Method and system for quality assessment of an elongated shaft forged into shape

Abstract

The present application relates to the technical field of quality management, in particular to a quality evaluation method and system for elongated shaft forging forming, comprising the following steps: calculating shaft core effective energy based on power consumption integral and radial attenuation, evaluating density, calculating streamline exposure rate through streamline simulation and boundary vector, judging streamline integrity, quantifying surface deformation complexity by statistical entropy, and constructing elongated shaft quality evaluation data.In the present application, the shaft core effective energy is calculated by integrating load displacement and combining with the radial transmission model to quantify the core density, the streamline simulation is driven by displacement and the vector dot product after removing the boundary is calculated to predict the streamline exposure rate in cutting, the accuracy of fatigue strength evaluation is ensured, the curvature distribution is obtained based on laser point cloud differentiation and the information entropy is calculated to map the surface microscopic deformation to the disorder degree index, and the multi-dimensional fusion evaluation from energy efficiency, streamline integrity to surface topological features is realized, and the comprehensiveness and reliability of elongated shaft forging quality determination are improved.

Description

Technical Field

[0001] This invention relates to the field of quality management technology, and in particular to a quality assessment method and system for forging slender shafts. Background Technology

[0002] The field of quality management technology involves activities such as quality planning, quality assurance, quality control, and quality improvement to formulate quality policies and objectives and achieve these objectives. It focuses on monitoring the entire life cycle of products or services through systematic management methods, using statistical analysis tools to compare the differences between actual production data and standard specifications, and coordinating internal resources based on data feedback to ensure that output meets established requirements. Among these methods, the traditional quality assessment method for slender shaft forging refers to the dimensional accuracy and internal defect detection work carried out on shaft parts with a large length-to-diameter ratio after the forging process. It usually uses a handheld vernier caliper or outside micrometer to measure the shaft diameter at multiple points, and uses a dial indicator to move along the axis to detect straightness error. For internal quality, the grain size of the sliced ​​sample is observed by metallographic microscope, or an ultrasonic probe is used to contact the workpiece surface to receive echo signals to determine whether there are cracks and porosity.

[0003] Current inspection methods for shaft forgings rely on manual discrete measurement or contact flaw detection. These methods are limited by the sparse sampling points, which cannot cover the entire deformation area. Furthermore, metallographic testing requires destroying the sample, making it difficult to conduct a full non-destructive assessment of the internal density of the workpiece. Traditional methods only focus on the final dimensions, ignoring the attenuation characteristics of energy transmission along the radial direction during forging. They cannot determine whether the accumulated plastic work in the core meets the densification requirements, lack the ability to predict the relationship between the processing removal boundary and the distribution of metal streamlines, and cannot identify the potential strength reduction caused by the cutting off of streamlines. This results in a single and lagging dimension for quality assessment. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a quality assessment method for forging slender shafts, comprising the following steps:

[0005] S1: Collect forging pressure and slider displacement, perform integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collect workpiece diameter and length and calculate the ratio of length to diameter, and construct a set of basic forging parameters;

[0006] S2: Analyze the set of forging basic parameters, call the radial transmission efficiency coefficient, multiply it with the total deformation work data, obtain the net residual deformation energy of the core region, calculate the ratio of net residual deformation energy to core volume, obtain the unit effective deformation energy and compare it with the benchmark, and generate a core density assessment record.

[0007] S3: Analyze the set of forging basic parameters, generate a set of streamline trajectories based on slider displacement simulation, construct the processing removal boundary, calculate the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtain the streamline exposure rate, and generate a streamline integrity assessment record;

[0008] S4: Collect and differentiate the surface point cloud using a laser contour scanning device to obtain a curvature sequence, count the number of curvature sequences in the statistical interval and calculate the frequency ratio to construct a statistical distribution record of surface curvature.

[0009] S5: Analyze the surface curvature statistical distribution record, perform logarithmic multiplication and accumulation on the frequency proportion data to obtain the curvature distribution disorder data, map the deformation complexity, and combine the core density assessment record and the streamline integrity assessment record to construct the slender shaft quality assessment data.

[0010] As a further aspect of the present invention, the forging basic parameter set specifically includes cumulative deformation work value, slider displacement time sequence data, and workpiece geometric length-to-diameter ratio value; the core density assessment record specifically includes the net residual deformation energy value of the shaft center, effective deformation energy density per unit volume, and internal porosity risk indicator; the streamline integrity assessment record specifically includes the internal fiber streamline trajectory set and streamline boundary intersection coordinate set; the surface curvature statistical distribution record specifically includes the axial section curvature data sequence, curvature interval distribution quantity set, and interval falling frequency histogram; and the slender shaft quality assessment data specifically includes the curvature distribution disorder value, geometric deformation complexity classification data, and workpiece comprehensive quality classification instruction.

[0011] As a further aspect of the present invention, the step of obtaining the forging basic parameter set specifically includes:

[0012] S101: Real-time forging load parameters are collected by forging load sensor, slider displacement parameters are collected by slider displacement sensor, slider displacement increment between adjacent sampling times is calculated, the product operation of real-time forging load parameters and slider displacement increment is performed to obtain micro-element deformation work, the micro-element deformation work in the entire forging stroke is accumulated and integrated to calculate the total energy value of slider input to billet, and the accumulated deformation work value is obtained.

[0013] S102: Collect the real-time diameter and length parameters of the workpiece through online measurement equipment, extract the real-time length parameters of the workpiece as axial extension measurement data, extract the real-time diameter parameters of the workpiece as radial section measurement data, perform the ratio calculation of the axial extension measurement data and the radial section measurement data, and obtain the geometric length-to-diameter ratio of the workpiece.

[0014] S103: Call the accumulated deformation work value and the workpiece geometric length-to-diameter ratio value, extract the slider displacement parameter as the time reference of the motion process, map the accumulated deformation work value to the energy data field, map the workpiece geometric length-to-diameter ratio value to the geometric data field, integrate the total deformation work data, slider displacement parameter and length-to-diameter ratio parameter, and construct a set of forging basic parameters.

[0015] As a further aspect of the present invention, the steps for obtaining the cardiac density assessment record are specifically as follows:

[0016] S201: Analyze the set of forging basic parameters, extract the length-to-diameter ratio parameter and total deformation work data, retrieve the preset mapping table based on the length-to-diameter ratio parameter and match the corresponding radial energy transfer efficiency coefficient, perform the product attenuation operation of the total deformation work data and the radial energy transfer efficiency coefficient, calculate the energy value of the workpiece axial region after radial transmission loss, and obtain the net remaining deformation energy value of the axial region.

[0017] S202: Call the net remaining deformation energy value of the shaft center, construct the geometric volume data of the workpiece shaft center region according to the workpiece size, perform the ratio calculation of the net remaining deformation energy value of the shaft center and the geometric volume data, calculate the effective energy distributed in the unit volume of the shaft center region, and obtain the effective deformation energy density per unit volume.

[0018] S203: Based on the effective deformation energy density per unit volume, call the preset densification energy benchmark value, perform a numerical comparison between the effective deformation energy density per unit volume and the densification energy benchmark value, determine the degree of densification of the core material of the workpiece under the current energy state, and generate a core density assessment record.

