An adaptive cutting process for preventing silicon rod hidden cracks

Abstract

The application discloses a kind of self-adaptive cutting processes for preventing silicon rod hidden crack, including the following steps: silicon rod pretreatment, cutting equipment debugging and initialization, multidimensional real-time monitoring, parameter adaptive adjustment, cutting process closed-loop control, cutting completion and post-processing.The application detects the whole dimension through the silicon rod pretreatment stage, multidimensional real-time monitoring the key parameters such as internal stress and cutting line state of silicon rod, dynamically adjusts the cutting parameters through adaptive algorithm, realizes the accurate control of cutting stress, avoids the hidden crack caused by excessive stress and parameter mismatch from the root;According to the differences of silicon rod characteristics, the state changes in the cutting process, automatically adjust the cutting parameters, with strong adaptive ability, wide adaptability, improve the cutting efficiency and silicon rod cutting yield;Through the filing of cutting data and the construction of database, realize the continuous optimization of process, with high degree of automation, reduce labor cost.

Description

Technical Field

[0001] This invention belongs to the field of silicon rod cutting technology, specifically relating to an adaptive cutting process for preventing microcracks in silicon rods. It is applicable to the cutting and processing of various silicon rods, including monocrystalline and polycrystalline silicon rods, and is particularly suitable for silicon rod cutting operations in semiconductor and photovoltaic fields where high cutting accuracy and yield are required. Background Technology

[0002] Silicon rods are the core raw materials for semiconductor chips and photovoltaic cells. The cutting quality of silicon rods directly determines the performance and yield of subsequent products. Silicon material itself is characterized by high brittleness, high hardness and poor impact resistance. During the cutting process, it is very easy to generate microcracks. These microcracks will expand as stress is released during subsequent grinding, polishing and packaging processes, leading to silicon wafer breakage or scrap, which seriously affects production efficiency and product yield, and increases production costs.

[0003] Existing silicon rod cutting processes mainly employ fixed-parameter cutting methods. This means that based on the nominal dimensions and material of the silicon rod, fixed parameters such as cutting line speed, feed rate, cutting tension, and cooling flow rate are preset and remain constant throughout the cutting process. This traditional process has the following drawbacks: 1. Inability to adapt to the differences in silicon rod characteristics: The diameter tolerance, crystal defects, and internal stress distribution of different parts of the same silicon rod are different. The material uniformity of different batches of silicon rods is also different. When cutting with fixed parameters, for parts with stress concentration or uneven material, it is easy to cause microcracks due to excessive cutting stress. For parts with uniform material and low stress, the cutting efficiency will be low due to conservative cutting parameters.

[0004] 2. Parameters cannot be dynamically adjusted during the cutting process: During the cutting process, the wear of the cutting wire, the change in the cooling effect, and the increase in the cutting depth of the silicon rod will cause the actual cutting state to deviate from the preset state. Fixed parameters cannot respond to these changes in time, which will lead to the accumulation of cutting stress and the formation of microcracks. For example, after the cutting wire wears down, the cutting force increases. If the original feed rate is maintained, the internal stress of the silicon rod will exceed its bearing limit, forming microcracks.

[0005] 3. Lack of precise stress monitoring and feedback mechanisms: Existing processes rely heavily on the experience and judgment of operators, or only monitor external parameters such as the tension and speed of the cutting line. They cannot directly monitor the changes in internal stress during the silicon rod cutting process, making it difficult to predict the risk of microcracks in advance. Often, the microcracks are only discovered after they have formed, making it impossible to achieve proactive prevention.

[0006] In existing technologies, some improved solutions attempt to reduce microcracks by adjusting cutting parameters or adding support structures. For example, invention patent CN114589824B discloses a cutting device and a silicon rod cutting system, which dynamically adjusts the cutting position by detecting the shape of the cutting surface. However, this solution only focuses on the correction of the cutting position and does not adaptively adjust for changes in the internal stress of the silicon rod, thus failing to prevent microcracks at the root. Invention patent CN112078040A discloses a method for automatically adjusting process parameters for single-crystal silicon rod cutting. It adjusts the table speed through bow detection and torque modeling. Although it achieves automatic adjustment of some parameters, it does not combine multi-dimensional parameters such as the internal stress of the silicon rod and the state of the cutting line for coordinated adaptation. Therefore, its effect in preventing microcracks is limited and its adaptability is poor.

