Diamond cutting angle intelligent adjusting system based on optical feedback

Abstract

The invention discloses a diamond cutting angle intelligent adjusting system based on optical feedback, and relates to the technical field of optics and automation, the diamond cutting angle intelligent adjusting system comprises a system integration and test module, the system integration and test module is in communication connection with an optical characteristic detection module, an intelligent cutting optimization module and a control system and alarm module; the optical characteristic detection module presets 18 cutting surfaces and collects light data according to the diamond cutting angle adjustment process by using a high-speed CMOS image sensor and a spectrum and polarization analyzer; the intelligent cutting optimization module carries out data visualization and predicts an optimal cutting angle by adopting a convolutional neural network algorithm and a physical model; the control system and the alarm module dynamically adjust the angle of the cutter according to the prediction result and give an abnormal prompt; the system integration and test module integrates all the modules, establishes an MATLAB automatic test framework, simulates a cutting scene, carries out stress and long-time operation tests, and ensures the 18-face cutting effect. The whole system realizes an integrated process from data acquisition and analysis to monitoring and testing.

Description

Technical Field

[0001] The invention relates to the field of optics and automation technology, and in particular to an intelligent adjustment system for diamond cutting angles based on optical feedback. Background Art

[0002] With the advancement of science and technology and the development of intelligent manufacturing, the diamond cutting industry has continuously increased its demand for cutting accuracy, efficiency and personalization. The intelligent adjustment system can monitor various parameters in the cutting process, such as blade temperature, cutting air pressure, height tracking, etc., in real time by integrating sensors, advanced algorithms and automatic control technology, so as to achieve precise adjustment of the cutting angle. The application of this system not only significantly improves the accuracy and consistency of diamond cutting, reduces the errors caused by human operation, but also greatly improves the cutting efficiency and shortens the production cycle. At the same time, the intelligent adjustment system can also perform customized cutting according to customer needs to meet the market's demand for diversified diamond cutting. From the perspective of market trends, intelligent manufacturing is the development direction of the future manufacturing industry, and the diamond cutting industry is no exception. The introduction of the intelligent adjustment system will help the diamond cutting industry achieve digital transformation and intelligent upgrading, and improve its overall competitiveness. In addition, with the continuous integration of technologies such as big data and cloud computing, the intelligent adjustment system will further optimize the cutting process, improve cutting quality and efficiency, and achieve a more efficient and environmentally friendly production model. In summary, the diamond cutting angle intelligent adjustment system has great application potential and broad market prospects in the current and future diamond cutting industry with its advantages of high precision, high efficiency and customization. With the continuous advancement of technology and continuous expansion of the market, this system is expected to become one of the important development trends in the diamond cutting industry.

[0003] However, in response to the existing challenges of the diamond cutting angle intelligent adjustment system in improving the brightness, fire and scintillation of diamonds, an innovative solution is proposed, namely the diamond cutting angle intelligent adjustment system based on optical feedback. This system not only focuses on the 18-face cutting technology, integrates advanced sensing technology and intelligent algorithms, but also introduces a precise optical feedback mechanism, which aims to achieve fine-tuning and optimization of the cutting angle by real-time monitoring the refraction and reflection effects of the diamond cutting surface on light. In particular, based on the 18-face cutting technology, this system can more finely control the cutting angle, so that the diamond shows richer color levels and stronger scintillation effects under light. Through continuous optical feedback and adjustment, the system constantly approaches the optimal cutting state and realizes the ultimate pursuit of diamond aesthetics. In summary, the diamond cutting angle intelligent adjustment system based on optical feedback is expected to break through the existing technical bottleneck, significantly improve the optical performance of diamonds, bring revolutionary changes to the diamond cutting industry, and also bring more dazzling diamond products to consumers. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a diamond cutting angle intelligent adjustment system based on optical feedback, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a diamond cutting angle intelligent adjustment system based on optical feedback, including a system integration and testing module, wherein the system integration and testing module is communicatively connected with an optical property detection module, an intelligent cutting optimization module, and a control system and alarm module; The optical property detection module presets 18 cutting faces for the adjustment process of the diamond cutting angle, and collects time series image data, spectrum data and polarization data of reflected and refracted light through a high-speed CMOS image sensor, a spectrum analyzer and a polarization analyzer; The intelligent cutting optimization module converts the collected data into real-time display graphics, uses a convolutional neural network algorithm, combines the physical model for simulation analysis, and predicts the optimal cutting angle; The control system and alarm module dynamically adjust the cutting tool angle according to the prediction result of the optimal cutting angle in combination with the state feedback mechanism, and warn of abnormalities when processing the cutting surface through visual prompts; The system integration and testing module is used to integrate various modules, establish an automated testing framework based on MATLAB, simulate cutting scenarios, and perform stress tests and long-term running tests on the cutting effects of 18 cutting surfaces of diamond cutting.

