Door closer machining method based on computer-aided manufacturing

Abstract

This invention relates to the field of intelligent manufacturing and machining control technology, specifically a door closer machining method based on computer-aided manufacturing. The method includes: acquiring real-time machining status feedback signals from the door closer machining environment; calling a preset adaptive control model for feature extraction and optimization calculation to generate an adaptive parameter compensation command sequence; combining the actual machining feedback from the previous control cycle to extract parameter change frequency characteristics and amplitude fluctuation characteristics, and fusing and calculating the stress entropy values ​​of the control commands; reading and evaluating the underlying hardware tolerance threshold of the underlying servo drive system to generate an indicator for maintaining the optimal cutting state or a smooth degradation switching control indicator; and issuing the adaptive parameter compensation command sequence or the smooth control command sequence accordingly. This invention achieves coordinated control of machining cycle optimization and underlying execution stability boundaries, avoiding sudden torque reduction, tool breakage, and spindle impact.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and machining control technology, specifically to a door closer processing method based on computer-aided manufacturing. Background Technology

[0002] As a door control actuator, the key components of the door closer, such as the cylinder and housing, usually involve complex machining structures such as deep-hole hydraulic oil passages and steering transition zones. In computer-aided manufacturing, the stability of the machining process is directly related to the machining cycle time, dimensional consistency, and tool life. Therefore, precise control of the machining process is the key to ensuring the continuous and stable operation of the door closer production line.

[0003] Existing door closer machining control methods have many problems. For example, they often use fixed cutting parameters or only perform unidirectional adaptive adjustment based on real-time load. Although they can respond to changes in material hardness, tool wear, and thermal disturbances to a certain extent, they do not fully consider the matching relationship between the upper-level dynamic adjustment and the execution boundary of the lower-level servo drive system. This can easily lead to high-frequency oscillation of control commands, intervention of lower-level drive protection, sudden torque reduction, tool breakage, or spindle impact during high-cycle, unattended, and deep hole continuous machining processes. It is difficult to balance machining efficiency and equipment operation stability. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides a door closer manufacturing method based on computer-aided manufacturing. Specifically, the technical solution of the present invention includes:

[0005] S1: By using the preset cutting force acquisition unit and spindle load acquisition unit, the real-time machining status feedback signal of the door closer machining environment is obtained. The preset adaptive control model is called to perform feature extraction and optimization calculation on the real-time machining status feedback signal, and an adaptive parameter compensation instruction sequence is generated.

[0006] S2: Obtain the actual processing feedback from the previous control cycle. Based on the actual processing feedback and the adaptive parameter compensation command sequence, extract the parameter change frequency characteristics and amplitude fluctuation characteristics within the adjacent control cycles. Perform fusion and quantization processing on the parameter change frequency characteristics and amplitude fluctuation characteristics to calculate the stress entropy value of the control command.

[0007] S3: Read the underlying hardware tolerance threshold of the underlying servo drive system corresponding to the door closer processing environment from the preset system configuration library, correlate the control command stress entropy value with the underlying hardware tolerance threshold for evaluation and judgment, and obtain the evaluation result. If the evaluation result indicates that the processing system is within the stable control boundary, an indicator for maintaining the optimal cutting state is generated. If the evaluation result indicates that there is a risk of protection intervention of the underlying servo drive system, a smooth degradation switching control indicator is generated.

[0008] S4: In response to the indicator of maintaining the optimal cutting state, an adaptive parameter compensation command sequence is sent to the underlying servo drive system. In response to the indicator of smooth degradation switching control, a preset constant parameter degradation control strategy is called to generate a smooth control command sequence, and the smooth control command sequence is sent to the underlying servo drive system.

[0009] Preferably, the real-time machining status feedback signal includes high-frequency noise distortion components, cutting resistance fluctuation characteristics, and environmental thermodynamic pulse oscillation index; the adaptive parameter compensation instruction sequence includes dynamic adjustment of feed rate, dynamic adjustment of spindle speed, and microsecond-level compensation cycle; and the smooth control instruction sequence includes constant feed rate locking parameters, conservative cutting speed parameters, and underlying trust reconstruction handshake protocol.

[0010] Preferably, the specific steps of S1 are as follows:

[0011] S101: Acquire the real-time processing status feedback signal of the door closer processing environment, identify the high-frequency noise distortion component caused by the environmental thermodynamic pulse oscillation index in the real-time processing status feedback signal, perform filtering and stripping operation on the high-frequency noise distortion component, and generate feedback signal quality assessment value.

[0012] S102: Based on the quality assessment value of the feedback signal, call the preset adaptive control model to perform directional optimization and convergence on the real-time machining status feedback signal, and output the initial parameter set including the dynamic adjustment of feed rate and the dynamic adjustment of spindle speed.

[0013] S103: Set the lower limit threshold of feed rate based on the preset machining cycle efficiency index, set the upper limit threshold of spindle speed based on the preset tool remaining service life index, remove feed rate parameters that are less than or equal to the lower limit threshold of feed rate and spindle speed parameters that are greater than or equal to the upper limit threshold of spindle speed from the initial parameter set, and generate an adaptive parameter compensation instruction sequence.

[0014] Preferably, the specific steps of S2 are as follows:

[0015] S201: Based on the actual machining feedback and adaptive parameter compensation instruction sequence of the previous control cycle, obtain the instruction execution record in the previous continuous control cycle, extract the high-frequency fine-tuning action data in the instruction execution record and the cutting force feedback deviation data in the actual machining feedback, and generate a dynamic oscillation feature set.

[0016] S202: Call the dynamic oscillation feature set, calculate the dynamic oscillation index of the parameter adjustment action, and perform time integration calculation on the unit millisecond granularity within the preset time window to obtain the dynamic oscillation accumulation, and use the dynamic oscillation accumulation as the quantitative input of the underlying hardware control conflict.

[0017] S203: Based on the quantized input quantity and combined with the preset system historical downtime risk weights, the control command stress entropy value is calculated by mapping through the preset entropy evaluation function.

[0018] Preferably, the specific steps of S3 are as follows:

[0019] S301: Read the underlying hardware tolerance threshold of the corresponding underlying servo drive system from the preset system configuration library, calculate the difference between the control command stress entropy value and the underlying hardware tolerance threshold, and obtain the stress entropy margin value.

[0020] S302: Compare the stress entropy margin value with the preset safety margin. If the stress entropy margin value is greater than the preset safety margin, the machining system is determined to be within the stable control boundary, and an indicator for maintaining the optimal cutting state is generated.

[0021] S303: If the stress entropy margin is less than or equal to the preset safety margin, it is determined that there is a risk of protection intervention in the underlying servo drive system, and a smooth degradation switching control flag is generated.

[0022] Preferably, the preset safety margin is generated by reading the overload alarm frequency of the underlying servo drive system in the previous complete operating cycle; when calculating the difference between the control command stress entropy value and the underlying hardware tolerance threshold, the underlying control time granularity is in milliseconds, and the control command stress entropy value is subtracted from the underlying hardware tolerance threshold to obtain the stress entropy margin value.

[0023] Preferably, the specific steps of S4 are as follows:

[0024] S401: In response to maintaining the optimal cutting state indicator, the control weights of the adaptive control model are maintained, the adaptive parameter compensation instruction sequence is written into the data frame structure of the underlying control interface and sent to the underlying servo drive system to perform extreme cycle time machining.

[0025] S402: In response to the smooth degradation switching control flag, freeze the dynamic parameter output channel of the adaptive control model and activate the preset constant parameter degradation control strategy.

[0026] S403: Invoke the constant parameter degradation control strategy, lock the current feed rate and generate constant cutting parameters, encapsulate the constant cutting parameters into a smooth control command sequence, and send the smooth control command sequence to the underlying servo drive system to perform degradation protection machining.

[0027] Preferably, the historical spindle speed record is extracted from the adaptive parameter compensation instruction sequence and the historical average speed is calculated. The range of constant cutting parameters is limited to the preset lower limit ratio and the preset upper limit ratio of the historical average speed. The freezing operation of the dynamic parameter output channel is achieved by setting the write permission flag of the channel status register corresponding to the dynamic parameter output channel to the locked state.

[0028] Preferably, step S5 is also included:

[0029] S5: Collect the underlying servo running status data after executing the smooth control command sequence, calculate the real-time stress entropy repair rate of the current cycle based on the underlying servo running status data, compare the real-time stress entropy repair rate with the preset trust reconstruction threshold, if the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, generate a smooth control handover command, if the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, generate a maintain degraded operation command.

[0030] The control handover smoothing instruction is used to restore the parameter adjustment authority of the adaptive control model.

