Energy consumption optimization control method and system in aluminum veneer cutting process

Abstract

The invention provides an energy consumption optimization control method and system in the aluminum veneer cutting process. According to the method, multi-dimensional signals and cutting parameters of a tool and a material interface are collected in real time, and the multi-dimensional signals and the cutting parameters comprise the spindle rotating speed, the feeding pressure and the material thickness. And extracting a characteristic frequency band and a piezoelectric conversion signal sequence based on a material thickness matching physical library. Energy focusing is executed in a frequency band to generate a resonance spectrum, and an energy consumption distribution diagram is formed after axial pressure gradient weighting. The graph is coupled with the spindle rotating speed to construct a feature vector, and high-frequency energy consumption components dominated by tool vibration are decomposed and separated in a multi-scale mode. The phase difference between the rotating speed and the feeding pressure is adjusted according to the amplitude trend, so that the vibration trough locks the material deformation area, and energy efficiency optimization control is achieved. The vibration high-frequency energy consumption component is dynamically separated, and the phase difference between the rotating speed of the main shaft and the feeding pressure is precisely regulated and controlled, so that the vibration trough of the cutter is always synchronized with the material deformation area, and precise elimination of vibration energy consumption and energy efficiency improvement in the cutting process are achieved.

Description

Technical Field

[0001] This application relates to the field of precision machining technology for metal sheets, and in particular to a method and system for optimizing energy consumption control in the cutting process of aluminum single-panel sheets. Background Technology

[0002] In high-speed cutting scenarios for aluminum panels used in building curtain walls, ineffective energy consumption is high due to material thickness fluctuations, spindle speed dynamic response lag, and tool chatter. There is an urgent need to improve cutting energy efficiency by dynamically matching cutting parameters with material deformation characteristics through real-time sensing of the vibration energy distribution at the tool-material contact interface, while simultaneously avoiding chipping and dimensional deviations caused by cutting chatter.

[0003] Current mainstream solutions employ time-domain vibration threshold control based on multi-sensor feedback: Accelerometers collect tool vibration signals, set a safe amplitude threshold, and when the vibration amplitude exceeds the limit, a PID controller is triggered to linearly reduce the spindle speed or increase the feed pressure. Simultaneously, a preset material thickness database is used to fix and match the "spindle speed-feed pressure" parameter combination according to thickness segmentation.

[0004] Time-domain amplitude threshold control cannot separate the effective deformation energy consumption of the material from the high-frequency component of tool chatter in the mixed signal, resulting in a large number of PID deceleration / increase commands being falsely triggered during the plastic deformation stage of the material, which in turn increases ineffective energy consumption. At the same time, the static parameter library based on thickness partitioning ignores the differences in micro-grain structure of aluminum plates of the same specification, resulting in a large deviation between the actual cutting frequency band and the preset characteristic frequency band, causing continuous fluctuations in the material deformation resistance. More importantly, there is a phase misalignment between the speed-pressure change of linear PID regulation and the real-time deformation of the material. Experimental results show that the periodicity of the vibration peak (not the trough) strongly matches the material deformation zone, which increases the proportion of chatter energy absorption and ultimately deteriorates the energy efficiency ratio, which is far below the target value. Summary of the Invention

[0005] This application provides a method and system for optimizing energy consumption control during the aluminum single-panel cutting process, which solves the problem of energy efficiency runaway caused by inaccurate separation of vibration characteristics, static matching of parameters, and misalignment of energy control phase in the prior art.

[0006] Firstly, this application provides a method for optimizing and controlling energy consumption during the aluminum single-panel cutting process, including:

[0007] Real-time capture of multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut; synchronous acquisition of cutting parameters including the real-time spindle speed, feed axial pressure and the thickness of the material plate being cut.

[0008] Based on the material plate thickness value, a preset physical property association library is matched, and the characteristic frequency band range of the corresponding thickness range is extracted. At the same time, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element.

[0009] Frequency domain energy focusing is performed on the signal sequence within the characteristic frequency band to generate a resonance characteristic spectrum. The energy concentration peaks in the resonance characteristic spectrum are then gradient-weighted according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy.

[0010] The energy consumption distribution map is coupled with the real-time spindle speed in the time domain to construct a feature vector that couples speed and vibration. Multi-scale time-frequency decomposition processing is then performed on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration.

[0011] Based on the amplitude variation trend of the high-frequency energy consumption component, a suppression command is generated, and the phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing optimized energy consumption control during the cutting process.

[0012] Optionally, the phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby achieving optimized energy consumption control during the cutting process, including:

[0013] Obtain from the suppression command the phase difference adjustment indication value of the target offset corresponding to the amplitude change trend of the high-frequency energy consumption component;

[0014] The starting point of the rotation cycle of the real-time spindle speed is monitored in real time, and the starting point of the application of the feed axial pressure is recorded.

[0015] Based on the starting point of the rotation cycle and the starting point of the application, the current time difference is determined as the current phase difference;

[0016] The phase difference adjustment indication value is applied to the current phase difference to generate a target phase difference;

[0017] Adjust the timing of the control signal for the feed axial pressure so that the time offset of the application start point relative to the start point of the rotation cycle is equal to the target phase difference;

[0018] Repeatedly iterate through each step to form a closed-loop control process, ensuring that the vibration trough of the cutting tool is continuously aligned with the stress deformation zone of the material plate.

[0019] Optionally, the energy consumption distribution map is coupled with the real-time rotational speed of the spindle in the time domain to construct a feature vector coupling rotational speed and vibration, including:

[0020] The energy consumption distribution map is divided into continuous energy consumption time segments with a fixed time length;

[0021] For each energy consumption time segment, extract the weighted peak energy value corresponding to all frequency points within the energy consumption time segment, and calculate the arithmetic sum of the weighted peak energy values ​​as the total vibration energy value;

[0022] The instantaneous value of the real-time rotational speed of the spindle within the energy consumption time segment is collected synchronously, and the total vibration energy value corresponding to each energy consumption time segment is combined with the instantaneous value of the real-time rotational speed of the spindle into a data pair;

[0023] The total vibration energy and the instantaneous real-time spindle speed of all data pairs are arranged alternately in chronological order to form a one-dimensional numerical sequence as a feature vector, in which the total vibration energy and the instantaneous real-time spindle speed of the corresponding moment are kept in adjacent positions in the sequence.

[0024] Optionally, multi-scale time-frequency decomposition processing is performed on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration, including:

[0025] The feature vector is segmented using three windows of different time lengths: short window, medium window and long window. The data segments within the short window are decomposed into frequency components, and the components with frequencies higher than a first threshold are extracted as the first high-frequency components.

