Wood-working machine high-precision cutting process based on laser positioning

Abstract

The invention relates to the technical field of wood processing, and discloses a wood-working machine high-precision cutting process based on laser positioning, which comprises the following steps: S1, scanning the surface of wood through a laser sensor to obtain wood data, S2, combining visible laser and infrared laser, S3, analyzing the characteristics of the wood by using an AI algorithm, S4, dynamically compensating laser errors, and S5, carrying out laser positioning. The method comprises the following steps: S1, carrying out laser cutting, S2, carrying out laser cutting, S5, carrying out multi-process collaborative cutting, S6, carrying out a cooling and heat protection technology, S7, carrying out intelligent environment adjustment: monitoring factory environment parameters in real time and automatically adjusting the working state of a laser cutting system, S8, carrying out adaptive laser focal length adjustment, and S9, recording errors in each cutting process in real time by adopting an automatic feedback mechanism and recording the errors in each cutting process in real time. By adopting the dual-spectrum laser fusion distance measurement technology and combining visible light and infrared laser, accurate distance measurement on different wood materials and surface conditions is achieved, the distance measurement effect with higher adaptability and smaller error is achieved, and the cutting positioning precision is improved.

Description

Technical Field

[0001] The invention relates to the technical field of wood processing, in particular to a high-precision cutting process of a woodworking machine based on laser positioning. Background Art

[0002] In the modern wood processing industry, high-precision cutting technology is the key to improving processing efficiency and finished product quality. Among them, laser ranging, as a key positioning method before cutting, directly affects the accuracy of the cutting path. However, traditional laser ranging technology mostly relies on single-wavelength laser for measurement, which has poor adaptability to different types of wood and is difficult to meet the high-precision requirements in complex wood environments. For example, factors such as the material, color, texture, and surface roughness of the wood will affect the reflection characteristics of the laser, resulting in ranging errors.

[0003] Due to the different surface moisture content of some wood, it may affect the absorption and scattering of light, making it difficult for the ranging system to work stably. Especially in high-precision cutting applications, the accumulation of ranging errors will directly affect the cutting quality, and even lead to material waste and reduced production efficiency. How to improve the adaptability of the laser ranging system to wood of different materials and ensure accurate ranging under complex surface conditions has become an important challenge in the development of intelligent wood processing technology. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a high-precision cutting process for woodworking machinery based on laser positioning, which solves the problem that the single-wavelength laser ranging in the prior art has poor adaptability to wood of different materials and surface characteristics, is easily prone to ranging errors due to changes in reflectivity or surface texture, and is difficult to improve cutting accuracy.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A high-precision cutting process for woodworking machinery based on laser positioning includes the following steps:

[0006] S1. Application of laser positioning technology: Scan the wood surface with a laser sensor to obtain wood data, and then use laser measurement to obtain preliminary positioning information;

[0007] S2. Dual-spectrum laser fusion ranging: Dual-spectrum laser fusion technology is used to combine visible light laser and infrared laser to maintain ranging accuracy by adjusting the wavelength and power of the laser in real time;

[0008] S3, AI intelligent path optimization: Use AI algorithm to analyze the characteristics of wood, dynamically optimize the cutting path based on the structural characteristics of wood, and adjust the cutting sequence and process according to the real-time scanning data of wood before each cutting;

[0009] S4. Dynamic compensation of laser errors: During the cutting process, the laser system is used to monitor the errors caused by vibration, temperature and humidity changes or equipment offset in real time, and the IMU inertial sensors and deep learning algorithms are used to dynamically compensate for positioning errors.

[0010] S5. Multi-process collaborative cutting: By adopting a dual-process cutting method, low-power laser marking is used for pre-marking during processing to clarify the cutting path, and then CNC mechanical cutting equipment is used for precise cutting;

[0011] S6. Cooling and heat protection technology: During the laser cutting process, a high-pressure airflow or water mist spray device is used to reduce the temperature of the cutting area and avoid heat accumulation that may adversely affect the wood surface.

