Vertical and horizontal integrated gantry boring, milling and grinding composite numerical control machine tool and control method

Abstract

The invention relates to the technical field of machine tool machining, in particular to a vertical-horizontal integrated gantry boring-milling-grinding composite numerical control machine tool and a control method. The method comprises the steps that the machining abnormality significance degree of a to-be-analyzed machining position is determined according to the machining result data deviation of the to-be-analyzed machining position; the machining abrupt change rate is determined according to the machining abnormal significance degrees of the to-be-analyzed machining position and the neighborhood machining position of the to-be-analyzed machining position; determining a machining abnormal area of the to-be-adjusted machining parameters based on the machining abnormal significance degree and the machining abrupt change rate of each machining position; the machining result data deviation of the target machining position in the machining abnormal area is utilized to determine the machining abnormal index of the target machining position; and determining a target machining parameter of the next machining process at the target machining position by utilizing a machining characteristic formed by the machining abnormal index and the machining process data deviation. According to the technical scheme, the accuracy of determining the process parameters of the composite numerical control machine tool is improved, and error compensation of the composite numerical control machine tool is accurately achieved.

Description

Technical Field

[0001] This invention relates to the field of machine tool processing technology, specifically to a vertical and horizontal integrated gantry boring, milling and grinding composite CNC machine tool and its control method. Background Technology

[0002] The vertical / horizontal integrated gantry milling and grinding CNC machine tool is a high-precision machine tool that integrates vertical / horizontal machining postures and combines three core processes: boring, milling, and grinding. It is specifically designed for large, complex, and high-precision structural parts. Its core features are: completing multiple processes in a single setup, multi-position machining, avoiding repeated positioning errors, and balancing machining efficiency and accuracy. Compared to ordinary gantry machine tools that only support a single vertical / horizontal posture or a single machining process (such as only milling or grinding), this machine tool achieves seamless switching between boring, milling, and grinding through spindle vertical / horizontal conversion and a multi-process tool system.

[0003] The problem with existing technologies lies in the fact that while this boring, milling, and grinding composite machine tool integrates multiple functions and achieves seamless switching in workpiece processing, it involves numerous parameters that need to be set during workpiece processing, such as cutting speed, depth of cut, cutting depth, feed rate, and other related real-time machining parameters. Traditionally, parameter control is achieved through 3D modeling, such as CAD, determining the machining path based on the modeling results, and defining corresponding machining parameters for each path, such as the feed rate, generating corresponding G-code files. The machine tool then processes the workpiece by reading the G-code files in real time. However, the generation of these G-codes is based on ideal conditions for workpiece processing. Actual machining processes are easily affected by factors such as materials, systems, environment, and processes, resulting in deviations between the actual machining results and the system's target values. Therefore, real-time correction of the process parameters in the G-code machining process is necessary. Therefore, error compensation is required. However, traditional compensation systems are only applicable to a single machine tool function. Whenever the machine tool is changed, the corresponding process parameters need to be redefined. For example, the relevant parameters and processing results data of the boring process are difficult to use to control the process parameters of the milling process. Therefore, traditional compensation systems are also difficult to adapt to composite CNC machine tools. Summary of the Invention

[0004] To address the technical problem of inaccurate machining process parameters in existing vertical and horizontal integrated gantry milling and grinding CNC machine tools during actual workpiece processing, the present invention aims to provide a vertical and horizontal integrated gantry milling and grinding CNC machine tool and its control method. The specific technical solution adopted is as follows: This invention provides a method for combined vertical and horizontal gantry boring, milling, and grinding CNC control, the method comprising: By utilizing the deviation in the processing results data of the processing location to be analyzed, the degree of significance of the processing anomaly at the processing location to be analyzed is determined; The processing mutation rate of the processing location to be analyzed is determined by utilizing the significance of processing anomalies at the processing location to be analyzed and its neighboring processing locations. The abnormal processing regions for which processing parameters need to be adjusted are determined based on the significance of processing anomalies and the rate of processing mutation at each processing location. By utilizing the deviation in processing result data at the target processing location within the processing anomaly region, the processing anomaly index at the target processing location is determined. By utilizing the processing characteristics consisting of the processing anomaly index at the target processing position and the deviation of the processing data, the target processing parameters for the next processing step at the target processing position are determined.

[0005] Furthermore, the step of determining the significance of processing anomalies at the processing location to be analyzed by utilizing the deviation in processing result data at the processing location to be analyzed includes: The deviation between the actual processing result data and the theoretical processing result data of the processing location to be analyzed is determined and used as the point cloud data of the processing location to be analyzed; Using point cloud data of the processing location to be analyzed and its neighboring processing locations, the actual fitting plane of the neighborhood where the processing location to be analyzed is located and the curvature of the processing location to be analyzed are obtained. Determine the plane similarity between the actual fitted plane and the theoretical fitted plane corresponding to the theoretical processing result data. Use the plane similarity and the curvature of the processing position to be analyzed to determine the degree of significance of the processing anomaly at the processing position to be analyzed.