[0019] As a further aspect of the present invention, the process of obtaining the preset mapping table is specifically as follows:

[0020] A set of finite element models covering the range of aspect ratio parameters is established. A unified loading speed and friction coefficient boundary condition is set for the finite element model set. Nodal force and nodal displacement data of the contact area of ​​the outer surface are extracted. The total work input to the surface is calculated based on the nodal force and nodal displacement data. The stress tensor and strain increment tensor of the axial region element are extracted. The double shrinkage and time step integration of the stress tensor and strain increment tensor are performed to obtain the actual cumulative plastic deformation work. The ratio of the actual cumulative plastic deformation work to the total work input to the surface is calculated to obtain radial energy transfer efficiency data points. The functional relationship curve of the radial energy transfer efficiency data points is fitted. The functional relationship curve is discretized and sampled to construct a pre-set mapping table.

[0021] The process of obtaining the preset densification energy reference value is as follows:

[0022] Based on a cylindrical compression specimen containing pre-fabricated artificial pore defects, a uniaxial hot compression test with gradient deformation was performed on the cylindrical compression specimen. The load-displacement curve was recorded, and the deformation energy per unit volume was calculated. A quantitative porosity analysis was performed on the core of the cross-section of the cylindrical compression specimen. The evolution curve of the deformation energy per unit volume and the relative density value was fitted. The critical saturation position when the relative density value reaches the theoretical density of the material was identified. The deformation energy per unit volume corresponding to the critical saturation position was set as the preset densification energy benchmark value.

[0023] As a further aspect of the present invention, the steps for obtaining the streamline integrity assessment record are specifically as follows:

[0024] S301: Analyze the set of forging basic parameters, extract the slider displacement parameters, set the initial spatial coordinates of the virtual particles inside the slender shaft, construct a metal flow velocity field model that matches the workpiece geometry, use the time series of slider displacement parameters as the boundary driving input of the velocity field model, calculate the instantaneous velocity vector of each virtual particle in the velocity field and perform displacement update operation, record the motion path nodes of each virtual particle in the continuous deformation period, connect the motion path nodes of the same particle into a continuous geometric curve, and obtain the set of internal fiber streamline trajectories;

[0025] S302: Call the set of internal fiber streamline trajectories to obtain the three-dimensional geometric contour data of the finishing stage, construct the processing removal boundary space surface that defines the material retention domain and the cutting domain based on the three-dimensional geometric contour data, perform the spatial geometric intersection operation between the set of internal fiber streamline trajectories and the processing removal boundary space surface, identify the position where the streamline curve penetrates the boundary surface, and obtain the coordinate set of the streamline boundary intersection point.

[0026] S303: Based on the coordinate set of the streamline boundary intersection points, calculate the tangent vector of the streamline curve at the intersection point, calculate the normal vector of the processed and removed boundary space surface at the corresponding intersection point, perform the dot product operation of the unit tangent vector and the unit normal vector, obtain the streamline exposure value, and generate a streamline integrity assessment record.

[0027] As a further aspect of the present invention, the step of obtaining the surface curvature statistical distribution record specifically includes:

[0028] S401: Using a laser contour scanning device, collect point cloud data of the surface contour of a slender shaft, analyze the spatial geometric coordinates of the surface contour point cloud data along the workpiece axis, perform differential operation of the geometric coordinates with respect to the axial position, calculate the micro-bending degree of the workpiece surface at each continuous cross-section position, and obtain the axial cross-section curvature data sequence.

[0029] S402: Call the axial section curvature data sequence, project the curvature data sequence to the preset curvature statistical interval, count the number of data falling into each curvature statistical interval, quantify the clustering distribution of curvature features in each numerical segment, and obtain the curvature interval distribution set;

[0030] S403: Based on the curvature interval distribution set, sum the number of samples in each interval, determine the total sample size, perform the ratio calculation of the number of samples in each interval to the total sample size, calculate the frequency proportion of each curvature statistical interval in the overall surface morphology, integrate the frequency proportion data of each interval, and construct a surface curvature statistical distribution record.

[0031] As a further aspect of the present invention, the process of constructing the preset curvature statistical interval is specifically as follows:

[0032] The axial section curvature data sequence is invoked, and its arithmetic mean and standard deviation are calculated. An effective confidence interval is constructed with the arithmetic mean as the center and covering a range of ±3 times the standard deviation. Data in the axial section curvature data sequence that fall outside the effective confidence interval are marked as outliers and removed. An effective curvature sample set is generated. The effective curvature sample set is traversed, and the maximum and minimum curvature values ​​are extracted. The difference between the maximum and minimum curvature values ​​is calculated to obtain the effective curvature global fluctuation range. The micro-geometric shape identification accuracy specified in the surface roughness detection standard is obtained. The micro-geometric shape identification accuracy is set as the interval span step size. The ratio of the effective curvature global fluctuation range to the interval span step size is rounded up. The total number of intervals is determined. Using the minimum curvature value as the starting boundary, the total number of consecutive numerical segments are generated based on the interval span step size to construct the preset curvature statistical interval.

[0033] As a further aspect of the present invention, the steps for obtaining the quality assessment data of the slender shaft are specifically as follows:

[0034] S501: Analyze the surface curvature statistical distribution record, extract the frequency proportion data, calculate the logarithmic transformation value of the frequency proportion data, perform the product operation of the frequency proportion data and the logarithmic transformation value, accumulate and sum the product results in each statistical interval and take the absolute value to obtain the curvature distribution disorder value.

[0035] S502: Based on the curvature distribution disorder value, by performing deformation topology feature mapping, the calculated disorder value is mapped to the corresponding complexity range, the topology level of the current workpiece surface deformation feature is determined, the workpiece surface geometric quality status classification index is output, and geometric deformation complexity classification data is obtained.

[0036] S503: Call the geometric deformation complexity classification data, combine it with the core density assessment record and the streamline integrity assessment record, perform a weighted summation operation of multidimensional quality indicators, and construct slender shaft quality assessment data.

[0037] A quality assessment system for slender shaft forging includes:

[0038] The basic parameter construction module collects forging pressure and slider displacement, performs integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collects workpiece diameter and length and calculates the ratio of length to diameter, and constructs a set of basic forging parameters.

[0039] The dense feature analysis module analyzes the set of forging basic parameters, calls the radial transmission efficiency coefficient, multiplies it with the total deformation work data, obtains the net residual deformation energy of the core region, calculates the ratio of net residual deformation energy to core volume, obtains the unit effective deformation energy and compares it with the benchmark, and generates a core density assessment record.

[0040] The workpiece streamline simulation module analyzes the set of forging basic parameters, generates a streamline trajectory set based on slider displacement simulation, constructs a processing removal boundary, calculates the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtains the streamline exposure rate, and generates a streamline integrity evaluation record.

[0041] The surface topography statistics module collects and differentiates surface point clouds using a laser contour scanning device, obtains curvature sequences, counts the number of curvature sequences in the statistical interval and calculates the frequency ratio, and constructs a statistical distribution record of surface curvature.