[0012] S7, Intelligent environment adjustment: Real-time monitoring of factory environmental parameters to automatically adjust the working state of the laser cutting system, and automatically adjust the suction strength according to the dust concentration through the negative pressure dust suction system to prevent dust from interfering with the laser positioning system;

[0013] S8, Adaptive laser focus adjustment: Adopting adaptive laser focus adjustment mechanism, by real-time detection of wood thickness changes, automatically adjust the laser focus, so that the laser cutting always maintains the best focus and improves cutting accuracy;

[0014] S9. Automatic feedback and optimization learning: Adopt automatic feedback mechanism to record the errors in each cutting process in real time, continuously optimize cutting parameters through reinforcement learning algorithm, self-learn the best cutting path and parameter adjustment strategy, and improve cutting accuracy and efficiency as the use time increases.

[0015] Preferably, in S1, the wood data is used to obtain the three-dimensional surface data of the wood based on the laser reflection principle to create a digital model of the wood. The laser measurement frequency is set to 2000 times / second, and the positioning error is controlled to be ±0.08mm. The laser sensor adopts a line laser sensor and a surface laser sensor. The line laser sensor has a scanning frequency of 1000-1200Hz, a measuring range of 0-80m, and an accuracy of ±1mm, which is used to obtain the linear contour data of the wood surface. The surface laser sensor has a scanning rate of 45000-50000 points / second, and a measurement accuracy of ±2mm, which is used to obtain the two-dimensional plane data of the wood surface.

[0016] Preferably, in S2, the visible light laser adopts a semiconductor laser source with a wavelength of 400-450nm and a power range of 5-10mW, which is used to clearly capture the detailed information of the wood surface, and the detailed information includes texture and color detailed information. The infrared laser adopts an infrared laser source with a wavelength of 800-940nm and a power range of 10-20mW, which is used to deal with the situation where the wood surface is darker in color or has lower reflectivity. The real-time adjustment of the laser wavelength and power is completed within 45-50ms through the built-in photoelectric adjustment module according to the feedback information from the wood surface, and the feedback information includes the intensity and color of the reflected light.

[0017] Preferably, in S3, the features include texture, knots, cracks and uneven density, the optimized cutting path is used to avoid defective areas to avoid wasting wood, and the AI ​​algorithm adopts a U-Net neural network algorithm based on deep learning, whose neural network structure includes 5 convolutional layers, with convolution kernel sizes of 3×3, 5×5, and 7×7, respectively, and a step size of 1, and 3 fully connected layers with 128, 64, and 32 neurons, respectively, for analyzing the features of wood texture, knots, cracks and uneven density.

[0018] Preferably, in S4, the I MU inertial sensor is used to monitor the acceleration and angular velocity changes of the equipment in the X, Y, and Z directions in real time. The acceleration measurement range is ±16g, the accuracy is ±0.01g, the angular velocity measurement range is ±2000dps, and the accuracy is ±0.1dps. The deep learning algorithm adopts the long short-term memory network LSTM algorithm to analyze the data collected by the I MU sensor and the positioning data fed back by the laser positioning system, and construct an error prediction model. The error prediction model dynamically compensates for the positioning error in 140-150ms, so that the cutting path accuracy is improved to within ±0.08mm.

[0019] Preferably, in S5, the dual-process cutting includes laser pre-scribing and mechanical cutting, the laser power of the low-power laser pre-scribing is 3-5mW, the laser beam diameter is 0.1-0.2mm, the pre-scribing speed is 10-15mm / s, the cutting speed of the CNC mechanical cutting equipment is 50-150mm / s, and the cutting accuracy is ±0.03mm.

[0020] Preferably, in S6, the gas pressure range of the high-pressure airflow injection device is 0.5-0.8 MPa, and the gas flow rate is 2-3 m 3 / min, using a nozzle of model SV-10, the nozzle nozzle diameter is 2-3mm, the water mist particle diameter of the water mist injection device is 10-20μm, the water spray pressure is 0.3-0.5MPa, the water spray flow rate is 1-2L / min, and a fine water mist is formed by a nozzle of model WS-20, and the number of nozzle nozzle holes is 10-15.

[0021] Preferably, in S7, the environmental parameters include temperature, humidity and dust concentration, the temperature monitoring accuracy is ±0.2°C, the humidity monitoring accuracy is ±2%RH, and the dust concentration monitoring accuracy is ±1mg / m 3 The negative pressure dust collection system, when the dust concentration is less than 50mg / m 3 When the dust concentration is higher than 80mg / m 3 When cleaning, adjust the suction force to 400-500Pa.

