A cutting method for processing an automobile wire harness

Abstract

The application relates to the technical field of automobile wire harness processing, and discloses a cutting method for automobile wire harness processing. The method comprises the following steps: obtaining geometric feature data and material attribute data of a wire harness to be processed; constructing a dynamic cutting parameter mapping model based on the geometric feature data; generating a deformation compensation amount according to the material attribute data and inputting the deformation compensation amount into the model; driving a cutting device to perform a cutting operation based on the model output; collecting physical response data in a cutting process in real time, judging whether a preset iteration condition is met, outputting a final cutting result if the preset iteration condition is met, updating the model and repeating relevant steps if the preset iteration condition is not met. The method constructs a dynamic parameter model, introduces deformation compensation, combines real-time feedback, iteratively optimizes cutting parameters, solves the problems of poor adaptability of traditional fixed parameter cutting and easy occurrence of cutting quality problems, improves cutting quality stability and processing efficiency, reduces the dependence on artificial experience, and is suitable for processing requirements of diversified automobile wire harnesses.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of automobile wire harness processing, in particular to a cutting method for automobile wire harness processing. BACKGROUND

[0002] In the automobile manufacturing industry, automobile wire harnesses, as the key carriers connecting various electronic components, directly affect the overall operation performance and safety of automobiles. The cutting process, as an important part of the automobile wire harness processing flow, has a significant impact on the subsequent assembly and connection stability of the wire harness. With the continuous development of automobile technology, the structure of automobile wire harnesses is becoming increasingly complex. Not only are the diameters and numbers of wire harnesses diversified, but the materials used also exhibit a multi-element characteristic. There are not only traditional copper core wires, but also alloy wires with special properties. Some wire harnesses are also wrapped with insulation layers of different materials, which puts higher requirements on the cutting process.

[0003] The common automobile wire harness cutting method on the market uses fixed parameters for cutting operation. Before cutting, the operator usually sets the cutting speed, cutting force and other parameters according to experience or a simple specification manual, and then drives the cutting equipment to cut the batch of wire harnesses. However, this method has obvious limitations. Due to the differences in the geometric characteristics of different wire harnesses, such as the relatively thick diameter and the large number of internal wires of some wire harnesses, and the relatively thin diameter and relatively simple structure of some wire harnesses, when using fixed parameters for cutting, for wire harnesses with special geometric characteristics, incomplete cutting may occur, i.e. some wires are not cut off, which requires subsequent secondary processing, increasing the processing procedure and time cost; and for wire harnesses with simple structure, the end of the wire harness may be deformed or damaged due to excessive cutting parameters, affecting the subsequent connection effect.

[0004] Wire harnesses with different material properties have different deformation characteristics during cutting. For example, copper core wires have good ductility and may be extruded and deformed at the end during cutting if the force is too large. Alloy wires have high hardness and require more cutting force during cutting. If the parameters are not set properly, not only may the cutting efficiency be low, but also the cutting tool may be excessively worn, reducing its service life. The existing cutting method does not fully consider the deformation problems caused by the material properties of the wire harnesses, lacks targeted compensation measures, and makes it difficult to ensure the flatness of the wire harness end after cutting. Some wire harnesses may even have problems such as insulation layer damage and wire exposure, which poses a safety hazard to the subsequent use of automobile wire harnesses.

[0005] The existing cutting method lacks effective real-time feedback and adjustment mechanism during the cutting process. After the cutting equipment is started, it usually runs continuously according to the preset parameters, and even if the wire harness position offset, material property fluctuation and other situations occur during the cutting process, the cutting parameters cannot be adjusted in time. This "one-size-fits-all" mode leads to poor cutting quality stability, and the same batch of wire harnesses may have some cutting qualified and some cutting unqualified, increasing the product failure rate. At the same time, for the unqualified cutting products, screening, rework or scrap processing is needed, which not only wastes raw materials, but also reduces the overall production efficiency, and it is difficult to meet the current demand of automobile manufacturing industry for high precision, high efficiency and high stability of wire harness processing. SUMMARY

[0006] The purpose of the present application is to provide a cutting method for automobile wire harness processing to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides a cutting method for automobile wire harness processing, which comprises:

[0008] Step S1, obtaining the geometric feature data and material attribute data of the wire harness to be processed;

[0009] Step S2, constructing a dynamic cutting parameter mapping model based on the geometric feature data;

[0010] Step S3, generating a deformation compensation amount according to the material attribute data and inputting it into the dynamic cutting parameter mapping model;

[0011] Step S4, driving the cutting equipment to perform wire harness cutting operation based on the output of the dynamic cutting parameter mapping model;

[0012] Step S5, real-time collection of physical response data in the cutting process to determine whether the preset iteration condition is met;

[0013] Step S6, if the preset iteration condition is met, output the final cutting result; if not, update the dynamic cutting parameter mapping model and repeat steps S3 to S5.