[0042] The quality comprehensive assessment module analyzes the surface curvature statistical distribution record, performs logarithmic multiplication and accumulation on the frequency proportion data to obtain curvature distribution disorder data, maps deformation complexity, and constructs slender shaft quality assessment data by combining the core density assessment record and the streamline integrity assessment record.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0044] In this invention, the effective energy of the shaft center is calculated by integrating the load displacement and combining it with the radial transfer model, which quantifies the density of the core. The displacement-driven streamline simulation is used to calculate the vector dot product of the streamline with the removed boundary, predicting the streamline exposure rate during cutting and ensuring the accuracy of fatigue strength assessment. Based on the differential of laser point cloud, the curvature distribution is obtained and the information entropy is calculated, mapping the surface micro-deformation into a disorder index. This achieves a multi-dimensional fusion evaluation from energy efficiency, streamline integrity to surface topological features, improving the comprehensiveness and reliability of the quality judgment of slender shaft forgings. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the steps of the present invention;

[0047] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0048] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0049] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0050] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0051] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0054] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0055] Please see Figure 1 This invention provides a quality assessment method for forging slender shafts, comprising the following steps:

[0056] S1: Collect forging pressure and slider displacement, perform integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collect workpiece diameter and length and calculate the ratio of length to diameter, and construct a set of basic forging parameters;

[0057] S2: Analyze the set of basic forging parameters, call the radial transmission efficiency coefficient, multiply it with the total deformation work data, obtain the net residual deformation energy of the core region, calculate the ratio of net residual deformation energy to core volume, obtain the unit effective deformation energy and compare it with the benchmark, and generate a core density assessment record.

[0058] S3: Analyze the set of basic forging parameters, generate a set of streamline trajectories based on slider displacement simulation, construct the processing removal boundary, calculate the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtain the streamline exposure rate, and generate a streamline integrity assessment record;

[0059] S4: Collect and differentiate the surface point cloud using a laser contour scanning device to obtain a curvature sequence, count the number of curvature sequences in the statistical interval and calculate the frequency ratio to construct a statistical distribution record of surface curvature.

[0060] S5: Analyze the surface curvature statistical distribution records, perform logarithmic multiplication and accumulation operations on the frequency proportion data to obtain the curvature distribution disorder data, map the deformation complexity, and combine the core density assessment records and streamline integrity assessment records to construct the slender shaft quality assessment data.

[0061] The forging basic parameter set specifically includes the cumulative deformation work value, slider displacement time sequence data, and workpiece geometric length-to-diameter ratio value. The core density assessment record specifically includes the net residual deformation energy value of the shaft core, the effective deformation energy density per unit volume, and the internal porosity risk indicator. The streamline integrity assessment record specifically includes the internal fiber streamline trajectory set and the streamline boundary intersection coordinate set. The surface curvature statistical distribution record specifically includes the axial section curvature data sequence, the curvature interval distribution quantity set, and the interval falling frequency histogram. The slender shaft quality assessment data specifically includes the curvature distribution disorder value, geometric deformation complexity classification data, and workpiece comprehensive quality classification instructions.

[0062] Please see Figure 2 The specific steps for obtaining the set of basic forging parameters are as follows:

[0063] S101: Real-time forging load parameters are collected by forging load sensor, slider displacement parameters are collected by slider displacement sensor, slider displacement increment between adjacent sampling times is calculated, the product operation of real-time forging load parameters and slider displacement increment is performed to obtain micro-element deformation work, the micro-element deformation work in the entire forging stroke is accumulated and integrated to calculate the total energy value of slider input to billet, and the accumulated deformation work value is obtained.

[0064] This step is performed during the actual operation of a large hydraulic forging unit. The process involves synchronous data acquisition via a high-frequency pressure sensor installed in the hydraulic circuit of the forging machine's main cylinder and a magnetostrictive displacement sensor installed on the side of the slide guide. At the instant the forging stroke begins, the analog current signal from the forging load sensor is read in real-time at a sampling frequency of 1000 Hz and converted into a corresponding pressure value. Simultaneously, the pulse signal from the slide displacement sensor is read to determine the real-time vertical position of the slide. The process first calculates the difference in slide position between two adjacent sampling moments, i.e., subtracting the slide displacement parameter from the previous sampling moment from the current slide displacement parameter to obtain the slide displacement increment. Then, the real-time forging load parameter acquired at the current moment is multiplied by the slide displacement increment, i.e., the instantaneous pressure value is multiplied by the displacement change, thus obtaining the micro-element deformation work within this small time interval. This value represents the mechanical work done by the hydraulic press on the workpiece within this small stroke. To obtain the total energy input of the entire forging process, a trapezoidal integral algorithm or a Riemann sum algorithm is used. An initial cumulative variable with a value of 0 is set, and the infinitesimal deformation work calculated in each sampling cycle is added to this cumulative variable. As the forging stroke continues, this cumulative variable increases until the forging process ends. The final cumulative output is the total energy input of the slider to the billet. For example, in a slender shaft forging experiment, the real-time forging load parameters and slider displacement increments throughout the forging stroke are continuously sampled and multiplied at high frequency. The infinitesimal work of the entire stroke is then accumulated using the trapezoidal integral algorithm, ultimately obtaining a cumulative deformation work value of 35 megajoules. This cumulative deformation work value serves as the core energy benchmark data for subsequent evaluation of the material's internal energy absorption and densification degree.

[0065] S102: Collect the real-time diameter and length parameters of the workpiece through online measurement equipment, extract the real-time length parameters of the workpiece as axial extension measurement data, extract the real-time diameter parameters of the workpiece as radial section measurement data, perform the ratio calculation of the axial extension measurement data and the radial section measurement data, and obtain the geometric length-to-diameter ratio of the workpiece.

[0066] A non-contact online laser caliper is used to measure the geometric dimensions of workpieces under high-temperature forging conditions in real time. Two sets of laser emitters and receivers are positioned above the workpiece's axis; one set scans the axial end position of the workpiece, and the other scans its radial profile. In the data processing unit, the real-time length parameter of the workpiece obtained from the laser scan is first extracted. This parameter accurately represents the workpiece's axial extension. Simultaneously, the real-time diameter parameter is extracted; this parameter is typically the average or maximum value of measurements taken along multiple axial sections, serving as the radial section measurement. Then, a ratio calculation logic is executed: the axial extension measurement data is used as the numerator, and the radial section measurement data is used as the denominator. The division yields a dimensionless value, which is the workpiece's geometric length-to-diameter ratio. This value is the most significant geometric feature of slender shaft forgings and directly affects the stress distribution and energy transfer efficiency during subsequent deformation. For example, the online measuring device may acquire a real-time length parameter of 5000 mm and a real-time diameter parameter of 200 mm at a certain moment. Substituting these two values ​​into the ratio calculation logic, i.e., dividing 5000 mm by 200 mm, the geometric length-to-diameter ratio of the workpiece is calculated to be 25. This value indicates that the workpiece is a typical slender shaft with a large length-to-diameter ratio, and the attenuation of its radial energy transfer needs to be paid special attention to in subsequent analysis.

[0067] S103: Call the cumulative deformation work value and the workpiece geometric length-to-diameter ratio value, extract the slider displacement parameter as the time reference of the motion process, map the cumulative deformation work value to the energy data field, map the workpiece geometric length-to-diameter ratio value to the geometric data field, integrate the total deformation work data, slider displacement parameter and length-to-diameter ratio parameter, and construct the forging basic parameter set.