[0022] Preferably, in S8, the real-time detection of wood thickness changes uses a thickness sensor with a detection accuracy of ±0.05mm and a detection frequency of 450-500Hz, and the automatic adjustment of laser focal length uses an electric zoom lens.

[0023] Preferably, in S9, the automatic feedback mechanism is used to record the error data in the cutting process in real time, and the error data includes positioning error, cutting path deviation, and cutting depth error. The recording frequency is 10 times / cutting process. The reinforcement learning algorithm adopts a deep Q network DQN algorithm. After completing 20 cuts, the cutting parameters are optimized according to the recorded error data within 300-400ms. The cutting error gradually decreases at a rate of 0.02mm per optimization, and the cutting efficiency gradually increases at a rate of 2% per optimization.

[0024] The present invention provides a high-precision cutting process for woodworking machinery based on laser positioning. It has the following beneficial effects:

[0025] 1. The present invention adopts dual-spectrum laser fusion ranging technology, combines visible light and infrared laser to achieve accurate ranging of different wood materials and surface conditions, obtains ranging effects with stronger adaptability and smaller errors, and improves cutting positioning accuracy.

[0026] 2. The present invention combines AI intelligent path optimization and uses the U-Net deep learning algorithm to accurately identify wood texture, knots and cracks to achieve intelligent planning of the cutting path, thereby avoiding defective areas to improve wood utilization and reduce cutting losses.

[0027] 3. The present invention adopts a high-pressure airflow and water mist spray cooling system to dynamically control the temperature during the cutting process to reduce thermal effects, reduce wood burning and deformation, and thus improve the cutting surface quality and more stable precision.

[0028] 4. The present invention uses automatic feedback and reinforcement learning optimization mechanism to record error data in real time and optimize cutting parameters based on the DQN algorithm to achieve the effect of continuously reducing errors, improving processing efficiency, gradually improving accuracy, and stably optimizing cutting quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a process flow chart of the high-precision cutting process of woodworking machinery based on laser positioning according to the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0031] Please refer to the attached Figure 1 The embodiment of the present invention provides a high-precision cutting process for woodworking machinery based on laser positioning, comprising the following steps:

[0032] S1. Application of laser positioning technology: Scan the wood surface with a laser sensor to obtain wood data, and then use laser measurement to obtain preliminary positioning information;

[0033] S2. Dual-spectrum laser fusion ranging: Dual-spectrum laser fusion technology is used to combine visible light laser and infrared laser to maintain ranging accuracy by adjusting the wavelength and power of the laser in real time;

[0034] S3, AI intelligent path optimization: Use AI algorithm to analyze the characteristics of wood, dynamically optimize the cutting path based on the structural characteristics of wood, and adjust the cutting sequence and process according to the real-time scanning data of wood before each cutting;

[0035] S4. Dynamic compensation of laser errors: During the cutting process, the laser system is used to monitor the errors caused by vibration, temperature and humidity changes or equipment offset in real time, and the IMU inertial sensors and deep learning algorithms are used to dynamically compensate for positioning errors.

[0036] S5. Multi-process collaborative cutting: By adopting a dual-process cutting method, low-power laser marking is used for pre-marking during processing to clarify the cutting path, and then CNC mechanical cutting equipment is used for precise cutting;

[0037] S6. Cooling and heat protection technology: During the laser cutting process, a high-pressure airflow or water mist spray device is used to reduce the temperature of the cutting area and avoid heat accumulation that may adversely affect the wood surface.

[0038] S7, Intelligent environment adjustment: Real-time monitoring of factory environmental parameters to automatically adjust the working state of the laser cutting system, and automatically adjust the suction strength according to the dust concentration through the negative pressure dust suction system to prevent dust from interfering with the laser positioning system;

[0039] S8, Adaptive laser focus adjustment: Adopting adaptive laser focus adjustment mechanism, by real-time detection of wood thickness changes, automatically adjust the laser focus, so that the laser cutting always maintains the best focus and improves cutting accuracy;

[0040] S9. Automatic feedback and optimization learning: Adopt automatic feedback mechanism to record the errors in each cutting process in real time, continuously optimize cutting parameters through reinforcement learning algorithm, self-learn the best cutting path and parameter adjustment strategy, and improve cutting accuracy and efficiency as the use time increases.