[0031] Preferably, the specific steps of S5 are as follows:

[0032] S501: Collects the underlying servo running status data after executing the smooth control command sequence, extracts the motor current smoothness features and spindle vibration attenuation features from the underlying servo running status data, and generates a servo stability feedback set.

[0033] S502: Call the servo stability feedback set, calculate the descent gradient of the stress entropy of the underlying control command, and use the ratio of the descent gradient to the stress entropy value of the control command before degradation as the real-time stress entropy repair rate of the current cycle.

[0034] S503: Compare the real-time stress entropy repair rate with the preset trust reconstruction threshold. If the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, release the takeover state of the constant parameter degradation control strategy and generate a smooth handover instruction. If the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, maintain the takeover state of the constant parameter degradation control strategy and generate a maintain degradation operation instruction.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. By adopting the above technical solution, this invention obtains the real-time processing status feedback signal of the door closer processing environment, and calls the preset adaptive control model for feature extraction, state perception and dynamic optimization. Combined with filtering and stripping the high-frequency noise distortion component caused by environmental thermodynamic pulse oscillation and trimming the process boundary of the lower limit threshold of feed rate and the upper limit threshold of spindle speed, this invention effectively solves the problems in existing methods where the original feedback is easily contaminated by environmental disturbances and the dynamic parameter adjustment is easily deviated from the actual cutting state.

[0037] 2. This invention extracts the frequency and amplitude fluctuation characteristics of parameter changes by using the actual machining feedback from the previous control cycle, the high-frequency fine-tuning action data in the instruction execution record, and the cutting force feedback deviation data. It forms a dynamic oscillation feature set and calculates the stress entropy value of the control instruction. This invention can quantify the degree of conflict between the upper-level control output and the lower-level servo execution capability during high-cycle deep hole machining.

[0038] 3. This invention reads the underlying hardware tolerance threshold corresponding to the current underlying servo drive system from a preset system configuration library and performs a correlation evaluation and judgment based on the safety margin. This allows the control strategy to no longer adjust unidirectionally based solely on instantaneous load, but to generate an indicator to maintain the optimal cutting state or a smooth degradation switching control indicator in advance before approaching the underlying protection boundary. This effectively avoids intervention by the underlying drive protection, sudden torque reduction, tool breakage, and spindle impact. Simultaneously...

[0039] 4. This invention freezes the dynamic parameter output channel, activates the constant parameter degradation control strategy, limits the constant cutting parameters to a preset ratio range of the historical average speed, and issues a smooth control command sequence in conjunction with the underlying trust reconstruction handshake protocol. This enables a stable switch from aggressive adaptive mode to smooth protection mode in unattended and complex material fluctuation scenarios.

[0040] 5. This invention collects the underlying servo operating status data after executing the smooth control command sequence, calculates the real-time stress entropy repair rate, and generates a smooth control handover command or a degraded operation command accordingly. It can also gradually restore the parameter adjustment authority of the adaptive control model after the risk is eliminated.

[0041] 6. Through the synergistic effects of feedback purification, stress entropy assessment, hardware tolerance threshold matching, smooth degradation takeover, and trust reconstruction and back-intersection, this invention effectively balances the processing cycle time, equipment stability, and tool safety in door closer machining, ultimately significantly improving the continuous and stable operation capability in high-cycle, deep-hole, and unmanned door closer machining scenarios. Attached Figure Description

[0042] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0043] Figure 1 This is a flowchart illustrating a computer-aided manufacturing method for door closers provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0045] A computer-aided manufacturing method for door closers includes the following steps:

[0046] S1: By using the preset cutting force acquisition unit and spindle load acquisition unit, the real-time machining status feedback signal of the door closer machining environment is obtained. The preset adaptive control model is called to perform feature extraction and optimization calculation on the real-time machining status feedback signal, and an adaptive parameter compensation instruction sequence is generated.

[0047] S2: Obtain the actual processing feedback from the previous control cycle. Based on the actual processing feedback and the adaptive parameter compensation command sequence, extract the parameter change frequency characteristics and amplitude fluctuation characteristics within the adjacent control cycles. Perform fusion and quantization processing on the parameter change frequency characteristics and amplitude fluctuation characteristics to calculate the stress entropy value of the control command.

[0048] S3: Read the underlying hardware tolerance threshold of the underlying servo drive system corresponding to the door closer processing environment from the preset system configuration library, correlate the control command stress entropy value with the underlying hardware tolerance threshold for evaluation and judgment, and obtain the evaluation result. If the evaluation result indicates that the processing system is within the stable control boundary, an indicator for maintaining the optimal cutting state is generated. If the evaluation result indicates that there is a risk of protection intervention of the underlying servo drive system, a smooth degradation switching control indicator is generated.

[0049] S4: In response to the indicator of maintaining the optimal cutting state, an adaptive parameter compensation command sequence is sent to the underlying servo drive system. In response to the indicator of smooth degradation switching control, a preset constant parameter degradation control strategy is called to generate a smooth control command sequence, and the smooth control command sequence is sent to the underlying servo drive system.

[0050] This embodiment provides a door closer manufacturing control mechanism based on computer-aided manufacturing, such as... Figure 1 As shown; specifically, the mechanism is deployed in a fully automated, unmanned workshop on an automated production line for machining door closer housings. The object being machined is a door closer cylinder blank with deep-hole hydraulic oil passages. The machine tool includes a spindle unit, a feed servo unit, a cutting force acquisition unit, a spindle load acquisition unit, and a host controller for performing adaptive control.

[0051] The production line operates at a high cycle time for a long time. Due to the uneven hardness distribution inside the recycled cast aluminum alloy, the cutting load will fluctuate in a very short time when the tool enters the local hard spot area. Therefore, simply pursuing faster parameter adjustment is easy to conflict with the self-protection logic of the underlying servo drive system.

[0052] Specifically, the real-time machining status feedback signal in step S1 does not refer to the instantaneous value of a single sensor, but rather a complex set of symptoms that reflect the true physical state of the current cutting zone.

[0053] The host controller continuously receives signals such as spindle current, feed axis following error, cutting force changes, tool holder vibration, and coolant condition. Then, the preset adaptive control model determines whether the tool is currently in a state of stable cutting, local hard point entry, accelerated tool wear, or increased thermal disturbance, and generates an adaptive parameter compensation command sequence accordingly.

[0054] The preset adaptive control model is a neural network model based on reinforcement learning or a fuzzy proportional integral derivative control model. It is trained and tuned offline using pre-acquired historical normal machining data and limit test data of the machine tool, thereby establishing a nonlinear mapping relationship between machining state feedback characteristics and optimal cutting parameters.

[0055] The compensation here is essentially a dynamic correction of the feed rate, spindle speed and control refresh rate without deviating from the process allowable range, so that the tool always runs along the boundary of high parameters but not overload.

[0056] Furthermore, step S2 does not only evaluate the absolute amount of parameter adjustment, but also focuses on whether the control action begins to exhibit high-frequency oscillation and command conflict. When the feed rate and spindle speed are frequently corrected back and forth in adjacent control cycles, and the actual cutting load does not converge synchronously, it indicates that a mismatch has begun to appear between the upper-level adaptive control output and the lower-level servo execution capability.

[0057] To this end, the system extracts the frequency characteristics and amplitude fluctuation characteristics of parameter changes and merges them to form the stress entropy value of the control command. Specifically, the fusion and quantization process is as follows: preset dynamic weight coefficients are configured for the frequency characteristics and amplitude fluctuation characteristics of parameter changes respectively. The setting logic of the dynamic weight coefficients is: real-time evaluation of the variance of material hardness fluctuation in the cutting area. When the variance is greater than the preset hardness fluctuation threshold, the weight coefficient of the frequency characteristics of parameter changes is increased and the weight coefficient of the amplitude fluctuation characteristics is decreased.

[0058] Conversely, the weight coefficient of the parameter change frequency feature is reduced and the weight coefficient of the amplitude fluctuation feature is increased; the parameter change frequency feature and amplitude fluctuation feature are respectively weighted and summed with their corresponding dynamic weight coefficients, or input into a preset multivariate nonlinear regression function to output a comprehensive quantitative result;

[0059] This value can be understood as a state quantity that reflects whether the control stability has deteriorated: if this value approaches the preset stress limit threshold, it indicates that the degree of discretization of the control command and the frequency of high-frequency switching have increased, and the probability of triggering the active protection mechanism of the underlying servo drive system has increased accordingly.

[0060] Step S3 reads the underlying hardware tolerance threshold corresponding to the current machine tool model, underlying servo drive system version, and spindle power level, and evaluates the aforementioned control command stress entropy value with the threshold. The tolerance threshold here is not a theoretical value, but an engineering boundary that is fixed in the system configuration library after debugging for specific equipment. It is used to characterize the intensity of control disturbance that this type of servo drive system can accept during long-term operation.