[0006] Furthermore, by utilizing the significance of processing anomalies at the processing location to be analyzed and its neighboring processing locations, the processing mutation rate at the processing location to be analyzed is determined, including: Determine the significance of the differences in processing anomalies and the Euclidean distance between the processing location to be analyzed and its neighboring processing locations; The processing mutation rate at the processing location to be analyzed is determined by using the difference in the significance of the processing anomalies and the Euclidean distance.

[0007] Furthermore, using the differences in the significance of the processing anomalies and the Euclidean distance, the processing mutation rate at the processing location to be analyzed is determined, including: Using the difference in the significance of the processing anomalies and the Euclidean distance, the rate of change of the significance of the anomalies between the processing location to be analyzed and its neighboring processing locations is determined; Determine the dispersion of the significance of processing anomalies at all neighboring processing locations, and use the dispersion and the rate of change of the significance of the anomalies to determine the processing mutation rate of the processing location to be analyzed.

[0008] Furthermore, the determination of the abnormal processing regions for which processing parameters need adjustment based on the significance of processing anomalies and the rate of abrupt changes at each processing location includes: Based on the degree of significance of processing anomalies at each processing position, anomaly detection algorithms are used to obtain the set of abnormal positions of the processed workpiece and the processing area of ​​the normal workpiece. Based on the machining mutation rate of the normal workpiece machining area, the abnormal machining areas in the abnormal location set that require adjustment of machining parameters are identified.

[0009] Furthermore, determining the abnormal processing regions in the abnormal location set whose processing parameters need adjustment based on the processing mutation rate of the normal workpiece processing region includes: Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal processing mutation rate data set; Determine the processing mutation rate at the preset data distribution boundary in the normal processing mutation rate dataset and use it as the mutation rate threshold. The locations in the abnormal location set where the processing mutation rate is greater than the mutation rate threshold are taken as the boundary locations of the abnormal processing region for which the processing parameters to be adjusted, and the abnormal processing region is obtained.

[0010] Furthermore, the step of determining the processing anomaly index of the target processing position by utilizing the processing result data deviation of the target processing position in the processing anomaly region includes: By utilizing the deviation of processing result data at the target processing location in the processing anomaly area, the significance of the target processing anomaly, the target processing mutation rate, and the processing fault tolerance rate at the target processing location are determined. The average processing mutation rate at the boundary location of the processing anomaly region is determined, and the processing anomaly index at the target processing location is determined using the average processing mutation rate, the significance of the target processing anomaly, the target processing mutation rate, and the processing tolerance rate.

[0011] Furthermore, the step of determining the target processing parameters for the next processing step at the target processing position using processing features comprised of the processing anomaly index at the target processing position and processing data deviation includes: Based on the degree of significance of processing anomalies at each processing position, anomaly detection algorithms are used to obtain the set of abnormal positions of the processed workpiece and the processing area of ​​the normal workpiece. Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal position set; Using the processing anomaly index and processing process data deviation corresponding to each processing position in the normal position set as the processing feature vector, the processing feature vector with the largest modulus is determined and its modulus is used as the modulus threshold. Using the processing feature vector composed of the processing anomaly index of the target processing position and the deviation of the processing process data, and the modulus threshold, the target processing parameters for the next processing process at the target processing position are determined.

[0012] Further, the step of determining the target processing parameters for the next processing step at the target processing position using the processing feature vector composed of the processing anomaly index at the target processing position and the processing process data deviation, and the module length threshold, includes: The excess modulus of the processing feature vector at the target processing position that exceeds the modulus threshold is determined. The target machining parameters are obtained by using the original machining parameters of the next machining process at the target machining position that exceed the mold length correction.

[0013] The present invention also provides a vertical and horizontal integrated gantry boring, milling and grinding composite CNC machine tool, the CNC machine tool including a processor, a memory, and a composite CNC control program stored in the memory that can be executed by the processor, wherein when the composite CNC control program is executed by the processor, it implements the steps of the vertical and horizontal integrated gantry boring, milling and grinding composite CNC control method as described in any of the above claims.

[0014] The present invention has the following beneficial effects: This invention analyzes the deviations in processing results at various processing positions and their neighboring positions within a workpiece to obtain the significance and mutation rate of abnormalities at corresponding positions. This allows for the localization of abnormal processing regions requiring parameter adjustments. Based on the processing anomaly index and data deviations at each processing position within these regions, the processing parameters corresponding to each position are corrected. The vertical / horizontal integrated gantry boring, milling, and grinding CNC machine tool uses shared parameters to control parameters for the next processing step, maintaining the original parameters for normal processing areas and adjusting abnormal processing areas according to the corrected parameters. This achieves dynamic and precise control of the machine tool, improving the accuracy of process parameter determination and accurately compensating for errors. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are 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.