[0007] Therefore, developing an adaptive cutting process that can monitor key influencing factors in the cutting process in real time, dynamically and adaptively adjust cutting parameters, precisely control cutting stress, prevent microcracks in silicon rods from the root, and simultaneously take into account both cutting efficiency and cutting quality has become an urgent technical problem to be solved in the current silicon rod cutting field. Summary of the Invention

[0008] To address the shortcomings of existing silicon rod cutting processes, such as high microcrack rate, fixed parameters that cannot adapt to differences in silicon rod characteristics, and lack of precise stress feedback mechanisms, this invention provides an adaptive cutting process to prevent microcracks in silicon rods. Through multi-dimensional real-time monitoring and dynamic adaptive adjustment of parameters, precise control of cutting stress is achieved, thereby reducing the microcrack rate of silicon rods from the root cause, while improving cutting efficiency and product yield, and reducing production costs.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: An adaptive dicing process for preventing microcracks in silicon rods includes the following steps: S1. Silicon rod pretreatment: The silicon rod to be cut is cleaned and dried to remove surface stains and moisture. Ultrasonic testing technology is used to scan the entire length of the silicon rod to obtain data on the diameter tolerance, crystal defect distribution, and internal stress distribution of the silicon rod. A three-dimensional characteristic model of the silicon rod is established, stress concentration areas and crystal defect areas are marked, and initial cutting parameter thresholds for different areas are set.

[0010] S2. Cutting equipment debugging and initialization: Check whether the cutting line, guide wheel, cooling system, tension control system and stress monitoring system of the cutting equipment are normal; fix the pre-treated silicon rod on the cutting table, and set the initial cutting parameters according to the three-dimensional characteristic model of the silicon rod, including cutting line speed, feed speed, cutting tension and cooling flow rate. Among them, the initial feed speed of the stress concentration area and the crystal defect area should be reduced by 10%-20% and the cutting tension should be reduced by 5%-15% compared with the normal area.

[0011] S3. Multi-dimensional Real-time Monitoring: Start the cutting equipment and begin the silicon rod cutting operation. Simultaneously, activate the multi-dimensional monitoring system to collect the following parameters in real time: a) Internal stress parameters of silicon rod: The stress value of the cutting area is collected in real time by stress sensors attached to the surface of the silicon rod. The sampling frequency is 10-20Hz, and the data is synchronously transmitted to the control system. b) Cutting line status parameters: The wear degree, vibration amplitude, and actual tension value of the cutting line are collected in real time through the image acquisition module and tension sensor. The wear degree is obtained through image grayscale value analysis, and the vibration amplitude is collected through vibration sensor. c) Cutting environment parameters: Real-time acquisition of coolant temperature, cooling flow rate and ambient temperature in the cutting area to ensure that the coolant temperature is maintained at 20-25℃; d) Cutting progress parameters: Real-time acquisition of silicon rod cutting depth and cutting time, synchronous feedback to the control system, combined with the silicon rod three-dimensional characteristic model, to locate the current cutting position.

[0012] S4. Parameter Adaptive Adjustment: Based on the multi-dimensional real-time parameters collected in step S3 and combined with the three-dimensional characteristic model of the silicon rod, the control system dynamically adjusts the cutting parameters through a preset adaptive algorithm. The specific adjustment rules are as follows: a) Adjustment based on internal stress of silicon rod: When the real-time collected stress value exceeds 80% of the preset stress threshold, the feed rate is automatically reduced by 5%-10%, while the cooling flow rate is increased by 10%-15%; when the stress value exceeds the preset stress threshold, the cutting line speed is immediately reduced by 10%-20%, and the feed rate is reduced by 15%-25%, until the stress value drops below the preset threshold; when the stress value is below 50% of the preset stress threshold, the feed rate is increased by 5%-10% without exceeding the upper limit of the initial parameters, thereby improving cutting efficiency. b) Adjustment based on the cutting line status: When the wear of the cutting line is detected to exceed the preset threshold (i.e., the change in image grayscale value exceeds 20%), the cutting tension is automatically reduced by 5%-10%, and the feed speed is reduced by 5%-10% to avoid cutting line breakage and silicon rod microcracks; when the vibration amplitude of the cutting line exceeds the preset threshold (0.1-0.2mm), the guide wheel speed is adjusted to reduce the cutting line speed by 5%-10% to reduce the impact stress generated by vibration; when the actual tension of the cutting line deviates from the set tension by more than ±5%, the tension control system is automatically adjusted to restore the tension to the set range. c) Adjustments based on the cutting environment: When the coolant temperature exceeds 25℃, the cooling module of the cooling system is automatically activated, and the cooling flow rate is increased by 10%-15% to avoid microcracks caused by increased thermal stress on the silicon rod due to excessive temperature; when the cooling flow rate is lower than the preset value, coolant is added in time to ensure uniform cooling effect. d) Adjustment based on cutting position: When cutting to the stress concentration area or crystal defect area of ​​the silicon rod, the preset initial parameters of the area are automatically called. Combined with real-time stress data, the cutting parameters are further fine-tuned to ensure that the cutting stress is always within the range that the silicon rod can bear. When cutting to the normal area, the parameters are adjusted to the efficient cutting state on the premise of meeting the requirements for preventing microcracks.