[0041] In S1, wood data is used to obtain wood three-dimensional surface data based on the principle of laser reflection to create a wood digital model. The laser measurement frequency is set to 2000 times / second, and the positioning error is controlled to ±0.08mm. The laser sensor uses a line laser sensor and a surface laser sensor. The line laser sensor has a scanning frequency of 1000-1200Hz, a measurement range of 0-80m, and an accuracy of ±1mm. It is used to obtain linear contour data on the wood surface. The surface laser sensor has a scanning rate of 45000-50000 points / second, and a measurement accuracy of ±2mm. It is used to obtain two-dimensional plane data on the wood surface.

[0042] Specifically, high-precision wood cutting is achieved by combining multi-sensor three-dimensional positioning, dual-spectrum laser ranging, AI intelligent optimization and dynamic error compensation. The three-dimensional model of wood is constructed by line laser and surface laser sensors, with a measurement frequency of 2000 times / second and a positioning error controlled at ±0.08mm. Visible light and infrared light are used for ranging, and the laser wavelength and power are dynamically adjusted within 50ms to ensure ranging accuracy. AI intelligent optimization path, based on U-Net algorithm to identify wood texture, knots and cracks, improves utilization and reduces waste. IMU sensor combines LSTM algorithm for real-time error compensation, while adaptive laser focal length adjustment ensures cutting accuracy. The dual process method of "laser pre-scribing + CNC mechanical cutting" is adopted to optimize cutting quality, and the error is automatically fed back with the help of DQN reinforcement learning algorithm to achieve continuous parameter optimization. The overall solution improves cutting accuracy and efficiency and reduces material waste through intelligent control.

[0043] The S2 uses a semiconductor laser source with a wavelength of 400-450nm and a power range of 5-10mW for visible light lasers, which are used to clearly capture the details of the wood surface, including texture and color details. The infrared laser uses an infrared laser source with a wavelength of 800-940nm and a power range of 10-20mW, which is used to deal with situations where the wood surface is darker or has lower reflectivity. The wavelength and power of the laser are adjusted in real time through the built-in photoelectric adjustment module according to the feedback information from the wood surface, and the laser wavelength and power are adjusted within 45-50ms. The feedback information includes the intensity and color of the reflected light.

[0044] Specifically, the visible light laser uses a semiconductor laser source with a wavelength of 400-450nm and a power range of 5-10mW. It has the characteristics of high color resolution and can clearly capture the detailed information of the wood surface, including wood texture, color changes and surface structural characteristics, so that the system can accurately identify the characteristics of the wood surface, thereby providing more accurate path planning data in the subsequent cutting process. The infrared laser uses an infrared laser source with a wavelength of 800-940nm and a power range of 10-20mW. It is mainly used to adapt to the situation where the wood surface is darker or has lower reflectivity. Due to the longer wavelength of infrared light and stronger penetration, it can reduce the measurement error caused by the difference in surface reflectivity and improve the ranging stability. During the ranging process, the system uses the built-in high-precision photoelectric adjustment module to adjust the wavelength and power of the laser in real time according to the information fed back from the wood surface to adapt to different wood characteristics and environmental conditions. The photoelectric adjustment module can receive feedback data from the sensor in real time, including the reflected light intensity and color information of the wood surface, and dynamically adjust the laser wavelength and power within 45-50ms based on these data, ensuring that the ranging accuracy is always maintained within the predetermined range, avoiding measurement errors caused by changes in wood surface characteristics. In addition, the system also has an adaptive adjustment function, which can automatically optimize laser parameters in complex wood environments, making the measurement data more stable and reliable, thereby improving the accuracy and quality of subsequent cutting processes. Through the dual-spectrum laser fusion ranging technology, the present invention can effectively solve the problem of large ranging errors in traditional single-wavelength laser ranging methods when the surface characteristics of wood change greatly, improve the adaptability to different types of wood, and provide more accurate positioning information for subsequent intelligent cutting processes.

[0045] In S3, the features include texture, knots, cracks and uneven density. The optimized cutting path is used to avoid defective areas to avoid wasting wood. The AI ​​algorithm adopts the U-Net neural network algorithm based on deep learning. Its neural network structure includes 5 convolutional layers with convolution kernel sizes of 3×3, 5×5, and 7×7, respectively, and a step size of 1, as well as 3 fully connected layers with 128, 64, and 32 neurons respectively, which are used to analyze the characteristics of wood texture, knots, cracks and uneven density.