[0016] Figure 1 A flowchart illustrating the steps of a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in one embodiment of the present invention; Figure 2This is a detailed flowchart of step S1 in a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention; Figure 3 This is a detailed flowchart of step S2 in a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention; Figure 4 This is a detailed flowchart of step S3 in a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention. Figure 5 This is a detailed flowchart of step S4 in a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention. Figure 6 This is a detailed flowchart of step S5 in a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of the hardware operating environment of the vertical and horizontal integrated gantry boring, milling and grinding composite CNC machine tool involved in the embodiments of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a vertical and horizontal integrated gantry milling and grinding composite CNC control method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] Before proceeding with the following embodiments of the present invention, the objectives and main target scenarios of the invention will be explained in order to facilitate understanding of the invention.

[0020] The specific scheme of the vertical and horizontal integrated gantry milling composite CNC control method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Example 1: For the vertical and horizontal integrated gantry boring and milling composite CNC control method provided by this invention, please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a flowchart of the steps of a vertical and horizontal integrated gantry milling and grinding composite CNC control method provided in an embodiment of the present invention.

[0022] The integrated vertical and horizontal gantry boring and milling composite CNC control method includes: Step S1: Determine the significance of the processing anomaly at the processing location to be analyzed by using the deviation of the processing result data at the processing location to be analyzed; In this embodiment, the CNC (Computer Numerical Control) system of the vertical and horizontal integrated gantry boring and milling composite CNC machine tool (hereinafter referred to as "composite machine tool" or "machine tool") obtains the workpiece material information to be processed, such as titanium alloy or high-strength steel 45#. Based on the workpiece material information, the corresponding current machining process data (original machining parameters) are obtained from the process parameter database, such as the current spindle speed of 500-3000 rpm, feed rate of 0.02-0.2 mm / r, maximum depth of cut of 0.05 mm, and machine tool temperature of 60 degrees Celsius. That is to say, the corresponding range of process parameters can be obtained based on the current workpiece material information. It should be noted that the above process parameters are jointly controlled by the workpiece material information and the machining steps / processes, such as the current roughing boring process.

[0023] Since there is a deviation between the actual machining process and the target value (theoretical machining result data) processed by the composite machine tool system, the machining result data deviation between the actual machining result data and the theoretical machining result data corresponding to the previous machining process is obtained through image sampling, so it can also be called deviation image data (the deviation in this embodiment and subsequent embodiments takes the deviation of the cutting depth result as an example, but it can also be replaced with an image reflecting the error of other machining results, which will not be explained further). The deviation data retains positive and negative values. Negative numbers indicate that the cutting depth exceeds the current target, which is overcutting, and positive numbers indicate that the cutting depth is insufficient.

[0024] It should be noted that the processing result data deviation can be obtained by: selecting the sampling points (corresponding to the processing positions), calculating the corresponding deviations of the sampling points, storing the deviation data of each processing position in the form of point clouds, performing the above processing on each sampling point, and obtaining the deviation data of the corresponding position as the processing result data deviation in the form of image deviation data.

[0025] Specifically, please refer to Figure 2 Step S1 includes: Step S11: Determine the deviation between the actual processing result data and the theoretical processing result data of the processing location to be analyzed, and use it as the point cloud data of the processing location to be analyzed; Step S12: Using the point cloud data of the processing location to be analyzed and its neighboring processing locations, obtain the actual fitting plane of the neighborhood where the processing location to be analyzed is located and the curvature of the processing location to be analyzed. Step S13: Determine the plane similarity between the actual fitted plane and the theoretical fitted plane corresponding to the theoretical processing result data. Use the plane similarity and the curvature of the processing position to be analyzed to determine the degree of significance of the processing anomaly at the processing position to be analyzed.

[0026] Based on the above embodiments, the deviation data of each processing position is obtained as point cloud data. Any position is selected as the processing position to be analyzed, and the spatial point cloud adjacent to that processing position is obtained. For example, the neighborhood spatial radius R = 1mm, resulting in a neighborhood sampling point cloud. The value of the neighborhood radius R is related to the sampling density of the processing position point cloud and can be set according to the actual situation. For example, it can be set to 3-5 times the average point spacing. In this embodiment, R = 1mm is only an example value.

[0027] For the point cloud data of the machining location to be analyzed and its neighboring machining locations, an actual fitting plane is obtained through fitting. The curvature of the machining location to be analyzed can be calculated using Principal Component Analysis (PCA). This actual fitting plane reflects the machining flatness information of the composite machine tool at that location. Comparing the actual fitting plane fitted by the point cloud with the theoretical fitting plane corresponding to the target value (theoretical machining result data) reflects the local anomaly state of the current composite machine tool machining at that location. Furthermore, if the point cloud at that location has a large curvature, it indicates that the machining depth at that location is significantly inconsistent with the surrounding machining depth, exhibiting significant machining unevenness and thus significant machining anomalies. Therefore, the significance of the machining anomalies at the machining location to be analyzed (or the machining anomaly significance) can be reflected by the following formula. : In the above, q is the curvature of the current processing position to be analyzed, norm represents the normalization function, normalization is performed through norm to standardize the data and eliminate dimensions to obtain dimensionless data; n is the normal vector of the actual fitting plane. The normal vector corresponding to the theoretical fitting plane formed by the target value at this location. This represents the calculation of the sine of the angle between two vectors, used to reflect the similarity between two planes. The data is normalized using norm to obtain dimensionless data. This indicates the significance of the processing anomaly at the processing location to be analyzed.

