Intelligent control method and system for precise grinding of polytetrafluoroethylene tube connector

Abstract

The invention discloses an intelligent control method and system for precise grinding of a polytetrafluoroethylene pipe connector, relates to the technical field of precise grinding control, and aims to solve the technical problems that when a grinding tool is abraded, an automatic precise grinding system cannot accurately recognize the root of force feedback increase, so that misjudgment is caused, and an improper adjustment strategy is adopted. The method comprises the following steps: acquiring static pre-judgment information related to a to-be-processed area of the polytetrafluoroethylene pipe interface; obtaining at least one first dynamic process signal and at least one second dynamic process signal in the polishing operation; performing root analysis on the change of the first dynamic process signal according to the static pre-judgment information and the second dynamic process signal to obtain a root analysis result; the root analysis result is used for reflecting that the change of the first dynamic process signal is caused by characteristic fluctuation of the to-be-processed material or abrasion of a grinding tool; and executing a corresponding polishing adjustment strategy based on the root analysis result.

Description

Technical Field

[0001] This application relates to the field of precision grinding control technology, and in particular to an intelligent control method and system for precision grinding of polytetrafluoroethylene pipe interfaces. Background Technology

[0002] In cutting-edge industries such as semiconductors and biopharmaceuticals, polytetrafluoroethylene (PTFE) tubing is widely used for ultrapure fluid transport due to its excellent chemical inertness and purity. However, the precision polishing of PTFE tubing joints is a critical step in the production process, requiring extremely high dimensional tolerances and surface finishes to ensure a contamination-free, sealed connection. Traditional manual polishing methods are inefficient, inconsistent, and susceptible to human error, resulting in a high scrap rate.

[0003] To address the aforementioned issues, existing technologies have introduced automated precision grinding systems, which have been further upgraded to online adaptive control mechanisms. This adaptive system integrates force sensors to monitor the contact force between the grinding tool and the PTFE pipe in real time, and dynamically adjusts grinding parameters based on force feedback to compensate for fluctuations in the properties of the PTFE raw material, thereby improving dimensional accuracy and product consistency.

[0004] However, this adaptive mechanism presents new challenges in practical applications. When the grinding tool becomes less efficient due to wear, it requires higher contact force to achieve the same material removal rate. At this point, the adaptive control system, lacking the ability to discern the true source of the increased force, mistakenly interprets the increased grinding force as the grinding tool encountering a harder portion or batch of the PTFE pipe. In response to this misjudgment, the system automatically increases the grinding pressure or extends the grinding duration in an attempt to compensate for and maintain the preset material removal rate.

[0005] This misjudgment had serious consequences. First, it accelerated the wear of the grinding tools, shortened their lifespan, and increased replacement costs. Second, and more critically, the changes in grinding kinetics caused by the combination of worn tools and system compensation severely damaged the surface finish of the PTFE pipe interface, leading to defects such as microscopic scratches, localized thermal degradation (burn marks), or uneven surface texture. These defects are unacceptable for high-purity applications such as semiconductors and biomedicine, potentially causing particle shedding, bacterial adhesion, or damage to the material's long-term chemical inertness. Existing post-processing inspection systems often fail to detect these subtle but critical surface quality issues, causing a large number of parts that pass initial inspection to be deemed unqualified in more stringent quality assurance tests, resulting in hidden costs and production delays. Summary of the Invention

[0006] This application provides an intelligent control method and system for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces, aiming to solve the technical problem that existing automated precision grinding systems cannot accurately identify the root cause of increased force feedback when grinding tools wear, leading to misjudgment and inappropriate adjustment strategies, thereby accelerating tool wear, damaging product surface quality, and causing hidden costs and production delays.

[0007] In a first aspect, to solve the above-mentioned technical problems, the present invention provides an intelligent control method for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces. The method includes: before the grinding operation begins, acquiring static prediction information related to the area to be processed of the PTFE pipe interface; the static prediction information is used to characterize the inherent physical properties of the material to be processed in the PTFE pipe interface; acquiring at least one first dynamic process signal and at least one second dynamic process signal during the grinding operation; the first dynamic process signal is used to characterize the interaction state between the grinding tool and the PTFE pipe interface, and the second dynamic process signal is used to characterize the working performance state of the grinding tool itself; based on the static prediction information and the second dynamic process signal, performing root cause analysis on the changes in the first dynamic process signal to obtain root cause analysis results; the root cause analysis results are used to reflect whether the changes in the first dynamic process signal are caused by fluctuations in the characteristics of the material to be processed or by wear of the grinding tool; and executing a corresponding grinding adjustment strategy based on the root cause analysis results.

[0008] Secondly, this application provides an intelligent control system for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces. The system includes: a first acquisition unit, used to acquire static prediction information related to the area to be processed of the PTFE pipe interface before the grinding operation begins; the static prediction information is used to characterize the inherent physical properties of the material to be processed at the PTFE pipe interface; a second acquisition unit, used to acquire at least one first dynamic process signal and at least one second dynamic process signal during the grinding operation; the first dynamic process signal is used to characterize the interaction state between the grinding tool and the PTFE pipe interface, and the second dynamic process signal is used to characterize the working performance state of the grinding tool itself; an analysis unit, used to perform root cause analysis on the changes in the first dynamic process signal based on the static prediction information and the second dynamic process signal, and obtain root cause analysis results; the root cause analysis results are used to reflect that the changes in the first dynamic process signal are caused by fluctuations in the characteristics of the material to be processed or by wear of the grinding tool; and an execution unit, used to execute a corresponding grinding adjustment strategy based on the root cause analysis results.

[0009] This application has at least the following beneficial effects: The intelligent control method for precision grinding of PTFE tube interfaces disclosed in this application acquires static prediction information related to the area to be processed of the PTFE tube interface before the grinding operation begins. This information is used to characterize the inherent physical properties of the material to be processed. During the grinding operation, a first dynamic process signal characterizing the interaction state between the grinding tool and the PTFE tube interface, and a second dynamic process signal characterizing the working performance state of the grinding tool itself, are acquired. Based on this, according to the static prediction information and the second dynamic process signal, the changes in the first dynamic process signal are analyzed to determine whether the change is caused by fluctuations in the properties of the material to be processed or by wear of the grinding tool. Based on the analysis results, a corresponding grinding adjustment strategy is executed.

[0010] Through the above technical solution, this application effectively solves the technical problem that existing automated precision grinding systems cannot accurately identify the true source of increased force feedback when grinding tools wear, leading to misjudgments and inappropriate adjustment strategies. Specifically, this application achieves precise root cause analysis of dynamic signal changes during the grinding process by introducing static prediction information and a second dynamic process signal. When force feedback increases, the system no longer blindly attributes it to changes in material hardness, but can distinguish whether it is caused by fluctuations in material properties or wear of the grinding tool. This precise root cause analysis avoids the drawbacks of traditional systems that accelerate tool wear and damage product surface quality due to misjudgment. When tool wear is identified, the system can promptly provide maintenance prompts or adjust grinding parameters, thereby extending tool life and ensuring the surface finish and dimensional accuracy of PTFE pipe interfaces, effectively reducing scrap rates and production costs, and improving product reliability in high-purity applications such as semiconductors and biopharmaceuticals. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating an intelligent control method for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces provided in this application. Detailed Implementation

[0012] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] In cutting-edge industries such as semiconductors and biopharmaceuticals, PTFE tubing is widely used for ultrapure fluid transport due to its superior chemical inertness and purity. However, the precision polishing of PTFE tubing interfaces is a critical step in the production process, requiring extremely high dimensional tolerances and surface finishes to ensure a contamination-free, sealed connection. Traditional manual polishing methods are inefficient, inconsistent, and susceptible to human error, resulting in high scrap rates. While existing automated precision polishing systems and their online adaptive control mechanisms improve dimensional accuracy and product consistency by monitoring contact force in real time and dynamically adjusting polishing parameters using force sensors, they can still misinterpret increased polishing force as the tool encountering harder parts of the PTFE tubing when the polishing tool becomes worn and inefficient. This leads to misjudgment and improper compensation, accelerating tool wear and severely damaging the surface finish of the PTFE tubing interfaces, resulting in defects such as micro-scratches, localized thermal degradation, or uneven surface texture. These defects are unacceptable for high-purity applications and can lead to particle shedding, bacterial adhesion, or damage to the material's long-term chemical inertness.

