Machine tool control optimization system and method for polymer mineral casting

Abstract

The invention provides a machine tool control optimization system and method for polymer mineral castings, and relates to the technical field of machine tool control, and the method comprises the steps: carrying out the quality detection data collection of a plurality of polymer mineral castings; extracting control data in the historical window, and performing control deviation analysis on an extraction result; lathe control optimization is carried out based on the size deviation of the multiple mineral castings and the lathe deviation degree; grinding machine control optimization is carried out based on the multiple surface quality deviation degrees and the grinding machine deviation degrees; positioning and identifying the plurality of casting surface image sets in combination with drilling positioning information; drilling machine control optimization is carried out based on the multiple positioning defect sets and the drilling machine deviation degree; and the multiple lathe control parameters, the multiple grinding machine control parameters and the multiple drilling machine control parameters are transmitted for finishing machining. By means of the method, the technical problem that in the prior art, a casting possibly cannot meet the final design requirement and quality standard can be solved, and the technical effect of improving the machining precision and efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the field of machine tool control technology, and in particular to a machine tool control optimization system and method for polymer mineral castings. Background Art

[0002] By controlling the processing of polymer mineral castings through machine tools, the processing accuracy, efficiency and stability of machine tools can be improved, thereby ensuring that the castings can meet the final design requirements and quality standards.

[0003] At present, the polymer mineral castings produced by the existing technology need to be processed by machine tools. Although the mineral castings have obtained the desired shape during the casting process, they usually need to be processed and trimmed to meet the final design requirements and quality standards. For example, mineral castings may leave rough or uneven areas on the surface, which require machine tools to smooth, grind or polish the surface to improve the surface quality and appearance. The dimensions of the castings may deviate slightly from the design requirements, and machine tools need to be used for precise dimensional trimming to ensure that they meet the design specifications. If specific geometric features such as holes or threads are required in the design, machining may be required on the casting. The castings may have defects or flaws, which need to be trimmed or repaired by machine tools to meet the quality requirements. Therefore, a method is needed to solve the above problems.

[0004] In summary, the existing technology has technical problems such as the lack of subsequent CNC lathe, grinder and drilling processing and finishing of the polymer mineral castings obtained in production, which may lead to problems such as dimensional errors, shape deviations, and substandard surface roughness in the castings, resulting in the castings failing to meet the final design requirements and quality standards, further affecting the appearance quality of the castings, and may also affect the performance and life of the castings. Summary of the Invention

[0005] The purpose of this application is to provide a machine tool control optimization system and method for polymer mineral castings, so as to solve the technical problems in the prior art that most of the polymer mineral castings produced are not subsequently processed and trimmed by CNC lathes, grinders and drilling machines, resulting in dimensional errors, shape deviations, substandard surface roughness and other problems in the castings, causing the castings to fail to meet the final design requirements and quality standards, further affecting the appearance quality of the castings, and may also affect the performance and life of the castings.

[0006] In view of the above problems, the present application provides a machine tool control optimization system and method for polymer mineral castings.

[0007] In the first aspect, the present application also provides a machine tool control optimization system for polymer mineral castings, which is used to execute the machine tool control optimization method for polymer mineral castings as described in the first aspect, wherein the system includes: a mineral casting quality information generation module, the mineral casting quality information generation module is used to collect quality inspection data of multiple polymer mineral castings of a target batch, and generate multiple mineral casting quality information, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; a control deviation analysis module, the control deviation analysis module is used to interact with the control modules of the CNC lathe, grinder and drilling machine to extract control data within the history window, perform control deviation analysis on the extraction results, and generate lathe deviation, grinder deviation and drilling deviation; a lathe control parameter generation module, the lathe control parameter generation module is used to generate a control parameter based on the multiple mineral casting size deviations and The lathe deviation is used to optimize the lathe control and generate multiple lathe control parameters; a grinding machine control parameter generation module is used to optimize the grinding machine control based on the multiple surface quality deviations and the grinding machine deviation, and generate multiple grinding machine control parameters; a positioning defect set generation module is used to combine the drilling positioning information to perform positioning identification on the multiple casting surface image sets and generate multiple positioning defect sets; a drilling machine control parameter generation module is used to optimize the drilling machine control based on the multiple positioning defect sets and the drilling machine deviation and generate multiple drilling machine control parameters; a finishing processing module is used to transmit the multiple lathe control parameters, multiple grinding machine control parameters and the multiple drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and perform finishing processing on the multiple polymer mineral castings.