[0014] Preferably, the geometric feature data includes shape distribution vector of wire harness cross section, curvature change sequence in wire harness length direction and thickness gradient value of wire harness outer sheath.

[0015] Preferably, the dynamic cutting parameter mapping model includes feature encoding layer, parameterized operation layer and action decoding layer;

[0016] The feature encoding layer decomposes the geometric feature data into base function weight coefficients;

[0017] The parameterized operation layer performs a non-linear mapping of the base function weight coefficients according to the deformation compensation amount;

[0018] The action decoding layer reconstructs the mapped base function weight coefficients into a cutting speed control sequence and a cutting pressure control sequence.

[0019] Preferably, the deformation compensation amount is generated by the following steps:

[0020] Extracting an elastic modulus distribution and a thermal expansion coefficient matrix from the material attribute data;

[0021] According to the temperature fluctuation value of the wire harness processing environment, calculating a coupling offset of the elastic modulus distribution and the thermal expansion coefficient matrix;

[0022] Generating an axial deformation compensation component and a radial deformation compensation component based on the coupling offset.

[0023] Preferably, the preset iteration condition includes that the cumulative number of cutting operations reaches a maximum threshold, the absolute change amount of physical response data generated by adjacent two times of cutting is less than a convergence threshold, or the relative change rate of physical response data generated by adjacent two times of cutting is less than a convergence threshold.

[0024] Preferably, the physical response data includes a flatness index of a wire harness cutout topography, a distribution density value of a wire harness end face burr, and an offset distance of a wire harness internal conductor.

[0025] Preferably, the updating the dynamic cutting parameter mapping model includes:

[0026] Calculating a deviation amount of a target function value and a constraint function value of a current cutting result;

[0027] Based on the deviation amount, adjusting an internal weight matrix of the dynamic cutting parameter mapping model using a gradient projection algorithm.

[0028] Preferably, the constraint function value includes a maximum acceleration threshold of a cutting device, a wear safety boundary of a cutting tool, and a minimum yield strength of a wire harness material.

[0029] Preferably, in step S4, further includes:

[0030] According to the spatial layout topology of the wire harness processing device, the cutting speed control sequence and the cutting pressure control sequence are distributed to the main processing node and the auxiliary processing node;

[0031] The main processing node and the auxiliary processing node synchronously perform a cutting action based on a real-time data exchange protocol.

[0032] Preferably, the real-time data exchange protocol includes:

[0033] When the instruction transmission delay of the main processing node exceeds the delay threshold, the local cache instruction sequence of the auxiliary processing node is activated;

[0034] According to the wire harness processing progress feedback value, the instruction distribution frequency of the main processing node and the auxiliary processing node is dynamically adjusted.

[0035] Compared with the prior art, the beneficial effects of the present application are:

[0036] The cutting method for the automobile wire harness processing provides a more scientific and efficient solution for the wire harness cutting process through multi-link optimization design from the actual needs of wire harness processing. Before cutting, the geometric feature data and material attribute data of the wire harness to be processed are obtained, so that the subsequent cutting parameter setting is no longer dependent on experience, but is based on the accurate understanding of the specific situation of the bundle body. Based on the geometric feature data, a dynamic cutting parameter mapping model is constructed, which can form a corresponding relationship between the cutting parameters and the geometric characteristics of the wire harness. Different diameters and different structures of the wire harness can obtain adaptive initial cutting parameters, avoiding the problem of incomplete cutting or bundle body damage caused by the mismatch between the parameters and the geometric characteristics of the wire harness in traditional fixed parameter cutting, and improving the rationality of the cutting parameter setting from the source.

[0037] On the basis of considering the geometric characteristics, the method also generates a deformation compensation amount according to the material attribute data and inputs it into the dynamic cutting parameter mapping model. This design fully focuses on the deformation difference of different material wire harnesses in the cutting process, and through the introduction of the deformation compensation amount, the cutting parameters can be adjusted specifically. For example, for materials with strong ductility and easy deformation, the cutting force and speed can be adjusted through the compensation amount to reduce the extrusion deformation of the bundle body during cutting. For materials with high hardness, the cutting parameters can be optimized through the compensation amount to ensure the cutting efficiency while reducing the tool wear, effectively solving the cutting quality problem caused by ignoring the material attributes in the traditional cutting method, improving the flatness of the wire harness end after cutting, and reducing the occurrence of adverse phenomena such as insulation layer damage and exposed wires.