[0015] In view of the above problems, this application provides an intelligent control method for precision grinding of PTFE pipe interfaces. By introducing static prediction information and a second dynamic process signal, and combining the first dynamic process signal for multi-dimensional root cause analysis, this application can accurately distinguish whether the abnormality in the grinding process is caused by material property fluctuations or by grinding tool wear. This allows for the execution of a more precise grinding adjustment strategy, effectively avoiding the problems of accelerated tool wear and product surface quality degradation caused by misjudgment in the prior art, and significantly improving the grinding accuracy and surface quality of PTFE pipe interfaces.

[0016] The following specific embodiments will provide a detailed introduction and explanation of the precision fertilization program control method and system for intelligent agricultural equipment provided in this application.

[0017] Reference Figure 1 This application provides an intelligent control method for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces, which may include the following steps:

[0018] S1. Before the grinding operation begins, obtain static prediction information related to the area to be processed at the PTFE pipe interface.

[0019] Among them, static prediction information is used to characterize the inherent physical properties of the material to be processed at the PTFE tube interface. Static prediction information can be data that characterizes the inherent physical properties of the PTFE tube interface by detecting the area to be processed before the grinding operation begins, such as the material's hardness, density, and uniformity.

[0020] Polytetrafluoroethylene (PTFE) pipe joints refer to the pipe connection parts made of PTFE material, and their characteristics require that the integrity and purity of the material be maintained during the polishing process.

[0021] Before the grinding operation begins, static pre-judgment information related to the PTFE tube interface to be processed needs to be obtained. This static pre-judgment information characterizes the inherent physical properties of the material to be processed at the PTFE tube interface. For example, each batch of PTFE tube interfaces can be sampled and tested manually to measure physical parameters such as hardness and density, and these parameters can be input into the control system as static pre-judgment information. Alternatively, experienced operators can visually inspect the PTFE tube interfaces based on their color, texture, and other appearance characteristics to determine their material properties and use this information as static pre-judgment information.

[0022] S2. Acquire at least one first dynamic process signal and at least one second dynamic process signal during the grinding operation.

[0023] The first dynamic process signal characterizes the interaction state between the grinding tool and the PTFE tube interface, while the second dynamic process signal characterizes the working performance state of the grinding tool itself. The first dynamic process signal can be a signal of the interaction state between the grinding tool and the PTFE tube interface monitored in real time during the grinding operation, such as contact force, vibration, and torque. The second dynamic process signal can be a signal of the working performance state of the grinding tool itself monitored in real time during the grinding operation, such as the tool's power consumption, temperature, and degree of wear.

[0024] Grinding tools can be various types of grinding heads, grinding wheels, or abrasive discs, used for material removal and surface finishing of PTFE pipe joints.

[0025] In the grinding operation, it is necessary to acquire at least one first dynamic process signal and at least one second dynamic process signal. The first dynamic process signal is used to characterize the interaction state between the grinding tool and the PTFE tube interface. For example, the contact force between the grinding tool and the PTFE tube interface can be measured in real time by installing a strain gauge or piezoelectric sensor on the grinding actuator, and this contact force signal can be used as the first dynamic process signal. Alternatively, an accelerometer can be installed on the grinding spindle to monitor the vibration signal during the grinding process in real time, and the vibration signal can be used as the first dynamic process signal. The second dynamic process signal is used to characterize the working performance state of the grinding tool itself. For example, the power consumption of the grinding tool can be monitored in real time by installing a current sensor or power meter on the grinding spindle drive unit, and the power consumption signal can be used as the second dynamic process signal. Alternatively, an infrared thermal imager can be installed near the grinding tool to monitor the surface temperature of the grinding tool in real time, and the temperature signal can be used as the second dynamic process signal.

[0026] S3. Based on the static prediction information and the second dynamic process signal, perform root cause analysis on the changes in the first dynamic process signal to obtain the root cause analysis results.

[0027] Among them, the root cause analysis results are used to reflect whether the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed or by the wear of the grinding tool;

[0028] Root cause analysis can be performed by comprehensively analyzing the above-mentioned signals to determine the root cause of abnormalities in the polishing process.

[0029] For example, analysis can be performed using a pre-set expert system or decision tree model. When abnormal fluctuations occur in the first dynamic process signal (such as contact force), a preliminary judgment is first made by combining static predictive information (such as material hardness). If the material hardness is high, it may be judged to be caused by fluctuations in material properties. If the preliminary judgment cannot determine the cause, further analysis is performed by combining the second dynamic process signal (such as power consumption). If power consumption increases significantly, it may be judged to be caused by wear of the grinding tool.

[0030] S4. Based on the root cause analysis results, execute the corresponding polishing and adjustment strategies.

[0031] Among them, the grinding adjustment strategy refers to the measures taken to adjust grinding parameters (such as feed rate, spindle speed, grinding pressure, etc.) or maintain grinding tools based on the root cause analysis results.

[0032] For example, when root cause analysis indicates that the change in the first dynamic process signal is caused by fluctuations in the properties of the material being processed, the influence of material properties can be compensated by adjusting the grinding feed speed or grinding spindle speed based on the amplitude and direction of the change in the first dynamic process signal. For instance, if the contact force suddenly increases, it indicates that the material may have hardened; in this case, the feed speed can be appropriately reduced or the spindle speed increased to maintain a stable material removal rate. When root cause analysis indicates that the change in the first dynamic process signal is caused by wear of the grinding tool, the grinding feed speed or grinding pressure can be fine-tuned to temporarily maintain the grinding effect, while simultaneously generating maintenance prompts for the grinding tool to remind the operator to check or replace the grinding tool. For instance, if power consumption continues to rise, it indicates severe tool wear; in this case, the grinding pressure can be appropriately increased to maintain the removal rate, and a tool replacement alarm can be issued immediately.

[0033] The intelligent control method for precision grinding of PTFE tube interfaces disclosed in this application works by using multi-dimensional data acquisition and intelligent analysis to accurately identify and adaptively adjust abnormal situations during the grinding process. Traditional grinding systems often fail to distinguish between changes in material properties and tool wear when faced with abnormal grinding forces, leading to incorrect adjustments and exacerbating the problem. This application obtains static pre-judgment information before the grinding operation begins, such as the inherent physical properties of the material in the area to be processed (hardness, density, etc.), providing a benchmark for subsequent dynamic analysis. During the grinding operation, the system acquires first dynamic process signals in real time, such as the contact force between the grinding tool and the PTFE tube interface, which directly reflects the interaction state during the grinding process. Simultaneously, the system also acquires second dynamic process signals, such as the energy consumption or surface temperature of the grinding tool, which directly characterize the working performance of the grinding tool itself.

[0034] When the first dynamic process signal exhibits abnormal changes, such as a sudden increase in contact force, the system first performs a preliminary root cause analysis by combining pre-acquired static prediction information. If the static prediction information indicates that the material hardness of the current processing area is higher than average, the system tends to determine that the main reason for the increased contact force is fluctuations in material properties. However, if the static prediction information cannot explain this change, or if the trend of change is inconsistent with material properties, the system will further combine the second dynamic process signal for in-depth analysis. For example, if the power consumption of the grinding tool increases significantly at this time, or the surface temperature rises abnormally, the system will determine that the main reason for the increased contact force is wear of the grinding tool. Through this layered, multi-signal fusion root cause analysis mechanism, this application can accurately distinguish the root cause of grinding abnormalities.