[0061] The specific calibration process for the underlying hardware tolerance threshold is as follows: During the initialization and debugging phase of the equipment, a step control disturbance signal with an increasing gradient is gradually injected into the underlying servo drive system. The underlying servo drive system is monitored in real time for forced torque reduction or overload alarm actions, and the critical control command stress entropy value corresponding to the moment the protection action is triggered is recorded as the underlying hardware tolerance threshold of the equipment. When the evaluation result shows that the current condition is still within the stable control boundary, the system generates an indicator to maintain the optimal cutting state. When the evaluation result shows that the underlying hardware protection logic is about to be triggered, a smooth degradation switching control indicator is generated.

[0062] In step S4, if the system is within the stable boundary, the execution of the adaptive parameter compensation instruction sequence is maintained so that the machine tool continues to maintain high cycle time processing; if the system determines that there is a risk of protection intervention, it will no longer continue to amplify dynamic parameter tuning, but will call the preset constant parameter degradation control strategy to generate a smoother control instruction sequence and send it down to the underlying servo drive system.

[0063] This downgrade is not a shutdown, but rather a proactive reduction in machining cycle time to smooth the load in the cutting zone and stabilize the execution chain, thereby avoiding the forced sudden torque reduction of the underlying servo drive system and further preventing tool breakage and spindle impact.

[0064] In the fault-tolerant processing mechanism, if the real-time machining status feedback signal is missing in a certain cycle, such as the cutting force sensor temporarily losing connection, vibration data packet arriving late, or coolant condition sampling being abnormal, the upper controller will prioritize calling the stable state cache of the most recent valid cycle and reduce the aggressiveness of the adaptive control model's output. If the key feedback is invalid for several consecutive cycles, the smooth degradation switching process will be directly entered to ensure that the machine tool runs continuously under at least conservative conditions, rather than continuing to perform high-risk parameter tuning driven by distorted data.

[0065] During continuous night shift operation in a fully automated unmanned workshop, the deep hole oil passage of the door closer cylinder enters the area where the material hard points are concentrated, causing the spindle load to rise in a pulsed manner and the cutting force feedback to start to vibrate. The upper controller first generates a set of compensation instructions for quickly passing over the hard points based on the real-time feedback.

[0066] However, in the following two control cycles, the system found that the feed rate and spindle speed had been correcting back and forth at high frequency, while the actual cutting load was not correspondingly stable, indicating that the control command was accumulating stress entropy. At this time, the system compared this state with the tolerance threshold of the underlying servo drive system corresponding to the current machine tool configuration, and determined that it was close to the protection boundary. Therefore, it abandoned the pursuit of instantaneous optimal cutting and switched to constant parameter degradation control, so that the tool could pass through the complex area with a more stable cutting posture.

[0067] The purpose of this step is to integrate the upper-level adaptive optimization capabilities and the lower-level execution hardware stability boundary into the same control link in high-risk, high-cycle, unattended door closer processing scenarios, so as to achieve a processing control effect that can both actively optimize the processing cycle and complete smooth degradation before instability.

[0068] Furthermore, the real-time machining status feedback signal includes high-frequency noise distortion components, cutting resistance fluctuation characteristics, and environmental thermodynamic pulse oscillation index. The adaptive parameter compensation command sequence includes dynamic adjustment of feed rate, dynamic adjustment of spindle speed, and microsecond-level compensation cycle. The smooth control command sequence includes constant feed rate locking parameters, conservative cutting speed parameters, and underlying trust reconstruction handshake protocol.

[0069] This embodiment provides a refined mechanism for the composition of feedback signals and control commands. Specifically, in the aforementioned fully automated unmanned workshop door closer processing main line scenario, if all feedback signals are simply used as a unified input, it is difficult to distinguish between real material changes and pseudo-changes in environmental disturbances, which may lead to the adaptive control model over-responding to erroneous symptoms. Therefore, it is necessary to clearly constrain the composition of feedback signals and control commands.

[0070] Specifically, the real-time machining status feedback signal includes three key components: First, high-frequency noise distortion components, which usually originate from coolant pressure pulsations, unstable heat exchange, or electromagnetic interference, manifesting as abnormal jitter with short cycles and no corresponding stable cutting patterns; second, cutting resistance fluctuation characteristics, which characterize the actual physical force changes after the tool enters the material, reflecting hard points, hole corner areas, or tool wear conditions; and third, environmental thermodynamic pulse oscillation indicators, which describe whether the thermal environment around the cutting zone repeatedly deviates from steady state in a short period of time, such as the simultaneous periodic fluctuations in coolant temperature and flow rate.

[0071] The environmental thermodynamic pulse oscillation index is calculated by collecting temperature data and coolant flow data in the cutting zone in real time through temperature sensors and flow sensors arranged around the cutting zone and in the coolant pipeline, and extracting the frequency characteristics of the first derivative of the temperature data and flow data within a preset time window.

[0072] By modeling these three types of information separately, the upper controller can determine whether the current abnormal characteristics are due to changes in material properties or to perceived contamination caused by the external environment. On the output side, the adaptive parameter compensation instruction sequence includes at least the dynamic adjustment of the feed rate, the dynamic adjustment of the spindle speed, and the microsecond-level compensation cycle. Among them, the dynamic adjustment of the feed rate is used to adjust the speed at which the tool advances along the path per unit time; the dynamic adjustment of the spindle speed is used to affect the frequency of contact between the cutting edge and the material.

[0073] The microsecond-level compensation cycle is used to determine the time granularity at which the underlying interface updates the control commands. The reason for introducing the microsecond-level compensation cycle is that in deep hole oil passage machining, the tool is in a high slenderness ratio state. If the control refresh is too slow, it will not be able to respond to local hard points in time. If the control refresh frequency is higher than the underlying physical response bandwidth, it is easy to induce oscillation and divergence in the control link. Therefore, the microsecond-level compensation cycle is also used as a dynamic parameter for closed-loop adjustment.

[0074] Correspondingly, the smooth control command sequence includes constant feed rate locking parameters, conservative cutting speed parameters, and a low-level trust reconstruction handshake protocol. The constant feed rate locking parameters prevent the feed axis from continuing to chase noise at high frequency. The conservative cutting speed parameters are used to shift the spindle operating point to a range where thermal load and torque fluctuations are more controllable. The low-level trust reconstruction handshake protocol is used to explicitly inform the low-level servo drive system at the software layer that the upper layer has stopped aggressive parameter tuning and is now in a stable operating mode, so that the low-level servo drive system can release the suppression response to the continuous abnormal control flow and return to a predictable execution state.

[0075] In the fault-tolerant processing mechanism, if the environmental thermodynamic pulse oscillation index is available, but the high-frequency noise distortion component and the cutting resistance fluctuation characteristics are difficult to reliably separate, the system will prioritize reducing the compensation cycle refresh frequency and set a more conservative upper limit for the dynamic adjustment of the feed rate; if even the environmental thermodynamic pulse oscillation index is missing, it will be directly treated as a high-risk working condition, and the smooth control command sequence will be executed first to avoid mistaking noise for material changes due to incomplete environmental information.

[0076] On the same door closer production line, after the central cooling system aged, the coolant temperature fluctuated periodically, causing the cutting force sensor to vibrate at high frequency. The system identified that the vibration was accompanied by an increase in the thermodynamic pulse oscillation index, and thus determined that a significant portion of it did not come from actual changes in cutting resistance, but rather from environmental noise pollution.

[0077] At this time, although the upper controller can still output the dynamic adjustment of feed rate and spindle speed, if it finds that the underlying servo drive system is close to the unstable boundary, it will switch to issuing a smooth control command containing constant feed rate locking parameters and conservative cutting speed parameters, and notify the underlying servo drive system to enter the stable recovery phase through the handshake protocol.

[0078] The purpose of this mechanism is to decouple the control action organization form of the actual force change of environmental disturbance in complex processing scenarios, thereby achieving a clear division of labor between the two types of modes: adaptive control and degraded control.

[0079] Furthermore, the specific steps of S1 are as follows:

[0080] S101: Acquire the real-time processing status feedback signal of the door closer processing environment, identify the high-frequency noise distortion component caused by the environmental thermodynamic pulse oscillation index in the real-time processing status feedback signal, perform filtering and stripping operation on the high-frequency noise distortion component, and generate feedback signal quality assessment value.

[0081] S102: Based on the quality assessment value of the feedback signal, call the preset adaptive control model to perform directional optimization and convergence on the real-time machining status feedback signal, and output the initial parameter set including the dynamic adjustment of feed rate and the dynamic adjustment of spindle speed.

