Aluminum profile automatic cutting intelligent control method and device

Abstract

The present application provides an intelligent control method and device for automated cutting of aluminum profiles, which relates to the field of cutting control technology, including: extracting cutting-related characteristic parameters, extracting cutting yield, and constructing an initial cutting model; clustering and dividing the initial cutting model based on cutting requirements and aluminum profile differences, performing control parameter homogeneity simulation on multiple clustering models based on cutting yield, integrating to obtain a recombinant clustering model, and caching the recombinant clustering model; scanning the target aluminum profile based on cutting requirements and aluminum profile differences, and searching for a recombinant clustering model to obtain a matching recombinant clustering model; automatically controlling the cutting based on the matching recombinant clustering model inputted by the target aluminum profile to obtain the target cutting result. This application can solve the technical problem in the prior art of inaccurate cutting parameter settings caused by changes in conditions such as aluminum profile differences, which leads to fluctuations in cutting quality, thereby achieving the technical effect of improving cutting quality and production efficiency.

Description

Technical Field

[0001] The present application relates to the field of cutting control technology, and in particular to an intelligent control method and device for automated cutting of aluminum profiles. Background Art

[0002] With the continuous development of industrial automation, aluminum profile cutting technology has also been continuously improved. However, existing technologies still have some limitations, especially in how to deal with the differences in the characteristics of different aluminum profiles during the aluminum profile cutting process, optimize cutting parameters, and implement real-time feedback control.

[0003] Currently, in existing technologies, the cutting quality of aluminum profiles is affected by a variety of factors, including the material, thickness, hardness, and surface condition of the aluminum profile. These factors directly affect the selection of tools, cutting speed, feed rate, and other parameter settings during the cutting process. Many aluminum profile cutting systems rely on preset cutting parameters and static models, but in actual production, due to differences in aluminum profiles and changes in environmental conditions, the cutting effect often fails to meet expectations. Traditional systems lack dynamic adaptability and are unable to monitor and adjust parameters during the cutting process in real time. For example, when the tool is worn or the surface of the aluminum profile is uneven, the cutting quality may fluctuate, and existing systems find it difficult to detect problems and make adjustments in a timely manner, resulting in a large amount of waste and inefficient production during the cutting process.

[0004] In summary, the existing technology has technical problems such as inaccurate cutting parameter settings caused by changes in conditions such as differences in aluminum profiles, resulting in fluctuations in cutting quality, which further affects the stability of the cutting process and production efficiency. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent control method and device for automated cutting of aluminum profiles, so as to solve the technical problems in the prior art of inaccurate cutting parameter settings caused by changes in conditions such as differences in aluminum profiles, resulting in fluctuations in cutting quality, and further affecting the stability of the cutting process and production efficiency.

[0006] In view of the above problems, the present application provides an intelligent control method and device for automated cutting of aluminum profiles.

[0007] In the first aspect, the present application provides an intelligent control method for automated cutting of aluminum profiles, which is implemented by an intelligent control device for automated cutting of aluminum profiles, including: extracting cutting-related feature parameters based on cutting-related features according to historical aluminum profile cutting data, and extracting the cutting yield, and constructing an initial cutting model based on the cutting-related feature parameters and the cutting yield; clustering the initial cutting model according to cutting requirements and aluminum profile differences, performing control parameter homogenization simulation on multiple clustering models according to the cutting yield, integrating and obtaining a recombinant clustering model, and caching the recombinant clustering model; scanning the target aluminum profile based on the cutting requirements and aluminum profile differences, and searching the recombinant clustering model to obtain a matching recombinant clustering model; inputting the matching recombinant clustering model according to the target aluminum profile for automatic cutting control to obtain a target cutting result.