[0035] Once the root cause analysis is clear, the system can execute corresponding grinding adjustment strategies. If the problem is determined to be caused by fluctuations in material properties, the system will fine-tune the grinding feed rate or grinding spindle speed based on the amplitude and direction of the first dynamic process signal to compensate for the influence of material properties and ensure stable grinding quality. For example, when the material hardens, the feed rate can be appropriately reduced or the spindle speed increased to maintain a stable material removal rate and surface finish. If the problem is determined to be caused by wear on the grinding tools, the system will adopt different strategies, such as fine-tuning the grinding feed rate or grinding pressure to temporarily maintain the grinding effect. More importantly, the system will generate maintenance prompts for the grinding tools, reminding operators to check or replace worn grinding tools in a timely manner, fundamentally solving the problem and avoiding surface defects and shortened tool life caused by tool wear.

[0036] Through the aforementioned collaborative efforts, the intelligent control method of this application can effectively avoid the problems of accelerated tool wear and decreased product surface quality caused by misjudgment in the prior art. It not only improves the adaptability and robustness of the grinding process, but also significantly enhances the grinding accuracy and surface quality of PTFE tube interfaces, reducing scrap rates and production costs, and providing a reliable precision machining solution for high-purity applications such as semiconductors and biopharmaceuticals.

[0037] In some embodiments, this application further proposes the above-mentioned root cause analysis of the change in the first dynamic process signal based on static prediction information and the second dynamic process signal to obtain the root cause analysis result, including: performing a first root cause analysis on the change in the first dynamic process signal based on the static prediction information to determine whether the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed; if the changing trend of the interaction state between the grinding tool and the polytetrafluoroethylene pipe interface is consistent with the inherent physical characteristics of the material to be processed at the polytetrafluoroethylene pipe interface, then it is determined that the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed; otherwise, performing a second root cause analysis on the change in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the change is caused by the wear of the grinding tool.

[0038] Specifically, the first root cause analysis refers to the preliminary judgment made on the changes in the first dynamic process signal during the grinding process using pre-acquired static prediction information. The static prediction information characterizes the inherent physical properties of the material to be processed at the PTFE tube interface, such as hardness and density. When the interaction state between the grinding tool and the PTFE tube interface, such as contact force and vibration frequency, shows a trend consistent with these known inherent physical properties of the material, it can be preliminarily determined that the current change is caused by fluctuations in the properties of the material to be processed. For example, if the static prediction information shows that the material hardness is high in a certain area, and the first dynamic process signal (such as contact force) shows a continuous increasing trend in that area, then this change can be considered consistent with fluctuations in material hardness.

[0039] If the first root cause analysis fails to determine that the change is caused by fluctuations in the properties of the material being processed—that is, the changing trend of the interaction state between the grinding tool and the PTFE tube interface does not conform to the inherent physical properties of the material—a second root cause analysis is required. The second root cause analysis, based on the first analysis, further assesses the changes in the first dynamic process signal by incorporating a second dynamic process signal. The second dynamic process signal characterizes the working performance state of the grinding tool itself, such as its energy consumption and surface defects. By analyzing the second dynamic process signal, it can be determined whether the change in the first dynamic process signal is caused by wear of the grinding tool.

[0040] This application's solution effectively addresses the potential for misjudgment caused by a single analysis by introducing a phased root cause analysis mechanism. First, through a primary root cause analysis, static predictive information is prioritized to assess changes in the primary dynamic process signal. This preliminary assessment based on the inherent physical properties of the material efficiently filters out anomalies caused by fluctuations in the material's own characteristics, avoiding unnecessary tool condition checks. Since material properties are inherent attributes of the workpiece, their impact on the grinding process exhibits certain regularities. Therefore, when the changing trend of the interaction state matches these regularities, the material cause can be quickly and accurately identified. Second, if the primary root cause analysis fails to confirm a material cause, a secondary root cause analysis is conducted by combining the secondary dynamic process signal. The secondary dynamic process signal directly reflects the wear or performance degradation of the grinding tool, such as an abnormal increase in power consumption or the appearance of surface defects. This progressive analysis logic ensures that after ruling out material causes, attention can be focused on the tool condition, thereby accurately identifying anomalies caused by grinding tool wear. By adopting this material-first, tool-second logic, this approach avoids misjudging material wear as tool wear due to fluctuations in material properties, or vice versa, thereby improving the accuracy and efficiency of root cause analysis.

[0041] Through the above technical solution, this application can accurately identify the root cause of abnormal phenomena during the precision grinding of PTFE pipe interfaces. This phased and prioritized analysis method significantly improves the accuracy of root cause analysis and effectively avoids misjudgments caused by single or vague judgments. Specifically, by prioritizing the judgment of material property fluctuations, it can avoid mistakenly attributing the problem to the wear of the grinding tools when there are differences in the materials themselves, thereby reducing unnecessary tool maintenance or replacement costs. At the same time, after ruling out material causes, the judgment can be made in combination with the working performance status of the grinding tools themselves, which can more accurately locate tool wear problems, making subsequent grinding adjustment strategies or maintenance prompts more targeted and effective. Thus, not only is the control precision of the grinding process optimized, but the robustness and economy of the entire intelligent control system are also improved.

[0042] In some preferred embodiments, a specific example is given below. Suppose that during the polishing operation of a PTFE pipe joint, the contact force signal (as the first dynamic process signal) collected in real time by the force sensing unit suddenly shows a continuous increasing trend.

[0043] First, the system performs a root cause analysis on the change in the contact force signal based on static prediction information. For example, if the static prediction information shows that the relative hardness of the PTFE material in the current grinding area is higher than that of adjacent areas, and this increasing contact force trend is consistent with the known mechanical characteristics of high-hardness materials during grinding, the system will determine that the change in the contact force signal is caused by fluctuations in the properties of the material to be processed (i.e., a local increase in hardness). At this point, based on this root cause analysis result, the system will execute a corresponding grinding adjustment strategy, such as making a first fine adjustment to the grinding feed rate or the grinding spindle speed to adapt to the change in material hardness.

[0044] However, if the static prediction information shows that the material properties of the current grinding area do not fluctuate significantly, or if the trend of the contact force signal does not match the inherent physical properties of the material, the system will enter the second root cause analysis stage. At this point, the system will combine the second dynamic process signal for judgment. For example, if the power consumption signal collected by the power monitoring unit shows that the power consumption deviation signal of the grinding spindle is greater than the preset deviation, and the surface defect indication signal obtained by the multispectral imaging unit and thermal imaging unit indicates that there are microcracks or wear marks on the surface of the grinding tool, the system will determine that the change in the contact force signal is caused by the wear of the grinding tool. Based on this root cause analysis result, the system will execute the corresponding grinding adjustment strategy, such as making a second fine adjustment to the grinding feed speed or grinding pressure, and / or generating maintenance prompts for the grinding tool to remind the operator to check or replace the grinding tool. Through this step-by-step judgment mechanism, accurate identification of the cause of grinding abnormalities is ensured, thereby enabling the most appropriate countermeasures to be taken.

[0045] In some embodiments of this application, a first root cause analysis is proposed based on static prediction information to determine whether the change in the first dynamic process signal is caused by fluctuations in the characteristics of the material to be processed. If not, a second root cause analysis is performed on the change in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the change is caused by wear of the grinding tool. However, the above solutions do not specifically explain how to combine the second dynamic process signal to accurately determine the wear of the grinding tool, which may lead to an inaccurate or insufficient basis for the judgment of the wear of the grinding tool, thereby affecting the effectiveness of subsequent grinding adjustment strategies.