[0082] S103: Set the lower limit threshold of feed rate based on the preset machining cycle efficiency index, set the upper limit threshold of spindle speed based on the preset tool remaining service life index, remove feed rate parameters that are less than or equal to the lower limit threshold of feed rate and spindle speed parameters that are greater than or equal to the upper limit threshold of spindle speed from the initial parameter set, and generate an adaptive parameter compensation instruction sequence.

[0083] This embodiment provides a step for stratified purification of the adaptive parameter generation process; specifically, in the aforementioned scenario, if the unprocessed original feedback is used to drive adaptive optimization directly, when the coolant thermal pulse introduces a large number of pseudo-fluctuations, the system will incorrectly identify environmental noise as material hard points, thus resulting in over-parameter tuning; therefore, before the adaptive parameter compensation instruction is generated, the feedback quality is first judged, and then dynamic optimization and process boundary trimming are performed.

[0084] Specifically, in S101, the system first identifies the high-frequency noise distortion component caused by environmental thermodynamic pulse oscillation. This identification is not simply deleting all high-frequency components, but rather combining the synchronous fluctuation relationship of coolant temperature, flow rate, and injection pressure to screen out feedback anomalies that are inconsistent with the physical contact of cutting but highly synchronized with environmental disturbances.

[0085] Perform filtering and stripping to generate a feedback signal quality assessment value. This assessment value essentially represents the proportion of reliable cutting information in the current feedback. The higher the value, the closer the current data is to the true state of the cutting zone. The lower the value, the stronger the noise pollution, and it is not advisable to use aggressive optimization.

[0086] In S102, the adaptive control model determines the output strategy based on the feedback signal quality assessment value. If the feedback quality is high, the model can appropriately increase the speed of response to local hard points. If the feedback quality decreases, the model automatically weakens the high-frequency adjustment component to avoid amplifying noise into control commands.

[0087] As a specific implementation: Suppose that at a certain moment, the candidate output of the model contains three sets of initial parameters P1, P2, and P3, where P1 focuses on increasing the feed rate, P2 focuses on decreasing the spindle speed, and P3 slightly adjusts both. When the feedback quality is good, P3, which responds faster to changes in cutting resistance, can be retained first. When the feedback quality is poor, P2, which changes more gradually, is preferred. The selection logic here reflects the control strategy preference as the data reliability changes, rather than simply pursuing larger or faster parameter corrections.

[0088] In S103, the initial parameter set needs to be further filtered within the process boundary; the lower limit threshold of the feed rate is given by the machining cycle efficiency index in order to avoid excessive conservatism that causes the tool to stay in the cutting zone for too long, resulting in frictional heating and increased built-up edge.

[0089] The upper limit threshold of the spindle speed is given by the remaining tool life index in order to prevent the tool from continuing to maintain an excessively high linear speed when it is close to the fatigue boundary; the adaptive parameter compensation instruction sequence obtained after screening not only retains the ability to adapt to material changes, but also avoids exceeding the engineering balance point between cycle time and tool life.

[0090] In the fault-tolerant processing mechanism, if the quality evaluation value of the feedback signal is lower than the preset lower limit, aggressive optimization will not be enabled in this cycle, and only the candidate set with small change amplitude will be allowed to be output. If all candidate sets are filtered out by the feed rate lower limit threshold and the spindle speed upper limit threshold, the system will no longer forcibly search for new dynamic parameters, but will directly hand them over to the smooth degradation strategy to prevent the model from outputting unexecutable instructions under extreme constraints.

[0091] During the continuous machining of the deep hole oil passage of the door closer, the coolant nozzles pulsed due to pipeline aging, and the cutting force curve showed dense peaks. Based on the synchronous fluctuation of coolant pressure and temperature, the system identified the abnormal components that were consistent with the thermal pulse as noise and removed them, resulting in a lower but still usable feedback signal quality assessment value.

[0092] The adaptive control model limits the output feed rate parameter to an increase exceeding a first preset ratio, and outputs a set of parameters where both the spindle speed reduction and the feed rate change are within a preset conservative range. Combined with the fact that the tool has entered the later stage of its lifespan, the system filters out excessively high spindle speed schemes and finally generates a set of compensation instruction sequences with parameter change rates lower than the smoothing threshold.

[0093] The purpose of this step is to suppress adaptive parameter tuning distortion in noise pollution scenarios by first purifying the feedback, then dynamically optimizing, and applying process boundary constraints in a three-level manner.

[0094] Furthermore, the specific steps of S2 are as follows:

[0095] S201: Based on the actual machining feedback and adaptive parameter compensation instruction sequence of the previous control cycle, obtain the instruction execution record in the previous continuous control cycle, extract the high-frequency fine-tuning action data in the instruction execution record and the cutting force feedback deviation data in the actual machining feedback, and generate a dynamic oscillation feature set.

[0096] S202: Call the dynamic oscillation feature set, calculate the dynamic oscillation index of the parameter adjustment action, and perform time integration calculation on the unit millisecond granularity within the preset time window to obtain the dynamic oscillation accumulation, and use the dynamic oscillation accumulation as the quantitative input of the underlying hardware control conflict.

[0097] S203: Based on the quantized input quantity and combined with the preset system historical downtime risk weights, the control command stress entropy value is calculated by mapping through the preset entropy evaluation function.

[0098] This embodiment provides a quantitative step for controlling the stress entropy of commands. Specifically, when relying solely on the instantaneous feedback of the current cycle, the system may appear to be able to operate at this moment, but the degree of control disorder accumulated over multiple previous cycles cannot be seen. Especially in deep hole machining, actual instability is often not caused by a single large impact, but by the superposition of multiple high-frequency fine adjustments, which eventually triggers servo protection or tool breakage. Therefore, this embodiment introduces a dynamic oscillation feature set and dynamic oscillation accumulation as intermediate quantities that are closer to the actual pressure state of the equipment.

[0099] Specifically, in S201, the system simultaneously checks the actual processing feedback of the previous control cycle and the current sequence of compensation instructions to be executed, and then traces back the instruction execution records in the previous continuous control cycle; the continuous control cycle here can be understood as several uninterrupted high-speed control refresh windows.

[0100] The system extracts high-frequency fine-tuning action data, such as the feed rate being repeatedly adjusted up and down in a very short time, and the spindle speed continuously making small corrections in adjacent refresh windows. At the same time, it extracts cutting force feedback deviation data from the actual machining feedback to determine whether these fine-tuning actions have really improved the cutting condition. If the adjustment frequency is higher than the preset frequency threshold, but the cutting force deviation has not been reduced, it indicates that the control link is in an ineffective adjustment state.

[0101] In S202, the system forms a dynamic oscillation index based on the dynamic oscillation feature set and accumulates it at a unit millisecond granularity to obtain the dynamic oscillation accumulation; the time integral here can be understood as: not evaluating the instantaneous amplitude, but evaluating the duration span of high-frequency fluctuations;

[0102] For ease of explanation, we can define three consecutive millisecond segments T1, T2, and T3. In T1, both the feed rate and rotational speed undergo slight corrections, and the cutting force deviation decreases; this segment is then denoted as low oscillation. In T2, parameters frequently fluctuate, but the cutting force deviation remains essentially unchanged; this segment is then denoted as medium oscillation. In T3, parameter fluctuations are more frequent, and the cutting force deviation amplifies; this segment is then denoted as high oscillation. By continuously accumulating T1, T2, and T3 over time, the system no longer obtains the instantaneous intensity at a particular moment, but rather the continuous control conflict pressure borne by the underlying hardware.

[0103] In S203, the system further maps the aforementioned cumulative dynamic oscillations with historical shutdown risk weights to a control command stress entropy value; specifically, the system calculates the control command stress entropy value using a preset entropy evaluation function. The function is expressed as:

[0104]

[0105] in, Represented by natural constant The base-1 logarithmic operator; the logarithmic term uses... Normalization is performed to ensure the consistency of the dimensions of the formula;

[0106] In the calculation, the cumulative dynamic oscillation is obtained based on time integration at a unit millisecond granularity. Logarithmic and exponential evaluations are performed, with the logarithmic term supplemented by a preset weight for historical system downtime risk. The weight is obtained by normalizing the downtime frequency under similar historical operating conditions of this equipment and its value ranges from 0 to 1; the exponential term adopts the cumulative reference value of the extreme oscillation of this type of machine tool. As the denominator, multiplied by a preset penalty coefficient To construct a non-linear penalty;

[0107] Penalty coefficient The logic is as follows: extract fatigue damage data of machine tool spindle bearings under historical limit tests, fit the nonlinear curvature of spindle fatigue aggravated by oscillation accumulation, and use its absolute value as a penalty coefficient. The value of is between 1.5 and 3.0; among which, the variable As a natural constant, the specific mathematical meaning of the exponential term is: With base, and The ratio is in the form of an exponential power function, thereby constructing a nonlinear penalty weight that increases sharply as the cumulative oscillation approaches its limit.

