A method, device and medium for identifying deformation of an ultrathin saw blade

Abstract

The application discloses a kind of ultra-thin saw blade deformation degree identification method, equipment and medium, belong to precision machining online detection technical field.The application first monitors the temperature difference gradient evolution trend of saw blade edge and center in real time, and executes logic filtering using threshold value based on hardware noise reference automatic determination, to remove the non-thermal displacement interference caused by main shaft mechanical vibration or electromagnetic interference in time domain.On this basis, real-time temperature and axial displacement are synchronously collected, and a geometric constraint correction model containing logarithmic correction term is constructed to calculate real-time deformation coefficient.The logarithmic correction term corrects the physical distortion of the classical unconstrained thermal strain linearization model when dealing with limited disc structure, and successfully realizes the quantitative separation of "harmless thermal displacement" and "abnormal unstable deformation".Finally, the system executes closed-loop judgment according to the deviation degree of real-time deformation coefficient to theoretical thermal response reference, and outputs control signal.The application does not need experimental calibration weight, has very high signal-to-noise ratio and cross-device universality, can sensitively capture saw blade warping precursor, and effectively protects high-value ultra-thin saw blade from plastic damage.

Description

Technical Field

[0001] This invention relates to the field of precision machining online inspection technology, specifically to a method, device, and medium for identifying the deformation of an ultra-thin saw blade. Background Technology

[0002] As the precision machining industry demands increasingly higher material utilization rates, the application of ultra-thin saw blades is becoming more widespread. However, because the radial dimension of an ultra-thin saw blade is much larger than its axial thickness, its structural rigidity is extremely low, making it highly sensitive to thermal stress. During continuous cutting, the edges of the saw teeth generate high heat due to intense friction, resulting in a nonlinear temperature gradient within the saw blade that decreases from the edge to the center.

[0003] The existing identification methods and their shortcomings are as follows: Traditional online inspection methods often use displacement sensors to measure the runout of the saw blade's side. However, in the case of ultra-thin saw blades, the measured displacement is a combination of multiple factors, including: thermal expansion of the spindle, normal thermal expansion of the saw blade itself, installation misalignment, and warping deformation caused by thermal instability. Existing technologies cannot effectively decouple the "substantial deformation component" from the complex composite displacement signal, often leading to frequent false alarms due to normal thermal expansion fluctuations.

[0004] If thermal compensation algorithms are used, existing technologies are mostly based on traditional unconstrained thermal strain linearization models. However, ultra-thin saw blades, when fastened to the flange, are typical constrained disc structures, and their internal stress and displacement fields exhibit nonlinear logarithmic distributions. Directly applying traditional models will result in significant theoretical deviations, leading to insufficient detection accuracy to meet the requirements of precision machining.

[0005] Existing threshold alarm methods often have a lag effect, only identifying damage after deformation has already caused the workpiece to break. Furthermore, different saw blade specifications and materials require extensive cutting experiments to calibrate the threshold, lacking a universally applicable physical model. Summary of the Invention

[0006] To achieve the above-mentioned objectives, this invention provides a method for identifying the deformation degree of an ultra-thin saw blade, characterized by comprising: Step S1: acquiring the geometric constants of the saw blade, determining the sampling radius, and initializing the static reference distance; Step S2: synchronously acquiring the real-time edge temperature, center temperature, and real-time axial displacement of the saw blade; Step S3: determining whether to activate the deformation coefficient calculation process based on the comparison result of the rate of change of the temperature gradient and the trigger threshold; Step S4: calculating the real-time deformation coefficient using a geometric constraint correction model by combining the real-time axial displacement, static reference distance, real-time temperature difference, and sampling radius; Step S5: outputting a control signal to the actuator based on the determination result of the real-time deformation coefficient and the safety threshold.

[0007] Optionally, the geometric constants of the saw blade are obtained, the sampling radius is determined, and the static reference distance is initialized. Specifically, this includes: obtaining the base radius of the saw blade and the flange radius, subtracting the preset geometric avoidance constant from the base radius to obtain the sampling radius, and obtaining the static reference distance between the sensor and the saw blade base during the machine tool startup phase.

[0008] Optionally, the process of activating the deformation coefficient calculation is determined based on the comparison between the rate of change of the temperature gradient and the trigger threshold. Specifically, this includes: calculating the first derivative of the difference between the real-time edge temperature and the real-time center temperature; and activating the deformation coefficient calculation process when the absolute value of the first derivative is greater than the trigger threshold. The trigger threshold is determined based on the product of the standard deviation of the temperature gradient collected by the system during the no-load rotation phase and a preset multiple.

