinate system. It is obtained by superimposing the "cutting trajectory data" (theoretical path points) with the position compensation vector calculated by the "tool compensation parameters," truly reflecting the precise position of the tool in space at this moment. Real-time tool posture: refers to the instantaneous direction vector (usually represented by Euler angles or unit vectors) of the tool axis at its current real-time spatial position. It is obtained by superimposing the "tool posture data" (theoretical direction) with the posture rotation compensation calculated by the "tool compensation parameters," truly reflecting the tool's current cutting angle and direction. The lighting control command needs to determine the projection area of ​​the tool based on its real-time spatial position to adjust the corresponding light source. Furthermore, the lighting control command needs to predict the light and shadow relationship between the cutting edge and the workpiece surface based on the real-time tool posture, thereby determining the angle, brightness, and color temperature of the compensation illumination to eliminate shadows or reflections. Secondly, it calls the reference lighting mode defined by the lighting configuration parameters. Finally, it integrates the real-time tool posture with the reference lighting mode and dynamically compensates the reference lighting mode based on the real-time characteristics of the workpiece surface reflected by the feedback data. For example, if the feedback shows insufficient contrast in the current cutting edge area, the brightness of the corresponding light source in that area is increased accordingly; if excessive specular reflection is detected, the light source angle is fine-tuned or the color temperature is reduced. The final output is the optimized lighting control command, which is sent to the programmable lighting system for execution.

[0049] In some cases, the intelligent terminal controller's operation panel also features a one-button start / stop button to turn the terminal display on or off; a one-button pause button, which immediately pauses the cutting operation when pressed, and the screen displays the character "P" as a status indicator; and a one-button resume button, which resumes the cutting operation when pressed, and the screen displays the character "D" as a status indicator. The character labels are simple and intuitive, allowing operators to quickly identify the current control status.

[0050] Step 106: Determine the control command sequence based on the motion control command and the lighting control command; Furthermore, a unified timing framework can ensure precise synchronization of multiple system actions. A timestamp alignment mechanism guarantees accurate matching between motion and lighting state changes, avoiding coordination problems caused by timing deviations. Specifically, the control command sequence determination module aligns and integrates the motion control commands generated in step 104 and the lighting control commands generated in step 105 on a unified time axis, forming a structured control command sequence. This sequence ensures that the motion axis's movements and the changes in the lighting system are synchronized with millisecond-level precision.

[0051] Step 107: Send the control command sequence to the cutting machine via the isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

[0052] Furthermore, this step is the final stage for the reliable transmission and execution of control commands. The control module is responsible for sending the control command sequence through an isolated communication link. Specifically, the control command sequence is first converted into an anti-interference differential signal by the first signal isolation chip built into the intelligent terminal controller. Secondly, this differential signal is output through the physical communication port and transmitted to the cutting machine side via a shielded twisted-pair cable. On the cutting machine side, the second signal isolation chip on its interface board receives the differential signal, filters out noise coupled during transmission, and restores it to a clean digital control signal. Finally, the restored signal is sent to the servo drive unit and intelligent lighting controller of the cutting machine, thereby driving the machine tool to accurately complete the entire collaborative machining operation. This step employs signal isolation and differential transmission technology to electrically isolate interference sources, ensuring the reliability of control signal transmission in strong electromagnetic environments. This design effectively solves the signal interference problem commonly found in traditional control systems.

[0053] In some cases, this embodiment also includes providing a preview and limited adjustment interface for the parameters determined by the target process model on the human-machine interface of the intelligent terminal controller. The operator can input adjustment commands via a physical speed control knob or mode button, and the system updates the corresponding parameter values ​​in the motion control or lighting control commands in real time accordingly. Furthermore, the intelligent terminal controller has a built-in energy storage power supply and connects to a cloud server via a wireless communication module. This design achieves physical isolation of the controller from the cutting machine and the highly interfering environment of the production line in terms of power supply and data communication, significantly improving the system's portability, deployment flexibility, and anti-interference capability. During the cutting operation, the system has a built-in multi-level safety reminder mechanism. The intelligent terminal controller provides real-time status prompts through a combination of characters and buzzers: "A" indicates equipment startup completion with a buzzer; "U" indicates voltage abnormality; "S" indicates speed exceeding limits; "O" indicates excessive temperature; and "T" indicates communication abnormality. These reminder mechanisms can promptly alert operators when abnormalities occur, ensuring the safe operation of the cutting process. The cutting machine host has a built-in dynamic temperature control air duct indicator function. The system can monitor the host temperature in real time and dynamically display the current temperature control status and fan adjustment status on the controller screen to ensure that the host temperature is always within a reasonable operating range and improve the long-term stability of the equipment.