[0046] In response, this application further proposes the above-mentioned second root cause analysis of the changes in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the changes are caused by the wear of the polishing tool, including: extracting signals from the second dynamic process signal to obtain a power consumption deviation signal and a surface defect indication signal; the power consumption deviation signal is used to reflect the deviation between the current power consumption of the polishing tool and the reference power consumption, and the surface defect indication signal is used to reflect whether there are defects on the surface of the polishing tool; if the power consumption deviation signal is greater than a preset deviation, and the surface defect indication signal indicates that there are defects on the surface of the polishing tool, then it is determined that the changes are caused by the wear of the polishing tool.

[0047] Specifically, the second dynamic process signal can contain various information related to the working performance status of the grinding tool itself. To more accurately determine the wear condition of the grinding tool, these signals need to be effectively extracted and analyzed. "Signal extraction" refers to obtaining characteristic quantities with clear physical meaning from the original second dynamic process signal through specific processing algorithms or sensor data fusion techniques. For example, if the second dynamic process signal is the current or voltage data of the grinding spindle, the energy consumption deviation signal can be extracted; if the second dynamic process signal is an image or vibration data of the grinding tool surface, the surface defect indication signal can be extracted.

[0048] The energy consumption deviation signal is used to quantify the difference between the energy consumption of a grinding tool in its current working state and its baseline energy consumption in its ideal or initial state. After wear, the cutting efficiency of a grinding tool typically decreases, and friction may increase, resulting in the need for more energy to complete the same grinding task. Therefore, an increase in the energy consumption deviation signal can serve as an indirect indicator of grinding tool wear. The baseline energy consumption is usually the average energy consumption value obtained through experiments or calibration when the grinding tool is in brand new or optimal working condition.

[0049] Surface defect indication signals directly reflect the presence of physical defects on the surface of the grinding tool, such as abrasive dulling, chipping, cracks, or excessive wear. These defects directly affect the cutting ability and grinding quality of the grinding tool. Surface defect indication signals can be obtained in various ways, such as by acquiring and analyzing images of the grinding tool surface through a vision inspection system, or by identifying surface anomalies through non-contact detection methods such as acoustics and vibration.

[0050] "Preset deviation" is a pre-defined threshold used to determine whether the power consumption deviation signal is sufficient to indicate a degree of wear. This threshold can be set based on the type of grinding tool, material properties, expected lifespan, and practical application experience. When the power consumption deviation signal exceeds this preset deviation, it indicates that the power consumption of the grinding tool has significantly deviated from the normal range.

[0051] The solution in this application decomposes the second dynamic process signal into an energy consumption deviation signal and a surface defect indication signal using the aforementioned method, and sets dual judgment conditions. Only when the energy consumption deviation signal exceeds a preset deviation and the surface defect indication signal clearly indicates the presence of defects on the surface of the grinding tool, is it ultimately determined that the change in the first dynamic process signal is caused by wear of the grinding tool. This dual confirmation mechanism significantly improves the accuracy and reliability of the judgment.

[0052] This application's solution extracts refined signals from the second dynamic process signal, obtaining power consumption deviation signals and surface defect indication signals, thus providing a more specific and reliable basis for judging the wear of the grinding tool. After the first root cause analysis rules out the possibility of fluctuations in the properties of the material being processed, this solution, through comprehensive analysis of abnormal power consumption and surface physical defects of the grinding tool, can more accurately identify the root cause of grinding tool wear. The power consumption deviation signal reflects a decrease in the working efficiency of the grinding tool, while the surface defect indication signal directly reveals the physical damage to the grinding tool. Wear is only confirmed when both simultaneously meet preset conditions, avoiding misjudgments that may result from a single indicator, thereby ensuring the accuracy of the root cause analysis results.

[0053] The above technical solution significantly improves the accuracy and reliability of wear assessment for grinding tools. By combining two different types of wear evidence—power consumption deviation signals and surface defect indication signals—this solution can more comprehensively and accurately evaluate the actual condition of grinding tools. This multi-dimensional, dual-confirmation judgment mechanism effectively avoids erroneous judgments caused by fluctuations or misreading of a single signal, thus ensuring the targetedness and effectiveness of subsequent grinding adjustment strategies. This not only helps extend the service life of grinding tools and reduce production costs but also guarantees the grinding quality and consistency of PTFE pipe interfaces, improving overall production efficiency.

[0054] In some preferred embodiments, a specific example is given below. Assume the grinding tool is an electrically driven rotating grinding wheel, whose wear leads to increased friction and reduced cutting efficiency. During the grinding operation, a power monitoring unit connected to the grinding wheel drive motor collects power consumption signals in real time and calculates the power consumption deviation signal. Simultaneously, an industrial camera positioned near the grinding area periodically acquires images of the grinding wheel surface and uses image processing algorithms to analyze the degree of passivation of the abrasive grains or the presence of chipping, thereby obtaining a surface defect indication signal. For example, when the power consumption deviation signal continuously exceeds a preset 10% (i.e., the current power consumption is more than 10% higher than the baseline value), and the surface defect indication signal shows that more than 20% of the abrasive grains on the grinding wheel surface exhibit significant passivation or chipping, the system will clearly determine that the change in the first dynamic process signal (such as the contact force signal) is caused by grinding wheel wear. Based on this accurate root cause analysis result, the system can execute corresponding grinding adjustment strategies, such as reducing the grinding feed speed or generating maintenance prompts for replacing the grinding wheel.

[0055] In some of the embodiments described above in this application, a corresponding polishing adjustment strategy is proposed based on the root cause analysis results. However, in the implementation process, how to specifically execute the adjustment strategy and which parameters to adjust for different root cause analysis results have not been elaborated in detail, which may lead to insufficient targeting or low efficiency of the adjustment.

[0056] In response, this application further proposes the above-mentioned grinding adjustment strategy based on the root cause analysis results, including: when the root cause analysis results show that the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed, making a first fine adjustment to the grinding feed speed or grinding spindle speed according to the change amplitude and direction of the first dynamic process signal; when the root cause analysis results show that the change in the first dynamic process signal is caused by the wear of the grinding tool, making a second fine adjustment to the grinding feed speed or grinding pressure, and / or generating maintenance prompt information for the grinding tool.

[0057] Specifically, when the root cause analysis indicates that the change in the first dynamic process signal is caused by fluctuations in the properties of the material being processed, such as changes in material hardness or toughness, adjustments to the kinematic parameters of the grinding process are necessary. The first fine-tuning can be understood as a precise adjustment of the grinding feed rate or the grinding spindle speed. For example, if the material becomes harder, to maintain a stable grinding effect and avoid overload, the grinding feed rate can be appropriately reduced or the grinding spindle speed increased to reduce the amount of material removed per unit time or increase the cutting frequency of the abrasive grains. Conversely, if the material becomes softer, the grinding feed rate can be appropriately increased or the grinding spindle speed decreased to improve grinding efficiency.

[0058] Specifically, when the root cause analysis indicates that the change in the first dynamic process signal is caused by the wear of the grinding tool, it means that the cutting capability of the grinding tool has decreased. The second fine-tuning can be understood as making precise adjustments to the grinding feed rate or grinding pressure. For example, to compensate for the decrease in cutting efficiency caused by grinding tool wear, the grinding pressure can be appropriately increased to enhance the contact between the abrasive grains and the workpiece, or the grinding feed rate can be reduced to prolong the contact time of the abrasive grains. Furthermore, to prevent excessive wear of the grinding tool from affecting machining quality or causing tool damage, maintenance prompts for the grinding tool can be generated, such as prompts to replace the grinding tool or perform repairs. The purpose is to intervene in a timely manner to ensure the stability of the grinding process and the machining quality.