[0108] The significance of this mapping calculation is that when the cumulative amount of dynamic oscillation is small, the stress entropy is mainly dominated by the logarithmic term and rises slowly; when the cumulative amount of dynamic oscillation approaches the limit value, the exponential term amplifies rapidly, thus truly reflecting the surge in downtime risk caused by control oscillations to the equipment; the so-called historical downtime risk weight refers to the strength of the correlation between a certain type of oscillation mode and events such as overload alarms, forced torque reduction, and tool breakage shutdown on this machine tool, this tool, and this process path.

[0109] The significance of this is that the same cumulative amount of dynamic oscillation contributes differently to catastrophic downtime in the new and old tool stages, and in the shallow groove and deep hole machining stages; the stress entropy value is the result of combining the current control oscillation level and the danger of the fluctuation on this equipment.

[0110] In the fault-tolerant processing mechanism, if the instruction execution record within a continuous control cycle is incomplete, such as due to communication buffer overflow causing individual millisecond windows to be missing, the system will prioritize using the statistical trend of adjacent complete windows to fill in the gaps. If the missing proportion exceeds the preset upper limit, the stress entropy evaluation result of that wheel will be marked as low confidence and a conservative mode will be triggered, preventing the model from continuing to enhance high-frequency parameter tuning. If the cutting force feedback deviation data itself has been identified as serious noise pollution, the feedback deviation weight in the dynamic oscillation feature set will be reduced to avoid misjudging sensor anomalies as control oscillations.

[0111] At the aforementioned door closer machining site, after the tool entered the deep hole bend area, the upper controller frequently corrected the feed rate and speed in several consecutive control cycles. System backtracking revealed that the parameters had repeatedly reversed in the last three high-speed refresh windows, but the spindle load and cutting force did not decrease steadily; instead, the following error increased.

[0112] Therefore, the system packages these fine-tuning actions and feedback deviations into a dynamic oscillation feature set and continuously accumulates it at the millisecond level, eventually obtaining a high dynamic oscillation accumulation amount; combined with the history of the machine tool having previously experienced an overload alarm at the end of deep hole machining, a higher control command stress entropy value is obtained.

[0113] The purpose of this step is to transform whether frequent parameter adjustments actually improve the processing status into quantifiable equipment risk indicators, thereby enabling early identification of underlying control conflicts.

[0114] Furthermore, the specific steps of S3 are as follows:

[0115] S301: Read the underlying hardware tolerance threshold of the corresponding underlying servo drive system from the preset system configuration library, calculate the difference between the control command stress entropy value and the underlying hardware tolerance threshold, and obtain the stress entropy margin value.

[0116] S302: Compare the stress entropy margin value with the preset safety margin. If the stress entropy margin value is greater than the preset safety margin, the machining system is determined to be within the stable control boundary, and an indicator for maintaining the optimal cutting state is generated.

[0117] S303: If the stress entropy margin is less than or equal to the preset safety margin, it is determined that there is a risk of protection intervention in the underlying servo drive system, and a smooth degradation switching control flag is generated.

[0118] This embodiment provides a boundary determination step based on stress entropy margin. Specifically, after the stress entropy of the control command is quantified, if an absolute judgment is made solely based on the value, misjudgment may still occur because different models of underlying servo drive systems, different spindle power levels, and different control firmware have different upper limits of control disturbances that they can withstand. Therefore, it is necessary to correlate the stress entropy with the hardware tolerance threshold of the specific device.

[0119] Specifically, in S301, the system configuration library pre-stores the underlying hardware tolerance thresholds corresponding to the machine tool control architecture. These thresholds are derived from equipment debugging, online verification, and historical operating experience, and are bound to the specific underlying servo drive system.

[0120] The system calculates the difference between the current control command stress entropy value and the threshold to obtain the stress entropy margin value. This margin is used to characterize the safety margin difference between the current control state and the protection trigger threshold of the underlying servo drive system. If the stress entropy margin value is greater than the preset first buffer threshold, it means that even if the upper layer control maintains the current dynamic compensation rhythm, the underlying servo drive system still has sufficient execution margin. If the stress entropy margin value is less than or equal to the preset second buffer threshold, it means that the device is approaching its own stable execution boundary.

[0121] In S302 and S303, the system further compares the stress entropy margin value with the preset safety margin. The purpose of the safety margin is to avoid taking action before the underlying servo drive system actually enters the protection phase. When the stress entropy margin value is less than or equal to the preset safety margin, and the margin is determined to be unable to compensate for the dynamic stress introduced by the sudden change in material hardness, thermodynamic disturbance, or increased load, the system generates a smooth degradation switching control flag to replace the current adaptive optimization cutting strategy.

[0122] As a specific implementation: Suppose that the permissible disturbance boundary of a certain machine tool under the current configuration corresponds to the upper limit of the interval H, the stress state obtained by the current evaluation is E, and the system reserves a safety buffer M; when there is still a significant margin between E and H, and it is greater than M, an indicator for maintaining the optimal cutting state is generated; when the margin has been reduced to within M, even if it has not actually exceeded the limit, it immediately switches to smooth degradation; this process emphasizes preventive switching rather than post-fault response.

[0123] In the fault tolerance mechanism, if the system configuration library does not read a tolerance threshold that is completely consistent with the current underlying servo drive system firmware version, it will preferentially call a more conservative set of historical thresholds in the same model of equipment; if the threshold for the same model is also missing, it will default to smooth degradation mode to avoid continuing to perform high-risk parameter tuning when the threshold is unknown; if the stress entropy margin repeatedly crosses the safety margin boundary in two adjacent cycles, the system will use hysteresis judgment to prevent the control mode from frequently switching back and forth between optimal cutting and degraded cutting.

[0124] On the same door closer production line, the system calculates that the stress entropy of the current control command has increased significantly; the controller reads the tolerance threshold corresponding to the bottom servo drive system of the deep hole machining center and calculates the remaining stress entropy buffer space; since the tool has entered the later stage of its life and the bottom of the deep hole is about to undergo turning cutting, the system judges that the existing margin is not enough to cover the impact load of the next stage. Therefore, even though the bottom servo drive system has not yet forced torque reduction, a smooth degradation switching control flag is generated in advance.

[0125] The purpose of this step is to establish an executable mapping between the abstract degree of control disorder and the specific equipment's tolerance, thereby enabling proactive identification of protection intervention risks.

[0126] Furthermore, the preset safety margin is generated by reading the overload alarm frequency of the underlying servo drive system in the previous complete operating cycle; the difference between the control command stress entropy value and the underlying hardware tolerance threshold is calculated as follows: with the underlying control time granularity in milliseconds, the control command stress entropy value is subtracted from the underlying hardware tolerance threshold to obtain the stress entropy margin value.

[0127] This embodiment provides a safety margin adaptive setting mechanism. Specifically, if the safety margin remains fixed, the previously sufficient buffer may no longer be adequate when the equipment condition ages, the tool wear intensifies, or the environmental disturbance increases. Therefore, this embodiment incorporates the overload alarm frequency in the previous complete operating cycle into the safety margin setting process, so that the system can adjust the degree of conservatism according to the recent changes in the stability of the equipment.

[0128] Specifically, the previous complete operating cycle can correspond to the time from when a tool is put into production to when it is taken out of production, the continuous production period within a shift, or the complete machining task of a batch of door closer housings. The system counts the frequency of overload alarms generated by the underlying servo drive system during this cycle. If the alarm frequency increases, it usually means that even if the machine is not stopped in the end, the drive has experienced pressure close to the load limit multiple times. At this time, the safety margin should be increased accordingly to allow the system to switch to smooth degradation earlier. If the alarm frequency remains low, it indicates that the stability of the underlying execution link is good and the safety margin can be maintained at a normal level. There is no need to excessively suppress the adaptive control capability.

[0129] For the calculation of the stress entropy margin, this embodiment uses milliseconds as the underlying control time granularity, comparing the stress entropy value of the control command with the tolerance threshold of the underlying hardware millisecond by millisecond to form a fine-grained perception of dynamic boundaries. The significance here is that instability in deep hole machining often manifests as extremely short-duration pulse impacts. If only averaging over a longer period is done, it may mask those transient dangerous segments that are enough to trigger the driver protection. Through millisecond-granular evaluation, the system can more timely detect scenarios where the average seems acceptable, but the instantaneous buffer is close to being exhausted.

