Chemical conversion-based surface treatment method and system for aluminum alloy templates

Abstract

The present disclosure provides a chemical conversion-based aluminum alloy template surface treatment method and system, relating to the technical field of metal surface processing, the method comprising: taking the demand feature set of the aluminum alloy template as the target feature to generate the theoretical control parameter; performing steady state monitoring on the surface treatment process to generate the steady state fluctuation space; performing surface image collection of the process timing node to generate the timing image set; fitting the zero timing image with the theoretical control parameter to generate the supervision image; performing image comparison on the mapping image of the timing image set by calling the supervision image to generate the compensation parameter; and completing surface treatment management based on the compensation result and the steady state fluctuation space. The technical problem of low surface treatment quality caused by low precision of aluminum alloy template surface treatment process control can be solved, and the precision and accuracy of aluminum alloy template surface treatment control can be improved, thereby improving the surface treatment quality.

Description

A Method and System for Surface Treatment of Aluminum Alloy Templates Based on Chemical Transformation Technical Field

[0001] This disclosure relates to the field of metal surface processing technology, and more specifically, to a method and system for surface treatment of aluminum alloy templates based on chemical conversion. Background Technology

[0002] Aluminum alloy formwork is an important material in modern construction, widely used in building construction, road and bridge construction, underground pipeline construction and other building projects. It has good mechanical properties, corrosion resistance and thermal conductivity, which can effectively improve the safety and overall quality of buildings.

[0003] However, because the surface of aluminum alloy formwork is susceptible to oxidation and corrosion, chemical treatment is necessary to improve its performance and appearance. Existing surface treatment methods for aluminum alloy formwork suffer from low precision control, failing to promptly adjust and optimize subsequent processes based on the current treatment status. This results in poor overall surface treatment quality, affecting the normal use of the aluminum alloy formwork.

[0004] The shortcomings of existing aluminum alloy template surface treatment methods are that the surface treatment quality is low due to the low precision of surface treatment process control. Summary of the Invention

[0005] Therefore, in order to solve the above-mentioned technical problems, the technical solutions adopted in the embodiments of this disclosure are as follows:

[0006] A surface treatment method for aluminum alloy templates based on chemical conversion includes the following steps: establishing a demand feature set for aluminum alloy templates, wherein the demand feature set is obtained by parsing surface treatment requirements, and the surface treatment requirements are obtained through user interaction; constructing a digital mapping of the process processing capabilities of the surface treatment process, using the demand feature set as the target feature, performing theoretical fitting based on the digital mapping to generate theoretical control parameters; performing steady-state monitoring of the surface treatment process to generate a steady-state fluctuation space; setting a zero-point time sequence, wherein the zero-point time sequence is the raw material time node of the untreated aluminum alloy template, and according to the time sequence node of the surface treatment process distribution, starting from the zero-point time sequence, sequentially executing the surface image acquisition of the process time sequence node to generate a time sequence image set; performing fitting of the zero-point time sequence image with the theoretical control parameters to generate a supervision image of the first process; calling the mapped image of the time sequence image set with the supervision image, performing image comparison, and generating compensation parameters; performing all parameter compensation of the time sequence image set in the time sequence, and completing the subsequent surface treatment management of the aluminum alloy template based on the compensation results and the steady-state fluctuation space.

[0007] A surface treatment system for aluminum alloy templates based on chemical conversion includes: a requirement feature set establishment module, which establishes a requirement feature set for the aluminum alloy template, wherein the requirement feature set is obtained by parsing surface treatment requirements, which are obtained through user interaction; a theoretical control parameter generation module, which builds a digital mapping of the process capability of the surface treatment process, using the requirement feature set as target features, and performs theoretical fitting based on the digital mapping to generate theoretical control parameters; a steady-state fluctuation space generation module, which performs steady-state monitoring of the surface treatment process to generate a steady-state fluctuation space; and a surface image acquisition module, which sets a zero point. The time sequence, where the zero-point time sequence is the raw material time node of the untreated aluminum alloy template, is used to sequentially acquire surface images of the process time sequence nodes according to the distribution sequence of surface treatment processes, starting from the zero-point time sequence, to generate a time sequence image set; the supervision image generation module is used to fit the zero-point time sequence image with the theoretical control parameters to generate a supervision image for the first process; the compensation parameter generation module is used to call the mapped image of the time sequence image set with the supervision image, perform image comparison, and generate compensation parameters; the surface treatment management module is used to perform all parameter compensation of the time sequence image set in the time sequence, and complete the subsequent surface treatment management of the aluminum alloy template based on the compensation results and the steady-state fluctuation space.