[0009] Optionally, the real-time deformation coefficient can be calculated using a geometric constraint correction model. Specifically, the real-time deformation coefficient is calculated based on the absolute value of the difference between the real-time axial displacement and the static reference distance, the difference between the real-time edge temperature and the real-time center temperature, the sampling radius, the linear expansion coefficient of the matrix material, and a logarithmic correction term including the ratio of the sampling radius to the flange radius.

[0010] Optionally, a control signal is output to the actuator based on the determination result of the real-time deformation coefficient and the safety threshold. Specifically, this includes: determining whether the real-time deformation coefficient exceeds the preset safety threshold; if the real-time deformation coefficient exceeds the preset safety threshold, then outputting an emergency stop signal to the program logic controller.

[0011] To achieve the above-mentioned objectives, this invention also provides a deformation degree identification device for ultra-thin saw blades, characterized by applying the deformation degree identification method for ultra-thin saw blades as described above, comprising: an initialization module for acquiring the geometric constants of the saw blade, determining the sampling radius, and initializing the static reference distance; a signal acquisition module for synchronously acquiring the real-time edge temperature, center temperature, and real-time axial displacement of the saw blade; a trigger verification module for determining whether to activate the deformation coefficient calculation process based on the comparison result between the rate of change of the temperature gradient and the trigger threshold; a model calculation module for calculating the real-time deformation coefficient using a geometric constraint correction model, combining the real-time axial displacement, static reference distance, real-time temperature difference, and sampling radius; and a control execution module for outputting control signals to the execution mechanism based on the determination result of the real-time deformation coefficient and the safety threshold.

[0012] To achieve the above-mentioned objectives, the present invention also provides a computer-readable storage medium, characterized in that it stores a computer program, which, when executed by a processor, implements the aforementioned method for identifying the deformation degree of an ultrathin saw blade.

[0013] This invention, by introducing a geometric constraint correction model and thermo-mechanical coupling recognition logic, has the following significant technical advancements compared to existing technologies: This invention achieves precise monitoring of the processing status of ultra-thin saw blades by constructing a hierarchical signal decoupling and identification mechanism. First, by using the time-domain coupling verification of temperature gradient and displacement fluctuation, non-thermodynamic background noise caused by mechanical vibration or electromagnetic interference is eliminated at the source. On this basis, by introducing a physical evaluation model that includes constraint boundary correction terms, quantitative decoupling of "harmless normal thermal response" and "harmful instability and warping deformation" is achieved. That is, the theoretical benchmark constructed using physical constants automatically offsets normal thermal expansion displacement, while only sensitively capturing nonlinear jumps caused by internal stress instability. This progressive identification logic from physical causes to macroscopic results not only corrects the fundamental deviation of conventional calculation models in the constrained disk scenario, but also ensures that the system can effectively intercept irreversible instability deformation without interfering with normal thermal drift, significantly improving the signal-to-noise ratio of online detection and the continuity of the production process.

[0014] The deformation coefficient evaluation model quantifies and separates "harmless normal thermal displacement" from "harmful instability and warping deformation." The system can automatically deduct normal positional shifts caused by environmental temperature rise and only issue warnings for modal transitions caused by stress instability, greatly improving the signal-to-noise ratio (SNR) of the identification.

[0015] This solution effectively eliminates the fundamental bias of conventional displacement calculation models based on the free expansion assumption when dealing with constrained disk structures by introducing a logarithmic correction term that incorporates the proportional relationship between the sampling radius and the constraint boundary. The model achieves accurate reproduction of the saw blade's true physical deformation under complex thermodynamic environments through precise quantification of the strain accumulation path in a non-uniform thermal field. Since the model's calculations are entirely based on the system's inherent geometric and material physical constants, redundant experimental data calibration for specific working conditions is unnecessary. This physically deterministic modeling approach significantly enhances the technical adaptability and robustness of the recognition algorithm across different product specifications and diverse processing scenarios. Unlike traditional hysteresis feedback, this scheme drives the identification model by monitoring the temperature gradient as a "trigger." The Γ coefficient sensitively detects this trend even before the saw blade exhibits visible macroscopic deformation, but when internal thermal stress is nearing instability. This closed-loop identification from cause to effect effectively protects high-value ultra-thin saw blades from plastic damage. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the deformation identification method for an ultra-thin saw blade according to the present invention. Figure 2 This is a schematic diagram of the structure of the deformation degree identification device for an ultra-thin saw blade according to the present invention; Figure 3This is a schematic diagram of the computer-readable storage medium structure described in this invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of the present invention.