[0038] This method does not operate statically according to initial parameters during the cutting process. Instead, it collects physical response data in real time and determines whether preset iteration conditions are met. This real-time feedback mechanism can promptly capture anomalies during the cutting process, such as changes in cutting force caused by wire harness positional shifts or differences in cutting response caused by fluctuations in local material properties. When the preset iteration conditions are not met, the dynamic cutting parameter mapping model is updated promptly, and the relevant steps are repeated, achieving dynamic adjustment of cutting parameters. This iterative optimization mode breaks the limitations of the traditional "one-cut" cutting method, allowing the cutting process to flexibly adapt to actual conditions, effectively improving the stability of cutting quality, reducing the number of defective products in the same batch of wire harnesses, and lowering the costs of subsequent screening and rework. Through precise parameter setting and dynamic adjustment, this method can optimize cutting efficiency while ensuring cutting quality. It avoids secondary processing caused by improper parameters, reducing ineffective processing time; reasonable parameter settings reduce excessive tool wear, extend tool life, reduce production interruptions caused by tool replacement, and indirectly improve overall production efficiency. Furthermore, this method requires no complex manual intervention. From data acquisition to parameter adjustment and cutting execution, it forms a relatively complete automated process, reducing reliance on operator experience, minimizing the impact of human error, improving the consistency and reliability of wire harness processing, and better adapting to the large-scale, high-precision production needs of the modern automotive manufacturing industry. Attached Figure Description

[0039] Figure 1 This is a timing diagram of the cutting method for automotive wiring harness processing according to the present invention;

[0040] Figure 2 A flowchart illustrating the geometric feature data of a cutting method used in automotive wiring harness manufacturing;

[0041] Figure 3 A flowchart for generating deformation compensation amount for cutting methods used in automotive wiring harness processing;

[0042] Figure 4 A flowchart for acquiring physical response data of a cutting method used in automotive wiring harness processing. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1The application provides a cutting method for automobile wire harness processing.

[0045] Geometric feature data and material attribute data of the wire harness to be processed are acquired. The geometric feature data includes a shape distribution vector of a wire harness cross section, a curvature variation sequence, and an outer sheath thickness gradient value; and the material attribute data includes an elastic modulus distribution and a thermal expansion coefficient matrix. A dynamic cutting parameter mapping model is constructed based on the geometric feature data, the model being composed of a feature coding layer, a parameterized operation layer, and an action decoding layer, and being used for converting input data into a cutting control sequence. A deformation compensation amount is generated according to the material attribute data and is input into the dynamic cutting parameter mapping model to correct the cutting parameters. A cutting device performs a cutting operation according to the control sequence output by the model, and simultaneously collects physical response data in real time, including incision topography flatness, burr distribution density, and conductor offset distance. If an iteration condition (such as the number of cutting times reaching a threshold value or the change amount of the physical response data being less than a convergence threshold value) is met, a final result is output; otherwise, the model parameters are updated and the adjustment and cutting process are repeated.

[0046] Embodiment 1: refer to Figure 2 The acquisition of the geometric feature data is realized by a specific device combination. A three-dimensional laser scanner performs circumferential scanning at an axial interval of 0.5 mm along the wire harness to be processed, and acquires cross section contour point cloud data. After preprocessing of the point cloud of each cross section, a closed polygon is generated by using a contour fitting algorithm. The polygon is equally divided into 36 sectors, the radial distance of the contour points of each sector to the center of the cross section is calculated, and a 36-dimensional vector is formed as the basis of the shape distribution vector. Further, the first 12 harmonic component amplitudes are extracted by discrete Fourier transform, and finally compressed into a 12-dimensional shape distribution vector. The measurement of the curvature variation sequence is completed by a contact probe arranged on the feeding track. The probe records the spatial coordinates of the wire harness center line at a sampling frequency of 10 Hz, and calculates the curvature value at every 5 mm interval by a three-point difference method, forming a curvature variation sequence in the length direction. The outer sheath thickness gradient value is acquired by using a non-contact ultrasonic thickness gauge. Eight circumferential measurement points are selected within every 10 cm length range, the thickness values of the points are recorded, and then a thickness distribution field is constructed by a bilinear interpolation algorithm, and finally the variation rates of the thickness along the circumferential and axial directions are extracted as the gradient values.

[0047] The feature encoding layer receives three types of geometric feature data: 12-dimensional shape distribution vector, curvature variation sequence (200 sampling points in length direction), and 8x8 thickness gradient matrix. The curvature sequence is first decomposed by wavelet packet, and db4 wavelet basis function is selected for 5-layer decomposition. The 3rd layer detail coefficient is extracted as the curvature feature component. The thickness gradient matrix is converted to the frequency domain by two-dimensional discrete cosine transform, and the first 16 low-frequency coefficients are retained. The shape distribution vector is directly used as an input component. The three types of feature components are connected in parallel to the fully connected network, which contains three hidden layers (the number of neurons is 64, 32, and 16, respectively), and outputs 32-dimensional basis function weight coefficients through the ReLU activation function.