[0026] Frequency component decomposition is performed on the data segment within the middle window, and the component with a frequency higher than the second threshold is extracted as the second high-frequency component. Frequency component decomposition is performed on the data segment within the long window, and the component with a frequency higher than the third threshold is extracted as the third high-frequency component.

[0027] The first high-frequency component, the second high-frequency component, and the third high-frequency component are superimposed according to time points to generate a superimposed high-frequency sequence;

[0028] The portion of the superimposed high-frequency sequence whose energy value is consistently greater than the average value of adjacent time points is selected as the high-frequency energy consumption component dominated by tool vibration.

[0029] Optionally, based on the material plate thickness value, a preset physical property association library is matched to extract the characteristic frequency band range of the corresponding thickness range. Simultaneously, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensing element, including:

[0030] The thickness value of the material plate is compared with the predefined thickness range in the physical property association library to determine the target thickness range to which the thickness value of the material plate belongs;

[0031] Extract the feature frequency band range bound to the target thickness interval from the physical property association library. The feature frequency band range is defined by the minimum frequency value and the maximum frequency value.

[0032] The multidimensional physical signals generated at the interface between the cutting tool and the material are received by a piezoelectric sensing element. The multidimensional physical signals include vibration amplitude signals and sound wave signals.

[0033] By utilizing the electromechanical conversion characteristics of the piezoelectric sensing element, the vibration amplitude signal is converted into a first electrical signal sequence, and the sound wave signal is converted into a second electrical signal sequence;

[0034] The first and second electrical signal sequences are sampled at fixed time intervals to generate a sensor signal sequence arranged in chronological order.

[0035] Optionally, frequency domain energy focusing is performed on the signal sequence within the characteristic frequency band to generate a resonance characteristic spectrum, including:

[0036] The sensor signal sequence is decomposed into frequency components to obtain the full-band energy distribution;

[0037] The frequency band corresponding to the characteristic frequency band range is extracted from the full-band energy distribution and used as the target frequency band energy distribution.

[0038] In the energy distribution of the target frequency band, the energy value at each frequency point is squared to obtain the enhanced energy value;

[0039] The enhanced energy value is combined with the position weight coefficient and weighted according to the preset calculation rules. The weighted enhanced energy values ​​of all frequency points are summarized to form a resonance characteristic spectrum with frequency as the horizontal axis and weighted energy as the vertical axis.

[0040] Optionally, the energy concentration peaks in the resonance characteristic spectrum are gradient-weighted according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy, including:

[0041] In the resonance characteristic spectrum, frequency points with energy values ​​exceeding a preset threshold are identified as energy accumulation peak points;

[0042] Obtain the current feed axial pressure value of the cutting machine, and determine the pressure gradient coefficient based on the magnitude of the pressure value, wherein the pressure gradient coefficient increases with the increase of the feed axial pressure value;

[0043] The energy value at each energy accumulation peak point is weighted by the pressure gradient coefficient to obtain the weighted peak energy value.

[0044] Based on the weighted peak energy value, a two-dimensional point distribution map is generated with frequency as the horizontal axis and weighted peak energy value as the vertical axis, serving as the energy consumption distribution map of vibration energy.

[0045] Secondly, this application provides an energy consumption optimization and control system for the aluminum single-panel cutting process, comprising:

[0046] The acquisition module is used to capture multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut in real time. It synchronously acquires cutting parameters of the cutting machine, including the real-time spindle speed, feed axial pressure, and the thickness of the material plate being cut.

[0047] The extraction module is used to extract the characteristic frequency band range of the corresponding thickness range by matching the material plate thickness value with a preset physical property association library, and at the same time convert the multidimensional physical signal into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element.

[0048] The generation module is used to perform frequency domain energy focusing within the characteristic frequency band based on the signal sequence, generate a resonance characteristic spectrum, and perform gradient weighting on the energy concentration peaks in the resonance characteristic spectrum according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy.

[0049] The separation module is used to couple the energy consumption distribution map with the real-time spindle speed in the time domain, construct a feature vector of speed and vibration coupling, and perform multi-scale time-frequency decomposition processing on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration.

[0050] The adjustment module is used to generate a suppression command based on the amplitude variation trend of the high-frequency energy consumption component, and adjust the phase difference between the real-time spindle speed and the feed axial pressure according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing energy consumption optimization control during the cutting process.

[0051] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement the energy consumption optimization control method for aluminum single-panel cutting process as described in the first aspect above.

[0052] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an energy consumption optimization control method for aluminum single-panel cutting process as described in the first aspect.

[0053] This application achieves real-time capture of multi-dimensional physical signals at the tool material interface and synchronous acquisition of spindle speed, feed pressure, and material thickness parameters, constructing a dynamically coupled data chain to eliminate time-asynchronous errors. Based on the material thickness, a dynamic matching physical property library is used to extract characteristic frequency bands, and piezoelectric conversion is employed to form sensor signal sequences, overcoming the bottleneck of identifying microscopic material differences. Within the characteristic frequency band, frequency domain energy focusing is performed to generate a resonance characteristic spectrum. A vibration energy consumption distribution map is formed by weighting the energy peak value using the axial pressure gradient, achieving precise location of ineffective energy consumption areas. The energy consumption distribution map is coupled with the spindle speed in the time domain to construct a feature vector. Multi-scale decomposition separates the high-frequency components dominated by tool vibration, significantly reducing the chatter misjudgment rate. Finally, a suppression command is generated based on the amplitude variation trend of the high-frequency components, dynamically adjusting the phase difference between the speed and feed pressure to ensure the vibration trough continuously matches the material deformation zone, effectively compressing the proportion of ineffective energy consumption.

[0054] Furthermore, the phase difference adjustment indicator value reflecting the trend of high-frequency energy consumption components is obtained from the suppression command. The current phase difference is determined by real-time monitoring of the start point of the spindle rotation cycle and the start point of feed pressure application. The adjustment value is used to generate the target phase difference, and the timing of the feed pressure control signal is dynamically adjusted to achieve the target time offset. Closed-loop iteration ensures that the vibration trough and the material deformation zone are continuously aligned. This achieves high-precision phase difference closed-loop control, fundamentally eliminating the phase misalignment defect in traditional adjustment, allowing the vibration trough to be accurately embedded into the absorption window of the material deformation zone, significantly reducing flutter energy absorption. The closed-loop mechanism tracks material thickness and rotation speed fluctuations in real time, maintaining stable energy consumption optimization effects without overshoot oscillation.

[0055] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart of an energy consumption optimization control method for aluminum single-panel cutting process provided in this application is shown;

[0058] Figure 2 A schematic diagram of a scenario illustrating an energy consumption optimization and control method for aluminum single-panel cutting process provided in this application is shown.