[0046] Specifically, firstly, the image data of the wood surface is acquired through a high-precision imaging system, and the internal density distribution of the wood is detected in combination with X-ray or laser scanning data. Subsequently, the U-Net neural network processes these data, analyzes the wood's texture direction, knot position, crack range, density change and other information, and forms a complete wood feature map. For texture analysis, the system extracts characteristic parameters of the grayscale co-occurrence matrix of the wood surface, such as contrast, correlation, energy and entropy, to determine the texture direction and optimize the cutting direction; for knots, the target detection algorithm is used to identify the area, grayscale value and edge features of circular or elliptical areas in the image, accurately determine the location and size of the knot, and prevent the quality of the finished product from being reduced due to the knife cutting the knot; for cracks, the length and depth of the cracks are detected through pixel gradient changes and continuity analysis algorithms, and an avoidance path is generated; for uneven density, the X-ray density imaging algorithm is combined to identify high-density and low-density areas, and the cutting path is adjusted to optimize the performance of the finished product;

[0047] Based on the above analysis results, the core goal of optimizing the cutting path is to avoid defective areas as much as possible while ensuring maximum utilization of wood. The intelligent path optimization algorithm will generate a cutting path based on the characteristics of the wood to minimize the cutting length and reduce waste generation. At the same time, it will intelligently allocate the cutting area to avoid the impact of knots and cracks on the quality of the finished product. The optimization algorithm can complete the path adjustment within 100ms, and update the cutting sequence and process parameters in real time to adapt to the actual conditions of different woods. Through the AI ​​intelligent path optimization technology of the present invention, the utilization rate of wood can be effectively improved by 12%-15%, processing losses can be reduced, and production efficiency can be improved. At the same time, the stability and consistency of the quality of the finished product can be ensured, thereby greatly improving the accuracy and intelligence level of the woodworking machinery cutting process.

[0048] In S4, the I MU inertial sensor is used to monitor the acceleration and angular velocity changes of the equipment in the X, Y, and Z directions in real time. The acceleration measurement range is ±16g, the accuracy is ±0.01g, the angular velocity measurement range is ±2000dps, and the accuracy is ±0.1dps. The deep learning algorithm uses the long short-term memory network LSTM algorithm to analyze the data collected by the I MU sensor and the positioning data fed back by the laser positioning system, and to build an error prediction model. The error prediction model dynamically compensates for the positioning error in 140-150ms, so that the cutting path accuracy is improved to within ±0.08mm.

[0049] Specifically, the long short-term memory network (LSTM) deep learning algorithm is used to analyze the motion data collected by the IMU sensor and the actual positioning data fed back by the laser positioning system to build an error prediction model. The LSTM model enables the system to predict the error change trend under different operating environments and complete the error compensation calculation within 150ms by storing historical data for a long time and dynamically updating it in a short time. Specifically, the error prediction model first uses sensor data to calculate the real-time displacement error, and analyzes the system error pattern in combination with historical data to generate an error correction value. Then, the system dynamically corrects the errors caused by equipment offset, vibration or environmental factors by adjusting the laser cutting path and focal length to ensure that the cutting path is consistent with the theoretical path;

[0050] In addition, to further optimize the compensation accuracy, the system combines laser ranging data and uses a deep fusion algorithm to synchronously correct sensor errors, so that the final positioning error is controlled within ±0.08mm. This compensation mechanism is not only suitable for static precise positioning, but also can adapt to dynamic cutting scenarios and improve the system's adaptability to complex working conditions. Through the laser error dynamic compensation technology of the present invention, it is possible to effectively reduce cutting deviations caused by equipment jitter or external interference, improve cutting accuracy, ensure the consistency of cutting quality, and at the same time reduce scrap rates and improve processing efficiency, providing stable and reliable technical support for high-precision woodworking machinery cutting.

[0051] S5 medium and double process cutting includes laser pre-scribing and mechanical cutting. The laser power of low-power laser pre-scribing is 3-5mW, the laser beam diameter is 0.1-0.2mm, the pre-scribing speed is 10-15mm / s, the cutting speed of CNC mechanical cutting equipment is 50-150mm / s, and the cutting accuracy is ±0.03mm.