[0048] The parameterized operation layer is composed of a nonlinear mapping module, which receives 32-dimensional basis function weight coefficients from the feature encoding layer and a 4-dimensional compensation vector (including 2-dimensional axial components and 2-dimensional radial components) from the deformation compensation module. The compensation vector is expanded to 32 dimensions by splicing and multiplied with the original weight coefficients by Hadamard product. The operation result is input into a recurrent unit with a gating mechanism, which contains 128 hidden states, and the features are nonlinearly reorganized by the tanh activation function to output the updated 32-dimensional weight coefficients.

[0049] The updated 32-dimensional weight coefficients are split into three groups of sub-vectors: the first group of 12-dimensional vectors is reconstructed into cross-sectional shape control parameters by inverse Fourier transform; the second group of 8-dimensional vectors is reconstructed into curvature compensation parameters by inverse wavelet transform; and the third group of 12-dimensional vectors is reconstructed into thickness control parameters by inverse discrete cosine transform. The three groups of parameters are input into a motion planner, which generates a tool path trajectory according to the cross-sectional shape control parameters, adjusts the feed speed curve according to the curvature compensation parameters, and calculates the pressure distribution according to the thickness control parameters. The final output is a discretized cutting speed control sequence (time-velocity value pairs, sampling interval 10ms) and a cutting pressure control sequence (spatial position-pressure value pairs, resolution 1mm).

[0050] In the specific implementation process, the wire harness is fixed on a six-degree-of-freedom positioning platform. A three-dimensional laser scanner is installed at the feeding inlet and synchronously collects geometric data during the feeding process. The thickness gauge adopts a rotating scanning head design and completes thickness measurement during the feeding pause gap. The calculation of the feature encoding layer is completed on an industrial control computer, and the processing period is controlled within 50ms. The control sequence output by the action decoding layer is transmitted to the cutting execution unit through real-time Ethernet, and the execution unit includes a servo motor driven rotary knife head and a gas pressure regulation system to realize accurate synchronous control of speed and pressure.

[0051] Example 2: see Figure 3The generation process of the deformation compensation quantity starts with the accurate acquisition of material property data. In the preprocessing stage, a sample section of the wire harness to be processed is placed in a thermostat, and a segmented tensile test is performed by an electronic universal testing machine. The test environment temperature is changed in steps of 10°C within the range of -10°C to 50°C, and the elastic modulus values of three axial positions are collected at each temperature point. The elastic modulus distribution data is stored in the form of a three-dimensional tensor, with the first dimension representing the temperature node, the second dimension representing the axial position coordinate, and the third dimension recording the modulus measurement value. The measurement of the thermal expansion coefficient matrix uses a dynamic thermal mechanical analyzer, and the wire harness sample is fixed on a quartz clamp. The temperature is raised and lowered at a rate of 2°C / min within the same temperature range, and the radial and axial expansion coefficient change curves are recorded synchronously, finally forming a three-dimensional coefficient matrix of temperature-position-direction.

[0052] Eight high-precision temperature sensors are evenly arranged around the cutting station, respectively located on the equipment rack, the feeding track, the pneumatic circuit, and the tool cooling area. The sensors collect the environmental temperature at a frequency of 10Hz, and fuse the node data through Kalman filtering algorithm to output the real-time spatial and temporal distribution of the current temperature fluctuation value. This distribution data is converted into dynamic boundary conditions of the finite element model, including the change of air convection coefficient in the processing area, the thermal conductivity gradient of the equipment metal parts, and the radiation exchange parameters of the wire harness surface.

[0053] A refined three-dimensional model of the wire harness to be cut is established, and each section of the wire harness in the model is discretized into multiple shell elements: the innermost conductor layer is rigidly constrained, the insulation layer is set as an orthotropic anisotropic material, and the outer coating layer is defined as a viscoelastic body. The elastic modulus distribution tensor is mapped to the element property field of the finite element model, and the thermal expansion coefficient matrix is input as the temperature load function. The solver performs step-by-step coupled calculation: first, it calculates the conduction process of the temperature field in the time dimension, and then inputs the temperature field results as the initial conditions into the stress field module. After solving, the displacement deviation of each element node is extracted, and after Gaussian filtering noise reduction processing, the coupled offset data set is formed.

[0054] 100 calculation sections are divided along the length direction of the wire harness, and the axial displacement deviation of all elements in each section is averaged. A continuous curve of axial displacement deviation is constructed by cubic spline interpolation, and combined with the time function of the wire harness feeding speed, the spatial curve is converted into a time series compensation component. This component is output in real time by a digital signal processor, in the format of a two-dimensional array of time stamp and displacement, with a time resolution of 1ms.