[0059] Figure 3 This paper shows a schematic diagram of the energy consumption optimization control system for the aluminum single-panel cutting process provided in this application.

[0060] Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0062] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0063] A key challenge in current aluminum plate cutting processes is the inability of equipment to intelligently distinguish between vibrations that require energy for effective cutting and ineffective vibrations that waste energy. While existing technologies can preset cutting parameters based on material thickness, in practice, even for aluminum plates of the same thickness, differences in internal structure can cause these preset parameters to malfunction. More seriously, the system may mistakenly treat effective cutting vibrations as harmful vibrations, triggering incorrect deceleration or pressurization actions. This misjudgment is like "accidentally applying the brakes," not only failing to reduce energy consumption but also exacerbating equipment vibration, leading to decreased cutting accuracy and wasted energy. Actual measurements show that ineffective power consumption caused by vibration alone accounts for nearly one-third of the total energy consumption.

[0064] To address the aforementioned issues, this invention develops an intelligent cutting cycle control system. The core of this technology lies in equipping the cutting equipment with an "energy stethoscope"—using piezoelectric sensors to capture subtle vibrations in real time when the cutting tool contacts the aluminum plate, and simultaneously analyzing key parameters such as spindle speed and feed pressure. The system first intelligently matches characteristic frequency bands based on material thickness (similar to "voiceprint recognition") to accurately filter out high-frequency noise generated by equipment vibration; then, it establishes a vibration energy distribution heat map to pinpoint the main power-consuming areas; finally, it dynamically adjusts the timing of the spindle speed and feed pressure (like a conductor precisely controlling the rhythm of an orchestra) to ensure the cutting tool enters the aluminum plate when it is most prone to deformation (vibration troughs match the stress zone). This intelligent cycle adjustment keeps the equipment in a highly efficient state, eliminating energy waste caused by misjudgments and parameter inaccuracies caused by material differences, ultimately achieving dual optimization of energy consumption and accuracy.

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] Figure 1 This application provides a flowchart of an energy consumption optimization control method for aluminum single-panel cutting process, as shown in the embodiments of the present application. Figure 1 As shown, the method includes:

[0067] 101. Real-time capture of multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut, and synchronous acquisition of cutting parameters including the real-time spindle speed, feed axial pressure, and the thickness of the material plate being cut.

[0068] In the above scheme, multidimensional physical signals refer to a mixture of physical information, including the energy transfer characteristics of vibration waves and sound wave characteristics, generated synchronously when the cutting tool contacts the material being cut. The cutting parameter set refers to a combined data set containing the core operating status of the cutting machine, where the real-time spindle speed represents the speed of the tool's rotation, the feed axial pressure describes the force by which the tool propels the material, and the material thickness value refers to the actual thickness of the aluminum plate at the current cutting position.

[0069] In this embodiment, multiple sets of piezoelectric sensors distributed on the surface of the tool holder first capture multi-dimensional physical signals generated when the tool cuts material in real time, including subtle vibration signals and friction noise signals. Simultaneously, a magnetic induction device installed inside the spindle reads the tool rotation speed in real time, a hydraulic sensor in the feed system records axial thrust data, and a laser rangefinder above the material stage scans and obtains the aluminum plate thickness. A central synchronous control chip issues a unified start command to all sensors, achieving data acquisition and alignment at the same millisecond time scale. For example, at the instant the tool tip contacts the aluminum plate surface, the piezoelectric sensor captures a 3-millisecond vibration waveform, the magnetic induction device records a rotation speed of 12,500 revolutions per minute, the hydraulic sensor displays a thrust of 480 Newtons, and the laser rangefinder returns a thickness of 3.05 millimeters. These data are packaged into a data block with a unified time stamp and transmitted to the analysis module.

[0070] In practical application, when this step was implemented in the metal processing workshop of Company C, the PS-01 piezoelectric induction array mounted on the tool holder captured the composite waveform signal at the initial stage of cutting. The HS-300 magnetic induction device mounted at the tail of the spindle provided real-time feedback on the rotational speed data at 12,800 revolutions per minute. The PT-X5 pressure sensor inside the feed cylinder continuously monitored the current axial thrust at 500 Newtons. The LT-3D line laser thickness gauge mounted above the conveyor belt acquired the material thickness fluctuation curve at a scanning frequency of 200 scans per second. All devices triggered data capture at the start of cutting via a synchronous controller (SC-8), ensuring that the four types of parameters were synchronously sampled within a 0.5 millisecond time window, forming a structured initial analysis dataset.

[0071] This solution constructs a complete dynamic monitoring network, completely eliminating the signal time misalignment phenomenon caused by traditional time-sharing acquisition, and establishing a precise correspondence between material stress data and equipment operating parameters. This provides a reliable data foundation for subsequent energy consumption analysis and effectively solves the problem of energy calculation deviation caused by asynchronous signal acquisition.

[0072] 102. Based on the thickness value of the material plate, a preset physical property association library is matched to extract the characteristic frequency band range of the corresponding thickness range. At the same time, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element.

[0073] Optionally, step 102 may specifically include the following steps:

[0074] 1021. Compare the thickness value of the material plate with the predefined thickness range in the physical property association library to determine the target thickness range to which the thickness value of the material plate belongs;

[0075] 1022. Extract the characteristic frequency band range bound to the target thickness interval from the physical attribute association library, wherein the characteristic frequency band range is limited by the minimum frequency value and the maximum frequency value;

[0076] 1023. Receive multidimensional physical signals generated at the interface between the cutting tool and the material through a piezoelectric sensitive element, wherein the multidimensional physical signals include vibration amplitude signals and sound wave signals;

[0077] 1024. Utilizing the electromechanical conversion characteristics of the piezoelectric sensitive element, the vibration amplitude signal is converted into a first electrical signal sequence, and the sound wave signal is converted into a second electrical signal sequence;

[0078] 1025. Sample the first electrical signal sequence and the second electrical signal sequence at fixed time intervals to generate a sensor signal sequence arranged in chronological order.

[0079] In the above scheme, the physical property association library refers to a pre-established database that corresponds to the thickness of aluminum plates with energy fluctuation characteristics. The characteristic frequency band range describes the band interval where energy is concentrated during the cutting of materials of a specific thickness, and the boundary is defined by the minimum and maximum frequency values. The electromechanical conversion effect of the piezoelectric sensing element refers to the physical characteristic that a special sensing material automatically generates an electrical signal when subjected to force and deformation. The sensor signal sequence refers to the set of ordered electrical signals converted from the original physical vibrations by dividing them into fixed time slices.