[0008] In a second aspect, the present application provides a machine tool control optimization method for polymer mineral castings, which is implemented by a machine tool control optimization system for polymer mineral castings, wherein the method comprises: collecting quality inspection data of a plurality of polymer mineral castings of a target batch, generating a plurality of mineral casting quality information, wherein the plurality of mineral casting quality information comprises a plurality of mineral casting size deviations, a plurality of surface quality deviations, and a plurality of casting surface image sets; interactively extracting control data within a history window by the control modules of the CNC lathe, grinder, and drill, performing control deviation analysis on the extraction results, and generating a lathe deviation, a grinder deviation, and a drill deviation; based on the plurality of mineral castings The method comprises the following steps: performing lathe control optimization based on the part size deviation and the lathe deviation, and generating a plurality of lathe control parameters; performing grinder control optimization based on the plurality of surface quality deviations and the grinder deviation, and generating a plurality of grinder control parameters; performing positioning identification on the plurality of casting surface image sets in combination with drilling positioning information, and generating a plurality of positioning defect sets; performing drilling control optimization based on the plurality of positioning defect sets and the drilling deviation, and generating a plurality of drilling control parameters; transmitting the plurality of lathe control parameters, the plurality of grinder control parameters and the plurality of drilling control parameters to the control modules of the CNC lathe, the grinder and the drilling machine respectively, and performing finishing processing on the plurality of polymer mineral castings.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting quality inspection data of multiple polymer mineral castings in a target batch, multiple mineral casting quality information is generated, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; the control modules of the interactive CNC lathes, grinders, and drillers extract the control data in the history window, perform control deviation analysis on the extraction results, and generate lathe deviations, grinder deviations, and driller deviations; based on the multiple mineral casting size deviations and the lathe deviations, lathe control optimization is performed to generate multiple lathe control parameters; based on the multiple surface quality deviations and the grinder deviations, grinder control optimization is performed to generate multiple grinding machine control parameters; combining the drilling positioning information, positioning and identifying the plurality of casting surface image sets, generating a plurality of positioning defect sets; optimizing the drilling machine control based on the plurality of positioning defect sets and the drilling machine deviation, generating a plurality of drilling machine control parameters; transmitting the plurality of lathe control parameters, the plurality of grinding machine control parameters and the plurality of drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and performing finishing processing on the plurality of polymer mineral castings, that is, through processing and finishing by the CNC lathe, grinder and drilling machine, optimizing the machine tool control method, and finally achieving the technical goal of improving processing accuracy and efficiency, and achieving the technical effect of improving the production quality of castings.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 This is a schematic diagram of the structure of the machine tool control optimization system for polymer mineral castings in this application; Figure 2 This is a flow chart of the machine tool control optimization method for polymer mineral castings in this application.

[0013] Description of reference numerals: Mineral casting quality information generation module 11, control deviation analysis module 12, lathe control parameter generation module 13, grinder control parameter generation module 14, positioning defect set generation module 15, drilling machine control parameter generation module 16, finishing processing module 17. DETAILED DESCRIPTION

[0014] This application provides a machine tool control optimization system and method for polymer mineral castings, addressing the existing technical issues that arise from the fact that most polymer mineral castings produced are not subsequently processed and trimmed using CNC lathes, grinders, and drill presses. This can lead to dimensional errors, shape deviations, and substandard surface roughness in the castings, causing the castings to fail to meet final design requirements and quality standards, further affecting the casting's appearance quality and potentially also affecting the casting's performance and lifespan. This achieves the technical goal of improving machining accuracy and efficiency, and achieves the technical effect of improving casting production quality.

[0015] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0016] Example 1 Please see the attached Figure 1 The present application provides a machine tool control optimization system for polymer mineral castings, wherein the system is applied to a machine tool control optimization method for polymer mineral castings, and the system specifically includes: a mineral casting quality information generating module 11, which is used to collect quality inspection data of a target batch of multiple polymer mineral castings and generate multiple mineral casting quality information, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; A control deviation analysis module 12 is configured to interact with the control modules of the CNC lathe, grinder, and drill press to extract control data within a history window, perform control deviation analysis on the extracted results, and generate lathe deviation, grinder deviation, and drill press deviation; a lathe control parameter generating module 13, configured to perform lathe control optimization based on the plurality of mineral casting size deviations and the lathe deviation, and generate a plurality of lathe control parameters; a grinding machine control parameter generation module 14, configured to perform grinding machine control optimization based on the plurality of surface quality deviations and the grinding machine deviations, and generate a plurality of grinding machine control parameters; A positioning defect set generation module 15 is configured to perform positioning identification on the plurality of casting surface image sets in combination with drilling positioning information to generate a plurality of positioning defect sets; a drilling machine control parameter generation module 16, configured to perform drilling machine control optimization based on the plurality of positioning defect sets and the drilling machine deviation, and generate a plurality of drilling machine control parameters; The finishing processing module 17 is used to transmit the multiple lathe control parameters, multiple grinder control parameters and multiple drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and perform finishing processing on the multiple polymer mineral castings.

[0017] Furthermore, the system further comprises: a historical lathe control data set generation module, the historical lathe control data set generation module being configured to interact with the control module of the CNC lathe to extract the lathe control data within the historical window and generate a historical lathe control data set, wherein the historical lathe control data set includes a historical lathe target cutting amount set and a historical lathe actual cutting amount set; a historical lathe cutting deviation amount set generation module, the historical lathe cutting deviation amount set generation module being used to identify cutting deviations based on the historical lathe target cutting amount set and the historical lathe actual cutting amount set, and generate a historical lathe cutting deviation amount set; A lathe deviation generation module is used to perform control deviation analysis on the historical lathe cutting deviation amount set to generate the lathe deviation.

[0018] Furthermore, the system further comprises: a first historical lathe cutting deviation median generating module, configured to identify the cutting deviation median of the historical lathe cutting deviation set and generate a first historical lathe cutting deviation median; a lathe cutting deviation scatter plot construction module, the lathe cutting deviation scatter plot construction module being configured to construct a lathe cutting deviation scatter plot based on the historical lathe cutting deviation set, wherein the abscissa axis of the lathe cutting deviation scatter plot is cutting time, and the ordinate axis is lathe cutting deviation; a starting straight line generating module, the starting straight line generating module being configured to use a straight line passing through the median value of the historical lathe cutting deviation and parallel to the abscissa axis of the lathe cutting deviation scatter plot as the starting straight line; a first diffusion area generating module, configured to diffuse the starting straight line toward both sides of the straight line according to a preset deviation step length, until a scatter point density gain of two adjacent diffusions is less than a preset scatter point density gain, stop the diffusion, and generate a first diffusion area; A lathe deviation generation module is used to calculate the mean of multiple scattered points in the first diffusion area to generate the lathe deviation.