[0013] S5. Closed-loop control of the cutting process: The control system compares the adjusted cutting parameters with the actual cutting effect in real time. The image detection module detects the flatness and cracks of the cutting surface. If a small microcrack is detected, the cutting parameters are immediately adjusted (feed speed reduced by 20%-30%, cooling flow increased by 20%), and the parameter adjustment data at that location is recorded and fed back to the silicon rod three-dimensional characteristic model to optimize the initial parameter settings of subsequent silicon rods of the same type. If an obvious crack is detected, the cutting is immediately stopped, the cause is investigated and dealt with.

[0014] S6. Cutting Completion and Post-processing: After the silicon rod is cut, the cutting equipment is stopped, and the cut silicon rod is cleaned and dried. Ultrasonic detection technology is used to detect microcracks on the cut surface and inside of the silicon rod, and the microcrack situation is recorded. The parameter adjustment data, monitoring data, and microcrack detection results during this cutting process are compiled and archived to form a database, which is used to optimize the adaptive algorithm and initial parameter thresholds to achieve continuous iterative upgrades of the process.

[0015] Furthermore, in step S1, the frequency of the ultrasonic detection is 2-5MHz, and the scanning accuracy is 0.01mm, ensuring that the minute defects and stress concentration areas inside the silicon rod can be accurately detected; the three-dimensional characteristic model of the silicon rod is constructed using CAD software, and the diameter tolerance, crystal defects, stress distribution data are correlated with the position in the length direction of the silicon rod to achieve precise positioning during the cutting process.

[0016] Furthermore, in step S2, the cutting wire is a diamond cutting wire with a diameter of 0.1-0.3mm; the initial cutting parameters are set according to the following principles: the cutting wire speed is 10-15m / s, the feed speed is 0.5-2mm / min, the cutting tension is 8-15N, and the cooling flow rate is 5-10L / min. The specific values ​​are finely adjusted according to the material and diameter of the silicon rod.

[0017] Furthermore, in step S3, the stress sensor is a strain gauge stress sensor, which is attached to both sides of the cutting area on the surface of the silicon rod at a spacing of 5-10mm to ensure accurate acquisition of stress changes during the cutting process; the image acquisition module uses a high-definition industrial camera with a shooting frequency consistent with the stress acquisition frequency, for real-time monitoring of the wear of the cutting line and the condition of the cutting surface.

[0018] Furthermore, in step S4, the adaptive algorithm adopts a combination of PID control algorithm and fuzzy control algorithm. The PID control algorithm is used to achieve precise adjustment of the cutting parameters, and the fuzzy control algorithm is used to dynamically optimize the adjustment coefficient based on the coordinated changes of multi-dimensional parameters, so as to ensure the timeliness and accuracy of parameter adjustment.

[0019] Furthermore, the preset stress threshold is determined based on the material and diameter of the silicon rod. The preset stress threshold for monocrystalline silicon rods is 50-80 MPa, and the preset stress threshold for polycrystalline silicon rods is 40-70 MPa. The preset threshold for the degree of wear of the cutting line is: the change in image grayscale value exceeds 20%. The preset threshold for the vibration amplitude is: 0.1-0.2 mm.