[0008] Due to the adoption of the above-mentioned technical methods, the technical advancements achieved by this disclosure compared to the prior art are as follows:

[0009] This method addresses the technical problem of low surface treatment quality due to low precision in surface treatment process control in existing aluminum alloy template surface treatment methods. First, it acquires the user's aluminum alloy surface treatment requirements and analyzes these requirements to establish a requirement feature set for the aluminum alloy template. Then, it constructs a digital mapping between surface treatment control parameters and process capabilities, using the requirement feature set as target features. Based on this digital mapping, it performs theoretical fitting to obtain theoretical control parameters. Finally, it performs steady-state monitoring of the surface treatment process based on historical surface treatment data of the aluminum alloy template, generating a steady-state fluctuation space. A zero-point timing sequence is defined, where the zero-point timing sequence refers to the raw material time node of the untreated aluminum alloy template. Then, based on the surface treatment process distribution sequence timing nodes, starting from the zero-point timing sequence, surface image acquisition is performed sequentially for each process timing node, generating a timing image set. Further, the zero-point timing images are fitted with the theoretical control parameters to generate a monitoring image for the first process. The monitoring image is used to call the mapped image of the timing image set, performing image comparison and generating compensation parameters. Finally, all parameters of the timing image set are compensated sequentially, and the subsequent surface treatment management of the aluminum alloy template is completed based on the compensation results and the steady-state fluctuation space. This method allows for timely optimization and adjustment of the surface treatment control parameters of the aluminum alloy template, improving the precision and accuracy of surface treatment control, thereby enhancing the surface treatment quality of the aluminum alloy template. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0011] Figure 1 is a schematic flowchart of a chemical conversion-based surface treatment method for aluminum alloy templates provided in this application.

[0012] Figure 2 is a schematic diagram of the process for establishing the surface quality space set of the raw material of the aluminum alloy template in the surface treatment method of aluminum alloy template based on chemical conversion provided in this application.

[0013] Figure 3 is a schematic diagram of the surface treatment system for aluminum alloy templates based on chemical conversion provided in this application.

[0014] Figure labeling: 01 Demand feature set establishment module, 02 Theoretical control parameter generation module, 03 Steady-state fluctuation space generation module, 04 Surface image acquisition module, 05 Supervision image generation module, 06 Compensation parameter generation module, 07 Surface treatment management module. Detailed Implementation

[0015] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0016] Based on the above description, as shown in Figure 1, this disclosure provides a surface treatment method for aluminum alloy templates based on chemical conversion, including:

[0017] Surface treatment of aluminum alloy templates refers to improving the performance requirements of aluminum alloy templates through chemical treatment and other methods, such as improving the appearance of aluminum alloy templates and enhancing their corrosion resistance. Common surface treatment methods for aluminum alloy templates include degreasing, etching, pickling, matte finishing, polishing, and chemical oxidation coating. The method provided in this application is used to optimize the surface treatment method of aluminum alloy templates based on chemical conversion, so as to improve the control precision and accuracy of aluminum alloy template surface treatment. The method is specifically implemented in a surface treatment system for aluminum alloy templates based on chemical conversion.

[0018] A requirement feature set for aluminum alloy templates is established, wherein the requirement feature set is obtained by parsing surface treatment requirements, and the surface treatment requirements are obtained by establishing user interaction.

[0019] In this embodiment, firstly, the target users' requirements for the aluminum alloy template are collected to obtain the surface treatment requirements. These surface treatment requirements can be set according to actual user needs, such as surface smoothness and corrosion resistance requirements. Then, the surface treatment requirements are analyzed for their characteristics. This analysis involves quantifying the index features of the surface treatment requirements, such as determining the surface finish index and corrosion resistance level of the aluminum alloy template, thus establishing a requirement feature set for the aluminum alloy template. By acquiring the user's surface treatment requirements and analyzing and constructing the requirement feature set, data support is provided for the next step of matching the control parameters of the aluminum alloy template, and the compatibility between the surface treatment control parameters and the user's surface treatment requirements can be improved.

[0020] A digital mapping of the processing capabilities of surface treatment processes is established. Using the set of required features as target features, theoretical fitting is performed based on the digital mapping to generate theoretical control parameters.