[0019] Furthermore, the system further comprises: a first size deviation mean value generating module, the first size deviation mean value generating module being used to calculate the mean value of the size deviations of the plurality of mineral castings to generate a first size deviation mean value; a first lathe control parameter generating module, configured to perform parameter identification on the first dimensional deviation mean and the lathe deviation using a lathe control parameter identifier to generate first lathe control parameters, wherein the first lathe control parameters include feed rate, cutting depth, and cutting speed; a size fluctuation factor generating module, the size fluctuation factor generating module being used to identify the size fluctuations of the plurality of mineral castings and generate size fluctuation factors; An adjustment lathe control parameter generation module is used to match a first fine-tuning bandwidth based on the dimensional fluctuation factor, and use the first fine-tuning bandwidth to adjust the first lathe control parameter multiple times according to a preset adjustment method to generate multiple adjustment lathe control parameters, wherein the preset adjustment method is to increase or decrease the first lathe control parameter according to the first fine-tuning bandwidth.

[0020] Furthermore, the system further comprises: a target parameter generation module, the target parameter generation module being configured to traverse the plurality of adjustment lathe control parameters to perform fitness identification, taking the adjustment lathe control parameter corresponding to the maximum fitness as the target parameter, and taking the remaining plurality of adjustment lathe control parameters as a follow-up parameter set; a following fine-tuning bandwidth generation module, configured to respectively calculate the inverse of the ratio of the fitness of a plurality of following parameters in the following parameter set to the fitness of the target parameter, and multiply the calculation result by the first fine-tuning bandwidth to generate a plurality of following fine-tuning bandwidths; a following parameter set adjustment module, configured to adjust the following parameter set based on the multiple following fine-tuning bandwidths with the target parameter as a direction; The basic lathe control parameter generation module is used to adjust the lathe control parameter corresponding to the maximum fitness value during the adjustment as the basic lathe control parameter after multiple adjustments.

[0021] Furthermore, the system further comprises: a calculation result generating module, configured to calculate ratios of the plurality of mineral casting size deviations and the first size deviation mean value to generate a plurality of calculation results; Multiple lathe control parameter generation modules are used to multiply the multiple calculation results with the basic lathe control parameters to generate multiple lathe control parameters.

[0022] Furthermore, the system further comprises: a drilling hole-surface image mapping set generation module, the drilling hole-surface image mapping set generation module being configured to generate a plurality of drilling hole-surface image mapping sets by respectively locating the plurality of drilling hole locations in the drilling hole positioning information with the plurality of casting surface image sets; a drilling defect set generation module, the drilling defect set generation module being configured to traverse the plurality of drilling hole-surface image mapping sets to perform drilling defect identification and generate a plurality of drilling defect sets; a positioning defect set generation module, the positioning defect set generation module being configured to determine whether the plurality of drilling defect degree sets meet a preset drilling defect degree, and if so, to use the plurality of drilling defect degree sets as a plurality of positioning defect sets; The early warning instruction generation module is used to generate an early warning instruction if no, and send the early warning instruction to the user end for early warning.

[0023] Furthermore, the system further comprises: A feedback monitoring window acquisition module, wherein the feedback monitoring window acquisition module is used to acquire a preset feedback monitoring window; The feedback warning instruction generating module, the feedback monitoring window acquiring module is used to perform feedback monitoring on the multiple polymer mineral castings in the preset feedback monitoring window, and generate feedback warning instructions according to the monitoring results.

[0024] As for the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0025] Example 2 Based on the same inventive concept as the machine tool control optimization system for polymer mineral castings in the aforementioned embodiment, this application also provides a machine tool control optimization method for polymer mineral castings, please refer to the attached Figure 2 , the method comprising: Step 1: collecting quality inspection data of multiple polymer mineral castings of a target batch to generate multiple mineral casting quality information, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; Specifically, the target batch is a batch to be subjected to machine tool control optimization. Polymer mineral castings are castings that have undergone preliminary processing on machine tools and have obtained the desired shape, but still require subsequent processing and finishing to meet the final design requirements and quality standards. Furthermore, quality inspection data of multiple polymer mineral castings in the target batch are collected through quality inspection equipment to generate multiple mineral casting quality information. For example, the quality inspection equipment includes a surface quality inspection instrument or an image acquisition device. Furthermore, the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets. Among them, the multiple mineral casting size deviations are size deviation data generated by comparing the sizes of multiple mineral castings. The multiple surface quality deviations are quality deviation data generated by comparing the quality of multiple mineral castings. The multiple casting surface image sets are image sets generated by integrating the surface acquisition images of multiple mineral castings.

[0026] Step 2: Interacting with the control modules of the CNC lathe, grinder, and drill to extract control data in a history window, performing control deviation analysis on the extracted results, and generating lathe deviation, grinder deviation, and drill deviation; Specifically, the machine tool control optimization system for polymer mineral castings is applied to a machine tool control optimization platform, which is in communication with the control modules of CNC lathes, grinders, and drilling machines. The control modules of CNC lathes, grinders, and drilling machines are used to receive instructions and control the movement of CNC lathes, grinders, and drilling machines. Furthermore, the control module of the CNC lathe extracts and analyzes lathe control data over a historical period, identifies the actual and target cutting deviations, and generates lathe deviations, providing a basis for subsequent fault diagnosis, performance optimization, and preventive maintenance. Furthermore, by obtaining the lathe deviation, the grinder control data is identified for deviations, and the grinder deviation is generated. The drilling control data is also identified for deviations, and the drilling deviation is generated.