[0054] As can be seen from the above technical solutions, the beneficial effects of this embodiment are as follows: The cutting processes for different materials are encapsulated into a cloud-based model library, allowing operators to call up optimal parameters with a single click, greatly reducing the time and technical threshold for process switching and promoting the standardized accumulation of process knowledge; By synchronously calculating motion trajectories and lighting strategies from the same process model and introducing visual feedback for dynamic compensation, optimal lighting conditions are ensured for the cutting area at any processing moment, significantly improving the visual monitoring quality and thermal management effect of the processing process; By employing signal isolation chips and wireless communication technology, the intelligent terminal controller is physically isolated from the cutting machine body at the power supply and communication levels, effectively resisting the impact of strong interference in the industrial environment on the control core, ensuring the stability of control signals and the reliability of system operation; The portable controller with built-in energy storage allows operators to move flexibly, and the parameter preview and fine-tuning functions of the human-machine interface retain necessary manual intervention capabilities on the basis of automation, giving the entire system the dual advantages of intelligence, efficiency, and ease of operation.

[0055] Figure 1 The embodiments shown are merely basic examples of the method of the present invention. Other preferred embodiments of the method can be obtained by making certain optimizations and extensions based on them.

[0056] like Figure 2The image shows another specific embodiment of the intelligent cutting machine collaborative control method for multi-process models according to the present invention. This embodiment further describes the method based on the foregoing embodiments, and includes the following steps:

[0057] Step 201: Collect actual operating data during the cutting operation of the cutting machine; Specifically, during the cutting operation executed by the cutting machine according to the control commands issued in step 107, the system collects and records multi-dimensional actual operating data in real time through data acquisition modules deployed at key locations. This actual operating data is a comprehensive dataset, covering motion execution data: including real-time current of the servo motor, actual speed and position feedback, actual tracking error of each motion axis, acceleration vibration spectrum, etc.; process data: such as spindle load changes, acoustic emission signals when the tool contacts the workpiece, cutting fluid flow and pressure, actual time consumption of key machining stages, etc.; status monitoring data: such as workpiece surface image sequences continuously collected by vision sensors (used to analyze chip morphology and machining marks), and local temperature rise data monitored by infrared thermal imagers; environmental auxiliary data: which may include ambient temperature and humidity, actual operating current and brightness feedback of the lighting system, etc. This data is recorded synchronously at a high sampling rate (usually milliseconds or higher) and strictly aligned with the timestamps in the control command sequence, thus forming a multi-source data stream with accurate timing information that can completely reproduce the current operation process.

[0058] Step 202: Compare the actual operating data with the expected parameters in the target process model to determine the comparison results; Furthermore, this step is a crucial step in quantitatively evaluating the processing performance. Expected parameters are derived from the target process model, including: theoretical motion trajectory, desired feed rate and acceleration curves, reasonable spindle load range, ideal surface morphology description, and expected time consumption for each stage. Comparative analysis is conducted across multiple dimensions, such as motion accuracy comparison: calculating the deviation between the actual trajectory and the theoretical trajectory (e.g., contour error, position error) to evaluate tracking performance; process stability assessment: analyzing whether fluctuations in load, vibration, and other data exceed the model's expected stability range; quality-related feature extraction: extracting features such as surface roughness and chipping size from actual images and comparing them with the ideal quality features defined by the model; and performance index calculation: calculating the differences between actual total energy consumption, total processing time, and expected values.

[0059] By using pre-set analysis algorithms (such as statistical process control, feature matching, and error calculation models), the system integrates the comparison results from various dimensions to generate structured comparison results. These results not only include quantitative deviation values ​​(such as "maximum trajectory error 0.02mm" and "finishing stage time exceeds expectations by 15%), but also include classification judgments of possible causes of deviations (such as "tool wear leads to increased load" and "uneven material hardness causes abnormal vibration").