[0021] In this embodiment, firstly, multiple processing control parameters and corresponding processing capabilities of the surface treatment process are obtained. The processing capability refers to the surface state of the aluminum alloy after processing according to the processing control parameters. Using the processing capability as a sub-node and the corresponding processing control parameter as a leaf node, a digital mapping between the surface treatment process's processing capabilities and processing control parameters is established. Then, the required feature set is input as target features into the digital mapping for theoretical processing control parameter matching to obtain theoretical control parameters. These theoretical control parameters include surface treatment control parameters under multiple process timing nodes. Obtaining the theoretical control parameters provides data support for subsequent optimization and adjustment of the processing control parameters.

[0022] The surface treatment process is monitored in a steady state to generate a steady-state fluctuation space;

[0023] In this embodiment, firstly, historical surface treatment logs of the aluminum alloy template are retrieved, and historical surface treatment data is extracted based on these logs. This historical surface treatment data includes historical surface treatment control parameters and historical surface treatment results. Then, a steady-state analysis of the surface treatment process control parameters is performed based on the historical surface treatment data. This steady-state analysis refers to the stability analysis of the historical surface treatment results corresponding to the processing control parameters. Since the surface treatment results produced by the processing control parameters when surface treating the aluminum alloy template are not fixed, there will be differences; that is, the same processing control parameter may correspond to multiple surface treatment results. The range of surface treatment results corresponding to each processing control parameter and the probability of multiple surface treatment results within that range are determined. The range of surface treatment results includes all historical surface treatment results corresponding to the processing control parameter. A higher probability of a surface treatment result indicates higher stability of the surface treatment result obtained through the processing control parameter. Finally, a steady-state fluctuation space is constructed based on the range of surface treatment results corresponding to each processing control parameter and the probability of multiple surface treatment results within that range.

[0024] By constructing a steady-state fluctuation space, data support is provided for the next step of processing control parameter compensation and adjustment, which can improve the accuracy of processing control parameter compensation and adjustment.

[0025] A zero-point timing sequence is set, which is the raw material time node of the untreated aluminum alloy template. According to the time sequence node of the surface treatment process distribution, starting from the zero-point timing sequence, the surface image acquisition of the process time sequence node is executed sequentially to generate a time sequence image set.

[0026] In this embodiment, firstly, the raw material time node of the untreated aluminum alloy template is set as the zero-point time sequence, that is, the starting time node of the surface treatment of the aluminum alloy template is set to zero. Then, according to the time sequence nodes of the surface treatment process distribution, starting from the zero-point time sequence, surface images of the aluminum alloy template under the execution process time sequence nodes are acquired sequentially. That is, a surface image is acquired once after each process node is completed, and the surface image acquisition results are stored in the order of acquisition time to obtain a time sequence image set. By obtaining the time sequence image set, a basis is provided for comparing the surface treatment results under the process nodes.

[0027] The zero-point timing image is fitted with the theoretical control parameters to generate a monitoring image for the first process;

[0028] In this embodiment, firstly, a first theoretical control parameter is selected from the theoretical control parameters. This first theoretical control parameter is the surface processing control parameter at the first time-series node. Then, based on the first theoretical control parameter, simulated surface processing is performed on the zero-point time-series image to obtain a supervisory image after surface processing using the first theoretical control parameter, i.e., the supervisory image of the first process. The supervisory image represents the standard processed image of the zero-point time-series image after processing by the first process. The simulated surface processing method can utilize digital twin technology to simulate and model the surface processing process, constructing a surface processing simulation model, and performing simulated surface processing based on the simulation model. Obtaining the supervisory image of the first process provides support for the next step of surface processing image comparison.

[0029] The supervised image is used to call the mapped image of the time series image set, perform image comparison, and generate compensation parameters;

[0030] In this embodiment, firstly, the surface image acquisition results of the aluminum alloy template under the first process are retrieved from the time-series image set and marked as the first time-series image. Then, the supervised image is compared with the first time-series image, and the first surface treatment deviation is obtained based on the image comparison results. Then, compensation parameter analysis is performed based on the first surface treatment deviation to obtain the compensation parameters under the first process. The compensation parameter analysis method can be selected by those skilled in the art according to the actual situation. For example, a compensation parameter analysis model can be constructed based on a BP neural network, where the input data of the compensation parameter analysis model is the surface treatment deviation, and the output data is the compensation parameters of the surface treatment control parameters. Then, sample training data is extracted based on historical surface treatment data, and the compensation parameter analysis model is supervised and trained using the sample training data to obtain a compensation parameter analysis model that meets the expected indicators. Then, the first surface treatment deviation is analyzed using the trained compensation parameter analysis model to obtain the compensation parameters under the first process. By obtaining the compensation parameters under the first process, data support is provided for the next step of parameter compensation of the surface treatment control parameters.