[0055] In each cross-section, a polar coordinate system is established with the center of the beam as the origin. The unit displacement vector is decomposed into radial and tangential components, and the data points with radial components greater than the threshold value are screened for cluster analysis to identify the weak areas of the outer layer. The thickness gradient data and the radial displacement data are multiplied in the polar coordinate grid, and the result is input into the convolutional neural network for feature extraction. The network outputs two compensation parameters: the circumferential non-uniform shrinkage adjustment factor and the local pressure intensity coefficient, the former is used to correct the radius size of the tool path trajectory, and the latter is converted into the control voltage curve of the air pressure servo valve.

[0056] The axial compensation component drives the phase compensator of the feeding servo motor, and the micro-displacement pulse is superimposed on the basic feeding speed. The radial compensation component is converted into four independent control signals: the first group of signals control the radial feed cylinder pressure of the rotating cutter, the second group adjusts the circumferential clamping force of the auxiliary support roller, the third group changes the local power density of the laser heater, and the fourth group controls the wind speed distribution of the cooling air curtain. All compensation signals are transmitted synchronously to the execution terminal through the fiber ring network, and the transmission delay is controlled within 0.5ms, and the timing error of the cutting action is not more than ±10μs.

[0057] In the process control cycle, the temperature monitoring system updates the environmental parameters every 200ms. The finite element solver uses an incremental calculation method, and only reloads the area grid whose change exceeds 5% each time, and the calculation time is compressed to within 80ms. The compensation output module has a double buffer mechanism: the current period executes the compensation parameters calculated in the last period, while the background updates the new generation of compensation data, avoiding interruption of the control flow.

[0058] Example 3: refer to Figure 4 The judgment mechanism of the preset iteration condition is based on real-time acquisition and analysis of multi-dimensional physical response data. The acquisition system of physical response data is composed of three independent detection modules, which monitor the cut shape, burr distribution and conductor position synchronously. The cut shape detection module uses a confocal laser microscope, whose optical probe is installed at 5mm behind the cutting tool at an angle of 45 degrees. The scanning frequency is synchronized with the cutting speed. After each cutting is completed, the probe performs layer scanning in the Z-axis direction along the cut section to obtain three-dimensional surface topography data. After median filtering, five characteristic parameters are extracted: the waviness amplitude of the cut edge, the peak-valley height difference of the cutting surface, the slope change rate of the transition area, the material accumulation volume and the heat affected zone width. The calculation of the flatness index uses a composite weighting method:

[0059]

[0060] Among them: represents the waviness amplitude, indicates the peak-valley height difference, is the slope change rate, is the material build volume, is the heat-affected zone width, to are the normalized weight coefficients for each parameter. These coefficients are dynamically adjusted according to the wire harness material type, for example, the weight of the slope change rate is set to 0.35 for PVC outer layer wire harness, while the weight of the heat-affected zone width is raised to 0.28 for silicone insulated wire harness.

[0061] The burr distribution detection module is integrated in the unloading area of the cutting station, containing a high-speed industrial camera and a ring-shaped LED light source. The camera takes wire harness end face images at a rate of 200 frames per second, and the image processing algorithm first extracts the edge profile through the Canny operator, and then separates the adherent burrs by morphological opening operation. Each identified burr feature is quantified into three parameters: projected area, aspect ratio, and tip curvature radius. The distribution density value is defined as the normalized number of burrs per square millimeter area, and the standardization process considers the size weight of the burrs, with a count coefficient of 1.5 for large size burrs and 0.8 for small size. The burr distribution change between adjacent two cuts is evaluated by calculating the cosine similarity of the feature vectors, and when the similarity exceeds 0.98, it is determined to reach the convergence state.

[0062] The X-ray tube works in pulse mode, with each pulse lasting 50μs, and the pulse interval is synchronized with the cutting period. The projection data obtained by the detector array is reconstructed into cross-sectional images by filtered back-projection algorithm, with an image resolution of 5μm. The identification of conductor positions uses an improved Hough transform algorithm, which first extracts the elliptical features of the conductor region, and then calculates the offset vector of each conductor center relative to the standard position. The statistical quantity of the offset distance is the average value of the L2 norm of all conductor offset vectors, and the rigid displacement component of the wire harness during detection is compensated.

[0063] The first level judgment is based on the cumulative number of cutting operations, and when the counter reaches the preset threshold value (typical value is 8 times), the iterative process is forcibly terminated. The second level judgment monitors the absolute change of the physical response data, and sets the convergence threshold of the flatness index to 0.02mm, the burr density change threshold to 1.2 / mm², and the conductor offset distance threshold to 15μm. The third level judgment is for the relative change rate, which calculates the percentage difference between adjacent two measurement values, and the relative change rate threshold of the flatness index is 1.8%, the burr density is 2.5%, and the conductor offset is 3%. The three judgment levels form an or logical relationship, and any condition is met to trigger the iteration termination.