[0068] The process calls upon the accumulated deformation work value and the workpiece's aspect ratio value, and extracts the slider displacement parameters collected by the slider displacement sensor as the time axis and motion reference for the entire dataset, ensuring the consistency of all parameters in the physical stroke. A dedicated data structure space is allocated in memory, defining energy data fields and geometric data fields. This process writes the accumulated deformation work value into the energy data field and the workpiece's aspect ratio value into the geometric data field, while simultaneously storing the corresponding slider displacement parameters, instantaneous pressure data, etc., forming a set of basic forging parameters containing a complete process history. This set not only records the final state but also includes dynamic evolution information during the deformation process. For example, in the constructed parameter set, the first sequence corresponds to a slider displacement of 100 mm, at which point the accumulated deformation work value is 50 MJ, and the workpiece's aspect ratio value is 20; the second sequence corresponds to a slider displacement of 105 mm, at which point the accumulated deformation work value increases to 55 MJ, and the workpiece's aspect ratio value changes to 20.1. In this way, discrete physical quantities are integrated into structured data with temporal correlation, providing a standardized input interface for subsequent steps to analyze energy transfer efficiency and streamline distribution.

[0069] Please see Figure 3 The specific steps for obtaining cardiac density assessment records are as follows:

[0070] S201: Analyze the set of basic forging parameters, extract the length-to-diameter ratio parameter and total deformation work data, retrieve the preset mapping table based on the length-to-diameter ratio parameter and match the corresponding radial energy transfer efficiency coefficient, perform the product attenuation operation of the total deformation work data and the radial energy transfer efficiency coefficient, calculate the energy value of the workpiece axial region after radial transmission loss, and obtain the net remaining deformation energy value of the axial region.

[0071] The process involves analyzing the set of basic forging parameters, extracting the aspect ratio parameter as a key index, and simultaneously extracting the total deformation work data as the basic energy value to be processed. To accurately obtain the radial energy transfer efficiency coefficient, this process relies on a pre-constructed radial energy transfer efficiency mapping table. This mapping table is constructed based on finite element simulation analysis: a set of finite element models covering the range of aspect ratio parameters (e.g., 10 to 50) is established, and uniform boundary conditions (e.g., 50 mm / s) and friction coefficient (e.g., 0.3) are set for each model. In the simulation, nodal force and nodal displacement data of the outer surface contact area are extracted, and the total surface input work is calculated through integration. Simultaneously, the stress tensor and strain increment tensor of the axial region elements are extracted, and a double contraction operation (i.e., multiplying each component of the stress tensor with the corresponding component of the strain increment tensor and then summing) and time-step integration are performed to obtain the actual cumulative plastic deformation work. Finally, the actual cumulative plastic deformation work is divided by the total surface input work to obtain radial energy transfer efficiency data points under different aspect ratios, and these data are fitted with generative function curves or discretized into lookup tables. In actual evaluation, the preset mapping table is retrieved based on the current workpiece's aspect ratio parameter. If the aspect ratio parameter lies between two discrete points in the table, a linear interpolation algorithm is used to calculate the accurate coefficient. After obtaining the radial energy transfer efficiency coefficient, a product attenuation operation is performed, that is, the total deformation work data is multiplied by this coefficient to eliminate energy wasted due to friction loss, surface redundant deformation, etc., and the energy value that truly acts on the workpiece's axial region and promotes material densification is calculated, i.e., the axial net residual deformation energy value. For example, as shown in Table 1, the preset mapping table records the correspondence between aspect ratio and transfer efficiency. Assuming the current workpiece's aspect ratio parameter is 25, the corresponding radial energy transfer efficiency coefficient obtained by looking up the table or interpolation is 0.65. If the total deformation work data obtained in the previous step is 35 megajoules, the operation is performed: 35 megajoules multiplied by 0.65, and the axial net residual deformation energy value is calculated to be 22.75 megajoules. This figure reveals that although a large amount of energy is input from the outside, only about 65% of the energy is effectively transferred to the core region, which is a key basis for assessing the quality of the slender core section.

[0072] Table 1. Preset Mapping Table for Radial Energy Transfer Efficiency (Partial Example)

[0073]

[0074] See Table 1, which lists the energy transfer efficiency calculated by the finite element model under different aspect ratios, as the basis for this step.

[0075] S202: Call the net residual deformation energy value of the shaft, construct the geometric volume data of the shaft region of the workpiece according to the workpiece size, perform the ratio calculation of the net residual deformation energy value of the shaft and the geometric volume data, calculate the effective energy distributed in the unit volume of the shaft region, and obtain the effective deformation energy density per unit volume.

[0076] The net residual deformation energy value at the shaft center, calculated using attenuation, is used. Then, based on the workpiece's real-time diameter and length parameters, a geometric volume model of the shaft center region is constructed. The shaft center region is typically defined as a cylindrical core region with a diameter of 1 / 4 or 1 / 3 of the workpiece's total diameter. The volume data of this core region is calculated using the cylinder volume calculation logic (pi multiplied by the square of the radius, then multiplied by the length). Next, a ratio operation is performed, dividing the net residual deformation energy value at the shaft center by the geometric volume data of the shaft center region. This operation aims to eliminate the influence of workpiece dimensions on the energy value, converting the absolute energy value into energy density per unit volume, i.e., effective deformation energy density per unit volume. This indicator directly reflects the accumulated plastic deformation work per unit volume of the shaft material and is positively correlated with the degree of material porosity closure. For example, if the net residual deformation energy value at the shaft center is known to be 22.75 megajoules (i.e., 22,750,000 joules), the calculated geometric volume data of the shaft center region is 0.0175 cubic meters (calculated at 1 / 3 of the diameter). Performing a division operation: 22,750,000 joules divided by 0.0175 cubic meters yields an effective deformation energy density of 1300 megajoules per cubic meter. This density value directly characterizes the degree of forging strengthening experienced by the core material.

[0077] S203: Based on the effective deformation energy density per unit volume, call the preset densification energy benchmark value, perform a numerical comparison between the effective deformation energy density per unit volume and the densification energy benchmark value, determine the degree of densification of the core material of the workpiece under the current energy state, and generate a core density assessment record.

[0078] The system invokes a pre-set densification energy benchmark value. This benchmark value is obtained through rigorous material testing: a uniaxial hot compression test with gradient deformation is performed on a cylindrical compression specimen (made of the same material as the workpiece, such as 42CrMo steel) containing pre-fabricated artificial pores. The load-displacement curve is recorded throughout the test, and the deformation energy per unit volume is calculated. After the experiment, quantitative porosity analysis is performed on the core of the specimen's cross-section (e.g., using an optical microscope to statistically analyze the micropore area ratio), and a curve showing the evolution of deformation energy per unit volume and relative density is fitted. The critical saturation position where the relative density reaches the material's theoretical density (e.g., 99.9%) is identified, and the deformation energy per unit volume corresponding to this position is set as the densification energy benchmark value. In actual evaluation, a numerical comparison logic is executed: it is determined whether the currently calculated effective deformation energy density per unit volume is greater than or equal to this densification energy benchmark value. If it is greater, the core is deemed to have met the densification requirements; if it is less, a risk of porosity is identified. For example, material experiments determined that the densification energy benchmark for 42CrMo steel at 1200 degrees Celsius is 1200 megajoules per cubic meter. Comparing this benchmark with the previously calculated 1300 megajoules per cubic meter, since 1300 is greater than 1200, the resulting core density assessment is recorded as "densification meets the standard, excellent." Conversely, if the calculated result is only 1100, it is recorded as "insufficient densification, potential porosity exists."