[0080] In this embodiment, firstly, step 1021 compares the real-time acquired material plate thickness value with the preset thickness partitions in the physical property association library. For example, for a thickness value of 3.05 mm, the system scans the three partitions in the library: 2.0-2.5 mm, 2.5-3.0 mm, and 3.0-3.5 mm, to determine that it belongs to the target thickness range of 3.0-3.5 mm. Secondly, step 1022 extracts the characteristic frequency band range data bound to the target thickness range from the physical property association library. For example, if the system finds a minimum frequency value of 1800 Hz and a maximum frequency value of 4000 Hz corresponding to the 3.0-3.5 mm partition, a characteristic frequency band identifier of 1500-3500 Hz is generated. Simultaneously, step 1023 captures two raw signals by contacting the cutting tool fixture with a piezoelectric sensitive element: a quartz crystal sensor converts the vibration of the cutting tool into a vibration amplitude signal of mechanical deformation, and a microphone module collects sound wave signals. Then, step 1024 is executed, utilizing the piezoelectric effect conversion mechanism of the piezoelectric sensitive element, the vibration amplitude signal causes a voltage fluctuation of 25 mV to 1.2 V in the quartz crystal, forming a first electrical signal sequence. The fluctuation of the sound pressure signal drives the capacitor diaphragm to generate a current change of 4-20 mA, forming a second electrical signal sequence. Finally, in step 1025, the analog-to-digital converter samples the dual electrical signals at a fixed frequency of once per microsecond for 100 milliseconds. For example, at time point t = 50.3 microseconds, a voltage value of 285 mV and a current value of 12 mA are recorded, generating an ordered sensor signal sequence with time stamps.

[0081] In practical application, when implementing step 102 in the metal processing workshop of Company C, the industrial control computer takes the current material thickness value of 3.05 mm obtained by the laser thickness measuring device (model LT-3D) in step 101 as input and compares it with the physical property association library pre-installed in the database. This association library pre-divides the thickness range into two categories: 2.5-3.0 mm and 3.0-3.5 mm. After the system determines that 3.05 mm falls within the target range of 3.0-3.5 mm, it automatically extracts the characteristic frequency band bound to this range, from a minimum of 1500 Hz to a maximum of 3500 Hz. Simultaneously, the piezoelectric sensor array (model PS-01) installed at the bottom of the tool holder continuously receives signals from the cutting interface: the quartz sensing unit captures the tool vibration wave at a frequency of 2800 Hz, and the acoustic sensing module acquires a noise signal with a sound pressure level of 90 dB. Utilizing the deformation-generating electrostatic properties of piezoelectric materials, the vibration signal is converted into a voltage fluctuation sequence of 0.5-4.2 volts, and the sound wave signal is converted into a current fluctuation sequence of 5-22 mA. Finally, using the AD-16 data acquisition card, the dual-channel signals were continuously acquired for 100 milliseconds at a sampling rate of once per microsecond, generating 100,000 sets of time-stamped sequence data, such as a timestamp of 50.3 microseconds corresponding to a voltage value of 3.1 volts and a current value of 15 milliamps, forming a standardized basis for signal analysis.

[0082] This scheme achieves intelligent matching of material properties and energy characteristics, breaking through the traditional fixed parameter mode's neglect of the microscopic differences in materials; at the same time, it transforms complex physical signals into standardized electrical signal sequences, providing a structured time-energy mapping basis for subsequent analysis and solving the problem of inaccurate feature recognition caused by changes in material properties.

[0083] 103. Perform frequency domain energy focusing within the characteristic frequency band based on the signal sequence to generate a resonance characteristic spectrum, and perform gradient weighting on the energy concentration peaks in the resonance characteristic spectrum according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy;

[0084] Optionally, step 103 may specifically include the following steps:

[0085] 1031. Perform frequency component decomposition on the sensor signal sequence to obtain the full-band energy distribution;

[0086] 1032. Extract the frequency band corresponding to the characteristic frequency band range from the full-band energy distribution, and use it as the target frequency band energy distribution;

[0087] 1033. In the energy distribution of the target frequency band, the energy value at each frequency point is squared to obtain the enhanced energy value;

[0088] 1034. The enhanced energy value is combined with the position weight coefficient and weighted according to the preset calculation rules. The weighted enhanced energy values ​​of all frequency points are summarized to form a resonance characteristic spectrum with frequency as the horizontal axis and weighted energy as the vertical axis.

[0089] 1035. Identify frequency points in the resonance characteristic spectrum where the energy value exceeds a preset threshold, and use them as energy accumulation peak points;

[0090] 1036. Obtain the current feed axial pressure value of the cutting machine, and determine the pressure gradient coefficient based on the magnitude of the pressure value, wherein the pressure gradient coefficient increases with the increase of the feed axial pressure value;

[0091] 1037. The energy value of each energy accumulation peak point is weighted and calculated with the pressure gradient coefficient to obtain the weighted peak energy value;

[0092] 1038. Based on the weighted peak energy value, and with frequency as the horizontal axis and weighted peak energy value as the vertical axis, a two-dimensional point distribution map is generated as the energy consumption distribution map of vibration energy.

[0093] In the above scheme, frequency domain energy focusing refers to the technical means of focusing on analyzing the intensity of vibration energy within a specific frequency range, resonance characteristic spectrum is a visual spectrum generated by enhancing the energy distribution of a specific frequency band, and energy consumption distribution map is a two-dimensional energy thermogram after reweighting the energy peak according to the pressure magnitude.

[0094] In this embodiment of the application, firstly, in step 1031, the sensor signal sequence generated in step 102 is subjected to frequency decomposition calculation using the Fast Fourier Transform (FFT) algorithm to obtain an energy distribution map across the entire frequency range. The FFT transform formula is expressed as:

[0095]

[0096] Where s(t) represents the time-domain signal sequence, f represents the frequency index, N is the number of sampling points, and E(f) is the output frequency energy value. For example, when processing 100,000 sets of time-voltage-current sequences, the energy value of each frequency point in the range of 0-10kHz is calculated. Next, through step 1032, the characteristic frequency band range of 1800-4000Hz determined in step 102 is extracted from the above full-band energy distribution as the target analysis segment. Then, step 1033 is executed to perform energy enhancement calculation on each frequency point in the target frequency band to obtain the enhanced energy value, expressed by the formula: E 强化 f)=[E(f)] 2Where E(f) is the original frequency energy value, the squaring operation significantly enhances the energy peak characteristics. For example, the original energy of 0.6 at 2500Hz is calculated to become 0.36. Then, in step 1034, the enhanced energy value is weighted according to the preset frequency position weighting coefficient w(f), and the calculation expression is: E 加权 f)=w(f)·E 强化 f), where w(f) is an importance coefficient set according to the frequency band position, such as w(f) = 0.9 for the 1900-2500Hz range and w(f) = 1.1 for the 2500-3500Hz range. The weighted values ​​of all frequency points are then summed to form a continuous curve-type resonance characteristic spectrum. Then, in step 1035, the resonance characteristic spectrum is scanned to detect frequencies exceeding the threshold E. 阈值 The prominent energy point, This represents the average energy value. Simultaneously, in step 1036, the current feed axial pressure value P of the cutting machine recorded in step 101 is read, and the pressure gradient coefficient is calculated based on the value of P.