[0031] By performing all parameter compensations on the time-series image set in a time-series order, the subsequent surface treatment management of the aluminum alloy template is completed based on the compensation results and the steady-state fluctuation space.

[0032] In this embodiment, all parameters of the time-series image set are compensated sequentially based on the time sequence, and the subsequent surface treatment process of the aluminum alloy template is controlled according to the compensation results and the steady-state fluctuation space. This method solves the technical problem of low surface treatment quality due to low precision in surface treatment process control in existing aluminum alloy template surface treatment methods. It allows for timely optimization and adjustment of the surface treatment control parameters of the aluminum alloy template, improving the precision and accuracy of surface treatment control, thereby enhancing the surface treatment quality of the aluminum alloy template.

[0033] In one embodiment, the method further includes:

[0034] Establish a spatial set of the raw material surface quality for aluminum alloy templates;

[0035] As shown in Figure 2, in one embodiment, the method further includes:

[0036] The aluminum alloy template is subjected to multi-dimensional feature detection of the raw material;

[0037] Obtain the impact value of unit change of each feature on the quality result, and use the impact value as the basic impact value;

[0038] Obtain the feature variation space of multidimensional features, evaluate the feature quality impact of each feature using the feature variation space and the basic impact value, and generate independent feature quality impact results.

[0039] The proportion of the quality influence results of the independent features is screened by using a quality influence threshold, and the main features and auxiliary features are determined based on the proportion screening results.

[0040] In this embodiment, a spatial set of raw material surface quality for the aluminum alloy template is established. First, multi-dimensional feature detection of the raw material is performed on the aluminum alloy template. These multi-dimensional features are those correlated with the surface treatment results of the raw material. Those skilled in the art can set these features according to the actual type of the raw material, such as surface finish. Then, the influence value of unit changes in each feature on the quality result is analyzed based on the multi-dimensional feature detection results. The greater the influence of unit changes in a feature on the quality result, the greater the influence value corresponding to that feature. This influence value is then used as the basic influence value of the feature.

[0041] A feature variation space for multidimensional features is obtained, where the feature variation space refers to the range of feature variations of the multidimensional features. Then, based on the feature variation space and the basic influence value, the feature quality influence of each feature is evaluated. That is, the feature quality influence is adjusted by the basic influence value through the feature variation space. The larger the feature variation space, the greater the upward adjustment of the basic influence value. The adjusted basic influence value is used as the independent feature quality influence result of the feature. The independent feature quality influence results of multiple features are obtained. The larger the independent feature quality influence result, the greater the influence of feature variation on the surface treatment result of aluminum alloy template.

[0042] A quality impact threshold is obtained, which can be set by those skilled in the art according to actual conditions. Then, the quality impact results of the independent features are filtered through the quality impact threshold. Features corresponding to the independent feature impact results greater than the quality impact threshold are marked as useful features, and features corresponding to the independent feature impact results less than or equal to the quality impact threshold are marked as useless features. Multiple useful features, i.e., proportional filtering results, are obtained. The influence ratio is determined according to the independent feature quality impact results of multiple useful features. The larger the independent feature quality impact result, the larger the influence ratio. Finally, the main feature and auxiliary features are determined according to the influence ratio of multiple useful features.

[0043] In one embodiment, the method further includes:

[0044] The ratio screening results are sorted in ratio order;

[0045] Based on the proportional difference, the difference between the first and second priority items is determined;

[0046] If the judgment passes, the first feature will be used as the primary feature, and the remaining features will be used as auxiliary features.

[0047] If the judgment fails, the difference between the second and third priority will be used to determine the difference based on the ratio difference.

[0048] If the determination passes, the first and second priority features will be used as the main features, and the remaining features will be used as auxiliary features.

[0049] If the judgment fails, the judgment iteration is performed, and the main feature and auxiliary feature are divided according to the iteration termination condition.

[0050] In this embodiment of the application, firstly, the useful features in the ratio screening results are sorted according to the influence ratio of the features, wherein the larger the influence ratio of the features, the higher the ranking of the useful features, thus obtaining a ratio order sequence.