[0079] Please see Figure 4 The specific steps for obtaining the streamline integrity assessment record are as follows:

[0080] S301: Analyze the set of basic forging parameters, extract the slider displacement parameters, set the initial spatial coordinates of virtual particles inside the slender shaft, construct a metal flow velocity field model that matches the workpiece geometry, use the time series of slider displacement parameters as the boundary driving input of the velocity field model, calculate the instantaneous velocity vector of each virtual particle in the velocity field and perform displacement update calculation, record the motion path nodes of each virtual particle in the continuous deformation period, connect the motion path nodes of the same particle into a continuous geometric curve, and obtain the set of internal fiber streamline trajectories;

[0081] The process analyzes the set of basic forging parameters and extracts the slider displacement parameters as the driving variable for the time step. First, the initial spatial coordinates of virtual particles inside the slender shaft are set. These particles are uniformly distributed on the mesh nodes of the virtual billet before the simulation begins. Then, a metal flow velocity field model matching the workpiece geometry and the current deformation stage is constructed. This model does not rely on complex finite element iterations but is an analytical velocity field constructed based on the principle of volume invariance and the stream function method. Inputting the current slider pressing speed outputs the instantaneous velocity vector at any coordinate point inside the workpiece. During the simulation, the time series of the slider displacement parameters is input into the velocity field model to calculate the instantaneous velocity vector of each virtual particle at the current time step, and a displacement update operation is performed, i.e., the new coordinates equal the old coordinates plus the velocity vector multiplied by the time step. The motion path nodes of each virtual particle are continuously recorded throughout the forging process. Finally, all historical path nodes belonging to the same particle are connected in chronological order to fit and generate a continuous geometric curve, thus obtaining the set of internal fiber streamline trajectories. For example, consider a point mass with initial coordinates (0, 50, 0). During the downward movement of the slider, it is calculated to move to (0, 50.5, 1) at time t1 and to (0, 51.2, 2.5) at time t2. The three-dimensional curve formed by connecting these points represents the final shape of the metal fiber at that location, providing a geometric basis for subsequent determination of whether the streamlines have been cut.

[0082] S302: Call the internal fiber streamline trajectory set to obtain the three-dimensional geometric contour data of the finishing stage. Based on the three-dimensional geometric contour data, construct the processing removal boundary space surface that defines the material retention domain and the cutting domain. Perform the spatial geometric intersection operation between the internal fiber streamline trajectory set and the processing removal boundary space surface to identify the position where the streamline curve penetrates the boundary surface and obtain the coordinate set of the streamline boundary intersection point.

[0083] The process involves calling upon the previously generated set of internal fiber streamline trajectories and acquiring the 3D geometric contour data (typically from the CAD design model or a digital twin model of the final product) for the workpiece finishing stage. Based on this 3D geometric contour data, a machining removal boundary space surface is constructed to define the material retention domain and the cutting domain. This surface is essentially the boundary line between the outer surface of the forging blank and the outer surface of the final part; any material located outside this surface will be removed during machining. Subsequently, a spatial geometric intersection operation is performed, traversing each streamline trajectory curve and calculating its intersection point with the machining removal boundary space surface. If a streamline intersects with the surface, it means that the streamline extends from the inside of the part into the area to be removed, i.e., an "outburst" phenomenon has occurred. The position coordinates of all streamline curves penetrating the boundary surface are identified and recorded to obtain the set of streamline boundary intersection coordinates. For example, if a streamline diverges outward from the axis, and its trajectory equation and the equation of the machined step surface have a real solution at coordinates (100, 50, 200), then this point is a streamline outburst point.

[0084] S303: Based on the coordinate set of streamline boundary intersection points, calculate the tangent vector of the streamline curve at the intersection point, calculate the normal vector of the processed and removed boundary space surface at the corresponding intersection point, perform the dot product operation of the unit tangent vector and the unit normal vector, obtain the streamline exposure value, and generate a streamline integrity assessment record.

[0085] Based on the coordinate set of streamline boundary intersection points, for each intersection point, the tangent vector of the streamline curve at that point is first calculated. This vector represents the extension direction of the metal fiber at the cut. Simultaneously, the normal vector of the machined boundary space surface (i.e., the final surface of the part) at the corresponding intersection point is calculated. Then, the dot product of the unit tangent vector and the unit normal vector is performed. Geometrically, the dot product reflects the cosine of the angle between the streamline direction and the surface normal direction. If the streamline is parallel to the surface, the dot product is 0, indicating excellent streamline integrity; if the streamline is perpendicular to the surface (i.e., protruding along the normal direction), the absolute value of the dot product is the largest, indicating that the streamline is severely cut and has the worst fatigue resistance. The dot product results of all intersection points are statistically analyzed or the maximum value is taken to obtain the streamline exposure value, and a streamline integrity assessment record is generated accordingly. For example, at intersection point P, the streamline tangent vector is (0.707, 0.707, 0), and the surface normal vector is (0, 1, 0). Performing the dot product operation: 0.707×0 + 0.707×1 + 0×0, the result is 0.707. This indicates that the streamline cuts out of the surface at a 45-degree angle, and the streamline exposure value is relatively high, but it is within the allowable threshold range of the roughing stage. The evaluation record will indicate "The streamline cut-out angle is relatively large, and the fatigue strength risk is moderate".

[0086] Please see Figure 5 The specific steps for obtaining the statistical distribution record of surface curvature are as follows:

[0087] S401: Using a laser contour scanning device, collect point cloud data of the surface contour of a slender shaft, analyze the spatial geometric coordinates of the surface contour point cloud data along the workpiece axis, perform differential operation of the geometric coordinates with respect to the axial position, calculate the micro-bending degree of the workpiece surface at each continuous cross-section position, and obtain the axial cross-section curvature data sequence.

[0088] Using laser profile scanning equipment (such as a line laser profile sensor), surface profile point cloud data is acquired over the entire length of a slender axis during workpiece rotation or sensor movement. This data consists of a massive number of three-dimensional coordinate points (x, y, z). To analyze the microscopic undulations and deformations of the surface, this point cloud data is analyzed along the workpiece's axial direction, treating the continuous scan lines as a series of cross-sections. Subsequently, a differential operation of the geometric coordinates with respect to the axial position is performed. Specifically, for each point on the axial profile line, the coordinates of its immediate and adjacent points are taken, and the radius of curvature at that point is calculated using the second-order difference method or circular arc fitting method. The reciprocal is then used to obtain the curvature value. By performing this operation on all scan points, the microscopic curvature of the workpiece surface at various continuous cross-sectional positions is calculated, thereby obtaining a large sequence of axial cross-sectional curvature data. For example, at the axial position x = 100 mm, the change in the y-coordinate of the measurement point relative to the reference plane shows a slight convexity. The local radius of curvature fitted at this point is calculated to be 20 m after differential operation, and the reciprocal is used to obtain a curvature value of 0.05 / m. This sequence records the details of the unevenness and undulation changes at every point on the workpiece surface. See Table 2, which shows a portion of the collected and calculated axial section curvature data.