[0059] This application's solution achieves refined intelligent control of the grinding process by linking root cause analysis results with specific grinding adjustment strategies. When the root cause analysis clearly indicates that the change in the first dynamic process signal is caused by fluctuations in the properties of the material being processed, the impact of material property changes on the grinding interaction state can be directly compensated by making a first fine adjustment to the grinding feed rate or grinding spindle speed. For example, increased material hardness leads to increased contact force; by reducing the feed rate or increasing the spindle speed, the material removal load per unit time can be effectively reduced, thereby restoring the contact force to the desired range and ensuring the stability of the grinding process.

[0060] Furthermore, when the root cause analysis results indicate that the change in the first dynamic process signal is caused by the wear of the grinding tool, a second fine-tuning of the grinding feed rate or grinding pressure can effectively address the problem of decreased cutting performance of the grinding tool. For example, grinding tool wear leads to reduced cutting efficiency, which, if not adjusted, may result in prolonged processing time or decreased surface quality. In this case, increasing the grinding pressure can enhance the cutting action of the abrasive grains, or decreasing the feed rate can prolong the contact time between the abrasive grains and the workpiece, thereby compensating for the performance loss caused by tool wear to some extent. Simultaneously, generating maintenance reminders provides a preventative maintenance mechanism, promptly reminding operators to inspect or replace the grinding tools to avoid processing defects or equipment failures caused by excessive tool wear.

[0061] Through the above technical solution, this application can execute a highly targeted grinding adjustment strategy based on the specific root cause of the change in the first dynamic process signal (i.e., material property fluctuations or grinding tool wear). This differentiated adjustment mechanism avoids a "one-size-fits-all" approach, significantly improving the control precision and response efficiency of the grinding process. Specifically, fine-tuning parameters for material property fluctuations effectively maintains the stability and processing quality of the grinding process, reducing the scrap rate caused by material inhomogeneity. Fine-tuning parameters for grinding tool wear and providing maintenance prompts not only extends the service life of the grinding tools and reduces production costs, but also effectively prevents processing defects caused by tool performance degradation, ensuring the surface quality and precision of the final product. Thus, the solution of this application achieves more intelligent, efficient, and reliable precision grinding control of PTFE pipe interfaces.

[0062] In some preferred embodiments, this application is implemented as follows:

[0063] Assume that during the polishing process of the PTFE pipe joint, the contact force signal (as the first dynamic process signal) collected in real time by the force sensing unit shows a continuous increasing trend. The system performs root cause analysis based on static prediction information and the second dynamic process signal.

[0064] Scenario 1: If the root cause analysis results show that the increase in the contact force signal is caused by the higher-than-expected hardness of the PTFE material in the area to be processed (i.e., fluctuation in material properties), the system will make a first fine adjustment to the grinding feed speed according to the degree and direction of the increase in the contact force signal, for example, reducing it from 5 mm / s to 4.5 mm / s, in order to reduce the amount of material removed per unit time, thereby restoring the contact force to the target range and avoiding grinding overload or surface damage caused by excessively hard material.

[0065] Scenario 2: If the root cause analysis indicates that the increase in contact force signal is caused by wear of the grinding tool (e.g., combined with a significant increase in power consumption deviation signal and a surface defect indicator signal showing defects on the grinding tool surface), the system will make a second fine adjustment to the grinding pressure, for example, increasing it from 10N to 12N, to compensate for the decreased cutting efficiency of the worn tool and maintain effective material removal. Simultaneously, the system will generate a maintenance prompt message, such as "Grinding tool is severely worn; replacement or repair recommended," to remind the operator to address the issue promptly and prevent a decline in processing quality or further tool damage.

[0066] Specifically, when acquiring at least one first dynamic process signal in the grinding operation, the following method can be used. Acquiring at least one first dynamic process signal in the grinding operation includes: acquiring a contact force signal in real time through a force sensing unit installed on the grinding actuator, the contact force signal being used to characterize the interaction force between the grinding tool and the polytetrafluoroethylene pipe interface; and using the acquired contact force signal as the first dynamic process signal.

[0067] The force sensing unit can be understood as a device that converts mechanical force into an electrical signal. Specifically, this force sensing unit can be configured as a piezoelectric sensor, a resistance strain gauge sensor, or a capacitive sensor, etc., and its purpose is to accurately measure the actual contact force between the grinding tool and the PTFE tube interface. This force sensing unit is preferably installed on the grinding actuator to directly sense the interaction force between the tool and the workpiece during the grinding process. The contact force signal refers to the electrical signal acquired in real time by the force sensing unit, used to quantify the interaction force between the grinding tool and the PTFE tube interface. The intensity and trend of this signal directly reflect the pressure exerted by the tool on the workpiece and the reaction force of the workpiece on the tool during the grinding process. The first dynamic process signal is defined as the aforementioned acquired contact force signal. By using the contact force signal as the first dynamic process signal, the interaction state between the grinding tool and the PTFE tube interface can be intuitively and accurately characterized.

[0068] The solution proposed in this application, by setting a force sensing unit on the grinding actuator and acquiring the contact force signal in real time as the first dynamic process signal, can directly and accurately obtain the interaction force between the grinding tool and the PTFE tube interface. This direct measurement method enables the system to monitor the actual force situation during the grinding process in real time, thereby providing reliable and high-precision input data for subsequent root cause analysis. When the interaction state between the grinding tool and the PTFE tube interface changes, for example, due to fluctuations in material hardness or tool wear causing changes in the contact force, this change can be captured by the force sensing unit in a timely manner and converted into a contact force signal, which is then processed by the system as the first dynamic process signal.

[0069] The above technical solution enables precise, real-time monitoring of the interaction state between the grinding tool and the PTFE tube interface. By directly measuring the contact force, this solution avoids errors that might arise from inferring the interaction state through indirect parameters, thus improving the accuracy and reliability of the first dynamic process signal. This high-precision signal acquisition facilitates more accurate root cause analysis, providing a more solid data foundation for subsequent grinding adjustment strategies, ensuring precise control of the grinding process and stability of product quality.

[0070] Specifically, when acquiring at least one second dynamic process signal in the grinding operation, the following method can be used. Acquiring at least one second dynamic process signal in the grinding operation includes: acquiring power consumption signals in real time through a power monitoring unit connected to the grinding spindle drive unit; determining a power consumption deviation signal based on the power consumption signal and a pre-established reference power consumption corresponding to the grinding tool in its initial state; and using the power consumption deviation signal as the second dynamic process signal.

[0071] The power monitoring unit can be understood as a device or module used to measure the power consumption of the grinding spindle drive unit in real time. It is configured to be electrically connected to the grinding spindle drive unit to accurately acquire the power consumption of the grinding tool during operation. The power consumption signal refers to the instantaneous or periodic power consumption data collected by the power monitoring unit. Further, the reference power consumption refers to a reference value established through pre-calibration or testing when the grinding tool is in its initial or good working state. This reference value reflects the typical power consumption level of the grinding tool under normal, wear-free conditions. The power consumption deviation signal is obtained by comparing and calculating the real-time power consumption signal with the pre-established reference power consumption. Specifically, the deviation signal can be expressed as the difference or ratio between the current power consumption and the reference power consumption, aiming to quantify the degree of change in the working performance of the grinding tool.

[0072] The solution in this application generates an energy consumption deviation signal by real-time monitoring of the power consumption of the grinding spindle drive unit and comparing it with a preset benchmark power consumption. This deviation signal can intuitively reflect the working performance status of the grinding tool in actual operation. For example, when the grinding tool wears down, its contact efficiency with the workpiece may decrease, or a greater torque may be required to maintain the rotational speed, which usually leads to an increase or abnormal fluctuation in power consumption. By capturing this deviation in power consumption, the wear degree or performance degradation of the grinding tool itself can be effectively characterized. Thus, the obtained power consumption deviation signal serves as a second dynamic process signal, providing crucial tool performance data for subsequent root cause analysis, enabling the system to distinguish between grinding state changes caused by material property fluctuations and grinding state changes caused by grinding tool wear.