[0020] Furthermore, in step S5, the image detection module adopts machine vision detection technology with a detection accuracy of 0.001mm, which can identify microcracks with a width greater than 0.005mm; the feedback cycle of the closed-loop control is 0.5-1s, ensuring that parameter adjustments can respond promptly to changes in the cutting state.

[0021] Furthermore, in step S6, the microcrack detection adopts a combination of ultrasonic detection and infrared thermal imaging detection. Ultrasonic detection is used to detect microcracks inside the silicon rod, and infrared thermal imaging detection is used to detect tiny microcracks on the cut surface, ensuring the comprehensiveness of microcrack detection. The database is stored in a cloud database, which can realize data sharing among multiple devices and collaborative optimization of process parameters.

[0022] Compared with the prior art, the present invention has the following advantages: 1. Significant effect in preventing microcracks: This invention achieves comprehensive detection of the silicon rod's stress distribution and crystal defects in advance through the silicon rod pretreatment stage, and sets targeted initial parameters. During the cutting process, it monitors key parameters such as the internal stress of the silicon rod and the state of the cutting line in real time from multiple dimensions. Through adaptive algorithms, it dynamically adjusts the cutting parameters to achieve precise control of the cutting stress, thus avoiding microcracks caused by excessive stress and parameter mismatch from the root. Experimental verification shows that the microcrack rate of silicon rods can be reduced from 8%-12% in the existing technology to below 1%.

[0023] 2. Strong adaptability and wide compatibility: This invention can automatically adjust the cutting parameters according to the differences in the characteristics of silicon rods and the state changes during the cutting process, such as the wear of the cutting line and the changes in the cooling effect, without the need for manual intervention. It is not only suitable for cutting silicon rods of different diameters and materials, but also adapts to various complex working conditions during the cutting process, solving the problem of poor adaptability of traditional fixed parameter processes.

[0024] 3. Balancing cutting efficiency and quality: Under the premise of preventing microcracks, this invention achieves a balance between cutting efficiency and cutting quality by dynamically adjusting parameters. In areas where the silicon rod stress is low and the state is stable, high-efficiency cutting parameters are used, while in areas of stress concentration or defects, conservative cutting parameters are used. Compared with the traditional fixed parameter process, the cutting efficiency is increased by 10%-15%, and the flatness of the cut surface is improved by more than 20%, reducing the workload of subsequent grinding and polishing processes.

[0025] 4. Achieve continuous process optimization: This invention archives cutting data and builds a database, feeding back the parameter adjustments and microcrack detection results of each cut to the adaptive algorithm, continuously optimizing the initial parameter thresholds and adjustment rules. As data accumulates, the microcrack prevention effect and cutting efficiency of the process will gradually improve, forming a virtuous cycle.

[0026] 5. High degree of automation and reduced labor costs: This invention realizes full automation of the process from silicon rod pretreatment, cutting parameter setting, real-time monitoring, parameter adjustment to post-processing. It eliminates the need for operators to monitor in real time and manually adjust parameters, reducing human intervention, lowering the labor intensity and skill requirements of operators, and avoiding the risk of microcracks caused by human judgment errors. Attached Figure Description

[0027] Figure 1 This is a flowchart of an adaptive cutting process for preventing microcracks in silicon rods proposed in this invention. Figure 2 This is a schematic diagram of the three-dimensional characteristic model of a silicon rod for an adaptive cutting process to prevent microcracks in silicon rods proposed in this invention. Figure 3 This is a logic block diagram of an adaptive cutting process for preventing microcracks in silicon rods proposed in this invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example

[0029] This embodiment uses the adaptive cutting process of the present invention to cut a single crystal silicon rod with a diameter of 150mm. The specific steps are as follows: S1. Silicon rod pretreatment: The single crystal silicon rod to be cut is cleaned with deionized water and then placed in a drying oven and dried at 80℃ for 30 minutes to remove surface stains and moisture. A 2MHz ultrasonic testing device is used to scan the entire length of the silicon rod with a scanning accuracy of 0.01mm to obtain the diameter tolerance (±0.02mm), crystal defect distribution, and internal stress distribution data of the silicon rod. A three-dimensional characteristic model of the silicon rod is constructed using CAD software to mark the stress concentration area (stress value 65-75MPa) and the crystal defect area. The initial cutting parameter thresholds for the normal area are set as follows: cutting line speed 12m / s, feed speed 1.2mm / min, cutting tension 12N, and cooling flow rate 8L / min. The initial feed speed for the stress concentration area and the crystal defect area is 1.0mm / min (reduced by 16.7%), and the cutting tension is 10.5N (reduced by 12.5%).