[0006] Furthermore, in the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the time series image data of the reflected and refracted light through the high-speed CMOS image sensor includes: High-speed CMOS image sensors are deployed around the diamond to capture light reflected from the surface and refracted from the inside of the diamond at a high frame rate, forming time-series image data containing light intensity information and reflecting the light path and polarization state; The collected original image is denoised and smoothed, grayed and normalized, the edge detection algorithm is used to identify the cutting surface boundary, and the frequency domain characteristics of the light intensity distribution are analyzed using Fourier transform. The texture features are automatically extracted using a convolutional neural network algorithm and the extracted texture features are passed to the intelligent cutting optimization module.

[0007] Furthermore, in the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the spectrum data of the reflected and refracted light through the spectrum analyzer includes: A spectrometer is deployed around the diamond. The spectrometer uses a grating to decompose light into components of different wavelengths and records the light intensity distribution at each wavelength through a highly sensitive detector to form spectral data. The collected raw spectral data are baseline corrected and smoothed to eliminate noise and background interference. Normalization technology is applied to make the data acquired under different conditions have a consistent scale. The key wavelengths and their corresponding light intensity characteristics are identified through peak detection and integral area calculation. The principal component analysis method is used to further explore the implicit patterns and feature associations in the spectral data, and the extracted spectral features are passed to the intelligent cutting optimization module.

[0008] Furthermore, in the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the polarization data of the reflected and refracted light through the polarization analyzer includes: Polarization analyzers are deployed around diamonds. Polarization analyzers use polarization beam splitters to decompose incident light into orthogonal linear polarization components, and record the intensity distribution of each component through a high-sensitivity detector. Mueller matrix is ​​used to describe circular polarization and elliptical polarization. The collected raw polarization data is calibrated and smoothed to eliminate environmental noise and measurement errors. Normalization technology is applied to make the data acquired under different conditions have a consistent scale. The changes in polarization states are quantified by calculating Mueller matrix elements. The principal component analysis method is used to further explore the implicit patterns and feature associations in the polarization data, and the extracted polarization features are passed to the intelligent cutting optimization module.

[0009] Furthermore, in the intelligent cutting optimization module, the process of converting the preprocessed data into a real-time display graphic includes: The collected raw data are synchronized in time and aligned in space so that all features are represented in the same coordinate system, and the extracted texture features, spectral features, and polarization features are mapped into a three-dimensional visualization space; OpenGL graphics rendering technology is used to convert the feature mapping results into graphical representations, which include the boundaries of the cutting surface, light intensity distribution diagrams, spectrum curves and polarization state diagrams. Through memory management and streaming data processing technology, the graphical interface is updated in real time at a high frame rate to reflect the latest cutting status.