[0060] Step 203: Based on the comparison results, optimize and iterate the corresponding model parameters in the digital process model library.

[0061] Furthermore, based on the quantitative comparison results generated in step 202, an automated optimization mechanism for the digital process model library is initiated. The optimization target is the corresponding process model (or a general model for similar workpieces) that triggered this operation. The optimization iteration process follows preset rules or algorithms. For example, parameter calibration: if the comparison results show systematic deviations (such as the actual feed rate consistently being lower than expected but still meeting quality requirements), the corresponding parameters in the model are automatically adjusted (such as increasing the recommended feed rate value for the material). Strategy optimization: if a certain indicator (such as vibration) is found to continuously exceed the standard in a specific processing stage, the system may attempt to find and test alternative toolpaths or cutting parameter combinations in the model library. After verification through simulation or historical data, the corresponding strategy of the model is updated. Constraint correction: if the correlation between environmental data (such as temperature) and processing quality is confirmed, this factor can be incorporated into the model as a new constraint. Model version management: each optimization generates a new version of the model, retaining historical versions and optimization logs, supporting rollback and traceability. The optimization iteration process in this embodiment can be fully automated or adopt a semi-automatic mode of "system suggestion - manual confirmation". Finally, the optimized process model will be updated to the cloud-based model library. When similar processing tasks are triggered again, the system will prioritize matching or recommending the optimized model, thus enabling the processing technology to continuously improve and evolve with the accumulation of production practice.

[0062] As can be seen from the above technical solution, the beneficial effects of this embodiment are as follows: The data acquisition, comparison, and optimization closed loop constituted by steps 201 to 203 in this embodiment brings significant self-evolution capabilities to the system. By collecting actual operating data in real time and comparing it accurately with the model's expected values, the effect of each processing step can be quantitatively evaluated, and process deviations can be identified. Furthermore, based on the comparison results, the corresponding parameters in the digital process model library are automatically optimized and iterated, allowing process knowledge to continuously improve itself with the accumulation of production data. This fundamentally realizes the transformation of process management from static execution to dynamic optimization, and from experience-based to data-driven, not only improving the accuracy and efficiency of a single processing step, but also enabling the entire system to continuously learn and self-optimize, driving the processing technology to continuously approach the optimal state.

[0063] This invention also provides an intelligent collaborative control device for cutting machines oriented towards multi-process models. See also Figure 3 The image shows a specific embodiment of an intelligent cutting machine collaborative control device for multi-process models provided by the present invention. This embodiment of the device is used to execute... Figures 1-2 The physical apparatus of the method. Its technical solution is essentially the same as the above embodiments, and the corresponding descriptions in the above embodiments also apply to this embodiment. The apparatus includes:

[0064] The digital process model library module 301 is configured to build and store digital process model libraries for different cutting materials and gem types in a cloud server; The matching module 302 is configured to, in response to the processing request of the intelligent terminal controller for the target workpiece, match and send the corresponding target process model from the digital process model library to the intelligent terminal controller. The parsing module 303 is configured to parse the received target process model to determine the cutting trajectory data, tool compensation parameters and lighting configuration parameters. The motion control command determination module 304 is configured to determine motion control commands based on cutting trajectory data and tool compensation parameters. The lighting control command determination module 305 is configured to acquire feedback data from the vision sensor in real time and determine the lighting control command based on the lighting configuration parameters, tool compensation parameters and feedback data. The control command sequence determination module 306 is configured to determine a control command sequence based on motion control commands and lighting control commands; The control module 307 is configured to send a sequence of control commands to the cutting machine via an isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

[0065] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0066] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Buses can be categorized as address buses, data buses, and other types. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0067] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0068] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into main memory and then executes them. Alternatively, it may obtain the corresponding execution instructions from other devices to form a collaborative control device for a multi-process model intelligent cutting machine at the logical level. The processor executes the execution instructions stored in the memory to implement the collaborative control method for a multi-process model intelligent cutting machine provided in any embodiment of the present invention.