[0018] If the present invention involves orientation (e.g., up, down, left, right, front, back, outside, inside, etc.) when described, then the orientations involved need to be defined.

[0019] The scope of the embodiments described herein includes the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitations, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0020] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientation or positional relationships are based on the orientation or positional relationships shown in the accompanying drawings and are used only for the convenience of describing this document and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements, or direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0021] The following is in conjunction with the appendix Figure 1This application provides a detailed description of a method for identifying the deformation degree of an ultrathin saw blade.

[0022] This invention provides a method for identifying the deformation degree of an ultrathin saw blade. The overall technical outline is as follows: Step S1, acquiring geometric parameters, locking the sampling ring, and initializing the cold-state reference; Step S2, simultaneously acquiring the edge temperature, center temperature, and axial spatial distance of the saw blade during processing; Step S3, performing logical filtering based on the first-order evolution trend of the temperature difference field to determine whether to activate the core calculation model; Step S4, using a geometric constraint correction model, coupling the measured displacement with the theoretical thermal response potential to produce the deformation coefficient. Step S5, based on The numerical deviation is determined by closed-loop logic, and a stop or maintain command is sent to the machine tool.

[0023] In step S1, the system does not merely record numerical values, but rather establishes the physical boundary conditions for the entire identification logic. The obtained flange radius... This effectively defines the zero-displacement constraint boundary for the radial displacement of the saw blade. For ultra-thin saw blades with a substrate thickness of only 0.5mm to 1.2mm, this central rigid constraint is the physical cause of their warping deformation after heating. Regarding the sampling radius... The determination, The value of reflects the trade-offs in engineering practice: if If the laser spot is too small, it is prone to diffraction noise due to the sawtooth oscillation under high-speed rotation; if it is too large, it will be far from the edge region where thermal stress is most intense. Setting it between 2mm and 5mm ensures that while avoiding geometric interference from the tooth gaps, it maximizes the capture of early displacement characteristics caused by thermal instability. Static geometric reference distance This is used to define the initial geometric state of the system.

[0024] In step S2, the system emphasizes high signal synchronization. Traditional detection methods often monitor displacement in isolation, but this cannot distinguish between normal thermal expansion and abnormal mechanical oscillation. This invention uses sensors to collect the temperature of the sawtooth edge in real time. Core temperature and the real-time measured axial spatial distance This multi-field coupled data acquisition method is a prerequisite for subsequent physical image reconstruction and removal of "harmless thermal drift".

[0025] In step S3, the system introduces a hierarchical filtering logic based on "causal coupling" to eliminate displacement interference caused by non-thermal factors such as spindle mechanical vibration and electromagnetic interference. Specifically, the process involves calculating the temperature gradient. first derivative .like Less than the preset trigger threshold If the current displacement fluctuation is determined to be caused by non-thermal noise, subsequent model calculations will not be activated. (Regarding the trigger threshold...) The specific acquisition process in this embodiment employs an automatic calibration method based on a system hardware noise benchmark. First, during the system self-test phase—the no-load rotation phase after machine tool startup and before actual cutting—the infrared temperature sensor begins operation. At this time, since there is no frictional heat input, the temperature difference should theoretically be zero. However, due to limitations in hardware sensitivity and transmission fluctuations, the system will collect minute random interference signals. The system continuously collects... Instantaneous temperature gradient data for each period And calculate the standard deviation of the static noise sequence. :

[0026] Finally, threshold mapping is performed based on the confidence interval, and the following settings are made: In this embodiment, the scaling factor The value is typically set to 3. Based on the three-standard-deviation principle, the system can eliminate the hardware's own electronic random noise with a 99.7% confidence level. Only when the rate of temperature change exceeds three times the system's own fluctuation capability is it determined to be an "effective thermodynamic driving force," thus activating the next level of verification.