[0028] In this embodiment of the invention, the normalization function can be, for example, a maximum-minimum normalization function, which normalizes the data to the range of [0,1]. In one embodiment of the invention, the normalization process can be, for example, a maximum-minimum normalization process, and subsequent normalization steps can all use maximum-minimum normalization. In other embodiments of the invention, other normalization methods can be selected according to the specific range of values, which will not be elaborated further.

[0029] Step S2: Determine the processing mutation rate of the processing location to be analyzed by utilizing the significance of processing anomalies of the processing location to be analyzed and its neighboring processing locations. In this embodiment, if the machining parameters of the composite machine tool are inaccurate, meaning the machining parameters are not applicable to the machining at that location—for example, due to impurities in the material at that location—the current machining parameters cannot reach the corresponding target machining value, resulting in a large system deviation. Therefore, it is necessary to compare the machining results with those at other locations to determine whether the deviation at that location falls within the reasonable fluctuation range of machining.

[0030] Specifically, please refer to Figure 3 Step S2 includes: Step S21: Determine the significance difference and Euclidean distance between the processing anomaly to be analyzed and its neighboring processing locations; Step S22: Using the difference in the significance of the processing anomalies and the Euclidean distance, determine the processing mutation rate of the processing location to be analyzed.

[0031] More specifically, step S22 includes: Using the difference in the significance of the processing anomalies and the Euclidean distance, the rate of change of the significance of the anomalies between the processing location to be analyzed and its neighboring processing locations is determined; Determine the dispersion of the significance of processing anomalies at all neighboring processing locations, and use the dispersion and the rate of change of the significance of the anomalies to determine the processing mutation rate of the processing location to be analyzed.

[0032] In this embodiment, a machining position to be analyzed is selected, and the significance of machining anomalies at this position is compared with that at adjacent positions. If the anomalies at this position have a significant difference compared to those at surrounding positions, and the anomalies at surrounding positions maintain a high degree of similarity (i.e., the smaller the dispersion), then the machining position at this location is considered a true anomaly, and the machine tool machining parameters at this position need to be adjusted. This can be achieved by calculating the machining mutation rate using the following formula. This method reflects the true degree of anomaly at the processing location: The above These represent the significance of the processing anomaly and its location coordinates at the processing location to be analyzed. These represent the significance of the processing anomaly and its location coordinates, respectively, corresponding to the processing locations in the neighborhood of the processing location to be analyzed.

[0033] This represents the rate of change in the significance of the processing anomaly between the processing position to be analyzed and the j-th neighboring position, i.e., the rate of change in the significance of the anomaly. This indicates the significant difference in the degree of processing anomalies between the two locations. This represents the Euclidean distance between two locations. This represents the change in the significance of processing anomalies within a unit Euclidean distance. This represents the mean of the rate of change of the significance of anomalies between the location to be analyzed and each location in its neighborhood; the mean is calculated and then... Normalization is performed to eliminate the influence of dimensions; it should be noted that... This represents the Euclidean distance between two different locations, and its value is not 0.

[0034] The standard deviation is used to calculate the significance of anomalies at neighboring processing positions. It represents the dispersion of the significance of processing anomalies at all neighboring processing positions. The negative of the standard deviation is normalized using the norm function to eliminate the influence of dimensions and achieve logical analysis. That is, the smaller the dispersion, the greater the degree to which the processing position of the workpiece belongs to the true anomaly.

[0035] This indicates the machining mutation rate at the machining location to be analyzed. The higher the mutation rate, the more obvious the machining abnormality at that location. In this case, the machining parameters of the corresponding machine tool have a large deviation at that location, and therefore the machining parameters at that location need to be adjusted.

[0036] Step S3: Determine the processing anomaly areas for which processing parameters need to be adjusted based on the significance of processing anomalies and the rate of processing mutation at each processing location; Specifically, please refer to Figure 4 Step S3 includes: Step S31: Based on the degree of significance of processing anomalies at each processing position, use an anomaly detection algorithm to obtain the set of abnormal positions of the processed workpiece and the normal workpiece processing area. Step S32: Based on the machining mutation rate of the normal workpiece machining area, determine the machining abnormal area in the abnormal location set for which the machining parameters to be adjusted.

[0037] More specifically, step S32 includes: Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal processing mutation rate data set; Determine the processing mutation rate at the preset data distribution boundary in the normal processing mutation rate dataset and use it as the mutation rate threshold. The locations in the abnormal location set where the processing mutation rate is greater than the mutation rate threshold are taken as the boundary locations of the abnormal processing region for which the processing parameters to be adjusted, and the abnormal processing region is obtained.