[0089] Table 2 Sample of Axial Section Curvature Data Sequence

[0090]

[0091] As shown in Table 2, by differentiating the radial deviation, the curvature abrupt change at coordinate 100.2 can be accurately captured.

[0092] S402: Call the axial section curvature data sequence, project the curvature data sequence to the preset curvature statistical interval, count the number of data falling into each curvature statistical interval, quantify the clustering distribution of curvature features in each numerical segment, and obtain the curvature interval distribution set;

[0093] The axial section curvature data sequence is retrieved, and the arithmetic mean and standard deviation of all values ​​within the sequence are calculated. Next, a valid confidence interval is constructed, centered on the arithmetic mean and covering a range of values ​​plus or minus three standard deviations. The sequence is traversed, and data falling outside this valid confidence interval are marked as outliers and removed, generating a cleaned valid curvature sample set. Subsequently, the valid curvature sample set is traversed to extract the maximum and minimum curvature values, and the difference between the maximum and minimum curvature values ​​is calculated to obtain the effective curvature global fluctuation range. To give the statistical intervals a clear physical meaning, the micro-geometric shape identification precision specified in the surface roughness testing standard (e.g., the micro-interval corresponding to Ra3.2) is obtained, and this micro-geometric shape identification precision is directly set as the interval span step size. Then, the ratio of the effective curvature global fluctuation range to the interval span step size is rounded up to determine the total number of intervals. Finally, using the minimum curvature value as the starting boundary, a total number of consecutive numerical segments are generated based on the interval span step size, constructing a preset curvature statistical interval. On this basis, the number of data points falling into each curvature statistical interval is counted, quantifying the clustering distribution of curvature features in each numerical segment, and obtaining the curvature interval distribution set. For example, the calculated mean value of the curvature sequence is 0.05, and the standard deviation is 0.01. The effective interval is [0.02, 0.08]. After removing noise, the maximum value of the effective sample set is 0.078, the minimum value is 0.022, and the fluctuation range is 0.056. If the set micro-geometric shape recognition accuracy (step size) is 0.005, then the total number of intervals is 0.056 divided by 0.005 and rounded up, i.e., 12 intervals. The intervals are set as [0.022, 0.027), [0.027, 0.032), and so on.

[0094] S403: Based on the curvature interval distribution data set, sum the number of samples in each interval, determine the total sample size, perform the ratio calculation of the number of samples in each interval to the total sample size, calculate the frequency proportion of each curvature statistical interval in the overall surface morphology, integrate the frequency proportion data of each interval, and construct the surface curvature statistical distribution record.

[0095] Based on the previously obtained curvature interval distribution data set, the number of samples in each interval is first summed to determine the total sample size for statistical analysis. Then, the ratio of the number of samples in each interval to the total sample size is calculated, i.e., the number of points in a given interval is divided by the total number of points to calculate the frequency proportion of each curvature statistical interval in the overall surface morphology. The frequency proportion data for each interval are integrated to construct a surface curvature statistical distribution record. This record is essentially a normalized histogram, reflecting the probability density distribution of workpiece surface deformation. For example, with a total sample size of 10,000 points, if 500 points fall within the first interval [0.022, 0.027), the frequency proportion for this interval is 500 divided by 10,000, which is 0.05. If 2,000 points fall within the fifth interval, the frequency proportion is 0.2. This distribution record will serve as the input basis for subsequent calculations of disorder.

[0096] Please see Figure 6 The specific steps for obtaining quality assessment data for slender shafts are as follows:

[0097] S501: Analyze the surface curvature statistical distribution record, extract the frequency proportion data, calculate the logarithmic transformation value of the frequency proportion data, perform the product operation of the frequency proportion data and the logarithmic transformation value, sum the product results in each statistical interval and take the absolute value to obtain the curvature distribution disorder value.

[0098] The surface curvature statistical distribution record is analyzed to extract the frequency proportion data sequence. A natural logarithmic (ln) transformation is performed on each frequency proportion data to obtain a logarithmic transformed value. Then, the frequency proportion data is multiplied by the corresponding logarithmic transformed value. The products of all intervals are summed, and the absolute value of the sum is inverted (because the probability is less than 1, the logarithm is negative, and the sum is negative), thus obtaining the degree of disorder in the curvature distribution. This value is the Shannon entropy, which does not depend on the specific curvature value but reflects the uniformity and disorder of the curvature distribution. If the surface is very smooth and uniform, the curvature is concentrated in a few intervals, and the entropy value is low; if the surface has complex wrinkles or orange peel defects, the curvature distribution is widespread and random, and the entropy value is high. For example, if the frequency distribution is [0.5, 0.5], then the entropy is -(0.5×ln0.5 + 0.5×ln0.5) ≈ 0.69. If the distribution is [0.1, 0.1, ... 0.1] (uniformly distributed across 10 intervals), then the entropy is -(10 * 0.1 * ln0.1) ≈ 2.30. Calculations show that if the obtained curvature distribution disorder value is 2.1, it indicates that the surface morphology has high randomness and complexity.

[0099] S502: Based on the curvature distribution disorder value, by performing deformation topology feature mapping, the calculated disorder value is mapped to the corresponding complexity range, the topology level of the current workpiece surface deformation feature is determined, the workpiece surface geometric quality status classification index is output, and the geometric deformation complexity classification data is obtained.

[0100] Based on the calculated curvature distribution disorder value, a deformation topological feature mapping operation is performed. This typically involves a preset hierarchical threshold logic: setting multiple complexity intervals (e.g., Level 1: 0-1.0, Level 2: 1.0-2.0, Level 3: >2.0). The current disorder value is determined by which interval it falls into, thus determining the topological level of the current workpiece surface deformation features. Level 1 represents a simple surface morphology and high smoothness; Level 3 represents the presence of complex micro-textures or defects on the surface. Finally, a hierarchical index of the workpiece surface geometric quality is output, obtaining geometric deformation complexity hierarchical data. For example, substituting the previously calculated disorder value of 2.1 into the hierarchical logic, it is determined to be greater than 2.0, belonging to "Level 3 complexity," meaning the surface may have forging orange peel or micro-crack precursors, and the geometric deformation complexity hierarchical data is marked as "High Risk / Level 3."