[0008] In the second aspect, the present application also provides an intelligent control device for automatic cutting of aluminum profiles, which is used to execute an intelligent control method for automatic cutting of aluminum profiles as described in the first aspect, including: an initial cutting model construction module, the initial cutting model construction module is used to extract cutting-related feature parameters with cutting-related features as a control based on historical aluminum profile cutting data, and extract the cutting yield, and construct an initial cutting model based on the cutting-related feature parameters and the cutting yield; a recombinant clustering model acquisition module, the recombinant clustering model acquisition module is used to cluster the initial cutting model based on cutting requirements and aluminum profile differences, perform control parameter homogeneity simulation on multiple clustering models based on the cutting yield, and integrate to obtain a recombinant clustering model, and cache the recombinant clustering model; a matching recombinant clustering model acquisition module, the matching recombinant clustering model acquisition module is used to scan the target aluminum profile based on the cutting requirements and aluminum profile differences, and search the recombinant clustering model to obtain a matching recombinant clustering model; a target cutting result acquisition module, the target cutting result acquisition module is used to input the matching recombinant clustering model according to the target aluminum profile for automatic cutting control to obtain a target cutting result.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By extracting cutting-related feature parameters with cutting-related features as a control based on historical aluminum profile cutting data, and extracting the cutting yield, an initial cutting model is constructed based on the cutting-related feature parameters and the cutting yield; the initial cutting model is clustered and divided according to the cutting requirements and aluminum profile differences, and a plurality of clustering models are simulated for control parameter homogeneity according to the cutting yield, and integrated to obtain a recombinant clustering model, and the recombinant clustering model is cached; the target aluminum profile is scanned based on the cutting requirements and aluminum profile differences, and the recombinant clustering model is searched to obtain a matching recombinant clustering model; the matching recombinant clustering model is input according to the target aluminum profile for automatic cutting control to obtain the target cutting result. That is to say, by realizing the technical goal of dynamic adaptive control in the cutting process, the technical effect of improving cutting quality and production efficiency is achieved.

[0011] 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

[0012] 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.

[0013] Figure 1 This is a flow chart of an intelligent control method for automatic cutting of aluminum profiles for this application;

[0014] Figure 2 This is a structural schematic diagram of an intelligent control device for automatic cutting of aluminum profiles in this application.

[0015] Description of reference numerals:

[0016] Initial cutting model building module 11, recombination clustering model acquisition module 12, matching recombination clustering model acquisition module 13, target cutting result acquisition module 14. DETAILED DESCRIPTION

[0017] This application provides an intelligent control method and device for automated cutting of aluminum profiles, which solves the technical problem in the prior art that the cutting parameter settings are not accurate due to changes in conditions such as differences in aluminum profiles, resulting in fluctuations in cutting quality and further affecting the stability of the cutting process and production efficiency. It realizes the technical goal of dynamic adaptive control in the cutting process and achieves the technical effect of improving cutting quality and production efficiency.

[0018] 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.

[0019] For example, see the attached Figure 1 The present application provides an intelligent control method for automatic cutting of aluminum profiles, which is applied to an intelligent control device for automatic cutting of aluminum profiles, and specifically includes the following steps:

[0020] Step 1: Extract cutting-related feature parameters with cutting-related features as a control based on historical aluminum profile cutting data, extract cutting yield, and construct an initial cutting model based on the cutting-related feature parameters and the cutting yield.

[0021] Specifically, based on historical aluminum profile cutting data, features related to cutting quality and cutting accuracy in the cutting process are extracted as cutting-related features. Cutting-related features can include cutting speed, tool pressure, material hardness, feed rate, etc., which can affect the quality of the cutting results. For example, when the cutting speed is too high, too many burrs may appear on the surface of the aluminum profile, resulting in a decrease in cutting quality. Next, the cutting-related features are combined with indicators for judging cutting quality during the cutting process as the cutting yield. The cutting yield refers to the proportion of aluminum profiles that meet quality standards among all cut parts. The neural network is trained using cutting-related features and cutting yield, and then an initial cutting model is constructed. It can predict the yield under given cutting conditions, which serves as the basis for subsequent optimization and adjustment, helping to more accurately control the cutting process in actual production and improve cutting quality and production efficiency.

[0022] Step 2: Cluster the initial cutting model based on cutting requirements and aluminum profile differences, perform control parameter homogenization simulation on multiple cluster models according to the cutting yield, integrate and obtain a recombinant cluster model, and cache the recombinant cluster model.

[0023] Specifically, the initial cutting models are clustered based on cutting requirements and aluminum profile characteristics. This means that all cutting models are divided into several groups based on different cutting requirements and aluminum profile characteristics, with each group of models performing similarly under specific conditions. For example, if cutting soft aluminum profiles, a lower cutting speed may be used, while hard aluminum profiles may require a higher cutting speed. Therefore, models with different conditions are divided into different clusters. Next, based on the cutting yield of each cluster model, its control parameters are homogenized. This means that by adjusting the control parameters of the cutting process (such as cutting speed and feed rate) to make them consistent within each cluster, the parameters of each cluster are simulated to ensure that different models can achieve the optimal cutting effect under similar conditions. For example, if the cutting yield of a cluster model is lower than expected, its parameters such as cutting speed and tool pressure are adjusted to bring its yield back to the predetermined standard. Then, these adjusted cluster models are integrated to obtain a reorganized cluster model, ensuring that all cutting solutions under different conditions are uniformly optimized, improving cutting efficiency and accuracy. Finally, the reorganized clustering model is cached to facilitate quick call and application in subsequent production, reducing the time and computational burden of real-time adjustment, thereby improving the level of production automation and cutting quality.