[0130] For ease of explanation, a simplified example can be used: In a consecutive three-millisecond window W1, W2, and W3, the drive state is stable in W1, an overload alarm-related segment occurs in W2, and although no alarm occurs in W3, the control oscillation continues; based on the alarm frequency of the previous complete operating cycle, the system sets the current safety margin to a conservative level; the current stress state is compared with the hardware tolerance boundary every millisecond; if the margin approaches the safety margin consecutively in W2 and W3, it is directly regarded as a sign that a switch is needed, without waiting for the long-term average value to rise before processing;

[0131] In the fault-tolerant processing mechanism, if the data of the previous complete operation cycle is incomplete, such as the alarm record gap caused by the device restarting midway, the system can use the weighted result of the most recent effective operation segments to generate a safety margin; if the alarm record is completely unavailable, the default conservative margin is enabled; if the stress entropy assessment at the millisecond granularity produces an isolated abnormal spike due to communication jitter, the system can require the spike to be continuous in the adjacent window before triggering the switch, in order to prevent single-point noise from causing malfunctions.

[0132] As the door closer production line entered the weekend overtime production phase, the servo drive of the same machine tool had repeatedly triggered minor overload alarms in the previous shift. Although the machine did not stop, it indicated that the drive link was in a critical load state. After the start of this cycle, the system automatically increased the safety margin and monitored the stress entropy margin in millisecond granularity. When the tool cut into the complex curvature area at the end of the deep hole, although the average load was not abnormal, the stress entropy margin of several adjacent millisecond windows had been compressed to within the safety margin, and the system immediately issued a smooth degradation switching control indicator in advance.

[0133] The purpose of this mechanism is to make the safety margin no longer a static constant, but a dynamic boundary that reflects the recent health status of the equipment and the risk of transient shocks, so as to achieve risk prediction that is more in line with engineering practice.

[0134] Furthermore, the specific steps of S4 are as follows:

[0135] S401: In response to maintaining the optimal cutting state indicator, the control weights of the adaptive control model are maintained, the adaptive parameter compensation instruction sequence is written into the data frame structure of the underlying control interface and sent to the underlying servo drive system to perform extreme cycle time machining.

[0136] S402: In response to the smooth degradation switching control flag, freeze the dynamic parameter output channel of the adaptive control model and activate the preset constant parameter degradation control strategy.

[0137] S403: Invoke the constant parameter degradation control strategy, lock the current feed rate and generate constant cutting parameters, encapsulate the constant cutting parameters into a smooth control command sequence, and send the smooth control command sequence to the underlying servo drive system to perform degradation protection machining.

[0138] This embodiment provides a control mode execution and switching step; specifically, after the aforementioned boundary determination is completed, the system needs to truly implement the decision of continuing optimal cutting or switching to smooth degradation to the executable data interface layer; if there is no clear control handover mechanism, even if the upper layer determines that degradation is necessary, the dynamic parameter channel may still retain the old high-frequency output, causing the lower-level driver to receive two different types of commands at the same time, thereby further aggravating the conflict.

[0139] Specifically, in S401, when the system determines that it is still within the stable control boundary, it maintains the control weights of the adaptive control model and writes the adaptive parameter compensation instruction sequence into the data frame structure of the underlying control interface. The data frame structure here may include fields such as timestamp, feed instruction, speed instruction, refresh cycle marker, and execution priority. Through standardized encapsulation, the underlying servo drive system can stably parse the upper-level commands under extreme cycle time processing conditions, avoiding further amplification of command jitter due to non-standard interfaces.

[0140] In S402, once a smooth degradation switching control flag is generated, the system freezes the dynamic parameter output channel of the adaptive control model. Freezing does not mean shutting down the entire model, but rather preventing it from injecting new high-frequency compensations into the execution interface, so that its state is retained but no longer dominates the current control output. At the same time, the constant parameter degradation control strategy is activated. The core idea of ​​this strategy is to prioritize restoring the consistency and predictability of the control chain in the material complexity zone or during the environmental disturbance stage, and to reduce the optimization weight of local optimal processing parameters.

[0141] In S403, the system locks the current feed rate and generates constant cutting parameters based on it. These parameters are then encapsulated into a smooth control command sequence and sent to the underlying servo drive system. Locking the current feed rate is important to prevent the axial load of the tool from fluctuating back and forth. Generating constant cutting parameters ensures that the spindle and feed form a relatively fixed cutting ratio, which makes it easier for the underlying driver to re-establish a stable current output and follow control.

[0142] As a specific implementation: Assume there are two output channels C1 and C2, C1 is used for dynamic parameters and C2 is used for degradation parameters; when the system maintains optimal cutting, only C1 is allowed to write to the execution frame; when smooth degradation is triggered, C1 is locked to read-only state, and C2 begins to write constant parameters to the execution frame; in this way, the underlying interface only accepts one control mode at any time, avoiding concurrent conflicts.

[0143] In the fault-tolerant processing mechanism, if the underlying interface still detects residual dynamic instruction frames after freezing the dynamic parameter output channel, the frame clearing and reloading process is executed to ensure that the downgraded instruction becomes the only valid source; if the current feed rate is locked and the current value is already outside the process allowable lower limit, the system will not directly use it, but will roll back to the feed rate reference value of the most recent stable cycle to regenerate constant cutting parameters; if the spindle temperature rise is abnormal during the downgrade period, while keeping the feed rate locked, a one-time downward adjustment of the conservative cutting speed is allowed, but high-frequency dynamic adjustment is prohibited from being restored.

[0144] When the deep hole machining of the door closer is close to the bottom, the system determines that the stress entropy of the control command has approached the tolerance boundary of the underlying servo driver. Therefore, it immediately freezes the dynamic output of the adaptive control model and no longer allows it to continuously issue high-frequency feed and speed correction.

[0145] The constant parameter degradation control strategy takes over the execution interface, locks the currently verified relatively stable feed rate, and generates a conservative spindle speed before writing it into the data frame structure; the underlying driver receives control commands with a smooth rhythm and clear refresh pattern during the machining process, thus enabling it to complete the machining of the remaining hole section with a more stable torque output;

[0146] The purpose of this step is to achieve a seamless switch from aggressive adaptive mode to smooth protection mode through a clear control freezing and handover mechanism.

[0147] Furthermore, the historical spindle speed records in the adaptive parameter compensation instruction sequence are extracted and the historical average speed is calculated. The range of constant cutting parameters is limited to the preset lower limit ratio and the preset upper limit ratio of the historical average speed. The freezing operation of the dynamic parameter output channel is achieved by setting the write permission flag of the channel status register corresponding to the dynamic parameter output channel to the locked state.

[0148] This embodiment provides a mechanism for constraining the source of degradation parameters and freezing the channel. Specifically, in the aforementioned degradation switching, if the constant cutting parameters adopt a fixed template, although a smooth transition is achieved in form, it may not match the current tool, material, and hole position, leading to problems such as excessively light cutting and increased frictional heat generation or excessively heavy cutting and still excessively high load after degradation. Therefore, this embodiment further limits the constant cutting parameters to extract a reference baseline from the recent adaptive control history.

[0149] Specifically, the range of constant cutting parameters is limited to a preset proportion of the historical average rotational speed. The purpose is that downgrading is not about creating a completely unfamiliar process point detached from the actual situation, but rather about selecting a more conservative and smoother central range as the downgrading target from the history of dynamic parameters that have recently proven to be executable by the equipment. This has two advantages: first, the underlying driver is more likely to accept it because the range is derived from recent operating data of the same tool, the same workpiece, and the same path; second, the change in tool force will not be too abrupt, avoiding new thermal or cutting shocks caused by sudden changes in rotational speed.

[0150] As a specific example: Suppose that in the most recent effective control cycles, the adaptive parameter compensation command sequence records the speed sets R1, R2, R3, and R4, among which the cutting states corresponding to R2 and R3 are the most stable;

[0151] The system first establishes a historical average speed baseline, and then sets a proportional range around this baseline, allowing only the downgraded parameters to be selected within this range; for example, if R1 is significantly high and corresponds to an oscillating increase, it will not be considered as a downgrade candidate; if R4 is too low, resulting in insufficient cutting, it will not be given priority; the final selected constant cutting parameters are both conservative and retain on-site adaptability.

[0152] Regarding the freezing of dynamic parameter output channels, this embodiment achieves this by writing the corresponding channel status register to the permission flag position as locked. This means that the freezing action is not a status flag at the logical level, but is directly implemented at the underlying access control of the control interface. As long as the register write permission is locked, even if the adaptive control model is still running in the background and generating new candidate parameters, it will not be able to write them to the execution channel, thereby preventing old task threads, communication delays, or cache residues from reactivating high-frequency dynamic output.