[0027] Step 3: performing lathe control optimization based on the multiple mineral casting size deviations and the lathe deviation, and generating multiple lathe control parameters; Specifically, the average value of the dimensional deviations of multiple mineral castings is calculated. Based on this average value and the lathe deviation, lathe control parameters are obtained. The dimensional deviations of multiple mineral castings are analyzed to identify fluctuations in lathe deviations. Based on the fluctuations in lathe deviations, a parameter adjustment range is determined, and the lathe control parameters are adjusted upward or downward to generate multiple lathe control parameters.

[0028] Step 4: performing grinding machine control optimization based on the multiple surface quality deviations and the grinding machine deviations to generate multiple grinding machine control parameters; Specifically, the average value of surface quality deviations is calculated by averaging multiple surface quality deviations. Based on this average value and the grinder deviation, the grinder control parameters are obtained. By analyzing multiple surface quality deviations, fluctuations in the grinder deviations are identified. Based on the fluctuations in the grinder deviations, the adjustment range of the grinder parameters is determined, and the grinder control parameters are adjusted upward or downward to generate multiple grinder control parameters.

[0029] Step 5: combining the drilling location information, performing location recognition on the plurality of casting surface image sets to generate a plurality of location defect sets; Specifically, multiple drilling locations for each drilling location in the drilling location information are matched against the casting surface image set to determine the corresponding position of each drilling hole in the image and obtain multiple drilling defect sets. Preset drilling defect levels are set based on production requirements and quality control standards. The multiple drilling defect sets are compared with the preset drilling defect levels. If the multiple drilling defect sets meet the preset drilling defect levels, the multiple drilling defect sets are used as multiple positioning defect sets.

[0030] Step 6: performing drilling machine control optimization based on the multiple positioning defect sets and the drilling machine deviation, and generating multiple drilling machine control parameters; Specifically, the average value of the positioning defects is calculated by averaging multiple sets of positioning defects. The drilling machine control parameters are then derived based on the average value of the positioning defects and the drilling machine deviation. By analyzing multiple sets of positioning defects, the fluctuation of the drilling machine deviation is identified. Based on the fluctuation of the drilling machine deviation, the parameter adjustment range is determined, and the drilling machine control parameters are adjusted up or down to generate multiple drilling machine control parameters.

[0031] Step seven: transmitting the multiple lathe control parameters, the multiple grinder control parameters and the multiple drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and performing finishing processing on the multiple polymer mineral castings.

[0032] Specifically, multiple lathe control parameters are transmitted to the control module of the CNC lathe, multiple grinder control parameters are transmitted to the control module of the grinder, and multiple drilling machine control parameters are transmitted to the control module of the drilling machine, and then multiple polymer mineral castings are trimmed and processed to meet design requirements and quality standards.

[0033] The machine tool control optimization method for polymer mineral castings is applied to a machine tool control optimization system for polymer mineral castings, which can achieve the technical goal of improving processing accuracy and efficiency and achieve the technical effect of improving the production quality of castings.

[0034] Furthermore, the present application further comprises the following steps: Interacting with the control module of the CNC lathe to extract the lathe control data in the history window to generate a historical lathe control data set, wherein the historical lathe control data set includes a historical lathe target cutting amount set and a historical lathe actual cutting amount set; Perform cutting deviation identification based on the historical lathe target cutting amount set and the historical lathe actual cutting amount set to generate a historical lathe cutting deviation amount set; A control deviation analysis is performed on the historical lathe cutting deviation amount set to generate the lathe deviation degree.

[0035] Specifically, a machine tool control optimization system for polymer mineral castings is applied to a machine tool control optimization platform, which is communicatively connected to the control module of a CNC lathe. The control module of the CNC lathe is used to receive instructions, control the movement and cutting of the CNC lathe. Furthermore, the history window refers to a historical time period or operation cycle, which is used to extract the lathe control data within the time period. The operation data set of the lathe is read from the history window by the control module to generate a historical lathe control data set, including a historical lathe target cutting amount set and a historical lathe actual cutting amount set. The historical lathe target cutting amount set is the target cutting amount set at each operation cycle or time point. The historical lathe actual cutting amount set is the cutting amount achieved by the CNC lathe in actual operation.

[0036] Cutting deviation is the difference between the actual cutting amount and the target cutting amount. By comparing the corresponding data in the historical lathe target cutting amount set and the historical lathe actual cutting amount set, the cutting deviation at each time point or operating cycle is calculated. All calculated cutting deviations are integrated to generate the historical lathe cutting deviation set.

[0037] Next, control deviation refers to the magnitude and changing trend of cutting deviation, reflecting the degree to which lathe operations deviate from the intended target. Control deviation analysis is performed by statistically analyzing a historical set of lathe cutting deviations, calculating statistical indicators such as the median deviation, and the trend of deviation over time. Based on the results of the control deviation analysis, a lathe deviation index is generated. For example, the lathe deviation index is graphically displayed to intuitively understand the lathe's performance status and potential problems.

[0038] The control module of the CNC lathe extracts and analyzes historical control data, identifies cutting deviations, and generates lathe deviation indicators, providing a basis for subsequent fault diagnosis, performance optimization, and preventive maintenance.

[0039] Furthermore, the present application further comprises the following steps: Performing cutting deviation median identification on the historical lathe cutting deviation set to generate a first historical lathe cutting deviation median; Constructing a lathe cutting deviation scatter plot based on the historical lathe cutting deviation set, wherein the abscissa axis of the lathe cutting deviation scatter plot is cutting time, and the ordinate axis is lathe cutting deviation; A straight line passing through the median value of the historical lathe cutting deviation and parallel to the abscissa axis of the lathe cutting deviation scatter plot is used as a starting straight line; Based on the starting straight line, the diffusion is performed toward both sides of the straight line according to a preset deviation step size until the scatter point density gain of two adjacent diffusions is less than the preset scatter point density gain, and the diffusion is stopped to generate a first diffusion area; The mean of a plurality of scattered points in the first diffusion area is calculated to generate the lathe deviation.