[0073] The above technical solution provides an objective and quantitative method for evaluating the working performance of grinding tools. The introduction of power consumption deviation signals enables the system to sensitively detect performance changes in grinding tools caused by wear and other factors, thus providing a reliable basis for subsequent root cause analysis. This power consumption monitoring-based method has the advantages of simple implementation, strong real-time performance, and relatively low cost. It helps improve the intelligence level and control precision of the grinding process, thereby extending the service life of grinding tools and ensuring the grinding quality of PTFE pipe interfaces.

[0074] In some embodiments described above, this application proposes acquiring at least one second dynamic process signal during the grinding operation to characterize the working performance state of the grinding tool itself. However, in actual implementation, acquiring only a generalized working performance state signal may not provide sufficiently detailed or direct information to accurately determine whether the grinding tool has wear or surface defects, potentially affecting the accuracy of root cause analysis. If the above problems are not addressed, minor wear or defects in the grinding tool may not be identified in a timely manner, leading to decreased grinding quality or shortened tool life.

[0075] In response, this application further proposes a specific method for obtaining the aforementioned second dynamic process signal, including: acquiring images of the surface of the PTFE pipe interface after polishing using a multispectral imaging unit to obtain a multi-band surface image; acquiring the temperature of the PTFE pipe interface surface after polishing using a thermal imaging unit to obtain a surface temperature distribution map; determining a surface defect indication signal based on the multi-band surface image and the surface temperature distribution map using preset image analysis and temperature analysis rules; and using the surface defect indication signal as the second dynamic process signal.

[0076] Specifically, a multispectral imaging unit can be understood as a device capable of simultaneously imaging a target within multiple discrete narrow spectral bands. Its purpose is to capture the reflection, absorption, or emission characteristics of the PTFE pipe interface surface at different wavelengths. These characteristics are often closely related to the material's microstructure, surface roughness, and the presence of foreign objects or defects—information that is difficult to obtain with traditional visible light imaging. For example, a multispectral imaging unit can be configured to acquire images in multiple bands, including visible light, near-infrared (NIR), and short-wave infrared (SWIR). A thermal imaging unit, on the other hand, is a device that uses the principle of infrared radiation to convert the surface temperature distribution of an object into a visual image. Its purpose is to monitor in real-time the temperature changes of the PTFE pipe interface surface during or after polishing, as wear or defects in polishing tools often lead to increased local friction, resulting in localized temperature increases or uneven temperature distribution.

[0077] Multi-band surface images refer to a collection of images acquired by a multispectral imaging unit at different spectral bands, with each image containing surface information within a specific wavelength range. Surface temperature distribution maps are images acquired by a thermal imaging unit, reflecting the temperature values ​​at various points on the surface of the PTFE pipe interface. In practical applications, image analysis and temperature analysis rules can be a series of preset algorithms and judgment criteria used for spectral feature extraction, texture analysis, and defect identification of multi-band surface images, and for hotspot detection and temperature gradient analysis of surface temperature distribution maps. For example, it can be set that abnormal reflectivity in a specific spectral band, the appearance of a specific texture pattern on the surface (such as scratches or burn marks), or a local temperature exceeding a preset threshold are considered defects. Therefore, the surface defect indication signal can be a binary signal (e.g., 0 indicates no defect, 1 indicates defect) or a composite signal containing detailed information such as defect type, location, and severity.

[0078] This application's solution achieves comprehensive and detailed inspection of the surface quality of PTFE pipe interfaces through the collaborative operation of a multispectral imaging unit and a thermal imaging unit. The multispectral imaging unit captures spectral features such as changes in surface microstructure, scratches, or material burns caused by grinding tool wear, while the thermal imaging unit monitors in real time the accumulation of localized frictional heat and temperature anomalies caused by the worn tool. By fusing and analyzing these multi-source heterogeneous data and applying preset image and temperature analysis rules, surface defects can be accurately identified and converted into surface defect indication signals. This signal directly reflects the impact of the grinding tool on the workpiece surface, thus providing a more accurate and direct characterization of the grinding tool's working performance and offering a reliable basis for subsequent root cause analysis.

[0079] The above technical solution enables the acquisition of more refined and comprehensive information on the working performance status of grinding tools. Specifically, multispectral imaging and thermal imaging technologies can detect minute surface defects and temperature anomalies that are difficult to detect using traditional methods. These defects and anomalies are often direct manifestations of early wear on grinding tools. Therefore, the introduction of surface defect indication signals makes the assessment of grinding tool wear more accurate and timely, significantly improving the reliability of root cause analysis. Compared to relying solely on generalized working performance status signals, this solution can detect potential problems with grinding tools earlier, thereby avoiding a decline in grinding quality due to tool wear, effectively extending the service life of grinding tools, and ensuring the processing accuracy and consistency of PTFE tube interfaces.

[0080] In some preferred embodiments, a specific example is given below. Suppose that during the precision polishing of a PTFE tube interface, the polishing tool may wear down due to prolonged use. To accurately determine the wear condition of the polishing tool, images are first acquired on the surface of the polished PTFE tube interface using a multispectral imaging unit. This unit can be configured to acquire images in multiple bands, including 450nm (blue), 550nm (green), 650nm (red), 850nm (near-infrared), and 1550nm (short-wave infrared). Simultaneously, a thermal imaging unit acquires the temperature of the same area, generating a surface temperature distribution map. These multi-band surface images are then input into an image analysis module, which uses a preset algorithm (e.g., a matching algorithm based on a spectral feature library or a texture analysis algorithm) to detect defects such as microscopic scratches, burn marks, or material discoloration. For example, if an abnormally high reflectivity is detected in the 1550nm band, accompanied by linear texture in a specific direction, it may indicate the presence of a scratch. Simultaneously, the surface temperature distribution map is input into the temperature analysis module, which detects the presence of "hot spots" where the local temperature is more than 10°C higher than the ambient temperature, or areas with abnormal temperature gradients. If the image analysis results show multiple microscopic scratches, and the temperature analysis results show local hot spots corresponding to the scratch areas, a surface defect indication signal is generated according to preset image and temperature analysis rules. This signal indicates that the surface of the polishing tool has a defect caused by wear. This surface defect indication signal is then used as a second dynamic process signal for subsequent root cause analysis to determine whether the change in the first dynamic process signal is caused by wear of the polishing tool.

[0081] In some embodiments described above, a method is proposed to acquire static predictive information related to the area to be processed at the PTFE tube interface before the grinding operation begins, in order to characterize the inherent physical properties of the material to be processed. However, in practical applications, how to efficiently, non-destructively, and accurately acquire this static predictive information is crucial to ensuring the accuracy of subsequent root cause analysis. If the static predictive information is not acquired accurately, it may lead to deviations in the root cause analysis of changes in the first dynamic process signal, thereby affecting the effectiveness of the grinding adjustment strategy.

[0082] In response, this application further proposes a method for obtaining the aforementioned static prediction information, specifically including: before the grinding operation begins, transmitting a probe acoustic wave signal to the area to be processed through a non-contact acoustic detection unit and receiving the reflected acoustic wave signal; performing spectral feature analysis on the reflected acoustic wave signal to obtain acoustic reflection spectrum features; determining material characteristic prediction information based on the acoustic reflection spectrum features and calibration data characterizing the physical properties of different polytetrafluoroethylene materials, wherein the material characteristic prediction information is used to characterize the relative hardness of the area to be processed; and using the material characteristic prediction information as the static prediction information.