[0027] In step S4, the system uses a core evaluation model to quantitatively decouple the instability state of the saw blade. This model achieves accurate reconstruction of the physical deformation through the following formula:

[0028] The core advancement of this formula lies in the introduction of a logarithmic correction term. Traditional unconstrained thermal strain linearization models are suitable for unrestricted long rod models, but ultrathin saw blades, under processing conditions, are essentially disks with rigid central constraints. According to the steady-state thermal conduction law of thermodynamics, their radial temperature gradient exhibits a logarithmic distribution. The logarithmic term introduced in this scheme successfully corrects the stress shielding effect caused by the flange constraint, accurately reproducing the strain accumulation path from the constraint boundary to the free edge. When the saw blade is under normal thermal elongation, the displacement increment at the numerator end is offset by the theoretical prediction value at the denominator end based on physical constants. It has remained within a stable range.

[0029] In step S5, the system through The offset of the coefficient is used to determine the fault mode. Unlike the lag in traditional threshold alarms, this scheme uses the deviation between the measured displacement and the theoretical potential as the criterion. When the saw blade is not unstable, the denominator will increase synchronously, making... Once warping occurs, the displacement components will exhibit nonlinear jumps, leading to... Rapid deviation from the baseline value. This closed-loop identification from cause to effect allows the system to detect anomalies through safety thresholds during the "instability precursor period" before permanent plastic damage to the saw blade, and immediately output an emergency stop signal to the programmable logic controller.

[0030] Specific application examples Typical scenario example: Taking the steady-state performance of an ultra-thin manganese steel saw blade during the initial stage of continuous cutting as an example. The saw blade base radius is set. The flange radius is 150mm. The sampling radius is 50mm. The material's inherent coefficient of linear expansion is set at 145mm near the edge. for .

[0031] During the cold start phase of the machine tool, the system calibrates the initial static reference. The thickness is 10.000 mm. As the cutting process continues, the saw teeth generate heat due to friction at the edges, causing heat to conduct towards the center. When the thermal field reaches dynamic equilibrium, the infrared sensor measures the edge temperature. for core temperature for The real-time temperature difference is .

[0032] At this moment, the laser sensor captured Real-time spatial distance This reflects the saw blade producing The axial displacement deviation. Model calculations show that, under these geometric constraints, The temperature difference should theoretically induce approximately The natural thermal elongation. The deformation coefficient is calculated. The value is approximately 0.998, indicating that the measured displacement is in high agreement with the theoretical thermal response potential.

[0033] Although the saw blade produces micron-level physical displacement, the system identifies its physical nature as "harmless" thermal breathing motion, determines that the processing state is in thermodynamic steady state, thereby maintaining continuous operation of the machine tool and effectively avoiding false alarms and shutdowns caused by normal thermal drift of traditional displacement sensors.

[0034] Extreme scenario example: Under extreme conditions such as continuous high-load cutting or instantaneous failure of the cooling system, the evolution of thermal stress inside the saw blade will disrupt the linear equilibrium. The same geometric parameters and material constants are maintained as described above.

[0035] As heat rapidly accumulates in the cutting area, the measured real-time temperature difference between the edge and the center further increases. Based on calculations using physical laws, if the saw blade remains in a state of normal thermal expansion within the confined plane, its theoretical axial displacement should be only [value missing]. about.

[0036] However, due to the radial compressive stress accumulated inside the saw blade exceeding the critical load of the ultrathin structure, the saw blade instantly transformed from a stable planar mode to a three-dimensional warp mode. At this moment, the laser sensor sensitively captured the nonlinear spatial jump signal, and the measured distance... Mutation This caused the total displacement deviation to surge to .

[0037] Substituting into the model, the deformation coefficient at this point is calculated. The measured displacement surged to approximately 1.807, far exceeding the theoretical thermal response range expected under this thermodynamic intensity. By identifying this significant energy-displacement imbalance, the system accurately determined that the saw blade was on the verge of plastic failure, deeming it an irreversible unstable deformation. It then immediately sent an emergency stop command to the programmable logic controller, preventing potential saw blade breakage and workpiece scrapping within milliseconds.

[0038] The above examples, by comparing the physical indicators under two modes of normal heating and abnormal instability, fully demonstrate the technical advantages of this invention in dealing with the problems of "signal aliasing" and "physical distortion".

[0039] System Implementation Examples The deformation identification system for ultrathin saw blades provided in this embodiment of the invention adopts a modular design to implement the above logic. The initialization module executes step S1, responsible for acquiring geometric parameters and zero-position calibration; the signal acquisition module executes step S2, responsible for real-time synchronous acquisition of multi-dimensional thermal field signals; the trigger verification module executes step S3, responsible for logic filtering and calculation activation based on a noise benchmark; and the model calculation module executes step S4, using geometric constraints to correct the model output deformation coefficients. The control execution module executes step S5, which is responsible for performing closed-loop actions such as shutdown or warning based on the evaluation results.