[0040] Specifically, the median is the number in the middle of a set of data after sorting them from smallest to largest. The set of historical lathe cutting deviations is sorted, and the cutting deviation in the middle is identified as the median, representing the average level of cutting deviation. This generates the first historical lathe cutting deviation median.

[0041] A scatter plot is a graphic used to display the relationship between multiple variables, where each dot represents a data pair—in this case, cutting time and cutting deviation. With cutting time as the horizontal axis and cutting deviation as the vertical axis, each data point in the historical lathe cutting deviation data set is plotted on the graph. This allows for an intuitive understanding of how cutting deviation changes over time, completing the construction of a scatter plot for lathe cutting deviation.

[0042] Next, a straight line passing through the median of the historical lathe cutting deviation and parallel to the horizontal axis of the lathe cutting deviation scatter plot is used as the starting point of diffusion, that is, the starting straight line, to determine the normal fluctuation range of the cutting deviation.

[0043] Next, starting from the starting line, the data is diffused toward both sides of the line at a preset deviation step size to determine the normal fluctuation boundary of the cutting deviation. Diffusion stops when the scatter point density gain between two consecutive diffusions is less than the preset scatter point density gain, indicating that further diffusion will not significantly increase the number of scatter points. Therefore, it can be considered that the main distribution area of ​​the cutting deviation has been found. Based on the diffusion boundary determined by the stopping condition, a region containing the main cutting deviation scatter points is generated, namely the first diffusion region.

[0044] Furthermore, within the first diffusion region, the mean of the cutting deviations of all scattered points is calculated, representing the average level of cutting deviation within that region. This mean is used as the lathe deviation, reflecting the lathe's performance stability and deviation during the cutting process. A large deviation indicates a performance issue or the need for adjustment.

[0045] By performing median identification on a set of historical lathe cutting deviations, constructing a scatter plot, determining the diffusion area, and calculating the mean, an indicator reflecting the lathe deviation can be generated, which helps to evaluate the performance stability and cutting accuracy of the lathe and provides an important basis for subsequent fault diagnosis and optimization.

[0046] Furthermore, the present application further comprises the following steps: Calculating the average of the dimensional deviations of the plurality of mineral castings to generate a first dimensional deviation average; Using a lathe control parameter identifier to perform parameter identification on the first dimension deviation mean and the lathe deviation to generate first lathe control parameters, wherein the first lathe control parameters include feed rate, cutting depth, and cutting speed; performing deviation fluctuation identification on the dimensional deviations of the plurality of mineral castings to generate dimensional fluctuation factors; Based on the size fluctuation factor matching the first fine-tuning bandwidth, the first lathe control parameter is adjusted multiple times according to a preset adjustment method using the first fine-tuning bandwidth to generate multiple adjusted lathe control parameters, wherein the preset adjustment method is to increase or decrease the first lathe control parameter according to the first fine-tuning bandwidth.

[0047] Specifically, the dimensional deviations of multiple mineral castings refer to the dimensional differences between the multiple mineral castings. The sum of the dimensional deviations of the multiple mineral castings is then divided by the number of castings to obtain an average value of the dimensional deviations. This value is used to reflect the overall dimensional deviations of the castings and generate a first dimensional deviation mean.

[0048] The lathe control parameter identifier is a tool or algorithm used to identify lathe control parameters based on information such as dimensional deviation and lathe deviation. The lathe control parameter identifier uses the first dimensional deviation mean and lathe deviation as inputs to calculate the first lathe control parameters. These first lathe control parameters include feed rate, cutting depth, and cutting speed.

[0049] Next, the dimensional fluctuation factor is an indicator used to reflect the magnitude of dimensional deviation fluctuations. By analyzing the dimensional deviations of multiple mineral castings, the fluctuations in the deviations are identified and the dimensional fluctuation factor is generated. During the deviation fluctuation identification process, dimensional deviation data is analyzed, and statistical quantities such as standard deviation and coefficient of variation are calculated. Alternatively, methods such as time series analysis can be used to identify the fluctuation characteristics and trends of dimensional deviations.

[0050] Next, the first fine-tuning bandwidth is the parameter adjustment range determined by the dimensional fluctuation factor. It reflects the range within which the lathe control parameters can be fine-tuned while maintaining machining stability. The preset adjustment method refers to the strategy for increasing or decreasing the first lathe control parameter according to the first fine-tuning bandwidth. The specific adjustment step size, direction, and number of times can be set based on actual conditions and needs. Within the fine-tuning bandwidth, the first lathe control parameter is adjusted multiple times according to the preset adjustment method, generating multiple adjusted lathe control parameters to obtain the optimal control parameter combination to minimize casting dimensional deviation.

[0051] By analyzing the dimensional deviations of multiple mineral castings and adjusting the lathe control parameters based on the analysis results, it is possible to improve the machining accuracy of castings and increase production efficiency. At the same time, by continuously optimizing and adjusting the control parameters, the performance and stability of the lathe can be improved.