[0051] Based on the aforementioned proportional order sequence, the first-ranked proportion is subtracted from the second-ranked proportion to obtain the proportional difference between the first and second ranks. A proportional difference threshold is obtained; this threshold can be set by those skilled in the art based on the actual degree of difference between useful features. Then, the proportional difference between the first and second ranks is judged according to the proportional difference threshold. When the proportional difference is greater than the threshold, the judgment is passed, indicating that the influence of the first-ranked useful feature is significantly greater than that of the second-ranked useful feature. In this case, the first-ranked useful feature is designated as the primary feature, and the remaining useful features are designated as auxiliary features. When the proportional difference is less than or equal to the threshold, the judgment is failed. The difference between the second and third ranks is then judged according to the threshold. When the judgment passes, the first and second-ranked useful features are designated as primary features, and the remaining features are designated as auxiliary features. When the judgment fails, the same method is used for iterative judgment until all useful features in the proportional screening result have been judged, thus completing the division of primary and auxiliary features in the proportional screening result.

[0052] By setting the main and auxiliary features according to the difference between useful features in the screening results, the accuracy of setting the main and auxiliary features can be improved, thereby improving the accuracy of setting the surface quality space set of the raw material.

[0053] A spatial set of the original surface quality of the aluminum alloy template is established based on the main features and the auxiliary features.

[0054] In this embodiment of the application, a spatial set of the raw material surface quality of the aluminum alloy template is constructed based on the main features and the auxiliary features. By obtaining the spatial set of the raw material surface quality, support is provided for the next step of analyzing the compensation results of surface control parameters.

[0055] Configure the control response accuracy of surface treatment, and use the control response accuracy as a segmentation reference to spatially segment the surface quality space set to generate a stepped reference value.

[0056] The corresponding raw material is called to perform surface treatment control based on the stepped reference value, and a mapping between the compensation result and the stepped reference value is established.

[0057] The subsequent surface treatment management of the aluminum alloy template is performed based on the mapping results and the steady-state fluctuation space.

[0058] In this embodiment, firstly, the control response precision of the surface treatment is configured. This precision is used to divide the surface treatment results under the first process. Those skilled in the art can set this precision according to actual needs; the higher the required precision, the higher the corresponding control response precision. Then, using the control response precision as the segmentation basis, the surface quality space set is spatially segmented. This involves extracting the surface treatment results corresponding to the control response precision from the surface quality space set. Different control response precisions correspond to different ranges of surface treatment results, resulting in a stepped reference value. This stepped reference value refers to the range of surface treatment results corresponding to the specified control response precision. Then, based on the stepped reference value, the corresponding raw material is called for surface treatment control, and a mapping between the compensation result and the stepped reference value is established. Finally, the subsequent surface treatment process of the aluminum alloy template is controlled based on the mapping result and the steady-state fluctuation space.

[0059] By generating stepped reference values ​​based on the control response accuracy of surface treatment, support is provided for obtaining supervisory images under the first process, which can improve the accuracy of supervisory image acquisition, thereby improving the accuracy of compensation parameter acquisition.

[0060] In one embodiment, the method further includes:

[0061] Read the surface quality of the raw aluminum alloy and use it as input data;

[0062] The input data is used to perform inter-step matching on the step reference value to obtain inter-step matching results;

[0063] The mapping result of the inter-step matching result is called, and the mapping result is adjusted proportionally according to the distribution of the input data in the inter-step. The surface treatment management of the current raw material is performed based on the proportional adjustment result.

[0064] In this embodiment, firstly, the surface quality of the raw aluminum alloy is read, and the surface quality of the raw aluminum alloy is used as input data to perform inter-step matching with the stepped reference value to obtain the inter-step matching result. The inter-step matching result refers to the range of multiple surface treatment results corresponding to multiple processes under the control response accuracy. The mapping result under the first process in the inter-step matching result is called, and the mapping result is adjusted proportionally according to the distribution of the input data between the steps, that is, the range of surface treatment results under the first process is determined, and the proportional adjustment result is obtained. Then, the supervision image corresponding to the proportional adjustment result is obtained, that is, the supervision image of the first process, and the surface treatment management of the current raw material is performed according to the supervision image of the first process.

[0065] In one embodiment, the method further includes:

[0066] The compensation result is identified using the steady-state fluctuation space.

[0067] If the compensation value meets the preset early warning threshold, a process early warning message will be generated.

[0068] The process control is maintained and managed through the aforementioned process early warning information.