[0064] The scanning trigger signal of the laser microscope lags behind the tool position signal by 200 μs, ensuring the capture of stable kerf morphology. The exposure time of the industrial camera is synchronized with the end-of-cut signal, with an additional 5 ms waiting period for mechanical vibration decay. The X-ray pulse is triggered during the tool return stroke, avoiding electromagnetic interference generated by cutting. All detection data are aligned by timestamps before input into the judgment module, which uses a sliding window mechanism to handle timing deviations, with a window width of 10 ms.

[0065] The original detection data retain the complete waveform and image, and are stored in the cache after compression, retaining the last three iteration records. The feature extraction results are stored in a structured array format, including timestamps, tool position coordinates, and various physical response parameters. The running log of the judgment logic records the intermediate variables and trigger states of each evaluation in detail, which is used for subsequent process analysis. When the iteration is terminated, the system automatically generates a process report containing all key parameters, and the report data format is compatible with the MES system interface standard.

[0066] The abnormality handling mechanism of physical response data includes three levels of response: the first level of response is for transient interference, such as detection noise or short-term environmental disturbance, and abnormal points are filtered by digital filtering and majority voting algorithm. The second level of response handles persistent deviations, and when three consecutive measurement values exceed the expected fluctuation range, the sensor calibration process is triggered. The third level of response deals with systematic failures, such as X-ray tube aging or optical lens contamination, and automatically switches to the redundant detection channel and alerts maintenance.

[0067] The updating process of the dynamic cutting parameter mapping model is based on an optimization algorithm. The calculation of the objective function value uses a multi-index fusion method, and the input parameters include the real-time collected kerf flatness index, burr distribution density value, and conductor offset distance. The flatness index comes from the scanning data of the laser microscope, the burr density is based on image analysis results, and the conductor offset distance is taken from the X-ray reconstruction data. The objective function value is generated by a weighted sum formula, and the weight coefficients are dynamically configured according to the wire type: for multi-core shielded wire, the weight of the conductor offset distance is increased to 0.6; for single-core power wire, the burr density weight is set to 0.55. The calculation process is completed on a special hardware acceleration card, with a processing delay controlled within 2 ms.

[0068] Device constraints are collected by vibration sensors and encoders installed on the cutting device spindle, monitoring acceleration values and position deviations in real time. Tool constraints use a tool state monitoring module, including a contact wear probe and an infrared temperature sensor. Material constraints rely on an online material testing unit, which performs micro-tension testing on wire sample segments during cutting intervals. The threshold values of each constraint parameter are set, as shown in Table 1.

[0069] Table 1: Threshold setting table for constraint functions.

[0070] Constraint type Monitoring parameter Threshold setting Equipment constraint Maximum acceleration ≤ 5.0 m / s² Vibration amplitude ≤ 0.05 mm (RMS) Tool constraint Edge wear width ≤ 0.12 mm Tool temperature ≤85℃ Material constraint Minimum yield strength ≥ 80% of material nominal value Breaking elongation ≥ 75% of material nominal value

[0071] The deviation amount calculation module receives the objective function value and the constraint function value, and performs standardization processing. The objective function value is normalized to the interval [0, 1], and the constraint function value is converted into a violation degree coefficient (0 represents no over-limit, and 1 represents serious over-limit). The deviation amount is defined as the Euclidean distance between the normalized value of the objective function and the constraint violation coefficient, and the calculation process is realized by using a fixed-point operation unit, and the output precision reaches 0.001.

[0072] The execution of the gradient projection algorithm includes four stages: the first stage calculates the partial derivative of the objective function with respect to the model weight matrix, which is realized by using perturbation analysis method: a small change of ±0.1% is applied to each element of the weight matrix, and the change amount of the objective function value is observed. The second stage calculates the gradient direction of the constraint function, and the finite difference method is used to sample near the constraint boundary. The third stage performs projection operation, which projects the gradient of the objective function to the constraint feasible region, and eliminates the component in the same direction as the normal vector of the constraint boundary. The fourth stage determines the update step, which is dynamically adjusted according to the current deviation amount and the historical convergence trend, and the maximum step is limited to 0.5% of the norm of the weight matrix.

[0073] The weight update amount of the feature encoding layer directly applies the gradient projection result; the weight of the parameterized operation layer adopts an update method with a forgetting factor, and the forgetting factor is set to 0.7; the weight update of the action decoding layer needs to be processed by orthogonalization to maintain the linear independence of the output space. The updated weight matrix is checked by singular value decomposition, and the ill-conditioned solution with a condition number greater than 1000 is removed.