[0038] In this embodiment, the significance of (processing) anomalies at each processing position is obtained. If it belongs to the normal operation process, the normal vector of the corresponding position is compared with the above. The theoretical normal vectors remain consistent, and because it is presented as a plane, the local curvature is zero, thus making the abnormality significant during normal processing less noticeable. The value is 0. Based on the significance of machining anomalies at each location, anomaly detection algorithms such as ocsvm (One-Class Support Vector Machine) are used to obtain the anomaly locations in the entire machined workpiece, forming an anomaly location set. The remaining (processing) positions are considered as normal workpiece processing areas / normal position sets. .

[0039] The processing mutation rate at each (processing) location within the normal processing area is obtained (forming a processing mutation rate dataset). This mutation rate data reflects the fluctuations in the rate of change between normal processing and surrounding locations, and the fluctuations in this mutation rate data reflect the range of fluctuations in the normal data. However, it is clear that the neighborhood of the location at the boundary between normal and abnormal processing includes the abnormal processing area, thus affecting the accuracy of the mutation rate analysis. Therefore, a box plot can be used to further analyze the data. The processing locations of the set (normal location set) are analyzed. The box plot method is used to analyze the mutation rate data set of normal processing, and its upper bound value (e.g. Q3+1.5×IQR) is taken as the mutation rate threshold. The mutation rate threshold represents the mutation rate change of normal processing.

[0040] In some extreme cases, if the entire workpiece surface is identified as abnormal, making it impossible to calculate the mutation rate threshold from the "normal workpiece processing area", the system can use a global mutation rate benchmark value obtained from historical processing data of similar workpieces as a backup mutation rate threshold.

[0041] From the set of abnormal locations Locations exceeding the mutation rate threshold are identified to represent boundary regions where processing anomalies exist (because in actual processing, the anomalous region may be too large, resulting in a small difference between the internal and surrounding processing, even if the internal processing exhibits abnormalities, it should still be considered an abnormal workpiece processing due to inaccurate processing parameters). This is used to determine the set of anomalous locations. The boundary positions that need to be adjusted are obtained, and the boundary positions consist of multiple sets of positions.

[0042] Subsequently, the existing convex hull algorithm is used on the above set of boundary locations to obtain the regions of processing anomalies. It should be noted that there may be multiple regions of processing anomalies.

[0043] Step S4: Determine the processing anomaly index of the target processing position by utilizing the processing result data deviation of the target processing position in the processing anomaly area; In this embodiment, for each processing abnormality region, the average processing mutation rate at the boundary position of the processing abnormality region is obtained. Used to represent the characteristics of the entire abnormal processing region, if the mean of the boundary mutation rate The larger the value, the further the abnormal processing area deviates from the target value, the more significant the deviation, and therefore the greater the need for parameter adjustment and the larger the adjustment range. Conversely, if the average boundary mutation rate is smaller... The smaller the value, the smaller the deviation in the abnormal processing area, and the less adjustment or fine-tuning of the processing parameters are needed to ensure that the current machine tool's processing operation on the workpiece position meets the requirements.

[0044] However, the above analysis is based on the workpiece machining results. It is necessary to combine the machining result data with machine operation data (machining process data) to jointly reflect the machining status and further improve the accuracy of process parameter adjustments. Therefore, it is essential to obtain relevant process monitoring data from the previous workpiece machining, such as cutting force data from the machining process data, to obtain the actual cutting force at each position. Next, a neural network, such as a spatiotemporal graph neural network (which has the ability to analyze time and spatial location, and therefore can be used for workpiece machining analysis), is constructed. The input of the network is the corresponding machining (process) parameters at each location, and the output is the theoretical cutting force data obtained through the above machining process parameters. The theoretical cutting force data was compared with the actual cutting force data. Due to the influence of factors such as system deviation, environment, and materials, there are differences between the actual and theoretical cutting force data. The inaccuracy of parameters such as cutting force during the machining process affects the machining results on the workpiece. Consequently, the machining process data and the machining results at this location show abnormalities simultaneously. The more obvious the abnormality in the machining process, the more obvious the abnormality in the corresponding machining result. For example, insufficient cutting force on the workpiece may prevent effective cutting of the workpiece material, resulting in disordered surface texture and numerous defects. Consequently, there is a large rate of abrupt changes in the machining at this location. .

[0045] Specifically, please refer to Figure 5 Step S4 includes: Step S41: Using the deviation of the processing result data of the target processing position in the processing anomaly area, determine the significance of the target processing anomaly, the target processing mutation rate, and the processing fault tolerance rate of the target processing position; Step S42: Determine the average processing mutation rate at the boundary position of the processing anomaly region, and use the average processing mutation rate, the significance of the target processing anomaly, the target processing mutation rate, and the processing tolerance rate to determine the processing anomaly index of the target processing position.