[0069] In this embodiment, firstly, the compensation result is identified through the steady-state fluctuation space. Firstly, the compensation value corresponding to the compensation result is obtained, and then judged according to a preset warning threshold. The preset warning threshold can be set by those skilled in the art based on the steady-state fluctuation space. When the compensation value is greater than the preset warning threshold, it indicates that the compensation result exceeds the steady-state fluctuation space. Then, process warning information is generated, and the compensation value is corrected according to the process warning information, completing the maintenance and management of process control parameters. Correcting the compensation value by generating process warning information can improve the accuracy of the compensation value setting, thereby improving the accuracy of subsequent adjustments to the surface treatment control parameters of the aluminum alloy template.

[0070] In one embodiment, the method further includes:

[0071] Continuous evaluation of surface treatment results;

[0072] Bias defects are extracted by continuously processing evaluation results, and a mapping analysis is performed to correct these bias defects. Surface treatment compensation for the aluminum alloy template is then performed based on the results of the corrected mapping analysis.

[0073] In this embodiment, the surface treatment results are continuously evaluated according to the temporal sequence of the surface treatment processes, resulting in continuous evaluation results under multiple processes. A processing result evaluation threshold is set, which can be determined by those skilled in the art based on actual conditions. The continuous processing evaluation results are judged according to the threshold, and those results lower than the threshold are designated as biased defects. Then, the subsequent adjacent surface treatment processes are corrected based on these biased defects to obtain corrected mapping analysis results. Finally, the surface treatment processes of adjacent aluminum alloy templates are corrected and compensated based on the corrected mapping analysis results. By extracting biased defects from the continuous processing evaluation results and correcting and compensating the surface treatment processes of adjacent aluminum alloy templates based on these defects, the precision and accuracy of aluminum alloy template surface treatment control can be further improved.

[0074] In one embodiment, as shown in Figure 3, a surface treatment system for aluminum alloy templates based on chemical conversion is provided, including: a demand feature set establishment module 01, a theoretical control parameter generation module 02, a steady-state fluctuation space generation module 03, a surface image acquisition module 04, a supervisory image generation module 05, a compensation parameter generation module 06, and a surface treatment management module 07, wherein:

[0075] The requirement feature set establishment module 01 is used to establish the requirement feature set of aluminum alloy template. The requirement feature set is obtained by parsing the surface treatment requirements, and the surface treatment requirements are obtained by establishing user interaction.

[0076] Theoretical control parameter generation module 02 is used to build a digital mapping of the processing capability of the surface treatment process, and to generate theoretical control parameters by performing theoretical fitting based on the digital mapping with the demand feature set as the target feature.

[0077] Steady-state fluctuation space generation module 03 is used to perform steady-state monitoring of the surface treatment process and generate a steady-state fluctuation space.

[0078] The surface image acquisition module 04 is used to set the zero-point timing sequence, which is the raw material time node of the untreated aluminum alloy template. According to the time node of the surface treatment process distribution sequence, the surface image acquisition of the process time node is executed sequentially from the zero-point timing sequence to generate a time sequence image set.

[0079] The supervision image generation module 05 is used to perform execution fitting on the zero-point time series image with the theoretical control parameters to generate a supervision image of the first process;

[0080] The compensation parameter generation module 06 is used to call the mapping image of the time series image set with the supervision image, perform image comparison, and generate compensation parameters.

[0081] The surface treatment management module 07 is used to perform all parameter compensation of the time-series image set in a time-series order, and complete the subsequent surface treatment management of the aluminum alloy template based on the compensation results and the steady-state fluctuation space.

[0082] In one embodiment, the system further includes:

[0083] A raw material surface quality space set establishment module is used to establish the raw material surface quality space set of the aluminum alloy template.

[0084] A stepped reference value generation module is used to configure the control response accuracy of surface treatment, and to spatially divide the surface quality space set using the control response accuracy as a segmentation reference to generate stepped reference values.

[0085] A mapping establishment module is used to call the corresponding raw material to perform surface treatment control based on the stepped reference value, and to establish a mapping between the compensation result and the stepped reference value.

[0086] A surface treatment management module is used to perform subsequent surface treatment management of aluminum alloy templates based on the mapping results and the steady-state fluctuation space.

[0087] In one embodiment, the system further includes:

[0088] An input data acquisition module is used to read the surface quality of the raw aluminum alloy and use it as input data.

[0089] A step-to-step matching result acquisition module is used to perform step-to-step matching on the step reference value with the input data to obtain step-to-step matching results.

[0090] The proportional adjustment module is used to call the mapping result of the inter-step matching result, and to make proportional adjustments to the mapping result according to the distribution of the input data between the steps, and to manage the surface treatment of the current raw material based on the proportional adjustment result.