[0030] S2. Cutting equipment debugging and initialization: Check whether the diamond cutting wire (0.2mm in diameter), guide wheel, cooling system, tension control system and stress monitoring system are normal; fix the pretreated silicon rod on the cutting table, input the initial cutting parameters according to the three-dimensional characteristic model of the silicon rod, start the equipment for no-load debugging, and ensure that each system is operating normally.

[0031] S3. Multi-dimensional Real-time Monitoring: Upon starting the cutting equipment and commencing the silicon rod cutting operation, the multi-dimensional monitoring system is simultaneously activated. Strain gauge stress sensors are attached to both sides of the silicon rod cutting area at a spacing of 8.0 mm, acquiring stress values ​​in the cutting area in real time at a frequency of 15 Hz. A high-definition industrial camera captures images of the cutting line at a frequency of 15 Hz and analyzes the degree of wear on the cutting line. Tension sensors acquire the tension of the cutting line in real time, and vibration sensors acquire the vibration amplitude of the cutting line. Temperature and flow sensors acquire the coolant temperature (maintained at around 22℃) and cooling flow rate in real time. The silicon rod cutting depth and cutting time are acquired in real time and synchronously fed back to the control system. Combined with the three-dimensional characteristic model of the silicon rod, the current cutting position is located.

[0032] S4. Adaptive Parameter Adjustment: The control system employs an adaptive algorithm combining PID control and fuzzy control to dynamically adjust parameters based on real-time data acquisition. When cutting into the normal area, the real-time stress value is 45MPa (80% lower than the preset threshold of 80MPa, i.e., 64MPa). The control system automatically increases the feed speed to 1.3mm / min (an increase of 8.3%), maintains the cutting line speed at 12m / s, the cutting tension at 12N, and the cooling flow rate at 8L / min, thereby improving the cutting efficiency. When cutting to the stress concentration area, the real-time stress value rises to 68MPa (80% of the preset threshold of 80MPa). The control system automatically reduces the feed rate to 0.9mm / min (a 10% reduction), increases the cooling flow rate to 9.2L / min (a 15% increase), maintains the cutting line speed at 12m / s, and maintains the cutting tension at 10.5N until the stress value drops below 60MPa, at which point the feed rate is restored to 1.0mm / min. During the cutting process, when the image grayscale value change of the detected cutting line wear reaches 22% (exceeding the preset threshold of 20%), the control system automatically reduces the cutting tension to 9.5N (a reduction of 10%) and the feed speed to 0.9mm / min (a reduction of 10%) to avoid cutting line breakage and silicon rod microcracks. When the coolant temperature rises to 26℃, the cooling system cooling module is automatically activated, and the cooling flow rate is increased to 9.2L / min, so that the coolant temperature drops rapidly to 23℃.

[0033] S5. Closed-loop control of the cutting process: The control system compares the adjusted cutting parameters with the actual cutting effect with a feedback cycle of 0.8s. The machine vision inspection module detects the cutting surface in real time. If no microcracks are found, the cutting continues normally. When cutting to the crystal defect area, a slight stress fluctuation is detected. The feed rate is immediately reduced to 0.8mm / min and the cooling flow rate is increased to 9.6L / min. After the stress stabilizes, the feed rate is restored to 1.0mm / min.

[0034] S6. Cutting Completion and Post-processing: After the silicon rod is cut, the cutting equipment is stopped, and the cut silicon rod is washed with deionized water and dried at 80℃ for 30 minutes. Ultrasonic detection and infrared thermal imaging detection are combined to detect microcracks on the cut surface and inside of the silicon rod. No microcracks were found. The parameter adjustment data, monitoring data and microcrack detection results of this cutting are compiled and archived into the cloud database for use in optimizing the initial parameter settings of subsequent silicon rods of the same type.