[0052] Specifically, first, before cutting, a low-power laser is used for pre-scribing to clarify the cutting path and ensure the accuracy of the cutting process. This pre-scribing not only provides an accurate reference for subsequent mechanical cutting, but also reduces cutting deviations caused by changes in wood density or different grain directions, and improves cutting consistency and accuracy;

[0053] After completing the pre-scribing, the CNC mechanical cutting equipment performs the final cutting. By combining laser pre-scribing with mechanical cutting, this process overcomes the deviation problem that may be caused by changes in wood hardness, knots or cracks in single mechanical cutting, and effectively reduces burrs, burns or edge tears generated during the cutting process. In addition, this dual-process cutting method can reduce the cutting resistance of the mechanical tool, thereby reducing tool wear, increasing the service life of the equipment, and ensuring the smoothness of the cutting surface, significantly improving the accuracy of wood processing, ensuring high-quality cutting, while reducing wood waste and improving production efficiency. Compared with the traditional single cutting process, the present invention uses dual-process collaborative cutting, which can not only improve accuracy, but also reduce waste loss caused by cutting errors, making the quality of the final product more stable and reliable.

[0054] The gas pressure range of S6 medium and high pressure air flow injection device is 0.5-0.8MPa, and the gas flow rate is 2-3m 3 / min, using a nozzle of model SV-10, the nozzle nozzle diameter is 2-3mm, the water mist particle diameter of the water mist injection device is 10-20μm, the water spray pressure is 0.3-0.5MPa, the water spray flow rate is 1-2L / min, and a fine water mist is formed through a nozzle of model WS-20, the number of nozzle nozzle holes is 10-15.

[0055] Specifically, through the powerful power of high-pressure airflow, the heat generated during the cutting process can be quickly and effectively taken away, and the temperature of the cutting area can be reduced, thereby preventing the wood from deforming, cracking or burning due to excessive temperature, and ensuring the flatness and quality of the wood cutting surface. The water mist spray device further improves the cooling effect by spraying fine water mist. The diameter range of the water mist particles is 10-20μm, the water spray pressure is set to 0.3-0.5MPa, and the water spray flow rate is 1-2L / min. A uniform water mist is formed by a nozzle model WS-20, and the number of nozzle holes is 10-15, which can form a uniform cooling coverage in the cutting area. The fine water mist not only helps to reduce the temperature of the cutting area and reduce the thermal stress on the wood surface, but also effectively prevents the wood surface from burning or scorching, and improves the surface quality and fineness of the wood after cutting.

[0056] The cooling and heat protection system can provide a stable cooling effect in the high temperature environment of laser cutting, ensuring that the temperature fluctuation of the wood during the cutting process is minimized, thereby improving the cutting accuracy, reducing material waste, and effectively extending the service life of the cutting equipment. Through the combined use of high-pressure airflow and water mist, the present invention can provide a multi-dimensional cooling solution to meet the processing requirements of wood of different materials and ensure that the final cutting quality meets high-precision requirements.

[0057] In S7, environmental parameters include temperature, humidity and dust concentration. The accuracy of temperature monitoring is ±0.2℃, the accuracy of humidity monitoring is ±2%RH, and the accuracy of dust concentration monitoring is ±1mg / m 3 , negative pressure dust collection system, when the dust concentration is less than 50mg / m 3 When the dust concentration is higher than 80mg / m 3 When cleaning, adjust the suction force to 400-500Pa.

[0058] Specifically, the intelligent environmental adjustment system continuously monitors the working environment to ensure a stable and reliable cutting process. The temperature control accuracy is ±0.2°C, the humidity detection error does not exceed ±2%RH, and the dust concentration perception accuracy is controlled within ±1mg / m 3 The negative pressure dust removal module automatically adjusts the operating state according to the dust level, maintaining 200-300Pa at low values ​​and increasing to 400-500Pa at high values, quickly removing particles and ensuring beam stability. The system optimizes the working environment, reduces interference factors, improves processing quality, and effectively extends the life of the equipment.

[0059] S8 can detect the change of wood thickness in real time, using thickness sensor with detection accuracy of ±0.05mm and detection frequency of 450-500Hz. It can automatically adjust the laser focal length using electric zoom lens.