[0083] Specifically, a non-contact acoustic testing unit can be understood as a device that uses sound waves for non-destructive testing. It emits probe sound wave signals of specific frequency and intensity, which penetrate the area to be processed at the interface of a PTFE (polytetrafluoroethylene) tube. When these probe sound wave signals encounter the internal structure or interface of the material, they are reflected, refracted, or scattered. The non-contact acoustic testing unit then receives these reflected sound wave signals. The purpose of using a non-contact method is to avoid any physical damage to the surface or internal structure of the PTFE tube interface during the testing process, while ensuring the real-time nature and convenience of the testing.

[0084] Among these, spectral characteristic analysis of reflected acoustic signals refers to converting the received time-domain reflected acoustic signals into frequency-domain signals using signal processing techniques such as Fourier transform, thereby analyzing their energy distribution, peak frequency, bandwidth, and other characteristics at different frequencies. The acoustic reflection spectrum characteristics are a comprehensive reflection of these spectral analysis results, and they can sensitively reflect the inherent physical properties of materials, such as density, elastic modulus, internal defects, and crystal structure.

[0085] In practical applications, based on acoustic reflection spectrum characteristics and pre-established calibration data characterizing the physical properties of different polytetrafluoroethylene (PTFE) materials, predictive information about material properties can be determined. The calibration data is obtained by performing acoustic testing and spectral analysis on PTFE materials with known physical properties, establishing a database of correspondences between acoustic reflection spectrum characteristics and material physical properties (such as hardness, density, and toughness). By comparing and matching the acoustic reflection spectrum characteristics of the area to be processed with this calibration data, the predictive information about the material properties of the area to be processed can be accurately inferred, such as its relative hardness. Relative hardness is an important indicator of a material's resistance to localized plastic deformation and has a direct impact on the material removal rate and surface quality during the grinding process. Finally, this predictive information about material properties is used as static predictive information to provide accurate data support for subsequent root cause analysis of the material's inherent properties.

[0086] This application's solution introduces a non-contact acoustic detection unit, enabling the non-destructive acquisition of internal physical property information of the PTFE pipe interface area to be processed before the grinding operation begins. The detection acoustic signal propagates and reflects within the material, and the reflected signal carries information about the material's inherent physical properties, such as density and elastic modulus. By performing spectral feature analysis on these reflected acoustic signals, unique acoustic reflection spectrum characteristics can be extracted. These characteristics are highly sensitive indicators of the material's internal structure and composition. Furthermore, by combining pre-established calibration data characterizing the physical properties of different PTFE materials, the abstract acoustic reflection spectrum characteristics can be transformed into specific, quantifiable predictive information about material properties, such as the relative hardness of the area to be processed. This method avoids surface damage that may result from traditional contact testing, while providing more in-depth and accurate data on the material's internal properties than macroscopic observation. Therefore, the obtained material property prediction information, as static prediction information, provides a solid and accurate foundation for subsequent root cause analysis of the changes in the first dynamic process signal based on the static prediction information and the second dynamic process signal, ensuring the reliability of the root cause analysis results, thereby enabling the grinding adjustment strategy to respond more accurately to the problems caused by material property fluctuations.

[0087] Through the above technical solution, this application can achieve non-destructive, efficient, and accurate acquisition of static pre-judgment information of the PTFE pipe interface to be processed area. Non-contact acoustic detection avoids potential damage to the workpiece surface and improves the safety of the detection. Spectral feature analysis combined with calibration data makes the judgment of the inherent physical properties of the material (especially relative hardness) more accurate and quantitative, overcoming the empirical or subjective biases that may exist in traditional methods. This accurate static pre-judgment information significantly improves the accuracy of subsequent root cause analysis, enabling the system to more reliably distinguish whether the change in the first dynamic process signal is caused by material property fluctuations or by wear of the grinding tool. This provides key data support for implementing more precise and effective grinding adjustment strategies, ultimately improving the overall control accuracy and efficiency of precision grinding of PTFE pipe interfaces.

[0088] In some preferred embodiments, a specific example is given below. Suppose that before precision polishing a batch of PTFE pipe interfaces, it is necessary to accurately obtain the material properties of the area to be processed for each interface. In this case, a non-contact acoustic detection unit, such as an ultrasonic probe, can be deployed and placed at a certain distance from the area to be processed on the PTFE pipe interface. The probe emits a series of high-frequency sound wave signals towards the area to be processed. When the sound wave signals propagate within the PTFE material and encounter microstructures or density inhomogeneities within the material, they are reflected. The probe then receives these reflected sound wave signals.

[0089] The received reflected acoustic wave signal is transmitted to a signal processing module, which performs spectral feature analysis on the signal. For example, using a Fast Fourier Transform (FFT) algorithm, the time-domain signal is converted into frequency-domain acoustic reflection spectrum features, and its dominant frequency, harmonic distribution, and energy attenuation characteristics are analyzed. Simultaneously, a calibration database is pre-stored within the system, containing acoustic reflection spectrum features of polytetrafluoroethylene (PTFE) materials with various known hardness grades. The signal processing module compares and matches the acoustic reflection spectrum features of the current area to be processed with the data in the calibration database.

[0090] By comparison, the system can determine that the acoustic reflection spectrum characteristics of the current processing area are closest to those of a specific hardness grade of PTFE material in the database, thus determining the material property prediction information for that area, such as its relative hardness being "medium to hard". This "medium to hard" material property prediction information is then used as static prediction information and input into the subsequent root cause analysis module. When fluctuations occur in the first dynamic process signal during grinding, the system will first combine this "medium to hard" static prediction information to perform the first root cause analysis, determining whether the fluctuation is consistent with the inherent hardness characteristics of the material, thereby more accurately determining whether the fluctuation is caused by material properties or by wear of the grinding tool, and then executing the corresponding grinding adjustment strategy.

[0091] In cutting-edge industries such as semiconductors and biopharmaceuticals, PTFE tubing is widely used for ultrapure fluid transport due to its superior chemical inertness and purity. However, the precision polishing of PTFE tubing interfaces is a critical step in the production process, requiring extremely high dimensional tolerances and surface finishes to ensure a contamination-free, sealed connection. Traditional manual polishing methods are inefficient, inconsistent, and susceptible to human error, resulting in high scrap rates. While existing automated precision polishing systems and their online adaptive control mechanisms improve dimensional accuracy and product consistency by monitoring contact force in real time and dynamically adjusting polishing parameters using force sensors, they can still misinterpret increased polishing force as the tool encountering harder parts of the PTFE tubing when the polishing tool becomes worn and inefficient. This leads to misjudgment and improper compensation, accelerating tool wear and severely damaging the surface finish of the PTFE tubing interfaces, resulting in defects such as micro-scratches, localized thermal degradation, or uneven surface texture. These defects are unacceptable for high-purity applications and can lead to particle shedding, bacterial adhesion, or damage to the material's long-term chemical inertness.

[0092] In some embodiments, this application proposes an intelligent control system for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces, comprising: a first acquisition unit, configured to acquire static prediction information related to the area to be processed of the PTFE pipe interface before the grinding operation begins; the static prediction information is used to characterize the inherent physical properties of the material to be processed of the PTFE pipe interface; a second acquisition unit, configured to acquire at least one first dynamic process signal and at least one second dynamic process signal during the grinding operation; the first dynamic process signal is used to characterize the interaction state between the grinding tool and the PTFE pipe interface, and the second dynamic process signal is used to characterize the working performance state of the grinding tool itself; an analysis unit, configured to perform root cause analysis on the changes in the first dynamic process signal based on the static prediction information and the second dynamic process signal, and obtain a root cause analysis result; the root cause analysis result is used to reflect that the changes in the first dynamic process signal are caused by the characteristic fluctuations of the material to be processed or by the wear of the grinding tool; and an execution unit, configured to execute a corresponding grinding adjustment strategy based on the root cause analysis result.