[0024] Step 3: Scan the target aluminum profile based on the cutting requirements and the aluminum profile differences, and search the recombinant clustering model to obtain a matching recombinant clustering model.

[0025] Specifically, the target aluminum profile is scanned based on the cutting requirements and the profile's profile specific properties, such as material hardness, thickness, and surface quality. This means that before cutting, a matching recombinant clustering model is searched based on the profile's specific properties, such as material hardness, thickness, and surface quality. These recombinant clustering models are generated by optimizing different cutting conditions and include a variety of cutting solutions tailored to different profiles and cutting requirements. Therefore, the matching process aims to find a clustering model that best matches the profile's characteristics and cutting requirements, ensuring optimal cutting results. For example, if the target aluminum profile is 5 mm thick and has a high hardness, the recombinant clustering model optimized for high-hardness aluminum profiles is selected and cutting parameters are adjusted accordingly to ensure cutting quality and yield. This matching process enables precise control of the cutting process, improving efficiency and reducing waste.

[0026] Step 4: Input the matching recombinant clustering model according to the target aluminum profile to perform automatic cutting control to obtain the target cutting result.

[0027] Specifically, based on the characteristics of the target aluminum profile, it is input into a matching and recombinant clustering model for automatic cutting control. This means that based on the actual properties of the target aluminum profile, such as thickness, hardness, and material type, the clustering model that best matches it is selected, containing the optimal combination of cutting parameters. Next, the cutting machine's operating state is automatically adjusted according to the cutting parameters in the model, controlling various factors in the cutting process, such as tool pressure, cutting depth, and feed speed, to ensure cutting stability and high quality. During the cutting process, the cutting results are monitored in real time to ensure that each cut meets the quality standard. Ultimately, through this automatic control, the target cutting result is achieved to ensure that this standard is met or exceeded, ultimately achieving a precise cutting effect.

[0028] The intelligent control method for automatic cutting of aluminum profiles is applied to an intelligent control device for automatic cutting of aluminum profiles, which can achieve the technical goal of dynamic adaptive control during the cutting process and achieve the technical effect of improving cutting quality and production efficiency.

[0029] Furthermore, this application also includes:

[0030] The cutting-related features are extracted according to the historical aluminum profile cutting data; the historical aluminum profile cutting data is divided into batches based on the cutting requirements and aluminum profile differences to obtain multiple batches of aluminum profile cutting data; the first batch of aluminum profile cutting data is extracted from the multiple batches of aluminum profile cutting data; the first batch of aluminum profile cutting data is interacted with the cutting-related features to obtain first cutting-related feature parameters; the first batch of cutting yield is extracted from the first batch of aluminum profile cutting data; a first cutting model is constructed through the first cutting-related feature parameters and the first batch of cutting yield, and is added to the initial cutting model.

[0031] Specifically, by analyzing historical aluminum profile cutting data, we extracted features related to the cutting process, including cutting speed, tool pressure, material hardness, feed rate, etc. By analyzing these features, we can identify the key factors that affect cutting quality and accuracy, and these factors form the associated features of cutting.

[0032] Next, this historical data is divided into batches based on the different types of aluminum profiles and cutting requirements. Different batches of aluminum profiles may behave differently during the cutting process due to differences in production processes, material sources, and environmental conditions. Therefore, dividing historical data into batches helps to accurately analyze cutting results under different conditions, thereby better adjusting control parameters.

[0033] After data division, cutting data are randomly extracted from multiple batches of data as the first batch of aluminum profile cutting data, providing a basis for subsequent analysis and model building.

[0034] Then, the cutting data of the first batch of aluminum profiles are subjected to parameter extraction according to the cutting-related features, and the cutting-related feature parameters of the first batch are obtained, thereby obtaining the key parameters that can describe the cutting process.

[0035] Next, we extract the cutting yield rates for multiple cutting lines or production runs within the first batch of aluminum extrusion cutting data, and use this as the first batch cutting yield rate. The first batch cutting yield rate is a metric used to measure aluminum extrusion cutting quality, generally referring to the proportion of all cut parts from multiple cutting lines or production runs that meet quality standards. The yield rate directly reflects the effectiveness of the cutting process.

[0036] By combining the first cutting-related characteristic parameters and the first batch yield, a first cutting model is constructed. According to the method for generating the first cutting model, multiple batches of aluminum profile cutting data are traversed to generate multiple corresponding cutting models, which are combined to obtain the initial cutting model.