[0074] The current running weight matrix is stored in the DDR4 memory block A, and the new generation of weight matrix is updated and calculated in block B. When the update is completed, the hardware switching switch is used to realize the seamless switching in milliseconds. The abnormality detection mechanism continuously monitors the numerical stability of the weight matrix, and when NaN value or numerical overflow is detected, it automatically rolls back to the last valid version.

[0075] The tool wear threshold value linearly decays with the cumulative cutting length, and the threshold value decreases by 0.01 mm for every 100 meters of cutting length. The material yield strength threshold value is compensated according to the ambient temperature, and the threshold value is lowered by 2% of the nominal value of the material for every 10℃ increase in temperature. The device acceleration threshold value is automatically relaxed by 20% in high-speed cutting mode, but the vibration amplitude threshold value is simultaneously tightened by 30%.

[0076] The verification process after model update includes three steps: first, perform virtual cutting in a simulation environment to predict physical response data; second, perform short stroke trial cutting with actual cutting length of 5 cm; finally, compare the difference between the predicted value and the measured value, and trigger secondary update when the error of the key parameters exceeds 5%. The verification data is input into the historical database for parameter self-tuning of the optimization algorithm.

[0077] Example 5: The distribution and execution of the cutting control sequence is implemented based on a distributed device architecture. The main machining node is equipped with a high-precision five-axis servo cutting machine with a rotating tool head and a pressure feedback system. The auxiliary machining nodes include four sets of pneumatic support clamps, each with an independent radial clamping force adjustment unit and an axial position servo mechanism. The spatial layout topology between devices is established by laser tracker measurement, recording the position matrix of each node in the global coordinate system. This matrix includes the relative distance, azimuth angle, and connection path length of the main node and each auxiliary node, which is used to calculate the transmission priority of the control sequence.

[0078] The cutting speed control sequence and the cutting pressure control sequence output by the dynamic cutting parameter mapping model are task-decomposed by the distribution algorithm. The speed control sequence is completely distributed to the main machining node, which contains the correspondence between time stamps and speed values, with a time resolution of 10 milliseconds. The pressure control sequence is weighted and distributed according to the spatial layout topology: first, calculate the topology distance weight coefficient of each auxiliary node to the main node, the closer the distance, the higher the weight; then map the spatial position information of the pressure sequence to the action area of the auxiliary node, generating a pressure-position sub-sequence exclusive to each node. The distribution result is packaged as an instruction data packet, containing the target node address, execution time window, and action parameters.

[0079] The main machining node serves as the time reference source, sending a synchronization clock signal every 100 milliseconds. The instruction data packet is broadcast to all nodes within 5 milliseconds after the rising edge of the clock signal, and the data packet transmission adopts a deterministic Ethernet protocol with a maximum jitter controlled within 50 microseconds. Each node synchronously executes the action at the start time of the next clock cycle after receiving the instruction. The execution state feedback data is returned to the main node 20 milliseconds before the end of the cycle, forming a closed-loop control.

[0080] The first level sets hardware timestamps at the transmission layer, and when the main node instruction transmission delay is detected to exceed the 50 millisecond threshold, the local cached instruction sequence of the auxiliary node is triggered. The cached sequence is taken from the last valid cycle data, and the current cycle parameters are predicted by a linear extrapolation algorithm. The second level establishes a delay prediction model, trains an LSTM network based on historical transmission delay data, and actively enables the cache for nodes that may exceed the limit in advance. The third level sets a safety fallback mode, and when the transmission fails for three consecutive cycles, the auxiliary node switches to a preset constant pressure clamping mode and alarms.

[0081] The progress monitoring system is composed of absolute value encoders installed on the feeding rollers, which measure the feeding length of the wire harness in real time. The progress feedback value is calculated as the percentage deviation of the actual feeding length from the planned length. The frequency adjustment algorithm inputs this deviation value into a proportional-integral controller, outputting a frequency adjustment coefficient. The basic distribution frequency is 10 Hz, which can be dynamically increased to 1 kHz. When the progress lags behind by more than 5%, the frequency is increased in steps; when the progress is ahead, the frequency is reduced in inverse proportion.

[0082] The action coordination of the main machining node and the auxiliary nodes is realized through kinematic compensation. When the main node performs the cutting action, the tool position coordinates are calculated in real time and broadcast to the auxiliary nodes. The auxiliary nodes calculate the follow-up trajectory of their support clamps according to the tool position, and the trajectory generation takes into account the real-time deformation compensation data of the wire bundle. The pressure adjustment units of each clamp receive the position-pressure mapping table, and increase the clamping force in advance when the tool approaches the area, and reduce the pressure when the tool moves away from the area. The relative position of the clamp and the tool is monitored by a laser range finder, and when the error exceeds 0.1 millimeter, online recalibration is triggered.