[0060] Specifically, the system monitors the change in wood thickness in real time and automatically adjusts the laser focal length to ensure that the laser always maintains the best focus state, thereby improving cutting accuracy and reducing errors caused by focus offset. The high-precision thickness sensor continuously detects wood thickness at a frequency of 450-500Hz with an accuracy of ±0.05mm and transmits the data to the control system. After the system calculates the thickness change, it drives the electric zoom lens to automatically adjust the laser focal length to ensure that the focus is always at the optimal cutting depth. The system can dynamically adapt to differences in wood thickness, avoid cutting too deep or too shallow due to thickness changes, and improve processing consistency. At the same time, it reduces the need for repeated adjustments, improves cutting efficiency, reduces material waste and equipment wear, and ensures long-term stability.

[0061] In S9, the automatic feedback mechanism is used to record the error data in the cutting process in real time. The error data includes positioning error, cutting path deviation, and cutting depth error. The recording frequency is 10 times / cutting process. The reinforcement learning algorithm adopts the deep Q network DQN algorithm. After completing 20 cuts, the cutting parameters are optimized according to the recorded error data within 300-400ms. The cutting error gradually decreases at a rate of 0.02mm each time the optimization is carried out, and the cutting efficiency gradually increases at a rate of 2% each time the optimization is carried out.

[0062] Specifically, the automatic feedback and optimization system improves cutting accuracy and efficiency through real-time error recording and reinforcement learning algorithms. The system records 10 error data during each cutting process, including positioning error, path deviation and depth error, and uses the deep Q network (DQN) algorithm for optimization. After completing 20 cuts, the system analyzes the error data within 300-400ms and dynamically adjusts the cutting parameters to gradually reduce the cutting error, reducing it by 0.02mm each time it is optimized, while increasing cutting efficiency by 2%. This mechanism ensures that the system continuously improves itself, reduces precision deviations, improves processing consistency, and achieves efficient and accurate intelligent cutting in long-term operation.

[0063] 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. High-precision cutting process for woodworking machinery based on laser positioning, characterized in that: The following steps are involved: S1. Application of laser positioning technology: Scan the wood surface with a laser sensor to obtain wood data, and then use laser measurement to obtain preliminary positioning information; S2. Dual-spectrum laser fusion ranging: Dual-spectrum laser fusion technology is used to combine visible light laser and infrared laser to maintain ranging accuracy by adjusting the wavelength and power of the laser in real time; S3, AI intelligent path optimization: Use AI algorithm to analyze the characteristics of wood, dynamically optimize the cutting path based on the structural characteristics of wood, and adjust the cutting sequence and process according to the real-time scanning data of wood before each cutting; S4. Dynamic compensation of laser error: During the cutting process, the laser system is used to monitor the errors caused by vibration, temperature and humidity changes or equipment offset in real time, and the IMU inertial sensor and deep learning algorithm are used to dynamically compensate for the positioning error; S5. Multi-process collaborative cutting: By adopting a dual-process cutting method, low-power laser marking is used for pre-marking during processing to clarify the cutting path, and then CNC mechanical cutting equipment is used for precise cutting; S6. Cooling and heat protection technology: During the laser cutting process, a high-pressure airflow or water mist spray device is used to reduce the temperature of the cutting area and avoid heat accumulation that may adversely affect the wood surface. S7, Intelligent environment adjustment: Real-time monitoring of factory environmental parameters to automatically adjust the working state of the laser cutting system, and automatically adjust the suction strength according to the dust concentration through the negative pressure dust suction system to prevent dust from interfering with the laser positioning system; S8, Adaptive laser focus adjustment: Adopting adaptive laser focus adjustment mechanism, by real-time detection of wood thickness changes, automatically adjust the laser focus, so that the laser cutting always maintains the best focus and improves cutting accuracy; S9. Automatic feedback and optimization learning: Adopt automatic feedback mechanism to record the errors in each cutting process in real time, continuously optimize cutting parameters through reinforcement learning algorithm, self-learn the best cutting path and parameter adjustment strategy, and improve cutting accuracy and efficiency as the use time increases.

2. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S1, the wood data is used to obtain the three-dimensional surface data of the wood based on the laser reflection principle to create a digital model of the wood. The laser measurement frequency is set to 2000 times / second, and the positioning error is controlled to be ±0.08mm. The laser sensor uses a line laser sensor and a surface laser sensor. The line laser sensor has a scanning frequency of 1000-1200Hz, a measurement range of 0-80m, and an accuracy of ±1mm. It is used to obtain the linear contour data of the wood surface. The surface laser sensor has a scanning rate of 45000-50000 points / second, and a measurement accuracy of ±2mm. It is used to obtain the two-dimensional plane data of the wood surface.

3. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S2, the visible light laser adopts a semiconductor laser source with a wavelength of 400-450nm and a power range of 5-10mW, which is used to clearly capture the detailed information of the wood surface, and the detailed information includes texture and color detailed information. The infrared laser adopts an infrared laser source with a wavelength of 800-940nm and a power range of 10-20mW, which is used to deal with the situation where the wood surface is darker in color or has lower reflectivity. The real-time adjustment of the laser wavelength and power is completed within 45-50ms through the built-in photoelectric adjustment module according to the feedback information from the wood surface. The feedback information includes the intensity and color of the reflected light.

4. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S3, the features include texture, knots, cracks and uneven density. The optimized cutting path is used to avoid defective areas to avoid wasting wood. The AI ​​algorithm adopts the U-Net neural network algorithm based on deep learning. Its neural network structure includes 5 convolution layers, and the convolution kernel sizes are 3×3, 5×5, and 7×7, respectively, with a step size of 1, and 3 fully connected layers. The number of neurons is 128, 64, and 32, respectively, which are used to analyze the characteristics of wood texture, knots, cracks and uneven density.

5. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S4, the IMU inertial sensor is used to monitor the acceleration and angular velocity changes of the equipment in the three directions of X, Y, and Z in real time. The acceleration measurement range is ±16g, the accuracy is ±0.01g, the angular velocity measurement range is ±2000dps, and the accuracy is ±0.1dps. The deep learning algorithm adopts the long short-term memory network LSTM algorithm to analyze the data collected by the IMU sensor and the positioning data fed back by the laser positioning system, and construct an error prediction model. The error prediction model dynamically compensates for the positioning error in 140-150ms, so that the cutting path accuracy is improved to within ±0.08mm.

6. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S5, the dual-process cutting includes laser pre-scribing and mechanical cutting. The laser power of the low-power laser pre-scribing is 3-5mW, the laser beam diameter is 0.1-0.2mm, the pre-scribing speed is 10-15mm / s, the cutting speed of the CNC mechanical cutting equipment is 50-150mm / s, and the cutting accuracy is ±0.03mm.

7. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S6, the gas pressure range of the high-pressure airflow injection device is 0.5-0.8MPa, and the gas flow rate is 2-3m 3 / min, using a nozzle of model SV-10, the nozzle nozzle diameter is 2-3mm, the water mist particle diameter of the water mist injection device is 10-20μm, the water spray pressure is 0.3-0.5MPa, the water spray flow rate is 1-2L / min, and a fine water mist is formed by a nozzle of model WS-20, and the number of nozzle nozzle holes is 10-15.

8. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1 is characterized in that: In S7, the environmental parameters include temperature, humidity and dust concentration. The accuracy of temperature monitoring is ±0.2°C, the accuracy of humidity monitoring is ±2%RH, and the accuracy of dust concentration monitoring is ±1mg / m 3 The negative pressure dust collection system, when the dust concentration is lower than 50mg / m 3 When the dust concentration is higher than 80mg / m 3 When cleaning, adjust the suction force to 400-500Pa.

9. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1, characterized in that: In S8, the real-time detection of wood thickness changes uses a thickness sensor with a detection accuracy of ±0.05mm and a detection frequency of 450-500Hz. The automatic adjustment of the laser focal length uses an electric zoom lens.

10. The high-precision cutting process for woodworking machinery based on laser positioning according to claim 1, characterized in that: In S9, the automatic feedback mechanism is used to record the error data in the cutting process in real time. The error data includes positioning error, cutting path deviation, and cutting depth error. The recording frequency is 10 times / cutting process. The reinforcement learning algorithm adopts a deep Q network DQN algorithm. After completing 20 cuts, the cutting parameters are optimized within 300-400ms according to the recorded error data. The cutting error gradually decreases at a rate of 0.02mm per optimization, and the cutting efficiency gradually increases at a rate of 2% per optimization.

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