[0093] In some embodiments, the analysis unit is specifically used to: perform a first root cause analysis on the change of the first dynamic process signal based on the static prediction information to determine whether the change of the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed; if the changing trend of the interaction state between the grinding tool and the polytetrafluoroethylene pipe interface is consistent with the inherent physical characteristics of the material to be processed at the polytetrafluoroethylene pipe interface, then it is determined that the change of the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed; otherwise, perform a second root cause analysis on the change of the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the change is caused by the wear of the grinding tool.

[0094] The system of this application acquires static pre-judgment information of the material before grinding through a first acquisition unit, providing a benchmark for subsequent analysis; during grinding, a second acquisition unit acquires the interaction state between the grinding tool and the pipe interface (first dynamic process signal) and the working performance state of the grinding tool itself (second dynamic process signal) in real time; the analysis unit integrates this information to accurately analyze the root cause of grinding anomalies, distinguishing between fluctuations in material properties and tool wear; finally, the execution unit executes targeted grinding adjustment strategies based on the analysis results. This multi-dimensional information fusion and intelligent decision-making mechanism effectively solves the problems of accelerated tool wear and decreased product surface quality caused by misjudgment in existing technologies, significantly improving the grinding accuracy and surface quality of PTFE pipe interfaces.

[0095] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent control of precision grinding of polytetrafluoroethylene (PTFE) pipe joints, characterized in that, include: Before the grinding operation begins, obtain static prediction information related to the area to be processed of the PTFE pipe interface. The static prediction information is used to characterize the inherent physical properties of the material to be processed for the polytetrafluoroethylene pipe interface; Acquire at least one first dynamic process signal and at least one second dynamic process signal during the polishing operation; the first dynamic process signal is used to characterize the interaction state between the polishing tool and the polytetrafluoroethylene pipe interface, and the second dynamic process signal is used to characterize the working performance state of the polishing tool itself. Based on the static prediction information and the second dynamic process signal, a root cause analysis is performed on the change of the first dynamic process signal to obtain the root cause analysis result; the root cause analysis result is used to reflect that the change of the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed or by the wear of the grinding tool; Based on the root cause analysis results, the corresponding polishing and adjustment strategies are implemented.

2. The method according to claim 1, characterized in that, The step of performing root cause analysis on the changes in the first dynamic process signal based on the static prediction information and the second dynamic process signal to obtain the root cause analysis results includes: Based on the static prediction information, a first root cause analysis is performed on the change of the first dynamic process signal to determine whether the change of the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed. If the changing trend of the interaction state between the grinding tool and the polytetrafluoroethylene pipe interface is consistent with the inherent physical properties of the material to be processed at the polytetrafluoroethylene pipe interface, then it is determined that the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed. Otherwise, a second root cause analysis is performed on the changes in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the changes are caused by the wear of the grinding tool.

3. The method according to claim 2, characterized in that, The step of performing a second root cause analysis on the changes in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the changes are caused by the wear of the grinding tool includes: The second dynamic process signal is extracted to obtain an energy consumption deviation signal and a surface defect indication signal; the energy consumption deviation signal is used to reflect the deviation between the current energy consumption of the polishing tool and the reference energy consumption, and the surface defect indication signal is used to reflect whether there are defects on the surface of the polishing tool. If the power consumption deviation signal is greater than the preset deviation, and the surface defect indication signal indicates that there is a defect on the surface of the polishing tool, then it is determined that the change is caused by the wear of the polishing tool.

4. The method according to claim 1, characterized in that, The step of implementing the corresponding polishing and adjustment strategy based on the root cause analysis results includes: When the root cause analysis results show that the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed, the grinding feed speed or the grinding spindle speed is finely adjusted according to the change amplitude and direction of the first dynamic process signal. When the root cause analysis results show that the change in the first dynamic process signal is caused by the wear of the grinding tool, a second fine adjustment is made to the grinding feed speed or grinding pressure, and / or, maintenance prompts are generated for the grinding tool.

5. The method according to claim 1, characterized in that, The acquisition of at least one first dynamic process signal during the polishing operation includes: The contact force signal is collected in real time by a force sensing unit installed on the grinding actuator. The contact force signal is used to characterize the interaction force between the grinding tool and the polytetrafluoroethylene pipe interface. The collected contact force signal is used as the first dynamic process signal.

6. The method according to claim 1, characterized in that, Acquire at least one second dynamic process signal during the grinding operation, including: The power consumption signal is collected in real time by a power monitoring unit connected to the grinding spindle drive unit; Based on the power consumption signal and the pre-established reference power consumption corresponding to the grinding tool in the initial state, the power consumption deviation signal is determined. The energy consumption deviation signal is used as the second dynamic process signal.

7. The method according to claim 1, characterized in that, Acquire at least one second dynamic process signal during the grinding operation, including: The multispectral imaging unit is used to acquire images of the surface of the PTFE pipe interface after polishing, and multi-band surface images are obtained. The surface temperature of the PTFE pipe interface after polishing is collected by a thermal imaging unit to obtain a surface temperature distribution map. Based on the multi-band surface image and the surface temperature distribution map, surface defect indication signals are determined through preset image analysis and temperature analysis rules; The surface defect indication signal is used as the second dynamic process signal.

8. The method according to claim 1, characterized in that, Before the grinding operation begins, static prediction information related to the area to be processed at the PTFE pipe interface is obtained, including: Before the grinding operation begins, a non-contact acoustic detection unit transmits a detection sound wave signal to the area to be processed and receives the reflected sound wave signal. The reflected acoustic wave signal is subjected to spectral feature analysis to obtain acoustic reflection spectrum features; Based on the acoustic reflection spectrum characteristics and calibration data characterizing the physical properties of different polytetrafluoroethylene materials, material property prediction information is determined, which is used to characterize the relative hardness of the area to be processed. The material property prediction information is used as the static prediction information.

9. A smart control system for precision grinding of polytetrafluoroethylene (PTFE) pipe interfaces, characterized in that, include: The first acquisition unit is used to acquire static prediction information related to the processing area of ​​the polytetrafluoroethylene pipe interface before the grinding operation begins. The static prediction information is used to characterize the inherent physical properties of the material to be processed for the polytetrafluoroethylene pipe interface; The second acquisition unit is used to acquire at least one first dynamic process signal and at least one second dynamic process signal during the grinding operation; the first dynamic process signal is used to characterize the interaction state between the grinding tool and the polytetrafluoroethylene pipe interface, and the second dynamic process signal is used to characterize the working performance state of the grinding tool itself. The analysis unit is used to perform root cause analysis on the changes in the first dynamic process signal based on the static prediction information and the second dynamic process signal, and obtain the root cause analysis result; the root cause analysis result is used to reflect that the changes in the first dynamic process signal are caused by the characteristic fluctuations of the material to be processed or by the wear of the grinding tool; The execution unit is used to execute the corresponding polishing adjustment strategy based on the root cause analysis results.

10. The system according to claim 9, characterized in that, The analysis unit is specifically used for: Based on the static prediction information, a first root cause analysis is performed on the change of the first dynamic process signal to determine whether the change of the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed. If the changing trend of the interaction state between the grinding tool and the polytetrafluoroethylene pipe interface is consistent with the inherent physical properties of the material to be processed at the polytetrafluoroethylene pipe interface, then it is determined that the change in the first dynamic process signal is caused by the characteristic fluctuation of the material to be processed. Otherwise, a second root cause analysis is performed on the changes in the first dynamic process signal in conjunction with the second dynamic process signal to determine whether the changes are caused by the wear of the grinding tool.

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