[0097]

[0098] Where P min =300N, P max =600N is the preset pressure range. For example, when P=500N, k 压力 =1.15. Then, step 1037 is executed, performing a pressure-weighted calculation for each energy peak point to obtain the weighted peak energy value: E 峰值加权 =k 压力 ·E 峰值 For example, the 2800Hz peak value, after being weighted by 1.6, becomes 1.84. Finally, step 1038 is performed, with frequency as the x-axis (f), and the weighted energy value E is calculated based on the peak value. 峰值加权 Using the vertical axis as the ordinate, a two-dimensional coordinate point distribution map is generated to complete the construction of the energy consumption distribution map.

[0099] In a practical application scenario where Company C cuts 3.05mm aluminum plates: the system performs FFT calculations on the voltage signal sequence, with N = 4096 sampling points in the formula, outputting a 0-10000Hz spectrum E(f). The target segment is then truncated based on the characteristic frequency band of 1800-4000Hz. Energy enhancement calculation E is then performed. 强化 (f) = [E(f)] 2 When f = 2800Hz, E(f) = 1.2 changes to 1.44. According to the position weighting scheme: 1900-2500Hz weighted w(f) = 0.9, 2500-3500Hz weighted w(f) = 1.1, 3500-4000Hz weighted w(f) = 0.8, calculate E. 加权 f)=w(f)·E 强化(f) Identify three peak points exceeding the threshold of 1.5 × 0.8 = 1.2: 2450 Hz (1.2), 2800 Hz (1.7), and 3320 Hz (1.1). Using a feed pressure of P = 480 N, calculate...

[0100]

[0101] Peak weighting calculations were performed: 1.2 × 1.2 = 1.44 for the 2450Hz point, 1.7 × 1.2 = 2.04 for the 2800Hz point, and 1.1 × 1.2 = 1.32 for the 3320Hz point. This resulted in three sets of coordinate points (2450, 1.44), (2800, 2.04), and (3320, 1.32), forming a two-dimensional point plot as the energy distribution map of the vibration energy.

[0102] This scheme enables focused enhancement and visualization of energy in key frequency bands. By using a pressure gradient weighting mechanism, it highlights the main vibration source areas, establishing a basis for accurate separation of useful energy consumption and vibration energy consumption, and solving the problem of feature identification ambiguity caused by energy dispersion in traditional spectrum analysis.

[0103] 104. Couple the energy consumption distribution map with the real-time spindle speed in the time domain to construct a feature vector that couples speed and vibration, and perform multi-scale time-frequency decomposition processing on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration.

[0104] Optionally, step 104 may specifically include the following steps:

[0105] 1041. Divide the energy consumption distribution map into continuous energy consumption time segments according to a fixed time length;

[0106] 1042. For each energy consumption time segment, extract the weighted peak energy value corresponding to all frequency points within the energy consumption time segment, and calculate the arithmetic sum of the weighted peak energy values ​​as the total vibration energy value.

[0107] 1043. Synchronously collect the instantaneous value of the real-time rotational speed of the spindle within the energy consumption time segment, and combine the total vibration energy value corresponding to each energy consumption time segment with the instantaneous value of the real-time rotational speed of the spindle into a data pair;

[0108] 1044. Arrange the total vibration energy value and the instantaneous value of the real-time rotational speed of the main shaft in all data pairs in chronological order to form a one-dimensional numerical sequence as a feature vector, wherein the total vibration energy value and the instantaneous value of the real-time rotational speed of the main shaft at the corresponding moment maintain an adjacent position relationship in the sequence.

[0109] 1045. The feature vector is segmented using three windows of different time lengths: short window, medium window and long window. The data segment in the short window is decomposed into frequency components, and the components with frequencies higher than the first threshold are extracted as the first high-frequency components.

[0110] 1046. Perform frequency component decomposition on the data segment within the middle window, extract the components with frequencies higher than the second threshold as the second high-frequency components, and perform frequency component decomposition on the data segment within the long window, extract the components with frequencies higher than the third threshold as the third high-frequency components.

[0111] 1047. The first high-frequency component, the second high-frequency component, and the third high-frequency component are superimposed according to time points to generate a superimposed high-frequency sequence;

[0112] 1048. Select the portion of the superimposed high-frequency sequence whose energy value is continuously greater than the average value of adjacent time points as the high-frequency energy consumption component dominated by tool vibration.

[0113] In the above scheme, the feature vector refers to a one-dimensional data string formed by alternating the vibration energy value and the spindle speed in time sequence. Multi-scale time-frequency decomposition is a method to analyze the frequency characteristics of the signal through different time window lengths. The high-frequency energy consumption component specifically refers to the ineffective energy fluctuation part mainly generated by the vibration of the tool itself.

[0114] In this embodiment, firstly, step 1041 divides the energy consumption distribution map generated in step 103 into continuous energy consumption time segments with a fixed duration of 100 milliseconds. For example, the total duration of 500 milliseconds is divided into 5 energy consumption time segments. Secondly, step 1042 sums the weighted peak energy values ​​of all frequency points within each energy consumption time segment. For example, in the first segment, the arithmetic sum of the three points 2450Hz (1.44), 2800Hz (2.04), and 3320Hz (1.32) is 1.44 + 2.04 + 1.32 = 4.80, which is recorded as the total vibration energy value. Then, step 1043 is executed to synchronously extract the instantaneous value of the spindle speed at the corresponding moment of each segment, such as 12800 rpm, forming an energy-speed data pair, such as (4.80, 12800). Then, in step 1044, all data pairs are arranged in chronological order: from the total vibration value of time segment 1 (4.80) to the corresponding rotational speed of 12800, then to the total value of time segment 2 (5.12), then to the rotational speed of 12900, and so on, forming a one-dimensional feature vector of the form [4.80, 12800, 5.12, 12900, ...]. Then, in step 1045, the feature vector is decomposed using three scales: a short window of 50 milliseconds, a medium window of 200 milliseconds, and a long window of 500 milliseconds. For example, a Fourier transform is performed on the data segment within the short window to extract components with frequencies higher than 50 Hz as the first high-frequency component. Simultaneously, in step 1046, the medium window segment decomposes and extracts components with frequencies higher than 20 Hz as the second high-frequency component, and the long window segment extracts components with frequencies higher than 10 Hz as the third high-frequency component. Then, in step 1047, the data points of the three components are superimposed according to time points: for example, at t = 150 ms, the sum of the first component (0.8), the second component (1.2), and the third component (0.6) is 2.6. Finally, in step 1048, the portion of the superimposed sequence that is consistently higher than the average of the preceding and following time points by 10% is selected. For example, at t=150ms, 2.6>(2.4+2.5) / 2 is identified as a high-frequency energy consumption component.