[0153] In the fault-tolerant processing mechanism, if there are insufficient valid samples of historical average speed, such as when a new tool has just been put into service or a new batch of workpieces has just been changed, the system can use the default conservative range of this tool type and this process path instead; if the register locking operation fails, the system should interrupt the sending of dynamic parameter frames and return a channel freeze failure alarm to the upper controller. At this time, it is still treated as a degraded risk and aggressive parameter tuning is not allowed to be restored; if there is no speed value that meets the spindle thermal load constraint within the proportional range, the conservative lower limit is expanded first, but the upper limit is not expanded, so as to ensure that the degraded direction is always towards the more stable side;

[0154] On the same door closer machining center, when the system is about to perform a downgrade switch, it first extracts several actual spindle speeds from the dynamic compensation records of the most recent period, calculates a historical average speed baseline, and determines a conservative range near the baseline.

[0155] The system selects a rotational speed from the range that is more stable for the current tool mechanical load as a constant cutting parameter; at the same time, the controller writes a lock flag to the status register corresponding to the dynamic parameter output channel to ensure that even if the adaptive model is still updating internally, it cannot overwrite the degraded parameters that are being executed.

[0156] The purpose of this mechanism is to enable degraded control to have both parameter smoothness, field continuity, and interface exclusivity, thereby achieving more reliable control takeover.

[0157] Furthermore, it also includes step S5:

[0158] S5: Collect the underlying servo running status data after executing the smooth control command sequence, calculate the real-time stress entropy repair rate of the current cycle based on the underlying servo running status data, compare the real-time stress entropy repair rate with the preset trust reconstruction threshold, if the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, generate a smooth control handover command, if the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, generate a maintain degraded operation command.

[0159] The control handover smoothing instruction is used to restore the parameter adjustment authority of the adaptive control model.

[0160] This embodiment provides a closed-loop repair mechanism for restoring control after degradation. Specifically, in the aforementioned scheme, smooth degradation can help the system avoid the risks of tool breakage and drive protection. However, if the system remains in conservative mode for a long time after degradation, it will continuously sacrifice cycle efficiency and will not be conducive to leveraging the advantages of the adaptive control model. Therefore, after degradation, it is also necessary to determine whether the underlying execution link has recovered from the high-stress state to a state that can accept dynamic parameter tuning again.

[0161] Specifically, the core of S5 lies in collecting the underlying servo operating status data after executing the smooth control command sequence, and calculating the real-time stress entropy repair rate based on this data. This repair rate can be understood as: compared to the high conflict state before degradation, whether the control pressure of the current underlying servo is effectively decreasing, and whether the rate of decrease is sufficient to indicate that the degradation control is effective.

[0162] If we only look at the instantaneous load reduction, we might mistake a temporary light load error for a true recovery. However, by introducing the recovery rate, the system focuses on a trend, namely, whether the drive current is smoother, whether the following error converges, whether the spindle vibration is reduced, and whether the protection logic exits the alarm state, among other comprehensive characteristics.

[0163] The system compares the real-time stress entropy repair rate with the preset trust reconstruction threshold. Trust reconstruction is essentially the process by which the underlying hardware restores the stable execution capability of the upper-level control mode. When the repair rate reaches or exceeds the threshold, it indicates that the smooth degradation has removed the driver from the stress state. At this time, the system generates a smooth control handover instruction to gradually restore the parameter adjustment authority of the adaptive control model. If the repair rate does not reach the threshold, the degraded operation continues to avoid premature restoration of dynamic parameter tuning, which could lead to another oscillation.

[0164] As a specific implementation: Suppose that after the system is downgraded, two recovery windows Q1 and Q2 are observed continuously. If Q1 only shows a temporary decrease in load while Q2 fluctuates again, it indicates that the recovery rate is insufficient and the downgrade should be maintained. If both Q1 and Q2 show a stable improvement trend, it is considered that the conditions for returning to the previous state have been met.

[0165] In the fault-tolerant processing mechanism, if the underlying servo operating status data collected after degradation is incomplete, the system extends the observation window and does not immediately submit the data back; if the repair rate fluctuates repeatedly around the threshold, a phased submission method is adopted, such as first restoring the small adjustment permission of the spindle speed, and then restoring the feed rate adjustment permission, rather than releasing it all at once; if a rapid increase in stress entropy is detected again in a short period of time after submission, the system can immediately cancel the submission command and re-enter degraded operation.

[0166] After successfully navigating the complex area at the bottom of the deep hole of the door closer through smooth degradation, the system began to monitor the operating status of the underlying servo. After several control cycles, the spindle load fluctuation gradually weakened, and the feed axis following error no longer increased, indicating that the degradation measures were repairing the previous control conflicts.

[0167] The system calculates that the real-time stress entropy repair rate continues to rise and reaches the trust reconstruction threshold. Therefore, it generates a control power smooth handover instruction, which enables the adaptive control model to regain parameter adjustment authority for subsequent high-cycle processing of relatively stable hole sections.

[0168] The purpose of this mechanism is to ensure that degraded control is not just a passive braking intervention, but also a transitional bridge for re-acceleration after stabilization, thereby achieving self-recovery operation of the control system.

[0169] Furthermore, the specific steps of S5 are as follows:

[0170] S501: Collects the underlying servo running status data after executing the smooth control command sequence, extracts the motor current smoothness features and spindle vibration attenuation features from the underlying servo running status data, and generates a servo stability feedback set.

[0171] S502: Call the servo stability feedback set, calculate the descent gradient of the stress entropy of the underlying control command, and use the ratio of the descent gradient to the stress entropy value of the control command before degradation as the real-time stress entropy repair rate of the current cycle.

[0172] S503: Compare the real-time stress entropy repair rate with the preset trust reconstruction threshold. If the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, release the takeover state of the constant parameter degradation control strategy and generate a smooth handover instruction. If the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, maintain the takeover state of the constant parameter degradation control strategy and generate a maintain degradation operation instruction.

[0173] This embodiment provides a specific step for extracting and backcrossing the stress entropy repair rate. Specifically, in the aforementioned recovery mechanism, if control is restored solely based on experience to determine that the surface tends to be stable, it is easy to introduce new uncertainties in high-risk deep hole machining scenarios. Therefore, this embodiment further specifies that the smoothness characteristics of motor current and the vibration attenuation characteristics of spindle are used as the core sources of the servo stability feedback set.

[0174] Specifically, in S501, the system collects the underlying servo operating status data after executing the smooth control command sequence, and extracts the motor current smoothness characteristics and spindle vibration attenuation characteristics from it; the motor current smoothness reflects whether the output torque of the driver tends to be consistent. If the current waveform gradually changes from dense peaks to smoothness, it indicates that the underlying execution chain is getting rid of frequent corrections.

[0175] The spindle vibration attenuation characteristics reflect whether the tool-workpiece contact state has changed from intense impact to stable cutting. If the vibration envelope continues to decrease, it indicates that the mechanical stress in the cutting zone is being released. Combining the two to form a servo stability feedback set can simultaneously cover the recovery signs of both the electric drive layer and the mechanical layer.

[0176] In S502, the system determines the decreasing gradient of the stress entropy of the underlying control command based on the servo stability feedback set, and then compares the decreasing trend with the stress entropy baseline before degradation to form a real-time stress entropy repair rate.

[0177] Specifically, the calculation logic for the real-time stress entropy repair rate is as follows: The current time span is calculated based on a weighted sum of the motor current smoothness and the spindle vibration attenuation characteristics. The decrease in stress entropy within the observation window Combined with the time span of the observation window Calculate the descent gradient ; this descent gradient Baseline value of control command stress entropy before degradation Perform ratio calculations to obtain the real-time stress entropy repair rate. ;

[0178] As a specific example: Let the baseline stress state before degradation be denoted as B. After degradation, three recovery segments U1, U2, and U3 are continuously observed. If the current spike in U1 decreases slightly but the vibration is still large, it only indicates that improvement has begun. If the current waveform in U2 and U3 continues to smooth and the vibration continues to decrease, it indicates that the stress state is continuously decreasing. The system determines whether the repair rate is sufficient to support the return. The key to this process is not the size of a single point, but whether the improvement is continuous and whether it covers both the electric drive and mechanical levels.

[0179] In S503, when the real-time stress entropy repair rate reaches or exceeds the trust reconstruction threshold, the system releases the takeover state of the constant parameter degradation control strategy and generates a smooth return command for control. This return can adopt a layered recovery method, for example, first releasing the spindle speed adjustment limit, and then restoring the feed rate fine-tuning authority in the subsequent stable window, thereby avoiding new oscillations caused by releasing all dynamic degrees of freedom at once. If the repair rate does not reach the threshold, the degradation control takeover state is maintained, and a command to maintain degradation operation is generated, so that the tool first completes the machining of the current high-risk area in a stable manner.