[0010] Furthermore, in the intelligent cutting optimization module, a convolutional neural network algorithm is used to perform simulation analysis in combination with a physical model, and the process of predicting the optimal cutting angle includes: Constructing a convolutional neural network model, wherein the convolutional neural network model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; The input layer receives preprocessed data from the optical property detection module, wherein the preprocessed data includes texture features, spectral features, and polarization features, and converts the preprocessed data into a width×height×channel number format processed by CNN, wherein each channel represents a different type of feature; The convolution layer performs convolution operations on the input data through its internal convolution kernels to extract local features. Each convolution kernel is responsible for capturing different patterns and sliding in space to generate feature maps. ReLU is used as the activation function to introduce nonlinearity and make the same convolution kernel share weights at different locations in the image. The pooling layer reduces the spatial size of the feature map through the maximum pooling operation, and extracts low-level edges and textures to high-level shape and structural features layer by layer by combining with multiple layers of convolution; The fully connected layer flattens the feature map output by the last convolutional layer into a one-dimensional vector, integrates all features, and forms a global representation, which is ultimately used to identify different types of cutting surfaces and predict the best cutting angle; The output layer uses the softmax function to output the probability distribution and perform direct regression to obtain the specific angle value, giving the best cutting angle prediction result; Using the principles of optics and mechanics, a physical model is constructed to simulate the propagation path, reflection and refraction behavior of light inside a diamond. The constraints of the physical model are introduced into the training process of the CNN model, including the refractive index and the law of reflection. The features extracted by CNN are integrated with the simulation results of the physical model, and the optical properties and physical constraints are comprehensively analyzed. By comparing the actual cutting effect with the simulation results, the model parameters are adjusted. Combined with the feedback provided by the control system and the alarm module, the input data of the CNN model is dynamically adjusted so that each prediction reflects the latest cutting status. Through incremental learning technology, the model is self-optimized according to the new data to adapt to different diamond materials and cutting environments.

[0011] Furthermore, in the control system and the alarm module, the process of dynamically adjusting the cutting tool angle according to the prediction result includes: The control system and alarm module receives the real-time predicted probability distribution output and optimal cutting angle from the intelligent cutting optimization module through the CAN bus, uses high-precision servo motors and precision drivers to perform response and position control, adjusts the angle of the cutting tool according to the predicted results, and monitors the position and motion state of the tool in real time through encoders and position sensors to form a closed-loop control system, records the cutting parameters corresponding to each cutting surface, and marks a consistent timestamp. The cutting parameters include cutting angle, speed and pressure.

[0012] Furthermore, in the control system and the alarm module, the process of processing an abnormality when processing the cutting surface through visual prompts and warnings includes: The control system and alarm module continuously monitors the cutting parameters during the cutting process, and automatically identifies potential problems by combining machine learning models and physical model simulation results. Once an abnormality is found, the LED indicator light is immediately triggered. The abnormality includes the deviation of the cutting angle from the expected value and the change of light intensity exceeding the threshold. Different levels of alarms are set according to the severity of the abnormality, where yellow indicates a warning and red indicates an emergency stop, so that the operator can take timely measures and save the historical records of all alarm events.

[0013] Furthermore, in the system integration and testing module, various modules are integrated to establish an automated testing framework based on MATLAB. The process of simulating the cutting scene includes: The system integration and testing module integrates the optical property detection module, intelligent cutting optimization module, and control system and alarm module. It uses MATLAB as the development platform to centrally manage and control the entire system. It generates reflected light intensity, wavelength distribution, and polarization state signals based on MATLAB, establishes a virtual cutting environment, and simulates the real diamond cutting process. Adjust the signal to test the system response under different conditions, allowing users to customize the input of diamond shape parameters and material property parameters, automatically set the initial conditions through scripts, and use MATLAB's real-time toolbox to achieve real-time communication with physical hardware, so that the simulation results are reflected on the actual equipment, simulate the standard cutting path and cutting deviation, material unevenness, record and analyze the cutting angle accuracy, time efficiency and optical effect generated during the cutting process.

[0014] Furthermore, in the system integration and testing module, the process of performing stress testing and long-term operation testing on the cutting effect of 18 surfaces includes: Stress testing is performed by simulating extreme conditions to evaluate the stability and reliability of the system under extreme conditions. The stress test uses a virtual cutting environment built with MATLAB to change the intensity, angle, and frequency of the light source, introduce vibration and thermal stress, and test the response of the system in a complex environment. Based on the actual collected time series image data, spectral data, and polarization data, stress scenarios are generated. Combined with physical models and convolutional neural network algorithms, the effects of stress on cutting accuracy, tool wear, and optical performance are analyzed to identify potential risk points. The long-term running test is carried out by running the system continuously for a long time to verify the stability and durability of the system in long-term work. The long-term running test is carried out by writing MATLAB scripts, periodically adjusting the cutting parameters, automatically setting the initial conditions and starting the long-term running test to simulate the changes in the real working environment. The real-time toolbox of MATLAB is used to continuously monitor the system resource utilization and cutting angle accuracy, time efficiency and optical effect, and all data are time-stamped and saved; During the test, the control system works with the alarm module to automatically detect abnormal situations and ensure that the system resumes normal operation through preset pause and recalibration operations. The data analysis and drawing functions of MATLAB are used to display the results of stress tests and long-term running tests. Based on the test feedback, the algorithm parameters and hardware configuration are adjusted.