[0101] S503: Call the geometric deformation complexity classification data, combine the core density assessment record and the streamline integrity assessment record, perform a weighted summation operation of multidimensional quality indicators, and construct the slender shaft quality assessment data;

[0102] The system retrieves data on geometric deformation complexity grading, core density assessment records, and streamline integrity assessment records. It then performs a weighted summation of multidimensional quality indicators. First, the weight coefficients for each indicator must be defined, typically based on expert scoring or the analytic hierarchy process (AHP) (e.g., density weight 0.5, streamline integrity weight 0.3, surface complexity weight 0.2). Qualitative descriptions in each record are converted into quantitative scores (e.g., "Excellent" = 100 points, "Medium" = 80 points, "High Risk" = 60 points), then multiplied by their respective weights and summed. The final slender shaft quality assessment data includes not only the total score but also detailed status of each sub-item. For example, core density score is 100 (weight 0.5), streamline integrity score is 80 (weight 0.3), and surface complexity score is 60 (weight 0.2). The calculation is: 100 × 0.5 + 80 × 0.3 + 60 × 0.2 = 50 + 24 + 12 = 86 points. The output quality assessment data for the slender shaft is "Total score 86, Quality grade: Good, Main deduction item: Surface morphology". See Table 3, which shows an example of the final generated quality assessment data structure for the slender shaft.

[0103] Table 3. Data Recording Table for Quality Assessment of Slender Shafts

[0104]

[0105] As shown in Table 3, the evaluation data intuitively demonstrates the contribution and final quality status of each dimension, achieving a comprehensive assessment from the inside out and from energy to geometry.

[0106] Please see Figure 7 A quality assessment system for slender shaft forging includes:

[0107] The basic parameter construction module collects forging pressure and slider displacement, performs integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collects workpiece diameter and length and calculates the ratio of length to diameter, and constructs a set of basic forging parameters.

[0108] The dense feature analysis module analyzes the set of forging basic parameters, calls the radial transmission efficiency coefficient, multiplies it with the total deformation work data, obtains the net residual deformation energy of the core region, calculates the ratio of net residual deformation energy to core volume, obtains the unit effective deformation energy and compares it with the benchmark, and generates a core density assessment record.

[0109] The workpiece streamline simulation module analyzes the set of basic forging parameters, generates a streamline trajectory set based on slider displacement simulation, constructs the processing removal boundary, calculates the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtains the streamline exposure rate, and generates a streamline integrity assessment record.

[0110] The surface topography statistics module collects and differentiates surface point clouds using a laser contour scanning device, obtains curvature sequences, counts the number of curvature sequences in the statistical interval and calculates the frequency ratio, and constructs a statistical distribution record of surface curvature.

[0111] The quality comprehensive assessment module analyzes the surface curvature statistical distribution records, performs logarithmic multiplication and accumulation operations on the frequency proportion data to obtain the curvature distribution disorder data, maps the deformation complexity, and combines the core density assessment records and streamline integrity assessment records to construct the slender shaft quality assessment data.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A quality assessment method for forging slender shafts, characterized in that, Includes the following steps: S1: Collect forging pressure and slider displacement, perform integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collect workpiece diameter and length and calculate the ratio of length to diameter, and construct a set of basic forging parameters; S2: Analyze the set of forging basic parameters, call the radial transmission efficiency coefficient, multiply it with the total deformation work data, obtain the net residual deformation energy of the core region, calculate the ratio of net residual deformation energy to core volume, obtain the unit effective deformation energy and compare it with the benchmark, and generate a core density assessment record. S3: Analyze the set of forging basic parameters, generate a set of streamline trajectories based on slider displacement simulation, construct the processing removal boundary, calculate the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtain the streamline exposure rate, and generate a streamline integrity assessment record; S4: Collect and differentiate the surface point cloud using a laser contour scanning device to obtain a curvature sequence, count the number of curvature sequences in the statistical interval and calculate the frequency ratio to construct a statistical distribution record of surface curvature. S5: Analyze the surface curvature statistical distribution record, perform logarithmic multiplication and accumulation on the frequency proportion data to obtain the curvature distribution disorder data, map the deformation complexity, and combine the core density assessment record and the streamline integrity assessment record to construct the slender shaft quality assessment data.

2. The quality assessment method for forging slender shafts according to claim 1, characterized in that, The forging basic parameter set specifically includes cumulative deformation work value, slider displacement time sequence data, and workpiece geometric length-to-diameter ratio value. The core density assessment record specifically includes the net residual deformation energy value of the shaft center, effective deformation energy density per unit volume, and internal porosity risk indicator. The streamline integrity assessment record specifically includes the internal fiber streamline trajectory set and streamline boundary intersection coordinate set. The surface curvature statistical distribution record specifically includes the axial section curvature data sequence, curvature interval distribution quantity set, and interval falling frequency histogram. The slender shaft quality assessment data specifically includes the curvature distribution disorder value, geometric deformation complexity classification data, and workpiece comprehensive quality classification instruction.

3. The quality assessment method for forging slender shafts according to claim 1, characterized in that, The specific steps for obtaining the set of basic forging parameters are as follows: S101: Real-time forging load parameters are collected by forging load sensor, slider displacement parameters are collected by slider displacement sensor, slider displacement increment between adjacent sampling times is calculated, the product operation of real-time forging load parameters and slider displacement increment is performed to obtain micro-element deformation work, the micro-element deformation work in the entire forging stroke is accumulated and integrated to calculate the total energy value of slider input to billet, and the accumulated deformation work value is obtained. S102: Collect the real-time diameter and length parameters of the workpiece through online measurement equipment, extract the real-time length parameters of the workpiece as axial extension measurement data, extract the real-time diameter parameters of the workpiece as radial section measurement data, perform the ratio calculation of the axial extension measurement data and the radial section measurement data, and obtain the geometric length-to-diameter ratio of the workpiece. S103: Call the accumulated deformation work value and the workpiece geometric length-to-diameter ratio value, extract the slider displacement parameter as the time reference of the motion process, map the accumulated deformation work value to the energy data field, map the workpiece geometric length-to-diameter ratio value to the geometric data field, integrate the total deformation work data, slider displacement parameter and length-to-diameter ratio parameter, and construct a set of forging basic parameters.

4. The quality assessment method for forging slender shafts according to claim 3, characterized in that, The specific steps for obtaining the cardiac density assessment record are as follows: S201: Analyze the set of forging basic parameters, extract the length-to-diameter ratio parameter and total deformation work data, retrieve the preset mapping table based on the length-to-diameter ratio parameter and match the corresponding radial energy transfer efficiency coefficient, perform the product attenuation operation of the total deformation work data and the radial energy transfer efficiency coefficient, calculate the energy value of the workpiece axial region after radial transmission loss, and obtain the net remaining deformation energy value of the axial region. S202: Call the net remaining deformation energy value of the shaft center, construct the geometric volume data of the workpiece shaft center region according to the workpiece size, perform the ratio calculation of the net remaining deformation energy value of the shaft center and the geometric volume data, calculate the effective energy distributed in the unit volume of the shaft center region, and obtain the effective deformation energy density per unit volume. S203: Based on the effective deformation energy density per unit volume, call the preset densification energy benchmark value, perform a numerical comparison between the effective deformation energy density per unit volume and the densification energy benchmark value, determine the degree of densification of the core material of the workpiece under the current energy state, and generate a core density assessment record.