[0040] Terminal and Media Examples The following is in conjunction with the appendix Figure 3 The electronic terminal and storage medium provided in the embodiments of this application will be described in detail.

[0041] The electronic terminal provided in this embodiment of the invention includes a processor, a memory, and a communication bus. The memory stores a computer program that implements the above-described identification method. When the processor executes the program, it implements the logic of steps S1 to S5 in the above method embodiment. In terms of hardware architecture, the processor connects to a sensor unit via the communication bus to acquire input data and connects to an actuator to output control signals. The computer-readable storage medium provided by this invention stores a computer program that, when executed by the processor, can correct physical distortions and achieve quantified separation of harmless thermal displacement and harmful warping deformation.

[0042] This invention can be an apparatus, method, and / or computer program product. A computer program product may include a readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0043] Storage media can be tangible devices that hold and store instructions for use by instruction execution devices. Storage media can include, for example, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof.

[0044] It should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0045] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the deformation degree of an ultrathin saw blade, characterized in that, include: Step S1: Obtain the geometric constants of the saw blade, determine the sampling radius, and initialize the static reference distance; Step S2: Synchronously collect the real-time edge temperature, center temperature, and real-time axial displacement of the saw blade; Step S3: Determine whether to activate the deformation coefficient calculation process based on the comparison between the rate of change of the temperature gradient and the trigger threshold; Step S4: Combine real-time axial displacement, static reference distance, real-time temperature difference, and sampling radius to calculate the real-time deformation coefficient using a geometric constraint correction model; Step S5: Output control signals to the actuator based on the determination results of the real-time deformation coefficient and the safety threshold.

2. The method for identifying the deformation degree of an ultrathin saw blade according to claim 1, characterized in that, The geometric constants of the saw blade are obtained, the sampling radius is determined, and the static reference distance is initialized. Specifically, the base radius of the saw blade and the flange radius are obtained, the sampling radius is obtained by subtracting the preset geometric avoidance constant from the base radius, and the static reference distance between the sensor and the saw blade base is obtained during the machine tool startup phase.

3. The method for identifying the deformation degree of an ultrathin saw blade according to claim 1, characterized in that, The determination of whether to activate the deformation coefficient calculation process is based on the comparison between the rate of change of the temperature gradient and the trigger threshold. Specifically, the first derivative of the difference between the real-time edge temperature and the real-time center temperature is calculated. When the absolute value of the first derivative is greater than the trigger threshold, the deformation coefficient calculation process is activated. The trigger threshold is determined by multiplying the standard deviation of the temperature gradient collected by the system during the no-load rotation phase with a preset multiple.

4. The method for identifying the deformation degree of an ultrathin saw blade according to claim 1, characterized in that, The real-time deformation coefficient is calculated using a geometric constraint correction model. Specifically, the real-time deformation coefficient is calculated based on the absolute value of the difference between the real-time axial displacement and the static reference distance, the difference between the real-time edge temperature and the real-time center temperature, the sampling radius, the linear expansion coefficient of the matrix material, and a logarithmic correction term that includes the ratio of the sampling radius to the flange radius.

5. The method for identifying the deformation degree of an ultrathin saw blade according to claim 1, characterized in that, Based on the determination result of the real-time deformation coefficient and the safety threshold, a control signal is output to the actuator. Specifically, it is determined whether the real-time deformation coefficient exceeds the preset safety threshold. If the real-time deformation coefficient exceeds the preset safety threshold, an emergency stop signal is output to the program logic controller.

6. A deformation degree identification device for ultra-thin saw blades, characterized in that, The method for identifying the deformation degree of an ultrathin saw blade as described in any one of claims 1 to 5 includes: The initialization module obtains the geometric constants of the saw blade, determines the sampling radius, and initializes the static reference distance; The signal acquisition module synchronously acquires the real-time edge temperature, center temperature, and real-time axial displacement of the saw blade; The trigger verification module determines whether to activate the deformation coefficient calculation process based on the comparison between the rate of change of the temperature gradient and the trigger threshold. The model calculation module combines real-time axial displacement, static reference distance, real-time temperature difference, and sampling radius, and uses geometric constraints to correct the model and calculate the real-time deformation coefficient. The control and execution module outputs control signals to the actuator based on the determination results of the real-time deformation coefficient and the safety threshold.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements a method for identifying the deformation degree of an ultrathin saw blade as described in any one of claims 1-5.

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