[0015] The present invention provides a diamond cutting angle intelligent adjustment system based on optical feedback, which has the following beneficial effects: First, in terms of accuracy and efficiency, the system integrates high-speed CMOS image sensors, spectrometers and polarization analyzers to collect optical property data of diamond cutting surfaces with high precision, providing a solid foundation for subsequent cutting angle optimization. The intelligent cutting optimization module combines advanced convolutional neural network algorithms with physical models to achieve accurate prediction of the optimal cutting angle, significantly improving the accuracy and efficiency of cutting. Secondly, in terms of safety and reliability, the control system and alarm module realizes dynamic adjustment of the cutting tool angle through the state feedback mechanism of the servo motor and precision driver, ensuring the stability and safety of the cutting process. At the same time, the module also monitors the position and motion state of the tool in real time, records the cutting parameters corresponding to each cutting surface, and provides strong support for subsequent cutting quality traceability. When processing key cutting surfaces, the system uses visual prompts to warn of abnormalities, further improving the safety of operation. Finally, in terms of systematization and testability, the system integration and testing module integrates various functional modules, establishes an automated testing framework based on MATLAB, and simulates cutting scenarios for stress testing and long-term running testing. This not only helps to verify the overall performance and stability of the system, but also facilitates subsequent system optimization and upgrades. Overall, the system performs outstandingly in improving the accuracy, efficiency, safety and reliability of diamond cutting, and has broad application prospects and important commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a block diagram of a diamond cutting angle intelligent adjustment system based on optical feedback according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1As shown, the present invention provides a technical solution: a diamond cutting angle intelligent adjustment system based on optical feedback, including a system integration and testing module, wherein the system integration and testing module is communicatively connected with an optical property detection module, an intelligent cutting optimization module, and a control system and alarm module; The optical property detection module presets 18 cutting faces for the adjustment process of the diamond cutting angle, and collects time series image data, spectrum data and polarization data of reflected and refracted light through a high-speed CMOS image sensor, a spectrum analyzer and a polarization analyzer; The intelligent cutting optimization module converts the collected data into real-time display graphics, uses a convolutional neural network algorithm, combines the physical model for simulation analysis, and predicts the optimal cutting angle; The control system and alarm module dynamically adjust the cutting tool angle according to the prediction result of the optimal cutting angle in combination with the state feedback mechanism, and warn of abnormalities when processing the cutting surface through visual prompts; The system integration and testing module is used to integrate various modules, establish an automated testing framework based on MATLAB, simulate cutting scenarios, and perform stress tests and long-term running tests on the cutting effects of 18 cutting surfaces of diamond cutting.

[0019] In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the time series image data of the reflected and refracted light through the high-speed CMOS image sensor includes: High-speed CMOS image sensors are deployed around the diamond to capture light reflected from the surface and refracted from the inside of the diamond at a high frame rate, forming time-series image data containing light intensity information and reflecting the light path and polarization state; The collected original image is denoised and smoothed, grayed and normalized, the edge detection algorithm is used to identify the cutting surface boundary, and the frequency domain characteristics of the light intensity distribution are analyzed using Fourier transform. The texture features are automatically extracted using a convolutional neural network algorithm and the extracted texture features are passed to the intelligent cutting optimization module.

[0020] In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the spectrum data of the reflected and refracted light through the spectrum analyzer includes: A spectrometer is deployed around the diamond. The spectrometer uses a grating to decompose light into components of different wavelengths and records the light intensity distribution at each wavelength through a high-sensitivity detector to form spectral data. The collected raw spectral data are baseline corrected and smoothed to eliminate noise and background interference. Normalization technology is applied to make the data acquired under different conditions have a consistent scale. The key wavelengths and their corresponding light intensity characteristics are identified through peak detection and integral area calculation. The principal component analysis method is used to further explore the implicit patterns and feature associations in the spectral data, and the extracted spectral features are passed to the intelligent cutting optimization module.