[0083] The data exchange network adopts a ring topology structure, and the main node acts as the network controller, forming a closed loop through double-channel optical fiber connection of each auxiliary node. The data transmission implements a double backup mechanism: the main channel transmits real-time control instructions, and the standby channel transmits state monitoring data. The network bandwidth allocation adopts a time division multiple access strategy, allocating 60% of the time slots to the main node and 10% of the time slots to each auxiliary node. When a node is detected to be offline, its time slot is automatically allocated to the adjacent node to expand the bandwidth.

[0084] The abnormality processing procedure includes two layers of responses, device level and system level. The device level response is executed by the local controller of each node, and when the actuator exceeds the limit or the sensor fails, it automatically switches to the safe torque mode. The system level response is coordinated by the main node, and when a key node fails, the topology weight is recalculated and the pressure distribution scheme is dynamically adjusted. All abnormal events are recorded with time-stamped logs, and the log data is stored synchronously with the process parameters for subsequent diagnostic analysis.

[0085] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0086] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A cutting method for processing an automobile wire harness, characterized by, The method comprises the following steps: Step S1, obtaining the geometric feature data and material attribute data of the wire harness to be processed; Step S2, constructing a dynamic cutting parameter mapping model based on the geometric feature data; Step S3, generating a deformation compensation amount according to the material attribute data and inputting it into the dynamic cutting parameter mapping model; Step S4, driving a cutting device to perform a wire harness cutting operation based on the output of the dynamic cutting parameter mapping model; Step S5, collecting physical response data in real time during the cutting process and determining whether a preset iteration condition is met; Step S6, if the preset iteration condition is met, outputting a final cutting result; if not, updating the dynamic cutting parameter mapping model and repeating steps S3 to S5; The dynamic cutting parameter mapping model comprises a feature encoding layer, a parameterized operation layer, and an action decoding layer; The feature encoding layer decomposes the geometric feature data into base function weight coefficients; The parameterized operation layer performs nonlinear mapping on the base function weight coefficients according to the deformation compensation amount; The action decoding layer reconstructs the mapped base function weight coefficients into a cutting speed control sequence and a cutting pressure control sequence; The deformation compensation amount is generated by the following steps: Extracting the elastic modulus distribution and thermal expansion coefficient matrix in the material attribute data; According to the temperature fluctuation value of the wire harness processing environment, calculating the coupling offset of the elastic modulus distribution and the thermal expansion coefficient matrix; Based on the coupling offset, generate the axial deformation compensation component and the radial deformation compensation component.

2. The cutting method for processing an automobile wire harness according to claim 1, characterized by The geometric feature data includes a shape distribution vector of the wire harness cross section, a curvature variation sequence in the length direction of the wire harness, and a thickness gradient value of the wire harness outer layer.

3. The cutting method for processing an automobile wire harness according to claim 1, characterized by The preset iteration condition includes that the cumulative number of cutting operations reaches a maximum threshold, the absolute change of physical response data generated by adjacent two times of cutting is less than a convergence threshold, or the relative change rate of physical response data generated by adjacent two times of cutting is less than a convergence threshold.

4. The cutting method for processing an automobile wire harness according to claim 3, characterized by The physical response data includes the flatness index of the wire harness incision topography, the distribution density value of the wire harness end face burr, and the offset distance of the wire harness internal conductor.

5. The cutting method for processing an automobile wire harness according to claim 1, characterized by The updating of the dynamic cutting parameter mapping model comprises: Calculating the deviation of the objective function value and the constraint function value of the current cutting result; Based on the deviation, adjust the internal weight matrix of the dynamic cutting parameter mapping model using the gradient projection algorithm.

6. The cutting method for processing an automobile wire harness according to claim 5, characterized by The constraint function value includes the maximum acceleration threshold of the cutting device, the wear safety boundary of the cutting tool, and the minimum yield strength of the wire harness material.

7. The cutting method for processing an automobile wire harness according to claim 1, characterized by In step S4, it also includes: According to the spatial layout topology of the wire harness processing device, the cutting speed control sequence and the cutting pressure control sequence are distributed to the main processing node and the auxiliary processing node; The main processing node and the auxiliary processing node synchronously perform cutting actions based on a real-time data exchange protocol.

8. The cutting method for processing an automobile wire harness according to claim 7, characterized by The real-time data exchange protocol comprises: When the instruction transmission delay of the main processing node exceeds the delay threshold, the local cache instruction sequence of the auxiliary processing node is activated; According to the wire harness processing progress feedback value, dynamically adjust the instruction distribution frequency of the main processing node and the auxiliary processing node.

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