[0052] Furthermore, the present application further comprises the following steps: Traversing the plurality of adjustment lathe control parameters to perform fitness identification, taking the adjustment lathe control parameter corresponding to the maximum fitness as the target parameter, and taking the remaining plurality of adjustment lathe control parameters as the follow-up parameter set; respectively calculating the inverse of the ratio of the fitness of the plurality of following parameters in the following parameter set to the fitness of the target parameter, and multiplying the calculation results by the first fine-tuning bandwidth to generate a plurality of following fine-tuning bandwidths; Taking the target parameter as a direction, adjusting the following parameter set based on the multiple following fine-tuning bandwidths; After multiple adjustments, the adjusted lathe control parameters corresponding to the maximum fitness value during the adjustment are used as the basic lathe control parameters.

[0053] Specifically, each lathe control parameter is accessed sequentially to evaluate its performance during the actual machining process, i.e., its fitness. This can be achieved, for example, through simulation, experimental verification, or historical data. The lathe control parameter with the maximum fitness is selected as the target parameter. The remaining lathe control parameters constitute the follow-up parameter set.

[0054] Next, the inverse of the ratio of the fitness of each following parameter in the following parameter set to the fitness of the target parameter is calculated to reflect the degree of performance difference between the following parameter and the target parameter. The calculated ratio is multiplied by the first fine-tuning bandwidth to generate multiple following fine-tuning bandwidths, which serve as reference ranges for subsequent adjustments to the following parameters.

[0055] Next, with the target parameter as the direction, when adjusting the follow parameter set, the adjustment is made in a direction closer to the target parameter.

[0056] Next, after multiple adjustments, the parameter set is iteratively optimized to find a more optimal solution. During these adjustments, the fitness value of each adjustment is recorded. The adjusted lathe control parameters corresponding to the maximum fitness value are selected as the basic lathe control parameters, representing the optimal control parameters under the current conditions.

[0057] By traversing and evaluating the fitness of multiple lathe control parameters, the target and following parameter sets are determined. Based on the performance difference between the following and target parameters, the following fine-tuning bandwidth is calculated and the following parameters are adjusted. After multiple iterations and optimizations, the basic lathe control parameters are ultimately selected, providing guidance for the actual machining process and helping to improve machining accuracy.

[0058] Furthermore, the present application further comprises the following steps: Calculating ratios of the plurality of mineral casting size deviations to the first size deviation average respectively to generate a plurality of calculation results; The multiple calculation results are multiplied by the basic lathe control parameters to generate multiple lathe control parameters.

[0059] Specifically, the ratio of the dimensional deviation of each mineral casting to the first dimensional deviation mean is calculated to reflect the size and direction of the dimensional deviation of a single casting relative to the overall average deviation, thereby generating multiple calculation results.

[0060] Then, the basic lathe control parameters with high adaptability obtained after multiple adjustments and optimizations are multiplied with multiple calculation results to generate multiple lathe control parameters. These are used to fine-tune the basic lathe control parameters according to the proportional relationship of the dimensional deviation of each casting to adapt to the processing requirements of different castings.

[0061] By combining the dimensional deviations of multiple mineral castings with basic lathe control parameters, multiple personalized lathe control parameters are generated to better adapt to the processing requirements of different castings and improve processing accuracy.

[0062] Furthermore, the present application further comprises the following steps: Based on the multiple drilling hole locations in the drilling hole location information, positioning them with the multiple casting surface image sets respectively to generate multiple drilling hole-surface image mapping sets; Traversing the plurality of borehole-surface image mapping sets to perform borehole defect recognition and generate a plurality of borehole defect sets; determining whether the plurality of drilling defect sets meet a preset drilling defect degree, and if so, using the plurality of drilling defect sets as a plurality of positioning defect sets; If not, a warning instruction is generated and sent to the user end for warning.

[0063] Specifically, the drill hole location information contains the specific location information of each drill hole on the polymer mineral casting, such as coordinates and dimensions. The multiple drill hole locations in each drill hole location information are matched with the casting surface image set to determine the corresponding position of each drill hole in the image, generating multiple sets of drill hole-surface image mappings.

[0064] Then, multiple sets of borehole-surface image mappings are sequentially accessed to obtain sample sets of historical borehole-surface image mappings. The sample sets of borehole-surface image mappings are used as drilling input data, and the drilling input data is divided into drilling training data and drilling verification data to construct a borehole defect identifier. The division ratio is customized by those skilled in the art based on actual conditions. For example, the division ratio is 7:3. The borehole defect identifier is trained using the drilling training data. When the output data of the borehole defect identifier tends to stabilize, the borehole defect identifier is verified using the drilling verification data. If the output accuracy of the borehole defect identifier is greater than or equal to the output accuracy threshold of the borehole defect identifier, the training of the borehole defect identifier is complete. The output accuracy threshold of the borehole defect identifier is customized by those skilled in the art based on actual conditions. For example, the output accuracy threshold of the borehole defect identifier is 80%. Furthermore, multiple sets of borehole-surface image mappings are sequentially input into the borehole defect identifier for drilling defect identification, generating multiple borehole defect sets.

[0065] Next, a preset drilling defect level is set based on production requirements and quality control standards. Multiple drilling defect level sets are compared with the preset drilling defect level. If the multiple drilling defect level sets meet the preset drilling defect level, the multiple drilling defect level sets are used as multiple positioning defect sets.

[0066] Next, if any of the drilling defect sets does not meet the preset drilling defect level, an early warning instruction is generated. The early warning instruction is sent to the user through communication methods such as email, SMS, and system notifications to remind the user to pay attention and take timely action.

[0067] By mapping drilling location information with casting surface images, the system automatically identifies and determines the degree of drilling defects. When defects exceeding the preset drilling defect degree are detected, an early warning is issued to ensure effective quality control during the production process, thereby helping to improve production efficiency and product quality.