5. The quality assessment method for forging slender shafts according to claim 4, characterized in that, The process of obtaining the preset mapping table is as follows: A set of finite element models covering the range of aspect ratio parameters is established. A unified loading speed and friction coefficient boundary condition is set for the finite element model set. Nodal force and nodal displacement data of the contact area of ​​the outer surface are extracted. The total work input to the surface is calculated based on the nodal force and nodal displacement data. The stress tensor and strain increment tensor of the axial region element are extracted. The double shrinkage and time step integration of the stress tensor and strain increment tensor are performed to obtain the actual cumulative plastic deformation work. The ratio of the actual cumulative plastic deformation work to the total work input to the surface is calculated to obtain radial energy transfer efficiency data points. The functional relationship curve of the radial energy transfer efficiency data points is fitted. The functional relationship curve is discretized and sampled to construct a pre-set mapping table. The process of obtaining the preset densification energy reference value is as follows: Based on a cylindrical compression specimen containing pre-fabricated artificial pore defects, a uniaxial hot compression test with gradient deformation was performed on the cylindrical compression specimen. The load-displacement curve was recorded, and the deformation energy per unit volume was calculated. A quantitative porosity analysis was performed on the core of the cross-section of the cylindrical compression specimen. The evolution curve of the deformation energy per unit volume and the relative density value was fitted. The critical saturation position when the relative density value reaches the theoretical density of the material was identified. The deformation energy per unit volume corresponding to the critical saturation position was set as the preset densification energy benchmark value.

6. The quality assessment method for forging slender shafts according to claim 4, characterized in that, The specific steps for obtaining the streamline integrity assessment record are as follows: S301: Analyze the set of forging basic parameters, extract the slider displacement parameters, set the initial spatial coordinates of the virtual particles inside the slender shaft, construct a metal flow velocity field model that matches the workpiece geometry, use the time series of slider displacement parameters as the boundary driving input of the velocity field model, calculate the instantaneous velocity vector of each virtual particle in the velocity field and perform displacement update operation, record the motion path nodes of each virtual particle in the continuous deformation period, connect the motion path nodes of the same particle into a continuous geometric curve, and obtain the set of internal fiber streamline trajectories; S302: Call the set of internal fiber streamline trajectories to obtain the three-dimensional geometric contour data of the finishing stage, construct the processing removal boundary space surface that defines the material retention domain and the cutting domain based on the three-dimensional geometric contour data, perform the spatial geometric intersection operation between the set of internal fiber streamline trajectories and the processing removal boundary space surface, identify the position where the streamline curve penetrates the boundary surface, and obtain the coordinate set of the streamline boundary intersection point. S303: Based on the coordinate set of the streamline boundary intersection points, calculate the tangent vector of the streamline curve at the intersection point, calculate the normal vector of the processed and removed boundary space surface at the corresponding intersection point, perform the dot product operation of the unit tangent vector and the unit normal vector, obtain the streamline exposure value, and generate a streamline integrity assessment record.

7. The quality assessment method for forging slender shafts according to claim 6, characterized in that, The specific steps for obtaining the surface curvature statistical distribution record are as follows: S401: Using a laser contour scanning device, collect point cloud data of the surface contour of a slender shaft, analyze the spatial geometric coordinates of the surface contour point cloud data along the workpiece axis, perform differential operation of the geometric coordinates with respect to the axial position, calculate the micro-bending degree of the workpiece surface at each continuous cross-section position, and obtain the axial cross-section curvature data sequence. S402: Call the axial section curvature data sequence, project the curvature data sequence to the preset curvature statistical interval, count the number of data falling into each curvature statistical interval, quantify the clustering distribution of curvature features in each numerical segment, and obtain the curvature interval distribution set; S403: Based on the curvature interval distribution set, sum the number of samples in each interval, determine the total sample size, perform the ratio calculation of the number of samples in each interval to the total sample size, calculate the frequency proportion of each curvature statistical interval in the overall surface morphology, integrate the frequency proportion data of each interval, and construct a surface curvature statistical distribution record.

8. The quality assessment method for forging slender shafts according to claim 7, characterized in that, The process of constructing the preset curvature statistical interval is as follows: The axial section curvature data sequence is invoked, and its arithmetic mean and standard deviation are calculated. An effective confidence interval is constructed with the arithmetic mean as the center and covering a range of ±3 times the standard deviation. Data in the axial section curvature data sequence that fall outside the effective confidence interval are marked as outliers and removed. An effective curvature sample set is generated. The effective curvature sample set is traversed, and the maximum and minimum curvature values ​​are extracted. The difference between the maximum and minimum curvature values ​​is calculated to obtain the effective curvature global fluctuation range. The micro-geometric shape identification accuracy specified in the surface roughness detection standard is obtained. The micro-geometric shape identification accuracy is set as the interval span step size. The ratio of the effective curvature global fluctuation range to the interval span step size is rounded up. The total number of intervals is determined. Using the minimum curvature value as the starting boundary, the total number of consecutive numerical segments are generated based on the interval span step size to construct the preset curvature statistical interval.

9. The quality assessment method for forging slender shafts according to claim 7, characterized in that, The specific steps for obtaining the quality assessment data of the slender shaft are as follows: S501: Analyze the surface curvature statistical distribution record, extract the frequency proportion data, calculate the logarithmic transformation value of the frequency proportion data, perform the product operation of the frequency proportion data and the logarithmic transformation value, accumulate and sum the product results in each statistical interval and take the absolute value to obtain the curvature distribution disorder value. S502: Based on the curvature distribution disorder value, by performing deformation topology feature mapping, the calculated disorder value is mapped to the corresponding complexity range, the topology level of the current workpiece surface deformation feature is determined, the workpiece surface geometric quality status classification index is output, and geometric deformation complexity classification data is obtained. S503: Call the geometric deformation complexity classification data, combine it with the core density assessment record and the streamline integrity assessment record, perform a weighted summation operation of multidimensional quality indicators, and construct slender shaft quality assessment data.

10. A quality assessment system for forging slender shafts, characterized in that, The system is used to implement the quality assessment method for forging slender shafts according to any one of claims 1-9, the system comprising: The basic parameter construction module collects forging pressure and slider displacement, performs integral calculation on forging pressure with respect to slider displacement to obtain total deformation work data, collects workpiece diameter and length and calculates the ratio of length to diameter, and constructs a set of basic forging parameters. The dense feature analysis module parses the set of forging basic parameters, calls the radial transmission efficiency coefficient, multiplies it with the total deformation work data, obtains the net residual deformation energy of the core region, calculates the ratio of net residual deformation energy to core volume, obtains the unit effective deformation energy and compares it with the benchmark, and generates a core density assessment record. The workpiece streamline simulation module analyzes the set of forging basic parameters, generates a streamline trajectory set based on slider displacement simulation, constructs a processing removal boundary, calculates the dot product of the tangent vector and the boundary normal vector at the intersection of the streamline trajectory set with the processing removal boundary, obtains the streamline exposure rate, and generates a streamline integrity evaluation record. The surface topography statistics module collects and differentiates surface point clouds using a laser contour scanning device, obtains curvature sequences, counts the number of curvature sequences in the statistical interval and calculates the frequency ratio, and constructs a statistical distribution record of surface curvature. The quality comprehensive assessment module analyzes the surface curvature statistical distribution record, performs logarithmic multiplication and accumulation on the frequency proportion data to obtain curvature distribution disorder data, maps deformation complexity, and constructs slender shaft quality assessment data by combining the core density assessment record and the streamline integrity assessment record.

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