[0069] The above is as described in the present invention. Figure 3The method for collaborative control of an intelligent cutting machine oriented towards a multi-process model, as provided in the illustrated embodiment, can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed through integrated logic circuits in the processor's hardware or through software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0070] The steps of the method disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0071] This invention also proposes a readable medium storing execution instructions. When these instructions are executed by a processor of an electronic device, the device can perform a collaborative control method for a multi-process model-oriented intelligent cutting machine provided in any embodiment of this invention, specifically for executing, as... Figure 1 , Figure 2 The method shown.

[0072] The electronic devices in the foregoing embodiments may be computers.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0074] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A collaborative control method for intelligent cutting machines oriented towards multi-process models, characterized in that, The method includes: Establish and store a digital process model library for different cutting materials and gem types on a cloud server; In response to the processing request from the intelligent terminal controller for the target workpiece, the corresponding target process model is matched from the digital process model library and sent to the intelligent terminal controller; The received target process model is analyzed to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters; Motion control commands are determined based on the cutting trajectory data and the tool compensation parameters; Real-time acquisition of feedback data from the vision sensor; determination of lighting control commands based on the lighting configuration parameters, the tool compensation parameters, and the feedback data. Determine the control command sequence based on the motion control command and the lighting control command; The control command sequence is sent to the cutting machine via an isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

2. The method according to claim 1, characterized in that, The step of parsing the received target process model to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters includes: Simultaneously calculate the geometric path information and material optical property information in the target process model; Based on the geometric path information, the cutting trajectory data and the tool posture data associated with the cutting trajectory data are determined; Based on the material optical property information and the tool posture data, the lighting configuration parameters are determined to adapt to different visual characteristics at different cutting stages.

3. The method according to claim 1, characterized in that, The step of determining the lighting control command based on the lighting configuration parameters, the tool compensation parameters, and the feedback data includes: The real-time spatial position and real-time tool attitude of the tool are determined based on the tool compensation parameters. Determine the reference lighting mode defined by the lighting configuration parameters; Based on the real-time spatial position, the tool posture, and the reference lighting mode, and using the real-time characteristics of the workpiece surface reflected in the feedback data, the reference lighting mode is dynamically compensated to determine the lighting control command that is adapted to the current cutting state.

4. The method according to claim 1, characterized in that, The step of sending the control command sequence to the cutting machine via an isolated communication link includes: The control command sequence is converted into a differential signal by the first signal isolation chip built into the intelligent terminal controller and output through the communication port; The differential signal is received by the second signal isolation chip on the interface board built into the cutting machine, and the motion control command and the lighting control command are restored to drive the corresponding actuators of the cutting machine.

5. The method according to claim 1, characterized in that, The method further includes: On the human-machine interface of the intelligent terminal controller, a preview and limited adjustment interface is provided for the parameters determined by the target process model; It receives adjustment commands input by the user through the speed control knob and mode button, and updates the corresponding parameters in the motion control command or the lighting control command in real time according to the adjustment commands.

6. The method according to claim 1, characterized in that, The intelligent terminal controller has a built-in energy storage power supply and is connected to the cloud server via a wireless communication module.

7. The method according to claim 1, characterized in that, The method further includes: During the cutting operation performed by the cutting machine, actual operating data is collected; The actual operating data is compared with the expected parameters in the target process model to determine the comparison results; Based on the comparison results, the corresponding model parameters in the digital process model library are optimized and iterated.

8. A collaborative control device for intelligent cutting machines oriented towards multi-process models, characterized in that, include: The digital process model library module is configured to create and store digital process model libraries for different cutting materials and gemstone types on a cloud server; The matching module is configured to, in response to a processing request from the smart terminal controller for a target workpiece, match and send the corresponding target process model from the digital process model library to the smart terminal controller. The parsing module is configured to parse the received target process model to determine the cutting trajectory data, tool compensation parameters, and lighting configuration parameters; The motion control command determination module is configured to determine motion control commands based on the cutting trajectory data and the tool compensation parameters. The lighting control command determination module is configured to acquire feedback data from the vision sensor in real time and determine the lighting control command based on the lighting configuration parameters, the tool compensation parameters, and the feedback data. The control command sequence determination module is configured to determine a control command sequence based on the motion control command and the lighting control command; The control module is configured to send the control command sequence to the cutting machine via an isolated communication link to control the cutting machine to perform the cutting operation corresponding to the target process model.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1 to 7.

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