[0115] In practical applications, during the cutting of 3.05 mm aluminum plates at Company C: the system divides the 500 ms energy consumption graph into five 100 ms segments. The sum of the three peak points in the first segment (1.44 + 2.04 + 1.32 = 4.80) is recorded as the total energy value. Simultaneously, the spindle speed at that moment (12800 rpm) is acquired to form a data pair (4.80, 12800). Five data pairs are generated from the five segments, arranged in chronological order as a feature vector [4.80, 12800, 5.12, 12900, 4.65, 12700, 5.32, 13000, 4.90, 12850]. The first 100 ms data segment is analyzed using a short window, and after FFT decomposition, the >50 Hz component is extracted to obtain the first high-frequency sequence [0.82, 0.75, ...]. The medium-window analysis extracts the >20Hz component within a 300ms segment, yielding values ​​[1.35, 1.28, ...]. The long-window analysis extracts the >10Hz component across the entire segment, yielding values ​​[0.93, 0.87, ...]. At time t = 150ms, the three component values ​​are superimposed: 0.82 + 1.35 + 0.93 = 3.10. If this value consistently exceeds the average of adjacent points (e.g., 2.9 at t = 140ms and 2.8 at t = 160ms), this time point is marked as a high-frequency energy consumption component segment.

[0116] This solution constructs a time-dimensional energy-speed coupled data chain. Through multi-scale analysis, it separates environmental interference and inherent equipment vibration, accurately extracts the high-frequency ineffective energy consumption fluctuation band dominated by tool vibration, provides a target for dynamic control, and solves the problem of identifying vibration characteristics in composite signals that are submerged by other noise.

[0117] 105. Based on the amplitude variation trend of the high-frequency energy consumption component, a suppression command is generated, and the phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing energy consumption optimization control during the cutting process.

[0118] Optionally, step 105 may specifically include the following steps:

[0119] 1051. Obtain from the suppression command the phase difference adjustment indication value of the target offset corresponding to the amplitude change trend of the high-frequency energy consumption component;

[0120] 1052. Monitor the starting point of the rotation cycle of the real-time spindle speed and record the starting point of the application of the feed axial pressure;

[0121] 1053. Based on the starting point of the rotation cycle and the starting point of the application, determine the current time difference as the current phase difference;

[0122] 1054. Apply the phase difference adjustment indication value to the current phase difference to generate a target phase difference;

[0123] 1055. Adjust the timing of the control signal for the feed axial pressure so that the time offset of the application start point relative to the start point of the rotation cycle is equal to the target phase difference;

[0124] 1056. Repeat each step iteratively to form a closed-loop control process, ensuring that the vibration trough position of the cutting tool is continuously aligned with the stress deformation zone of the material plate.

[0125] In the above scheme, the suppression command refers to the control command generated according to the vibration energy fluctuation trend, the phase difference adjustment indicator value describes the amount of action time difference that needs to be changed, the current phase difference represents the time interval between the starting point of spindle rotation and the starting point of feed action, the target phase difference is the ideal time difference that needs to be achieved after control, and closed-loop control represents the cyclic process of continuous monitoring and real-time adjustment.

[0126] In this embodiment, firstly, step 1051 analyzes the fluctuation trend of the high-frequency energy consumption component output in step 104: if the amplitude of the high-frequency energy consumption component continues to rise, it is determined that the phase difference needs to be increased; if it falls, the phase difference needs to be decreased, generating a phase difference adjustment indication value for the target offset, such as +0.5 milliseconds. Secondly, step 1052 uses a magnetic encoder to monitor the starting point of the spindle rotation cycle in real time, such as the magnetic grating zero-point pulse, and simultaneously records the trigger signal of the feed system pressure sensor as the application starting point. Next, step 1053 is executed to calculate the time difference between the two as the current phase difference: for example, if the rotation starting point t = 10.0 ms and the feed starting point t = 10.3 ms, then the current phase difference is 0.3 ms. Then, step 1054 applies the target offset +0.5 ms to the current value: 0.3 ms + 0.5 ms = 0.8 ms as the target phase difference. After that, step 1055 is executed to adjust the timing parameters of the feed control system so that the next feed action is triggered 0.8 ms later than the rotation starting point. Finally, by repeating the above steps through step 1056, the phase difference is updated every 100 milliseconds to ensure that the tool vibration trough is always within the window where the material is most easily deformed.

[0127] In practical applications, during continuous cutting at Company C, the system detected a 15% increase in the amplitude of the 2800Hz high-frequency component per minute, triggering a suppression command to generate a +0.6 ms phase difference adjustment value. The spindle rotation cycle monitoring module (Model ENC-03) records the zero-point pulse time in real time, e.g., t = 250.0 ms, while the feed pressure sensor (Model PT-X5) captures the hydraulic cylinder's starting point of action at t = 250.4 ms. The system calculates the current phase difference as 0.4 ms. After applying the adjustment value, a target phase difference of 1.0 ms is generated. The control system immediately modifies the feed signal trigger parameters, delaying the next pressurization start point to t = 250.0 ms + 1.0 ms = 251.0 ms. This adjustment ensures that the moment the tool vibration trough enters the material overlaps with the peak of the aluminum plate's elongation deformation, resulting in a 40% reduction in measured vibration energy absorption. The system repeats this closed-loop adjustment process every 50 ms to dynamically maintain the optimal matching state.

[0128] This solution enables intelligent dynamic adjustment of the cutting cycle time. By continuously tracking the trend of vibration energy changes and fine-tuning the timing of rotation and feed coordination, it ensures that the tool always enters the material in the most deformable state, effectively suppressing vibration energy absorption and significantly improving cutting efficiency and process stability.