[0091] In one embodiment, the system further includes:

[0092] A multi-dimensional feature detection module is used to perform multi-dimensional feature detection on the aluminum alloy template.

[0093] The basic influence value setting module is used to obtain the influence value of unit change of each feature on the quality result, and use the influence value as the basic influence value;

[0094] An independent feature quality impact result generation module is used to obtain the feature variation space of multi-dimensional features, evaluate the feature quality impact of each feature using the feature variation space and the basic impact value, and generate independent feature quality impact results.

[0095] A feature determination module is used to filter the proportion of the quality influence results of the independent features by a quality influence threshold, and determine the main features and auxiliary features based on the proportion filtering results.

[0096] A raw material surface quality space set establishment module is used to establish a raw material surface quality space set for aluminum alloy templates based on the main features and the auxiliary features.

[0097] In one embodiment, the system further includes:

[0098] A proportional order sorting module is used to sort the proportional screening results by proportional order.

[0099] The difference determination module is used to determine the difference between the first and second positions based on the proportional difference.

[0100] The main feature setting module is used to set the first priority feature as the main feature and the remaining features as auxiliary features if the determination passes.

[0101] The difference determination module is used to determine the difference between the second and third order based on the proportional difference if the determination fails.

[0102] The feature setting module is used to, if the determination passes, take the first and second priority features as the main features and the remaining features as auxiliary features.

[0103] The feature segmentation module is used to perform a judgment iteration if the judgment fails, and complete the segmentation of main features and auxiliary features according to the iteration termination condition.

[0104] In one embodiment, the system further includes:

[0105] A compensation identification module is used to identify the compensation result through the steady-state fluctuation space.

[0106] A process early warning information generation module is used to generate process early warning information if the compensation value meets a preset early warning threshold.

[0107] The maintenance management module is used for maintenance management of process control based on the process early warning information.

[0108] In one embodiment, the system further includes:

[0109] A continuous processing evaluation module is used to continuously evaluate the surface processing results.

[0110] A surface treatment compensation module is used to extract bias defects through continuous processing evaluation results, perform a mapping analysis to correct the bias defects, and perform surface treatment compensation for the aluminum alloy template based on the corrected mapping analysis results.

[0111] In summary, compared with the prior art, the embodiments of this disclosure have the following technical effects:

[0112] (1) By comparing images in chronological order, compensation parameters are generated based on the comparison results to compensate the surface treatment control parameters of the next adjacent process. This can optimize and adjust the surface treatment control parameters of the aluminum alloy template in a timely manner, improve the precision and accuracy of surface treatment control, and thus improve the surface treatment quality of the aluminum alloy template.

[0113] (2) By generating a stepped reference value based on the control response accuracy of surface treatment, support is provided for obtaining the supervision image under the first process, which can improve the accuracy of the supervision image acquisition, thereby improving the accuracy of the compensation parameter acquisition.

[0114] (3) By extracting biased defects from the continuous processing evaluation results and correcting and compensating the surface treatment process of adjacent aluminum alloy templates based on the biased defects, the precision and accuracy of surface treatment control of aluminum alloy templates can be further improved.

[0115] The embodiments described above are merely illustrative of several implementations of this disclosure and should not be construed as limiting the scope of the invention. Therefore, those skilled in the art can make various types of substitutions, modifications, and alterations without departing from the scope of the concept as defined by the appended claims, and all such substitutions, modifications, and alterations fall within the protection scope of this disclosure.

Claims

1. A surface treatment method for aluminum alloy templates based on chemical conversion, characterized in that, The method includes: establishing a demand feature set for aluminum alloy templates, wherein the demand feature set is obtained by parsing surface treatment requirements, and the surface treatment requirements are obtained through user interaction; constructing a digital mapping of the process processing capabilities of the surface treatment process, using the demand feature set as the target feature, performing theoretical fitting based on the digital mapping to generate theoretical control parameters; performing steady-state monitoring of the surface treatment process to generate a steady-state fluctuation space; setting a zero-point time sequence, wherein the zero-point time sequence is the raw material time node of the untreated aluminum alloy template, and according to the time sequence node of the surface treatment process distribution, starting from the zero-point time sequence, sequentially executing the surface image acquisition of the process time sequence node to generate a time sequence image set; and setting the zero-point time sequence... The sequence image is fitted with the theoretical control parameters to generate a supervision image for the first process. A first theoretical control parameter is selected from the theoretical control parameters, which is the surface treatment control parameter under the first time-series node. Then, based on the first theoretical control parameter, the zero-point time-series image is subjected to simulated surface processing to obtain a supervision image after surface processing with the first theoretical control parameter, i.e., the supervision image for the first process. The supervision image is used to call the mapped image of the time-series image set, perform image comparison, and generate compensation parameters. All parameters of the time-series image set are compensated sequentially, and the subsequent surface treatment management of the aluminum alloy template is completed based on the compensation results and the steady-state fluctuation space.