[0035] In this embodiment, the silicon rod cutting efficiency is improved by 12% compared with the traditional fixed parameter process, the flatness of the cutting surface is improved by 25%, and the microcrack rate is 0, which is a significant effect. Example

[0036] This embodiment uses the adaptive cutting process of the present invention to cut a polycrystalline silicon rod with a diameter of 200mm. The specific steps are as follows: S1. Silicon rod pretreatment: The polycrystalline silicon rod to be cut is cleaned with deionized water and then placed in a drying oven and dried at 60℃ for 40 minutes to remove surface stains and moisture. A 5MHz ultrasonic testing device is used to scan the entire length of the silicon rod with a scanning accuracy of 0.01mm to obtain the diameter tolerance (±0.03mm), crystal defect distribution, and internal stress distribution data of the silicon rod. A three-dimensional characteristic model of the silicon rod is constructed using CAD software to mark the stress concentration area (stress value 55-65MPa) and the crystal defect area. The initial cutting parameters for the normal area are set as follows: cutting line speed 10m / s, feed rate 0.8mm / min, cutting tension 10N, and cooling flow rate 6L / min. The initial feed rate for the stress concentration area and the crystal defect area is 0.7mm / min (reduced by 12.5%), and the cutting tension is 8.5N (reduced by 15%).

[0037] S2. Cutting equipment debugging and initialization: After checking that all components such as the diamond cutting wire (0.3mm in diameter), guide wheel, and cooling system are normal, fix the silicon rod on the cutting table, input the initial parameters, and start cutting after the no-load debugging is normal.

[0038] S3. Multi-dimensional Real-time Monitoring: Upon starting the cutting equipment and commencing the silicon rod cutting operation, the multi-dimensional monitoring system is simultaneously activated. Strain gauge stress sensors are attached to both sides of the silicon rod cutting area at a spacing of 8.0 mm, acquiring stress values ​​in the cutting area in real time at a frequency of 10 Hz. A high-definition industrial camera captures images of the cutting line at a frequency of 10 Hz and analyzes the degree of wear on the cutting line. Tension sensors acquire the tension of the cutting line in real time, and vibration sensors acquire the vibration amplitude of the cutting line. Temperature and flow sensors acquire the coolant temperature (maintained at around 22℃) and cooling flow rate in real time. The silicon rod cutting depth and cutting time are acquired in real time and synchronously fed back to the control system. Combined with the three-dimensional characteristic model of the silicon rod, the current cutting position is located.

[0039] S4. Adaptive Parameter Adjustment: The control system employs an adaptive algorithm combining PID control and fuzzy control to dynamically adjust parameters based on real-time data acquisition. When cutting to the normal area, the real-time stress value is 35MPa (50% lower than the preset threshold of 70MPa, i.e., 35MPa). The control system automatically increases the feed speed to 0.85mm / min (an increase of 6.25%), maintains the cutting line speed at 10m / s, the cutting tension at 10N, and the cooling flow rate at 6L / min to improve cutting efficiency. When cutting to the stress concentration area, the real-time stress value rises to 62MPa (exceeding 80% of the preset threshold of 70MPa, i.e., 56MPa). The control system automatically reduces the feed rate to 0.6mm / min (a reduction of 14.3%), increases the cooling flow rate to 6.9L / min (an increase of 15%), maintains the cutting line speed at 12m / s, and maintains the cutting tension at 10.5N until the stress value drops below 60MPa, at which point the feed rate is restored to 1.0mm / min. During the cutting process, when the vibration amplitude of the cutting line is detected to reach 0.22mm (exceeding the preset threshold of 0.2mm), the rotation speed of the guide wheel is adjusted to reduce the cutting line speed to 9m / s (a reduction of 10%). After the vibration amplitude returns to normal, the line speed is restored to 10m / s. When the coolant temperature rises to 26℃, the cooling system cooling module is automatically activated, and the cooling flow rate is increased to 9.2L / min, so that the coolant temperature drops rapidly to 23℃.

[0040] S5. Closed-loop control of the cutting process: The control system compares the adjusted cutting parameters with the actual cutting effect with a feedback cycle of 1.0s. The machine vision detection module detects a tiny microcrack (0.006mm wide) and immediately reduces the feed speed to 0.5mm / min (a reduction of 28.6%) and increases the cooling flow rate to 8.4L / min (an increase of 20%). After cutting for 5mm, the microcrack disappears and the feed speed returns to 0.7mm / min.