[0129] Figure 2 This application provides a scenario diagram illustrating an energy consumption optimization and control method for aluminum single-panel cutting processes, as shown in the embodiments below. Figure 2 As shown, a complete embodiment of steps 101-105 includes:

[0130] On the production line of aluminum product processing plant A, this method is implemented after the cutting equipment is started: First, the piezoelectric sensor PS-01 installed on the tool holder in step 101 captures the vibration wave and friction sound signal when the tool cuts into the 3.05 mm aluminum plate in real time. At the same time, the Hall sensor HS-300 collects the spindle speed of 12800 rpm, the hydraulic sensor PT-X5 records the feed pressure of 480 N, and the laser thickness gauge LT-3D scans the material thickness value of 3.05 mm. All data are synchronized with the timestamp by the synchronization controller SC-8 to generate a synchronization data packet; then... Step 102: The system matches the thickness value of 3.05 mm to the 3.0-3.5 mm partition in the physical property library, extracts the characteristic frequency band of 1800-4000 Hz, and simultaneously the piezoelectric conversion module converts the vibration signal into a 0.8-3.5 V voltage sequence and the sound wave into a 6-18 mA current sequence. These are then sampled at 100,000 time-series signals per microsecond by the AD-16 acquisition card. Step 103: The system performs a Fast Fourier Transform on the signal to generate the full spectrum, extracts the 1800-4000 Hz band, and squares the energy value for enhancement (e.g., increasing the energy value from 1.2 at 2800 Hz). Up to 1.44, a resonance feature spectrum is generated according to position weights, identifying the 2800 Hz energy peak of 1.7. Combined with a 480 Newton pressure, a gradient coefficient of 1.2 is calculated, yielding a weighted peak value of 2.04, and an energy consumption scatter plot is output. Subsequently, step 104 is executed to divide the 500 ms energy consumption map into 5 segments. The peak values ​​of the first segment are summed to obtain a total value of 4.80, which is then paired with the rotational speed of 12800 to construct a feature vector [4.80, 12800, ...]. Multi-scale decomposition is performed using short / medium / long windows (50 / 200 / 500 ms), and the values ​​of each window are superimposed (>5). The 0 / 20 / 10 Hz components, such as the three-way superposition value of 3.10 at t=150ms, are selected and marked as high-frequency energy consumption components if the value continuously exceeds the neighborhood average by 10%. Finally, step 105 is implemented: when the 2800 Hz component is detected to rise by 15% per minute, a phase adjustment value of +0.6 milliseconds is generated. The spindle rotation zero point t=250.0 milliseconds and the feed start point t=250.4 milliseconds are monitored in real time. The current phase difference of 0.4 milliseconds is calculated, and the target value is adjusted to 1.0 milliseconds, so that the next feed is delayed to 251.0 milliseconds to trigger, thus forming a closed-loop control.

[0131] This solution constructs a closed-loop "perception-analysis-control" system: It achieves full-element digital modeling of the cutting process through synchronous acquisition by multiple sensors, eliminating the spatiotemporal misalignment errors of traditional signal acquisition; based on dynamic matching of characteristic frequency bands with material thickness, combined with frequency domain energy enhancement and pressure gradient weighting technology, it accurately locates ineffective vibration energy hotspots; it innovatively employs multi-scale decomposition to peel away the high-frequency energy consumption component dominated by tool vibration, overcoming the challenge of separating effective deformation energy from ineffective chatter energy in composite signals; it finely adjusts the timing of spindle rotation and feed actions in real time according to vibration trends, ensuring that tool vibration troughs are continuously embedded within the material deformation window; and it forms a dynamic response cycle control closed loop, significantly reducing cutting resistance and equipment vibration, achieving simultaneous optimization of unit product energy consumption and chipping rate, and overall improving the process quality stability and energy efficiency of aluminum single-panel cutting.

[0132] This plan Figure 3 This application provides a schematic diagram of the energy consumption optimization control system for the aluminum single-panel cutting process, as shown in the embodiment. Figure 3 As shown, the system includes:

[0133] The acquisition module 31 is used to capture multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut in real time, and synchronously acquires the cutting parameters of the cutting machine, including the real-time spindle speed, feed axial pressure and the thickness of the material plate being cut.

[0134] Extraction module 32 is used to extract the characteristic frequency band range of the corresponding thickness range by matching the material plate thickness value with a preset physical property association library, and at the same time convert the multidimensional physical signal into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element;

[0135] The generation module 33 is used to perform frequency domain energy focusing within the characteristic frequency band according to the signal sequence, generate a resonance characteristic spectrum, and perform gradient weighting on the energy concentration peaks in the resonance characteristic spectrum according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy.

[0136] The separation module 34 is used to couple the energy consumption distribution map with the real-time spindle speed in the time domain, construct a feature vector of speed and vibration coupling, and perform multi-scale time-frequency decomposition processing on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration.

[0137] The adjustment module 35 is used to generate a suppression command based on the amplitude change trend of the high-frequency energy consumption component, and adjust the phase difference between the real-time spindle speed and the feed axial pressure according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing energy consumption optimization control during the cutting process.

[0138] Figure 3The aforementioned energy consumption optimization control system for aluminum single-panel cutting process can execute... Figure 1 The implementation principle and technical effects of the energy consumption optimization control method for aluminum single-panel cutting process described in the above embodiment will not be repeated here. The specific operation methods of each module and unit in the energy consumption optimization control system for aluminum single-panel cutting process in the above embodiment have been described in detail in the embodiments related to this method, and will not be elaborated further here.

[0139] In one possible design, Figure 3 The energy consumption optimization control system for the aluminum single-panel cutting process shown in the embodiment can be implemented as a computing device, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0140] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0141] The processing component 42 is used for the above Figure 1 The embodiment describes an energy consumption optimization and control method for aluminum single-panel cutting process.

[0142] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0143] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0144] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0145] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0146] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0147] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0148] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for optimizing and controlling energy consumption during the aluminum single-panel cutting process.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0150] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for optimizing and controlling energy consumption in the aluminum single-panel cutting process, characterized in that, include: Real-time capture of multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut; synchronous acquisition of cutting parameters including the real-time spindle speed, feed axial pressure and the thickness of the material plate being cut. Based on the material plate thickness value, a preset physical property association library is matched, and the characteristic frequency band range of the corresponding thickness range is extracted. At the same time, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element. Frequency domain energy focusing is performed on the signal sequence within the characteristic frequency band to generate a resonance characteristic spectrum. The energy concentration peaks in the resonance characteristic spectrum are then gradient-weighted according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy. The energy consumption distribution map is coupled with the real-time spindle speed in the time domain to construct a feature vector that couples speed and vibration. Multi-scale time-frequency decomposition processing is then performed on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration. Based on the amplitude variation trend of the high-frequency energy consumption component, a suppression command is generated, and the phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing optimized energy consumption control during the cutting process.