2. The method as described in claim 1, characterized in that, The method further includes: establishing a surface quality space set of raw materials for aluminum alloy templates; configuring the control response accuracy of surface treatment, using the control response accuracy as a segmentation reference, spatially segmenting the surface quality space set to generate stepped reference values; calling the corresponding raw materials to perform surface treatment control using the stepped reference values, and establishing a mapping between the compensation result and the stepped reference values; and performing subsequent surface treatment management of aluminum alloy templates based on the mapping result and the steady-state fluctuation space.

3. The method as described in claim 2, characterized in that, The method further includes: reading the surface quality of the raw aluminum alloy and using it as input data; performing inter-step matching on the stepped reference value with the input data to obtain inter-step matching results; calling the mapping result of the inter-step matching results and making proportional adjustments to the mapping result according to the distribution of the input data in the steps; and managing the surface treatment of the current raw material based on the proportional adjustment result.

4. The method as described in claim 2, characterized in that, The method further includes: performing multi-dimensional feature detection on the aluminum alloy template; obtaining the influence value of unit change of each feature on the quality result, and using the influence value as the basic influence value; obtaining the feature change space of the multi-dimensional features, evaluating the feature quality influence of each feature using the feature change space and the basic influence value, and generating independent feature quality influence results; screening the proportion of the independent feature quality influence results through a quality influence threshold, and determining the main feature and auxiliary feature based on the proportion screening results; and establishing a surface quality space set of the aluminum alloy template based on the main feature and the auxiliary feature.

5. The method as described in claim 4, characterized in that, The method further includes: sorting the proportion screening results by proportion order; determining the difference between the first and second priorities based on the proportion difference; if the determination passes, the first priority is taken as the main feature and the remaining features are taken as auxiliary features; if the determination fails, determining the difference between the second and third priorities based on the proportion difference; if the determination passes, the first and second priorities are taken as the main features and the remaining features are taken as auxiliary features; if the determination fails, performing a determination iteration, and completing the division of main and auxiliary features according to the iteration termination condition.

6. The method as described in claim 1, characterized in that, The method further includes: identifying the compensation result through the steady-state fluctuation space; generating process early warning information if the compensation value meets a preset early warning threshold; and performing process control maintenance and management through the process early warning information.

7. The method as described in claim 1, characterized in that, The method further includes: continuously evaluating the surface treatment results; extracting bias defects from the continuous evaluation results; performing a corrective mapping analysis based on the bias defects; and compensating for the surface treatment of the aluminum alloy template based on the corrective mapping analysis results.

8. A surface treatment system for aluminum alloy templates based on chemical conversion, characterized in that, The system is used to perform the steps of any one of the chemical conversion-based aluminum alloy template surface treatment methods according to claims 1-7, the system comprising: a demand feature set establishment module, which is used to establish a demand feature set for the aluminum alloy template, wherein the demand feature set is obtained by parsing surface treatment requirements, and the surface treatment requirements are obtained through user interaction; a theoretical control parameter generation module, which is used to build a digital mapping of the process processing capability of the surface treatment process, and, using the demand feature set as the target feature, performs theoretical fitting based on the digital mapping to generate theoretical control parameters; a steady-state fluctuation space generation module, which is used to perform steady-state monitoring of the surface treatment process to generate a steady-state fluctuation space; and a surface image acquisition module, which... The surface image acquisition module is used to set the zero-point timing sequence, which is the raw material time node of the untreated aluminum alloy template. According to the time sequence node of the surface treatment process distribution, starting from the zero-point timing sequence, the surface image acquisition of the process time sequence node is executed sequentially to generate a time sequence image set. The supervision image generation module is used to fit the zero-point time sequence image with the theoretical control parameters to generate a supervision image of the first process. The compensation parameter generation module is used to call the mapping image of the time sequence image set with the supervision image, perform image comparison, and generate compensation parameters. The surface treatment management module is used to perform all parameter compensation of the time sequence image set in the time sequence, and complete the subsequent surface treatment management of the aluminum alloy template based on the compensation results and the steady-state fluctuation space.

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