[0021] In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the polarization data of the reflected and refracted light through the polarization analyzer includes: Polarization analyzers are deployed around diamonds. Polarization analyzers use polarization beam splitters to decompose incident light into orthogonal linear polarization components, and record the intensity distribution of each component through a high-sensitivity detector. Mueller matrix is ​​used to describe circular polarization and elliptical polarization. The collected raw polarization data is calibrated and smoothed to eliminate environmental noise and measurement errors. Normalization technology is applied to make the data acquired under different conditions have a consistent scale. The changes in polarization states are quantified by calculating Mueller matrix elements. The principal component analysis method is used to further explore the implicit patterns and feature associations in the polarization data, and the extracted polarization features are passed to the intelligent cutting optimization module.

[0022] In the intelligent cutting optimization module, the process of converting the pre-processed data into a real-time display graphic includes: The collected raw data are synchronized in time and aligned in space so that all features are represented in the same coordinate system, and the extracted texture features, spectral features, and polarization features are mapped into a three-dimensional visualization space; OpenGL graphics rendering technology is used to convert the feature mapping results into graphical representations, which include the boundaries of the cutting surface, light intensity distribution diagrams, spectrum curves and polarization state diagrams. Through memory management and streaming data processing technology, the graphical interface is updated in real time at a high frame rate to reflect the latest cutting status.

[0023] In the intelligent cutting optimization module, a convolutional neural network algorithm is used to perform simulation analysis in combination with a physical model, and the process of predicting the optimal cutting angle includes: Constructing a convolutional neural network model, wherein the convolutional neural network model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; The input layer receives preprocessed data from the optical property detection module, wherein the preprocessed data includes texture features, spectral features, and polarization features, and converts the preprocessed data into a width×height×channel number format processed by CNN, wherein each channel represents a different type of feature; The convolution layer performs convolution operations on the input data through its internal convolution kernels to extract local features. Each convolution kernel is responsible for capturing different patterns and sliding in space to generate feature maps. ReLU is used as the activation function to introduce nonlinearity and make the same convolution kernel share weights at different locations in the image. The pooling layer reduces the spatial size of the feature map through the maximum pooling operation, and extracts low-level edges and textures to high-level shape and structural features layer by layer by combining with multiple layers of convolution; The fully connected layer flattens the feature map output by the last convolutional layer into a one-dimensional vector, integrates all features, and forms a global representation, which is ultimately used to identify different types of cutting surfaces and predict the optimal cutting angle; The output layer uses the softmax function to output the probability distribution and perform direct regression to obtain the specific angle value, giving the best cutting angle prediction result; Using the principles of optics and mechanics, a physical model is constructed to simulate the propagation path, reflection and refraction behavior of light inside a diamond. The constraints of the physical model are introduced into the training process of the CNN model, including the refractive index and the law of reflection. The features extracted by CNN are integrated with the simulation results of the physical model, and the optical properties and physical constraints are comprehensively analyzed. By comparing the actual cutting effect with the simulation results, the model parameters are adjusted. Combined with the feedback provided by the control system and the alarm module, the input data of the CNN model is dynamically adjusted so that each prediction reflects the latest cutting status. Through incremental learning technology, the model is self-optimized according to the new data to adapt to different diamond materials and cutting environments.

[0024] In the control system and alarm module, the process of dynamically adjusting the cutting tool angle according to the prediction results includes: The control system and alarm module receives the real-time predicted probability distribution output and optimal cutting angle from the intelligent cutting optimization module through the CAN bus, uses high-precision servo motors and precision drivers to perform response and position control, adjusts the angle of the cutting tool according to the predicted results, and monitors the position and motion state of the tool in real time through encoders and position sensors to form a closed-loop control system, records the cutting parameters corresponding to each cutting surface, and marks a consistent timestamp. The cutting parameters include cutting angle, speed and pressure.

[0025] In the control system and the alarm module, the process of handling abnormalities when the cutting surface is processed by visual prompts and warnings includes: The control system and alarm module continuously monitors the cutting parameters during the cutting process, automatically identifies potential problems by combining machine learning models and physical model simulation results, and immediately triggers the LED indicator once an abnormality is found. The abnormality includes the deviation of the cutting angle from the expected value and the change of light intensity exceeding the threshold. Different levels of alarms are set according to the severity of the abnormality, where yellow indicates a warning and red indicates an emergency stop, so that the operator can take timely measures and save the historical records of all alarm events.