[0068] Furthermore, the present application further comprises the following steps: Get the preset feedback monitoring window; Feedback monitoring is performed on the plurality of polymer mineral castings in the preset feedback monitoring window, and a feedback warning instruction is generated according to the monitoring result.

[0069] Specifically, within the machine tool control process, a time interval or processing step is set to collect characteristic data during or after the machining process. The preset feedback monitoring window is pre-set based on the machining cycle, process structure, and monitoring capabilities. For example, it can be set to automatically collect casting quality data every 10 minutes or within 5 minutes after completing a group of castings.

[0070] Next, within a preset feedback monitoring window, real-time or quasi-real-time feedback monitoring is performed on the processing status of multiple polymer mineral castings. This feedback monitoring includes whether dimensions meet standards, whether surface roughness exceeds standards, and whether hole positions are offset. This information can be obtained through laser scanning, image recognition, and three-dimensional coordinate measurement. For example, within 30 seconds of machining completion, a camera can capture an image of the hole position and compare it with the design template to determine whether the error exceeds the allowable threshold.

[0071] Based on the monitoring results, the system automatically analyzes whether any anomalies exceed tolerance limits. If a particular problem recurs frequently, such as if more than 10% of a batch of castings has a surface roughness greater than 50 microns or a hole offset exceeding 0.5 mm, a feedback warning command is generated. This warning signal prompts operators or the automated system to adjust its strategy, triggering equipment shutdown, self-diagnosis, or parameter fine-tuning.

[0072] In summary, the machine tool control optimization method for polymer mineral castings provided in this application has the following technical effects: By collecting quality inspection data of multiple polymer mineral castings in a target batch, multiple mineral casting quality information is generated, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; the control modules of the interactive CNC lathes, grinders, and drillers extract the control data in the history window, perform control deviation analysis on the extraction results, and generate lathe deviations, grinder deviations, and driller deviations; based on the multiple mineral casting size deviations and the lathe deviations, lathe control optimization is performed to generate multiple lathe control parameters; based on the multiple surface quality deviations and the grinder deviations, grinder control optimization is performed to generate multiple grinding machine control parameters; combining the drilling positioning information, positioning and identifying the plurality of casting surface image sets, generating a plurality of positioning defect sets; optimizing the drilling machine control based on the plurality of positioning defect sets and the drilling machine deviation, generating a plurality of drilling machine control parameters; transmitting the plurality of lathe control parameters, the plurality of grinding machine control parameters and the plurality of drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and performing finishing processing on the plurality of polymer mineral castings, that is, through processing and finishing by the CNC lathe, grinder and drilling machine, optimizing the machine tool control method, and finally achieving the technical goal of improving processing accuracy and efficiency, and achieving the technical effect of improving the production quality of castings.

[0073] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A machine tool control optimization system for polymer mineral castings, characterized in that: The system is applied to a machine tool control optimization platform, which is communicatively connected to the control modules of a CNC lathe, a grinder, and a drilling machine, respectively. The system includes: a mineral casting quality information generation module, the mineral casting quality information generation module being used to collect quality inspection data of a target batch of multiple polymer mineral castings and generate a plurality of mineral casting quality information, wherein the plurality of mineral casting quality information includes a plurality of mineral casting size deviations, a plurality of surface quality deviations, and a plurality of casting surface image sets; a control deviation analysis module, the control deviation analysis module being used to interact with the control modules of the CNC lathe, grinder, and drill press to extract control data within a history window, perform control deviation analysis on the extracted results, and generate lathe deviation, grinder deviation, and drill press deviation; a lathe control parameter generation module, the lathe control parameter generation module being configured to perform lathe control optimization based on the plurality of mineral casting size deviations and the lathe deviation, and generate a plurality of lathe control parameters; a grinding machine control parameter generation module, the grinding machine control parameter generation module being used to perform grinding machine control optimization based on the multiple surface quality deviations and the grinding machine deviations, and generate multiple grinding machine control parameters; a positioning defect set generation module, the positioning defect set generation module being used to perform positioning identification on the plurality of casting surface image sets in combination with drilling positioning information to generate a plurality of positioning defect sets; a drilling machine control parameter generation module, the drilling machine control parameter generation module being configured to perform drilling machine control optimization based on the plurality of positioning defect sets and the drilling machine deviation, and generate a plurality of drilling machine control parameters; A finishing processing module is used to transmit the multiple lathe control parameters, multiple grinder control parameters and multiple drilling machine control parameters to the control modules of the CNC lathe, grinder and drilling machine respectively, and perform finishing processing on the multiple polymer mineral castings.

2. The system according to claim 1, wherein The control deviation analysis module includes: a historical lathe control data set generation module, the historical lathe control data set generation module being configured to interact with the control module of the CNC lathe to extract the lathe control data within the historical window and generate a historical lathe control data set, wherein the historical lathe control data set includes a historical lathe target cutting amount set and a historical lathe actual cutting amount set; a historical lathe cutting deviation amount set generation module, the historical lathe cutting deviation amount set generation module being used to identify cutting deviations based on the historical lathe target cutting amount set and the historical lathe actual cutting amount set, and generate a historical lathe cutting deviation amount set; A lathe deviation generation module is used to perform control deviation analysis on the historical lathe cutting deviation amount set to generate the lathe deviation.