[0046] As mentioned above, since some locations within the processing anomaly region may still be considered abnormally processed, albeit with a smaller mutation rate, the mutation rate data for the processing anomaly region is still needed to correct the processing parameters. Therefore, the processing anomaly index of the target processing location x (referring to any location) within the processing anomaly region is further obtained. : The workpiece machining anomaly index is the target machining position x. norm is a normalization function, such as the maximum and minimum value normalization function, with a range of [0,1]. The significance of the target processing anomaly at the target processing location x is given; it is dimensionless data. The target is the mutation rate, which is also dimensionless data. This is the average processing mutation rate at the boundary of the processing anomaly region where the target processing location is located. The significance of the target processing anomaly and the target processing mutation rate are obtained based on the deviation of the processing result data at the target processing location. The specific calculation is the same as in the above embodiment, and will not be repeated here.

[0047] In other embodiments of the present invention, the following can also be used separately: , as well as" "Different weights are assigned and weighted sums are performed, such as 0.5, 0.3, and 0.2. The specific weight values ​​given in the embodiments of this invention (such as 0.5, 0.3, and 0.2, etc.) are empirical values ​​obtained under typical hardware configurations and test scenarios, intended to facilitate understanding of this invention. In practical applications, those skilled in the art can adjust, calibrate, or optimize these parameters according to specific hardware performance, scenario complexity, and data characteristics, which does not constitute a limitation of this invention."

[0048] The processing tolerance rate is the processing error rate of the target processing position. The higher the tolerance, the smaller the corresponding processing anomaly index. It is dimensionless data. For processing tolerance The analysis and acquisition methods are as follows: Since the above embodiments are based on the processing mutation rates obtained from the previous processing results, the following problems exist: as the processing progresses, the processed workpiece gradually takes shape, and thus the redundant processing space gradually decreases. (Redundant processing space reflects the tolerance for the next processing step. For example, if the current processing requires a depth error of 1mm, but the actual processing only retains an error of 0.5mm, that is, the original target plan had a remaining space of 1mm to allow for subsequent processing errors, but in reality there is only a remaining space of 0.5mm. Less redundant space requires higher accuracy of processing parameters; otherwise, the workpiece will not meet the manufacturing requirements.)

[0049] To address this, the tolerance rate for a given processing position can be represented by deviation image data (processing result data deviation). The smaller the deviation (negative), the smaller the corresponding processing tolerance rate. The relevant deviation data is mapped to the [0,1] interval using max-min normalization, and the tolerance rate for the positive part of the original deviation greater than 0 can be set to 1.

[0050] Step S5: Using the processing features consisting of the processing anomaly index of the target processing position and the deviation of the processing process data, determine the target processing parameters for the next processing process at the target processing position.

[0051] Specifically, please refer to Figure 6 Step S5 includes: Step S51: Based on the degree of significance of processing anomalies at each processing position, use an anomaly detection algorithm to obtain the set of abnormal positions of the processed workpiece and the normal workpiece processing area. Step S52: Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal position set. Step S53: Using the processing anomaly index and processing process data deviation formed by each processing position in the normal position set, determine the processing feature vector with the largest modulus and use its modulus as the modulus threshold. In this embodiment, based on the above embodiments, the normal position set MC of abnormal points detected by box plot is excluded, and the machining anomaly index of each position in the MC set is obtained. Therefore, for each position, there exists a corresponding machining anomaly index and cutting force data deviation (machining process data deviation, taking cutting force as an example). , Represents the theoretical cutting force. This represents the actual cutting force. This represents the absolute value of the difference between the two, or the Euclidean norm between them. This indicates normalization processing; the processing location constitutes the processing feature vector. The vector formed by the set of normal positions represents the feature vector corresponding to the condition where the error during processing is within a reasonable fluctuation range. The closer the feature vector is to the origin, the more likely the processing at that position will achieve the target value. Therefore, the module length of the feature vector with the largest distance (module length) is selected as the module length threshold. , Representing each eigenvector A set / sequence of moduli, where the moduli reflect the degree of anomaly at the corresponding position.

[0052] Step S54: Using the processing feature vector composed of the processing anomaly index of the target processing position and the processing process data deviation, and the module length threshold, determine the target processing parameters for the next processing process at the target processing position.

[0053] More specifically, step S54 includes: The excess modulus of the processing feature vector at the target processing position that exceeds the modulus threshold is determined. The target machining parameters are obtained by using the original machining parameters of the next machining process at the target machining position that exceed the mold length correction.

[0054] In this embodiment, the feature vector corresponding to the target processing position in the processing anomaly area is obtained, and the corresponding modulus is obtained. The modulus is compared with the modulus threshold of the normal region mentioned above. Compare the values ​​and obtain a modulus greater than the threshold. The portion exceeding the modulus length (normalized) is called the part exceeding the modulus length. The greater the deviation, the more severely the machining at that location deviates from the target value. Therefore, the machine tool process parameters for that location need to be adjusted in the next machining process. The more severe the deviation, the greater the adjustment required. Take cutting speed as an example among machining parameters: V represents the initial cutting speed at the target machining position; This indicates the target cutting speed at the same machining position in the next machine tool machining process.