[0180] In the fault-tolerant handling mechanism, if the motor current smoothness characteristics show improvement, but the spindle vibration attenuation characteristics are still not obvious, the system will not immediately switch back, because this may indicate that the electric drive layer recovers faster than the mechanical layer, and the contact between the tool and the material is still unstable; conversely, if the vibration decreases but the current spikes are still dense, it also indicates that the underlying drive is still compensating at a high intensity, and the degradation is maintained; if any feedback characteristic deteriorates again after switching back, the system can close the dynamic parameter output channel again and restore the degradation takeover to form a reversible safety closed loop;

[0181] During the aforementioned door closer machining process, after several cycles of smooth degradation operation, the system found that the spindle current waveform had changed from dense spikes to relatively smooth, and the peak envelope of the tool holder vibration continued to decrease. The system combined these two characteristics into a servo stability feedback set and determined that the underlying stress state had continuously decreased. When the downward trend reached the trust reconstruction requirement relative to the high stress baseline before degradation, the system released the takeover of the constant parameter degradation control strategy, first restored the small adjustment permission of the spindle speed, and then gradually restored the feed rate adjustment permission after confirming subsequent stability.

[0182] The purpose of this step is to support the crossover determination through observable underlying electric drive characteristics and mechanical vibration characteristics, thereby achieving a verifiable closed loop for adaptive control to recover after degradation.

[0183] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A door closer manufacturing method based on computer-aided manufacturing, characterized in that, Includes the following steps: S1: The real-time processing status feedback signal of the door closer processing environment is obtained through the preset cutting force acquisition unit and spindle load acquisition unit. The preset adaptive control model is called to perform feature extraction and optimization calculation on the real-time processing status feedback signal to generate an adaptive parameter compensation instruction sequence. S2: Obtain the actual processing feedback of the previous control cycle, and based on the actual processing feedback and the adaptive parameter compensation command sequence, extract the parameter change frequency characteristics and amplitude fluctuation characteristics in adjacent control cycles, perform fusion quantization processing on the parameter change frequency characteristics and amplitude fluctuation characteristics, and calculate the control command stress entropy value. S3: Read the underlying hardware tolerance threshold of the underlying servo drive system corresponding to the door closer processing environment from the preset system configuration library, correlate the control command stress entropy value with the underlying hardware tolerance threshold for evaluation and judgment, and obtain the evaluation result. If the evaluation result indicates that the processing system is within the stable control boundary, generate an indicator to maintain the optimal cutting state. If the evaluation result indicates that there is a risk of protection intervention of the underlying servo drive system, generate a smooth degradation switching control indicator. S4: In response to the "maintain optimal cutting state" flag, the adaptive parameter compensation instruction sequence is sent to the underlying servo drive system. In response to the "smooth degradation switching control" flag, a preset constant parameter degradation control strategy is invoked to generate a smooth control instruction sequence, and the smooth control instruction sequence is sent to the underlying servo drive system.

2. The door closer manufacturing method based on computer-aided manufacturing according to claim 1, characterized in that, The real-time machining status feedback signal includes high-frequency noise distortion components, cutting resistance fluctuation characteristics, and environmental thermodynamic pulse oscillation index. The adaptive parameter compensation command sequence includes dynamic adjustment of feed rate, dynamic adjustment of spindle speed, and microsecond-level compensation cycle. The smooth control command sequence includes constant feed rate locking parameters, conservative cutting speed parameters, and underlying trust reconstruction handshake protocol.

3. The door closer manufacturing method based on computer-aided manufacturing according to claim 2, characterized in that, The specific steps of S1 are as follows: S101: Obtain the real-time processing status feedback signal of the door closer processing environment, identify the high-frequency noise distortion component caused by the environmental thermodynamic pulse oscillation index in the real-time processing status feedback signal, perform filtering and stripping operation on the high-frequency noise distortion component, and generate a feedback signal quality assessment value. S102: Based on the quality evaluation value of the feedback signal, the preset adaptive control model is invoked to perform directional optimization and convergence on the real-time machining status feedback signal, and an initial parameter set including the dynamic adjustment amount of the feed rate and the dynamic adjustment amount of the spindle speed is output. S103: Set the feed rate lower limit threshold based on the preset machining cycle efficiency index, set the spindle speed upper limit threshold based on the preset tool remaining service life index, remove feed rate parameters that are less than or equal to the feed rate lower limit threshold and spindle speed parameters that are greater than or equal to the spindle speed upper limit threshold from the initial parameter set, and generate the adaptive parameter compensation instruction sequence.

4. The door closer manufacturing method based on computer-aided manufacturing according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the actual machining feedback of the previous control cycle and the adaptive parameter compensation instruction sequence, obtain the instruction execution record in the previous continuous control cycle, extract the high-frequency fine-tuning action data in the instruction execution record and the cutting force feedback deviation data in the actual machining feedback, and generate a dynamic oscillation feature set; S202: Call the dynamic oscillation feature set, calculate the dynamic oscillation index of the parameter adjustment action, and perform time integration calculation on the unit millisecond granularity within the preset time window to obtain the dynamic oscillation accumulation, and use the dynamic oscillation accumulation as the quantitative input of the underlying hardware control conflict. S203: Based on the quantized input quantity and combined with the preset system historical downtime risk weight, the control command stress entropy value is calculated by mapping through the preset entropy evaluation function.

5. The door closer manufacturing method based on computer-aided manufacturing according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Read the underlying hardware tolerance threshold corresponding to the underlying servo drive system from the preset system configuration library, calculate the difference between the control command stress entropy value and the underlying hardware tolerance threshold, and obtain the stress entropy margin value. S302: Compare the stress entropy margin value with the preset safety margin. If the stress entropy margin value is greater than the preset safety margin, it is determined that the machining system is within the stable control boundary, and the indicator for maintaining the optimal cutting state is generated. S303: If the stress entropy margin value is less than or equal to the preset safety margin, it is determined that the underlying servo drive system has the risk of protection intervention, and the smooth degradation switching control flag is generated.

6. The door closer manufacturing method based on computer-aided manufacturing according to claim 5, characterized in that, The preset safety margin is generated by reading the overload alarm frequency of the underlying servo drive system in the previous complete operating cycle. The calculation of the difference between the control command stress entropy value and the underlying hardware tolerance threshold is specifically as follows: with the underlying control time granularity in milliseconds, the control command stress entropy value is subtracted from the underlying hardware tolerance threshold to obtain the stress entropy margin value.

7. The door closer manufacturing method based on computer-aided manufacturing according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: In response to the indicator of maintaining the optimal cutting state, the control weights of the adaptive control model are maintained, the adaptive parameter compensation instruction sequence is written into the data frame structure of the underlying control interface, and sent to the underlying servo drive system to perform extreme cycle time machining. S402: In response to the smooth degradation switching control flag, freeze the dynamic parameter output channel of the adaptive control model and activate the preset constant parameter degradation control strategy; S403: Invoke the constant parameter degradation control strategy, lock the current feed rate and generate constant cutting parameters, encapsulate the constant cutting parameters into the smooth control instruction sequence, and send the smooth control instruction sequence to the underlying servo drive system to perform degradation protection machining.

8. The door closer manufacturing method based on computer-aided manufacturing according to claim 7, characterized in that, Extract the historical spindle speed records from the adaptive parameter compensation instruction sequence and calculate the historical average speed. The range of constant cutting parameters is limited to the preset lower limit ratio and the preset upper limit ratio of the historical average speed. The freezing operation of the dynamic parameter output channel is achieved by setting the write permission flag of the channel status register corresponding to the dynamic parameter output channel to a locked state.

9. The door closer manufacturing method based on computer-aided manufacturing according to claim 1, characterized in that, It also includes step S5: S5: Collect the underlying servo operating status data after executing the smooth control command sequence, calculate the real-time stress entropy repair rate of the current cycle based on the underlying servo operating status data, compare the real-time stress entropy repair rate with a preset trust reconstruction threshold, if the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, generate a control smooth handover command, if the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, generate a maintain degraded operation command. The control handover smoothing instruction is used to restore the parameter adjustment authority of the adaptive control model.

10. The door closer manufacturing method based on computer-aided manufacturing according to claim 9, characterized in that, The specific steps of S5 are as follows: S501: Collect the underlying servo operating status data after executing the smooth control command sequence, extract the motor current smoothness features and spindle vibration attenuation features from the underlying servo operating status data, and generate a servo stability feedback set. S502: Call the servo stability feedback set, calculate the descent gradient of the stress entropy of the underlying control command, and use the ratio of the descent gradient to the stress entropy value of the control command before degradation as the real-time stress entropy repair rate of the current cycle. S503: Compare the real-time stress entropy repair rate with the preset trust reconstruction threshold. If the real-time stress entropy repair rate is greater than or equal to the preset trust reconstruction threshold, release the takeover state of the constant parameter degradation control strategy and generate the control smooth handover instruction. If the real-time stress entropy repair rate is less than the preset trust reconstruction threshold, maintain the takeover state of the constant parameter degradation control strategy and generate the maintain degradation operation instruction.

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