3. The system according to claim 2, wherein: The lathe deviation generation module includes: a first historical lathe cutting deviation median generating module, configured to identify the cutting deviation median of the historical lathe cutting deviation set and generate a first historical lathe cutting deviation median; a lathe cutting deviation scatter plot construction module, the lathe cutting deviation scatter plot construction module being configured to construct a lathe cutting deviation scatter plot based on the historical lathe cutting deviation set, wherein the abscissa axis of the lathe cutting deviation scatter plot is cutting time, and the ordinate axis is lathe cutting deviation; a starting straight line generating module, the starting straight line generating module being configured to use a straight line passing through the median value of the historical lathe cutting deviation and parallel to the abscissa axis of the lathe cutting deviation scatter plot as the starting straight line; a first diffusion area generating module, configured to diffuse the starting straight line toward both sides of the straight line according to a preset deviation step length, until a scatter point density gain of two adjacent diffusions is less than a preset scatter point density gain, stop the diffusion, and generate a first diffusion area; A lathe deviation generation module is used to calculate the mean of multiple scattered points in the first diffusion area to generate the lathe deviation.

4. The system according to claim 1, wherein: The lathe control parameter generation module includes: a first size deviation mean value generating module, the first size deviation mean value generating module being used to calculate the mean value of the size deviations of the plurality of mineral castings to generate a first size deviation mean value; a first lathe control parameter generating module, configured to perform parameter identification on the first dimensional deviation mean and the lathe deviation using a lathe control parameter identifier to generate first lathe control parameters, wherein the first lathe control parameters include feed rate, cutting depth, and cutting speed; a size fluctuation factor generating module, the size fluctuation factor generating module being used to identify the size fluctuations of the plurality of mineral castings and generate size fluctuation factors; An adjustment lathe control parameter generation module is used to match a first fine-tuning bandwidth based on the dimensional fluctuation factor, and use the first fine-tuning bandwidth to adjust the first lathe control parameter multiple times according to a preset adjustment method to generate multiple adjustment lathe control parameters, wherein the preset adjustment method is to increase or decrease the first lathe control parameter according to the first fine-tuning bandwidth.

5. The system according to claim 4, wherein: The lathe control parameter adjustment generating module includes: a target parameter generation module, the target parameter generation module being configured to traverse the plurality of adjustment lathe control parameters to perform fitness identification, taking the adjustment lathe control parameter corresponding to the maximum fitness as the target parameter, and taking the remaining plurality of adjustment lathe control parameters as a follow-up parameter set; a following fine-tuning bandwidth generation module, configured to respectively calculate the inverse of the ratio of the fitness of a plurality of following parameters in the following parameter set to the fitness of the target parameter, and multiply the calculation result by the first fine-tuning bandwidth to generate a plurality of following fine-tuning bandwidths; a following parameter set adjustment module, configured to adjust the following parameter set based on the multiple following fine-tuning bandwidths with the target parameter as a direction; The basic lathe control parameter generation module is used to adjust the lathe control parameter corresponding to the maximum fitness value during the adjustment as the basic lathe control parameter after multiple adjustments.

6. The system according to claim 5, wherein: The lathe control parameter adjustment generating module also includes: a calculation result generating module, configured to calculate ratios of the plurality of mineral casting size deviations and the first size deviation mean value to generate a plurality of calculation results; Multiple lathe control parameter generation modules are used to multiply the multiple calculation results with the basic lathe control parameters to generate multiple lathe control parameters.

7. The system according to claim 1, wherein: The positioning defect set generation module includes: a drilling hole-surface image mapping set generation module, the drilling hole-surface image mapping set generation module being configured to generate a plurality of drilling hole-surface image mapping sets by respectively locating the plurality of drilling hole locations in the drilling hole positioning information with the plurality of casting surface image sets; a drilling defect set generation module, the drilling defect set generation module being configured to traverse the plurality of drilling hole-surface image mapping sets to perform drilling defect identification and generate a plurality of drilling defect sets; a positioning defect set generation module, the positioning defect set generation module being configured to determine whether the plurality of drilling defect degree sets meet a preset drilling defect degree, and if so, to use the plurality of drilling defect degree sets as a plurality of positioning defect sets; The early warning instruction generation module is used to generate an early warning instruction if no, and send the early warning instruction to the user end for early warning.

8. The system according to claim 1, wherein: The finishing module includes: A feedback monitoring window acquisition module, wherein the feedback monitoring window acquisition module is used to acquire a preset feedback monitoring window; The feedback warning instruction generating module, the feedback monitoring window acquiring module is used to perform feedback monitoring on the multiple polymer mineral castings in the preset feedback monitoring window, and generate feedback warning instructions according to the monitoring results.

9. A machine tool control optimization method for polymer mineral castings, characterized in that: The method is applied to a machine tool control optimization platform, which is communicatively connected to control modules of a CNC lathe, a grinder, and a drilling machine, respectively. The method includes: Collecting quality inspection data of a target batch of multiple polymer mineral castings to generate multiple mineral casting quality information, wherein the multiple mineral casting quality information includes multiple mineral casting size deviations, multiple surface quality deviations, and multiple casting surface image sets; Interacting with the control modules of the CNC lathe, grinder, and drill to extract control data in a history window, performing control deviation analysis on the extracted results, and generating lathe deviation, grinder deviation, and drill deviation; Perform lathe control optimization based on the multiple mineral casting size deviations and the lathe deviation to generate multiple lathe control parameters; performing grinding machine control optimization based on the plurality of surface quality deviations and the grinding machine deviations to generate a plurality of grinding machine control parameters; In combination with the drilling positioning information, positioning and identifying the plurality of casting surface image sets are performed to generate a plurality of positioning defect sets; Performing drilling machine control optimization based on the multiple positioning defect sets and the drilling machine deviation to generate multiple drilling machine control parameters; The multiple lathe control parameters, the multiple grinder control parameters and the multiple drilling machine control parameters are respectively transmitted to the control modules of the CNC lathe, grinder and drilling machine to perform finishing processing on the multiple polymer mineral castings.

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