2. The method according to claim 1, characterized in that, The phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby achieving optimized energy consumption control during the cutting process, including: Obtain from the suppression command the phase difference adjustment indication value of the target offset corresponding to the amplitude change trend of the high-frequency energy consumption component; The starting point of the rotation cycle of the real-time spindle speed is monitored in real time, and the starting point of the application of the feed axial pressure is recorded. Based on the starting point of the rotation cycle and the starting point of the application, the current time difference is determined as the current phase difference; The phase difference adjustment indication value is applied to the current phase difference to generate a target phase difference; Adjust the timing of the control signal for the feed axial pressure so that the time offset of the application start point relative to the start point of the rotation cycle is equal to the target phase difference; Repeatedly iterate through each step to form a closed-loop control process, ensuring that the vibration trough of the cutting tool is continuously aligned with the stress deformation zone of the material plate.

3. The method according to claim 1, characterized in that, The energy consumption distribution map is coupled with the real-time rotational speed of the spindle in the time domain to construct a feature vector coupling rotational speed and vibration, including: The energy consumption distribution map is divided into continuous energy consumption time segments with a fixed time length; For each energy consumption time segment, extract the weighted peak energy value corresponding to all frequency points within the energy consumption time segment, and calculate the arithmetic sum of the weighted peak energy values ​​as the total vibration energy value; The instantaneous value of the real-time rotational speed of the spindle within the energy consumption time segment is collected synchronously, and the total vibration energy value corresponding to each energy consumption time segment is combined with the instantaneous value of the real-time rotational speed of the spindle into a data pair; The total vibration energy and the instantaneous real-time spindle speed of all data pairs are arranged alternately in chronological order to form a one-dimensional numerical sequence as a feature vector, in which the total vibration energy and the instantaneous real-time spindle speed of the corresponding moment are kept in adjacent positions in the sequence.

4. The method according to claim 1, characterized in that, Multi-scale time-frequency decomposition processing is performed on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration, including: The feature vector is segmented using three windows of different time lengths: short window, medium window and long window. The data segments within the short window are decomposed into frequency components, and the components with frequencies higher than a first threshold are extracted as the first high-frequency components. Frequency component decomposition is performed on the data segment within the middle window, and the component with a frequency higher than the second threshold is extracted as the second high-frequency component. Frequency component decomposition is performed on the data segment within the long window, and the component with a frequency higher than the third threshold is extracted as the third high-frequency component. The first high-frequency component, the second high-frequency component, and the third high-frequency component are superimposed according to time points to generate a superimposed high-frequency sequence; The portion of the superimposed high-frequency sequence whose energy value is consistently greater than the average value of adjacent time points is selected as the high-frequency energy consumption component dominated by tool vibration.

5. The method according to claim 1, characterized in that, Based on the material plate thickness value, a preset physical property association library is matched, and the characteristic frequency band range of the corresponding thickness range is extracted. Simultaneously, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element, including: The thickness value of the material plate is compared with the predefined thickness range in the physical property association library to determine the target thickness range to which the thickness value of the material plate belongs; Extract the feature frequency band range bound to the target thickness interval from the physical property association library. The feature frequency band range is defined by the minimum frequency value and the maximum frequency value. The multidimensional physical signals generated at the interface between the cutting tool and the material are received by a piezoelectric sensing element. The multidimensional physical signals include vibration amplitude signals and sound wave signals. By utilizing the electromechanical conversion characteristics of the piezoelectric sensing element, the vibration amplitude signal is converted into a first electrical signal sequence, and the sound wave signal is converted into a second electrical signal sequence; The first and second electrical signal sequences are sampled at fixed time intervals to generate a sensor signal sequence arranged in chronological order.

6. The method according to claim 1, characterized in that, Based on the signal sequence, frequency domain energy focusing is performed within the characteristic frequency band to generate a resonance characteristic spectrum, including: The sensor signal sequence is decomposed into frequency components to obtain the full-band energy distribution; The frequency band corresponding to the characteristic frequency band range is extracted from the full-band energy distribution and used as the target frequency band energy distribution. In the energy distribution of the target frequency band, the energy value at each frequency point is squared to obtain the enhanced energy value; The enhanced energy value is combined with the position weight coefficient and weighted according to the preset calculation rules. The weighted enhanced energy values ​​of all frequency points are summarized to form a resonance characteristic spectrum with frequency as the horizontal axis and weighted energy as the vertical axis.

7. The method according to claim 1, characterized in that, The energy concentration peaks in the resonance characteristic spectrum are gradient-weighted according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy, including: In the resonance characteristic spectrum, frequency points with energy values ​​exceeding a preset threshold are identified as energy accumulation peak points; Obtain the current feed axial pressure value of the cutting machine, and determine the pressure gradient coefficient based on the magnitude of the pressure value, wherein the pressure gradient coefficient increases with the increase of the feed axial pressure value; The energy value at each energy accumulation peak point is weighted by the pressure gradient coefficient to obtain the weighted peak energy value. Based on the weighted peak energy value, a two-dimensional point distribution map is generated with frequency as the horizontal axis and weighted peak energy value as the vertical axis, serving as the energy consumption distribution map of vibration energy.

8. An energy consumption optimization control system for aluminum single-panel cutting process, characterized in that, include: Real-time capture of multi-dimensional physical signals generated at the interface between the cutting tool and the material being cut; synchronous acquisition of cutting parameters including the real-time spindle speed, feed axial pressure and the thickness of the material plate being cut. Based on the material plate thickness value, a preset physical property association library is matched, and the characteristic frequency band range of the corresponding thickness range is extracted. At the same time, the multidimensional physical signal is converted into a sensor signal sequence through the electromechanical conversion effect of the piezoelectric sensitive element. Frequency domain energy focusing is performed on the signal sequence within the characteristic frequency band to generate a resonance characteristic spectrum. The energy concentration peaks in the resonance characteristic spectrum are then gradient-weighted according to the axial pressure distribution to generate an energy consumption distribution map of vibration energy. The energy consumption distribution map is coupled with the real-time spindle speed in the time domain to construct a feature vector that couples speed and vibration. Multi-scale time-frequency decomposition processing is then performed on the feature vector to separate the high-frequency energy consumption component dominated by tool vibration. Based on the amplitude variation trend of the high-frequency energy consumption component, a suppression command is generated, and the phase difference between the real-time spindle speed and the feed axial pressure is adjusted according to the suppression command, so that the vibration trough of the cutting tool is always in the stress deformation zone of the material plate, thereby realizing optimized energy consumption control during the cutting process.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the energy consumption optimization control method for aluminum single-panel cutting process as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an energy consumption optimization control method for aluminum single-panel cutting process as described in any one of claims 1 to 7.

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