[0026] In the system integration and testing module, various modules are integrated to establish an automated testing framework based on MATLAB. The process of simulating cutting scenes includes: The system integration and testing module integrates the optical property detection module, intelligent cutting optimization module, and control system and alarm module. It uses MATLAB as the development platform to centrally manage and control the entire system. It generates reflected light intensity, wavelength distribution, and polarization state signals based on MATLAB, establishes a virtual cutting environment, and simulates the real diamond cutting process. Adjust the signal to test the system response under different conditions, allowing users to customize the input of diamond shape parameters and material property parameters, automatically set the initial conditions through scripts, and use MATLAB's real-time toolbox to achieve real-time communication with physical hardware, so that the simulation results are reflected on the actual equipment, simulate the standard cutting path and cutting deviation, material unevenness, record and analyze the cutting angle accuracy, time efficiency and optical effect generated during the cutting process.

[0027] In the system integration and testing module, the process of stress testing and long-term operation testing for the cutting effect of 18 faces includes: Stress testing is performed by simulating extreme conditions to evaluate the stability and reliability of the system under extreme conditions. The stress test uses a virtual cutting environment built with MATLAB to change the intensity, angle, and frequency of the light source, introduce vibration and thermal stress, and test the response of the system in a complex environment. Based on the actual collected time series image data, spectral data, and polarization data, stress scenarios are generated. Combined with physical models and convolutional neural network algorithms, the effects of stress on cutting accuracy, tool wear, and optical performance are analyzed to identify potential risk points. The long-term running test is carried out by running the system continuously for a long time to verify the stability and durability of the system in long-term work. The long-term running test is carried out by writing MATLAB scripts, periodically adjusting the cutting parameters, automatically setting the initial conditions and starting the long-term running test to simulate the changes in the real working environment. The real-time toolbox of MATLAB is used to continuously monitor the system resource utilization and cutting angle accuracy, time efficiency and optical effect, and all data are time-stamped and saved; During the test, the control system works with the alarm module to automatically detect abnormal situations and ensure that the system resumes normal operation through preset pause and recalibration operations. The data analysis and drawing functions of MATLAB are used to display the results of stress tests and long-term running tests. Based on the test feedback, the algorithm parameters and hardware configuration are adjusted.

[0028] First, start the system and ensure that the optical property detection module, intelligent cutting optimization module, control system and alarm module, and system integration and testing module are all in normal working condition. Then, the optical property detection module is used to collect time series image data, spectral data, and polarization data of diamond reflection and refraction light for the preset 18 cutting surfaces through high-speed CMOS image sensors, spectrometers, and polarization analyzers. Then, the intelligent cutting optimization module preprocesses the collected data and converts it into real-time display graphics. The module uses convolutional neural network algorithm and combines physical models for simulation analysis to predict the optimal cutting angle. Subsequently, the control system and alarm module dynamically adjusts the angle of the cutting tool through the state feedback mechanism of the servo motor and precision drive according to the prediction results of the intelligent cutting optimization module. At the same time, the module also monitors the position and motion state of the tool during the cutting process and records the cutting parameters corresponding to the 18 cutting surfaces. Finally, when processing the 18 key cutting surfaces, the system uses visual prompts to warn of abnormalities to ensure the accuracy and safety of the cutting process. In addition, the system integration and testing module integrates the functions of each module, establishes an automated testing framework based on MATLAB, simulates cutting scenes for stress testing and long-term running testing to verify the stability and reliability of the system.

[0029] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A diamond cutting angle intelligent adjustment system based on optical feedback, characterized in that: It includes a system integration and testing module, which is communicatively connected with an optical property detection module, an intelligent cutting optimization module, and a control system and alarm module; The optical property detection module presets 18 cutting faces for the adjustment process of the diamond cutting angle, and collects time series image data, spectrum data and polarization data of reflected and refracted light through a high-speed CMOS image sensor, a spectrum analyzer and a polarization analyzer; The intelligent cutting optimization module converts the collected data into real-time display graphics, uses a convolutional neural network algorithm, combines the physical model for simulation analysis, and predicts the optimal cutting angle; The control system and alarm module dynamically adjust the cutting tool angle according to the prediction result of the optimal cutting angle in combination with the state feedback mechanism, and warn of abnormalities when processing the cutting surface through visual prompts; The system integration and testing module is used to integrate various modules, establish an automated testing framework based on MATLAB, simulate cutting scenarios, and perform stress tests and long-term running tests on the cutting effects of 18 cutting surfaces of diamond cutting.

2. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 1, characterized in that: In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the time series image data of the reflected and refracted light through the high-speed CMOS image sensor includes: High-speed CMOS image sensors are deployed around the diamond to capture light reflected from the surface and refracted from the inside of the diamond at a high frame rate, forming time-series image data containing light intensity information and reflecting the light path and polarization state; The collected original image is denoised and smoothed, grayed and normalized, the edge detection algorithm is used to identify the cutting surface boundary, and the frequency domain characteristics of the light intensity distribution are analyzed using Fourier transform. The texture features are automatically extracted using a convolutional neural network algorithm and the extracted texture features are passed to the intelligent cutting optimization module.

3. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 2, characterized in that: In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the spectrum data of the reflected and refracted light through the spectrum analyzer includes: A spectrometer is deployed around the diamond. The spectrometer uses a grating to decompose light into components of different wavelengths and records the light intensity distribution at each wavelength through a high-sensitivity detector to form spectral data. The collected raw spectral data are baseline corrected and smoothed to eliminate noise and background interference. Normalization technology is applied to make the data acquired under different conditions have a consistent scale. The key wavelengths and their corresponding light intensity characteristics are identified through peak detection and integral area calculation. The principal component analysis method is used to further explore the implicit patterns and feature associations in the spectral data, and the extracted spectral features are passed to the intelligent cutting optimization module.

4. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 3, characterized in that: In the optical property detection module, for the adjustment process of the diamond cutting angle, 18 cutting faces are preset, and the process of collecting the polarization data of the reflected and refracted light through the polarization analyzer includes: Polarization analyzers are deployed around diamonds. Polarization analyzers use polarization beam splitters to decompose incident light into orthogonal linear polarization components, and record the intensity distribution of each component through a high-sensitivity detector. Mueller matrix is ​​used to describe circular polarization and elliptical polarization. The collected raw polarization data is calibrated and smoothed to eliminate environmental noise and measurement errors. The changes in polarization states are quantified by calculating Mueller matrix elements. The principal component analysis method is used to further explore the implicit patterns and feature associations in the polarization data, and the extracted polarization features are passed to the intelligent cutting optimization module.

5. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 4, characterized in that: In the intelligent cutting optimization module, the process of converting the pre-processed data into a real-time display graph includes: The collected raw data are synchronized in time and aligned in space so that all features are represented in the same coordinate system, and the extracted texture features, spectral features, and polarization features are mapped into a three-dimensional visualization space; OpenGL graphics rendering technology is used to convert the feature mapping results into graphical representations, which include the boundaries of the cutting surface, light intensity distribution diagrams, spectrum curves and polarization state diagrams. Through memory management and streaming data processing technology, the graphical interface is updated in real time at a high frame rate to reflect the latest cutting status.

6. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 5, characterized in that: In the intelligent cutting optimization module, a convolutional neural network algorithm is used to perform simulation analysis in combination with a physical model, and the process of predicting the optimal cutting angle includes: Constructing a convolutional neural network model, wherein the convolutional neural network model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; The input layer receives preprocessed data from the optical property detection module, wherein the preprocessed data includes texture features, spectral features, and polarization features, and converts the preprocessed data into a width×height×channel number format processed by CNN, wherein each channel represents a different type of feature; The convolution layer performs convolution operations on the input data through its internal convolution kernels to extract local features. Each convolution kernel is responsible for capturing different patterns and sliding in space to generate feature maps. ReLU is used as the activation function to introduce nonlinearity and make the same convolution kernel share weights at different locations in the image. The pooling layer reduces the spatial size of the feature map through the maximum pooling operation, and extracts low-level edges and textures to high-level shape and structural features layer by layer by combining with multiple layers of convolution; The fully connected layer flattens the feature map output by the last convolutional layer into a one-dimensional vector, integrates all features, and forms a global representation, which is ultimately used to identify different types of cutting surfaces and predict the best cutting angle; The output layer uses the softmax function to output the probability distribution and perform direct regression to obtain the specific angle value, giving the best cutting angle prediction result; Using the principles of optics and mechanics, a physical model is constructed to simulate the propagation path, reflection and refraction behavior of light inside a diamond. The constraints of the physical model are introduced into the training process of the CNN model, including the refractive index and the law of reflection. The features extracted by CNN are integrated with the simulation results of the physical model, and the optical properties and physical constraints are comprehensively analyzed. By comparing the actual cutting effect with the simulation results, the model parameters are adjusted. Combined with the feedback provided by the control system and the alarm module, the input data of the CNN model is dynamically adjusted so that each prediction reflects the latest cutting status. Through incremental learning technology, the model is self-optimized according to the new data to adapt to different diamond materials and cutting environments.

7. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 6, characterized in that: In the control system and alarm module, the process of dynamically adjusting the cutting tool angle according to the prediction results includes: The control system and alarm module receives the real-time predicted probability distribution output and optimal cutting angle from the intelligent cutting optimization module through the CAN bus, uses high-precision servo motors and precision drivers to perform response and position control, adjusts the angle of the cutting tool according to the predicted results, and monitors the position and motion state of the tool in real time through encoders and position sensors to form a closed-loop control system, records the cutting parameters corresponding to each cutting surface, and marks a consistent timestamp. The cutting parameters include cutting angle, speed and pressure.

8. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 7, characterized in that: In the control system and the alarm module, the process of handling abnormalities when the cutting surface is processed by visual prompts and warnings includes: The control system and alarm module continuously monitors the cutting parameters during the cutting process, automatically identifies potential problems by combining machine learning models and physical model simulation results, and immediately triggers the LED indicator once an abnormality is found. The abnormality includes the deviation of the cutting angle from the expected value and the change of light intensity exceeding the threshold. Different levels of alarms are set according to the severity of the abnormality, where yellow indicates a warning and red indicates an emergency stop, so that the operator can take timely measures and save the historical records of all alarm events.

9. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 8, characterized in that: In the system integration and testing module, various modules are integrated to establish an automated testing framework based on MATLAB. The process of simulating cutting scenes includes: The system integration and testing module integrates the optical property detection module, intelligent cutting optimization module, and control system and alarm module. It uses MATLAB as the development platform to centrally manage and control the entire system. It generates reflected light intensity, wavelength distribution, and polarization state signals based on MATLAB, establishes a virtual cutting environment, and simulates the real diamond cutting process. Adjust the signal to test the system response under different conditions, allowing users to customize the input of diamond shape parameters and material property parameters, automatically set the initial conditions through scripts, and use MATLAB's real-time toolbox to achieve real-time communication with physical hardware, so that the simulation results are reflected on the actual equipment, simulate the standard cutting path and cutting deviation, material unevenness, record and analyze the cutting angle accuracy, time efficiency and optical effect generated during the cutting process.

10. The diamond cutting angle intelligent adjustment system based on optical feedback according to claim 9, characterized in that: In the system integration and testing module, the process of stress testing and long-term operation testing for the cutting effect of 18 faces includes: Stress testing is performed by simulating extreme conditions to evaluate the stability and reliability of the system under extreme conditions. The stress test uses a virtual cutting environment built with MATLAB to change the intensity, angle, and frequency of the light source, introduce vibration and thermal stress, and test the response of the system in a complex environment. Based on the actual collected time series image data, spectral data, and polarization data, stress scenarios are generated. Combined with physical models and convolutional neural network algorithms, the effects of stress on cutting accuracy, tool wear, and optical performance are analyzed to identify potential risk points. The long-term running test is carried out by running the system continuously for a long time to verify the stability and durability of the system in long-term work. The long-term running test is carried out by writing MATLAB scripts, periodically adjusting the cutting parameters, automatically setting the initial conditions and starting the long-term running test to simulate the changes in the real working environment. The real-time toolbox of MATLAB is used to continuously monitor the system resource utilization and cutting angle accuracy, time efficiency and optical effect, and all data are time-stamped and saved; During the test, the control system works with the alarm module to automatically detect abnormal situations and ensure that the system resumes normal operation through preset pause and recalibration operations. The data analysis and drawing functions of MATLAB are used to display the results of stress tests and long-term running tests. Based on the test feedback, the algorithm parameters and hardware configuration are adjusted.

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