[0041] S6. Cutting Completion and Post-processing: After cutting, the cutting equipment is stopped, and the cut silicon rod is cleaned with deionized water and dried at 80℃ for 30 minutes. Ultrasonic detection and infrared thermal imaging are used to detect microcracks on the cut surface and inside of the silicon rod. No obvious microcracks were found, only one tiny microcrack with a width of 0.004mm, which meets industry standards. The cutting data is archived, and the adjustment coefficient of the adaptive algorithm is optimized.

[0042] In this embodiment, the silicon rod cutting efficiency is improved by 10% compared with the traditional process, the flatness of the cutting surface is improved by 22%, and the microcrack rate is 0.5%, which is far lower than the 8%-12% of the existing technology.

[0043] In summary, the technical solution of this invention can prevent microcracks in silicon rods from the source. Experiments have verified that the microcrack rate is reduced from 8%-12% to below 1%. It has strong adaptability and can automatically adjust parameters according to the characteristics of silicon rods and cutting conditions, adapting to silicon rods of different specifications and materials without manual intervention. It balances cutting efficiency and quality, improving efficiency by 10%-15% and cutting surface flatness by more than 20%, reducing the workload of subsequent processes. Through data archiving and algorithm optimization, a virtuous cycle is formed, continuously improving process performance. At the same time, it realizes full-process automation, reducing labor costs, labor intensity and the risk of operational errors. Its adaptability and practicality are outstanding.

[0044] Three-dimensional property model of silicon rod: 1 Silicon rods cylindrical base Length: 1000-2000mm; Diameter: 150mm / 200mm; Tolerance: ±0.02~0.03mm As the core detection object of the model, it provides basic physical dimension parameters. 2 Stress Concentration Area At 1 / 3 and 2 / 3 of the length of the silicon rod Stress range: 65-75 MPa Characterizing the location of key stress distribution provides a positioning basis for the cutting process. 3 Crystal defect region Near the corresponding stress concentration area Defect type: dislocation, void; defect size: 0.01-0.05mm Identifying the distribution of microscopic defects helps in assessing the internal integrity of materials. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive cutting process for preventing microcracks in silicon rods, characterized in that, Includes the following steps: S1. Silicon rod pretreatment: The silicon rod to be cut is cleaned and dried to remove surface stains and moisture. Ultrasonic testing technology is used to scan the entire length of the silicon rod to obtain data on the diameter tolerance, crystal defect distribution, and internal stress distribution of the silicon rod. A three-dimensional characteristic model of the silicon rod is established, stress concentration areas and crystal defect areas are marked, and initial cutting parameter thresholds for different areas are set. S2. Cutting equipment debugging and initialization: Check whether the cutting line, guide wheel, cooling system, tension control system and stress monitoring system of the cutting equipment are normal; fix the pre-treated silicon rod on the cutting table, and set the initial cutting parameters according to the three-dimensional characteristic model of the silicon rod, including cutting line speed, feed speed, cutting tension and cooling flow rate. S3. Multi-dimensional Real-time Monitoring: Upon starting the cutting equipment, the monitoring system is activated simultaneously to collect relevant parameters such as internal stress of the silicon rod, cutting line status, cutting environment, and cutting progress in real time. a) Internal stress parameters of silicon rod: The stress value of the cutting area is collected in real time by stress sensors attached to the surface of the silicon rod. The sampling frequency is 10-20Hz, and the data is synchronously transmitted to the control system. b) Cutting line status parameters: The wear degree, vibration amplitude, and actual tension value of the cutting line are collected in real time through the image acquisition module and tension sensor. The wear degree is obtained through image grayscale value analysis, and the vibration amplitude is collected through vibration sensor. c) Cutting environment parameters: Real-time acquisition of coolant temperature, cooling flow rate and ambient temperature in the cutting area to ensure that the coolant temperature is maintained at 20-25℃; d) Cutting progress parameters: Real-time acquisition of silicon rod cutting depth and cutting time, synchronous feedback to the control system, combined with the silicon rod three-dimensional characteristic model, to locate the current cutting position; S4. Parameter Adaptive Adjustment: Based on the multi-dimensional real-time parameters collected in step S3 and combined with the three-dimensional characteristic model of the silicon rod, the control system dynamically adjusts the cutting line speed, feed speed, cutting tension and cooling flow rate through a preset adaptive algorithm. S5. Closed-loop control of the cutting process: The control system compares the adjusted cutting parameters with the actual cutting effect in real time. The image detection module detects the flatness and cracks of the cutting surface. If a small microcrack is detected, the cutting parameters are adjusted immediately and the parameter adjustment data at that location is recorded and fed back to the silicon rod three-dimensional characteristic model to optimize the initial parameter settings of subsequent silicon rods of the same type. If an obvious crack is detected, the cutting is stopped immediately, the cause is investigated and dealt with. S6. Cutting Completion and Post-processing: After the silicon rod is cut, the cutting equipment is stopped, and the cut silicon rod is cleaned and dried. Ultrasonic detection technology is used to detect microcracks on the cut surface and inside of the silicon rod, and the microcrack situation is recorded. The parameter adjustment data, monitoring data, and microcrack detection results during this cutting process are compiled and archived to form a database, which is used to optimize the adaptive algorithm and initial parameter thresholds to achieve continuous iterative upgrades of the process.

2. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S1, the frequency of the ultrasonic detection is 2-5MHz, the scanning accuracy is 0.01mm, and the detection data of the three-dimensional characteristic model of the silicon rod corresponds to the position of the silicon rod along its length.

3. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S2, the cutting wire is a diamond cutting wire with a diameter of 0.1-0.3 mm. The initial cutting parameters are: cutting wire speed 10-15 m / s, feed rate 0.5-2 mm / min, cutting tension 8-15 N, and cooling flow rate 5-10 L / min. The initial feed rate of the stress concentration area and the crystal defect area is reduced by 10%-20% and the cutting tension is reduced by 5%-15% compared with the normal area.

4. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S3, the monitoring parameters specifically include: internal stress of the silicon rod: collected by a strain gauge stress sensor with a sampling frequency of 10-20Hz; cutting line status: the wear degree is detected by an image acquisition module, the tension is collected by a tension sensor, the vibration amplitude is collected by a vibration sensor; the cutting environment is: coolant temperature 20-25℃, cooling flow rate, and ambient temperature; cutting progress: cutting depth and cutting time.

5. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S4, the adaptive algorithm combines PID control and fuzzy control algorithms, and its parameter adjustment rules are as follows: a) When the stress value exceeds 80% of the preset threshold, reduce the feed rate by 5%-10% and increase the cooling flow rate by 10%-15%. When the stress value exceeds the preset threshold, reduce the cutting line speed by 10%-20% and the feed rate by 15%-25%. When the stress value is below 50% of the preset threshold, increase the feed rate by 5%-10%. b) When the wear of the cutting line exceeds the preset threshold, reduce the cutting tension by 5%-10% and the feed speed by 5%-10%. When the vibration amplitude exceeds the standard, adjust the guide wheel speed and reduce the cutting line speed by 5%-10%. When the tension deviation exceeds ±5%, adjust the tension to the set range. c) When the coolant temperature exceeds 25°C, activate the cooling module and increase the cooling flow rate by 10%-15%. If the cooling flow rate is insufficient, add coolant. d) Cut to the stress concentration area or crystal defect area, call the preset initial parameters and combine them with real-time stress fine-tuning.

6. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, The preset threshold for stress is: 50-80 MPa for monocrystalline silicon rods and 40-70 MPa for polycrystalline silicon rods. The preset threshold for the degree of wear of the cutting line is: the change in image grayscale value exceeds 20%. The preset threshold for vibration amplitude is: 0.1-0.2 mm.

7. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S5, machine vision is used to detect cracks on the cutting surface with a detection accuracy of 0.001mm and a closed-loop control feedback cycle of 0.5-1s. When a small microcrack is detected, the feed rate is reduced by 20%-30% and the cooling flow rate is increased by 20%.

8. The adaptive cutting process for preventing microcracks in silicon rods according to claim 1, characterized in that, In step S6, the microcrack detection adopts a combination of ultrasonic detection and infrared thermal imaging detection, and the cutting data is archived to the cloud database to realize data sharing among multiple devices and process optimization.

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