[0055] If there is obvious machining abnormality at the target machining location, it means that the machined surface does not meet the requirements. In this case, it is necessary to increase the cutting speed to reduce the deterioration of the workpiece surface flatness.

[0056] Because cutting speed and feed rate are highly coupled and mutually restrictive machining parameters during the cutting process, they jointly determine the machining state of the workpiece surface. Adjusting the cutting speed necessitates a simultaneous adjustment of the feed rate. Otherwise, cutting overload may occur, leading to spindle overheating, workpiece surface burns, thermal deformation, and other abnormalities. Similarly, adjusting other machining parameters can be done simultaneously with adjusting the corresponding coupled machining parameters.

[0057] Therefore, the feed rate needs to be adaptively adjusted based on the adjustment of the cutting speed. When the cutting speed increases, in order to offset the overload caused by the increase in the amount of cutting per unit time, it is necessary to reduce the cutting heat, which in turn requires a decrease in the feed rate; conversely, when the cutting speed decreases, in order to compensate for the efficiency loss caused by the decrease in the amount of cutting per unit time, it is necessary to increase the feed rate.

[0058] For the existing The standardization function maps values ​​to the range of -1 to 1 and removes the dimensions, resulting in dimensionless data. The required mean and standard deviation of the function are based on the cutting speed adjustment at all locations within the current machining anomaly area. The calculation is performed on the set that constitutes it. When When =V, =0; at this time, the cutting speed and feed rate remain unchanged.

[0059] This is the initial feed rate; This is the target feed amount for the next iteration.

[0060] This invention analyzes the deviations in processing results at various processing positions and their neighboring positions within a workpiece to obtain the significance and mutation rate of abnormalities at corresponding positions. This allows for the localization of abnormal processing regions requiring parameter adjustments. Based on the processing anomaly index and data deviations at each processing position within these regions, the processing parameters corresponding to each position are corrected. The vertical / horizontal integrated gantry boring, milling, and grinding CNC machine tool uses shared parameters to control parameters for the next processing step, maintaining the original parameters for normal processing areas and adjusting abnormal processing areas according to the corrected parameters. This achieves dynamic and precise control of the machine tool, improving the accuracy of process parameter determination and accurately compensating for errors.

[0061] Example 2: This invention also proposes a vertical and horizontal integrated gantry boring, milling and grinding composite CNC machine tool.

[0062] like Figure 7 As shown, Figure 7 This is a schematic diagram of the hardware operating environment of the vertical and horizontal integrated gantry boring, milling and grinding composite CNC machine tool involved in the embodiments of the present invention.

[0063] like Figure 7As shown, the vertical / horizontal integrated gantry boring, milling, and grinding composite CNC machine tool may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM or a stable, non-volatile memory, such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a composite CNC control program.

[0064] Those skilled in the art will understand that Figure 7 The hardware structure shown does not constitute a limitation on the machine tool and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0065] Continue to refer to Figure 7 , Figure 7 The memory 1005, which is a computer-readable storage medium, may include an operating system, a user interface module, a network communication module, and a composite numerical control program.

[0066] exist Figure 7 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the composite numerical control program stored in the memory 1005 and execute the steps in the above embodiments.

[0067] Based on the hardware structure of the above-mentioned vertical and horizontal integrated gantry boring and milling composite CNC machine tool, various embodiments of the vertical and horizontal integrated gantry boring and milling composite CNC control method of the present invention are used to implement.

[0068] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a composite numerical control program, wherein when executed by a processor, the composite numerical control program implements the steps of the above-described vertical / horizontal integrated gantry boring and milling composite numerical control method.

[0069] The method implemented when the composite CNC control program is executed can be referred to in various embodiments of the vertical and horizontal integrated gantry milling composite CNC control method of the present invention, and will not be repeated here.

[0070] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, machine tools, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A method for combined vertical and horizontal gantry boring, milling, and grinding CNC control, characterized in that, The method includes: By utilizing the deviation in the processing results data of the processing location to be analyzed, the degree of significance of the processing anomaly at the processing location to be analyzed is determined; The processing mutation rate of the processing location to be analyzed is determined by utilizing the significance of processing anomalies at the processing location to be analyzed and its neighboring processing locations. The abnormal processing regions for which processing parameters need to be adjusted are determined based on the significance of processing anomalies and the rate of processing mutation at each processing location. By utilizing the deviation in processing result data at the target processing location within the processing anomaly region, the processing anomaly index at the target processing location is determined. By utilizing the processing characteristics consisting of the processing anomaly index at the target processing position and the deviation of the processing data, the target processing parameters for the next processing step at the target processing position are determined.

2. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 1, characterized in that, The method of determining the significance of processing anomalies at the processing location to be analyzed by utilizing the deviation in the processing result data includes: The deviation between the actual processing result data and the theoretical processing result data of the processing location to be analyzed is determined and used as the point cloud data of the processing location to be analyzed; Using point cloud data of the processing location to be analyzed and its neighboring processing locations, the actual fitting plane of the neighborhood where the processing location to be analyzed is located and the curvature of the processing location to be analyzed are obtained. Determine the plane similarity between the actual fitted plane and the theoretical fitted plane corresponding to the theoretical processing result data. Use the plane similarity and the curvature of the processing position to be analyzed to determine the degree of significance of the processing anomaly at the processing position to be analyzed.

3. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 1, characterized in that, By utilizing the significance of processing anomalies at the processing location to be analyzed and its neighboring processing locations, the processing mutation rate at the processing location to be analyzed is determined, including: Determine the significance of the differences in processing anomalies and the Euclidean distance between the processing location to be analyzed and its neighboring processing locations; The processing mutation rate at the processing location to be analyzed is determined by using the difference in the significance of the processing anomalies and the Euclidean distance.

4. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 3, characterized in that, Using the differences in the significance of the processing anomalies and the Euclidean distance, the processing mutation rate at the processing location to be analyzed is determined, including: Using the difference in the significance of the processing anomalies and the Euclidean distance, the rate of change of the significance of the anomalies between the processing location to be analyzed and its neighboring processing locations is determined; Determine the dispersion of the significance of processing anomalies at all neighboring processing locations, and use the dispersion and the rate of change of the significance of the anomalies to determine the processing mutation rate of the processing location to be analyzed.

5. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 1, characterized in that, The process of determining the abnormal processing regions for which processing parameters need adjustment based on the significance of processing anomalies and the rate of abrupt changes at each processing location includes: Based on the degree of significance of processing anomalies at each processing position, anomaly detection algorithms are used to obtain the set of abnormal positions of the processed workpiece and the processing area of ​​the normal workpiece. Based on the machining mutation rate of the normal workpiece machining area, the abnormal machining areas in the abnormal location set that require adjustment of machining parameters are identified.

6. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 5, characterized in that, The determination of abnormal processing regions in the abnormal location set, based on the processing mutation rate of the normal workpiece processing region, and the processing parameters to be adjusted, includes: Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal processing mutation rate data set; Determine the processing mutation rate at the preset data distribution boundary in the normal processing mutation rate dataset and use it as the mutation rate threshold. The locations in the abnormal location set where the processing mutation rate is greater than the mutation rate threshold are taken as the boundary locations of the abnormal processing region for which the processing parameters to be adjusted, and the abnormal processing region is obtained.

7. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 1, characterized in that, The method of determining the processing anomaly index of the target processing location by utilizing the processing result data deviation of the target processing location in the processing anomaly area includes: By utilizing the deviation of processing result data at the target processing location in the processing anomaly area, the significance of the target processing anomaly, the target processing mutation rate, and the processing fault tolerance rate at the target processing location are determined. The average processing mutation rate at the boundary location of the processing anomaly region is determined, and the processing anomaly index at the target processing location is determined using the average processing mutation rate, the significance of the target processing anomaly, the target processing mutation rate, and the processing tolerance rate.

8. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 1, characterized in that, The method of determining the target processing parameters for the next processing step at the target processing position using processing features comprised of processing anomaly indices at the target processing position and processing data deviations includes: Based on the degree of significance of processing anomalies at each processing position, anomaly detection algorithms are used to obtain the set of abnormal positions of the processed workpiece and the processing area of ​​the normal workpiece. Based on the processing mutation rate data set of all processing positions in the normal workpiece processing area, abnormal data points in the processing mutation rate data set are screened out to obtain the normal position set; Using the processing anomaly index and processing process data deviation corresponding to each processing position in the normal position set as the processing feature vector, the processing feature vector with the largest modulus is determined and its modulus is used as the modulus threshold. Using the processing feature vector composed of the processing anomaly index of the target processing position and the deviation of the processing process data, and the modulus threshold, the target processing parameters for the next processing process at the target processing position are determined.

9. The vertical and horizontal integrated gantry boring, milling, and grinding composite CNC control method according to claim 8, characterized in that, The process of determining the target processing parameters for the next processing step at the target processing position using a processing feature vector composed of a processing anomaly index at the target processing position and processing process data deviation, and the module length threshold, includes: The excess modulus of the processing feature vector at the target processing position that exceeds the modulus threshold is determined. The target machining parameters are obtained by using the original machining parameters of the next machining process at the target machining position that exceed the mold length correction.

10. A vertical and horizontal integrated gantry boring, milling, and grinding composite CNC machine tool, characterized in that, The CNC machine tool includes a processor, a memory, and a composite CNC control program stored in the memory that can be executed by the processor, wherein when the composite CNC control program is executed by the processor, it implements the steps of the vertical and horizontal integrated gantry boring and milling composite CNC control method as described in any one of claims 1 to 9.

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