[0037] By obtaining the initial cutting model, not only can the efficiency of optimizing the cutting process be improved, but also accurate predictions and optimization suggestions can be provided for subsequent cutting processes.

[0038] Furthermore, this application also includes:

[0039] A preset clustering benchmark is configured for the cutting requirements and aluminum profile differences; the initial cutting model is clustered and divided according to the preset clustering benchmark to obtain the multiple clustering models; a first clustering model is extracted according to the multiple clustering models; the first clustering cutting yield of the first clustering model is screened according to the cutting yield threshold as the screening benchmark to obtain a screened first clustering model and a filtered first clustering model; the filtered first clustering model is subjected to a control parameter homogenization simulation tending to the screened first clustering model until the first clustering cutting yield of the filtered first clustering model meets the screening benchmark and is converted into the screened first clustering model; the screened first clustering model is reorganized to obtain a reorganized first clustering model, and is added to the reorganized clustering model.

[0040] Specifically, during aluminum profile cutting, varying cutting requirements and profile variations affect the cutting results, necessitating a pre-defined clustering benchmark for these variations. This pre-defined clustering benchmark is determined based on cutting requirements (such as precision and cutting speed) and profile variations (such as material hardness and thickness), grouping profiles with similar characteristics and cutting conditions. For example, a lower cutting speed might be used for softer aluminum profiles, while a higher speed might be required for harder ones.

[0041] Next, based on these pre-defined clustering benchmarks, the initial cutting model is clustered and divided into multiple cluster models. Clustering groups cutting data according to different characteristics, with each cluster model representing a specific cutting condition and result. For example, one cluster model is suitable for low cutting speeds for soft aluminum profiles, while another is suitable for high cutting speeds for hard aluminum profiles. Each cluster model contains a set of parameters that optimize the cutting quality of a specific aluminum profile.

[0042] Then, from the multiple cluster models obtained, a single cluster model was randomly selected for research. The first cluster model represents the cutting process characteristics under a specific cutting requirement and aluminum profile variation, resulting in corresponding cutting accuracy and yield. By extracting the first cluster model, the cutting effect under these specific conditions was analyzed.

[0043] Next, the first cluster of models in the first cluster is screened using the cutting yield threshold as a criterion. The yield threshold is a set standard that indicates a minimum yield requirement. For example, if the yield threshold is 95%, only cluster models with a cutting yield above 95% meet the requirement. This screening process selects the first cluster model that meets the criteria, while models with yields below the threshold are filtered out.

[0044] The first filtered cluster model is then adjusted through a control parameter homogenization simulation until its cutting yield meets the screening criteria. Control parameter homogenization simulation involves adjusting control parameters (such as cutting speed and feed rate) while maintaining the same cutting conditions, so that the model's output approaches the screening criteria. For example, if a cluster model has an 80% yield, by adjusting parameters such as cutting speed and tool pressure, the yield can be increased to 95%, thus meeting the requirements.

[0045] Finally, after all cluster models are optimized, the selected and optimized first cluster models are reorganized to obtain a reorganized first cluster model. Then, following the same method used to obtain the reorganized first cluster model, multiple cluster models are traversed for parameter optimization, and the reorganized cluster model is combined. This reorganized cluster model can better adapt to different cutting requirements and aluminum profile characteristics, providing more accurate cutting predictions.

[0046] By continuously adjusting and optimizing different clustering models, we can eventually screen out the most suitable control parameter combination based on the specific cutting requirements and aluminum profile characteristics, and improve the yield rate in the cutting process, and then obtain a reorganized clustering model, which can obtain a more efficient and accurate cutting model, thereby improving the cutting quality and efficiency of production.

[0047] Furthermore, this application also includes:

[0048] A yield correlation analysis is performed based on the cutting association feature to obtain a feature yield correlation coefficient; a first control model is extracted from the screened first clustering model based on the first cluster cutting yield of the screened first clustering model; a first adjustment model is extracted based on the filtered first clustering model; the absolute value of the difference between the cutting association feature parameters of the first adjustment model and the first control model is used as a first adjustment threshold; the cutting association feature parameters of the first adjustment model are extracted based on the cutting association feature to obtain first feature parameters; the feature yield correlation coefficient is used as an adjustment sequence, and the first feature parameters are adjusted toward the first control model in sequence, and when the adjustment amplitude of the previous sequence in the adjustment sequence meets the adjustment threshold, the next adjustment sequence is executed until the first adjustment cutting yield of the first adjustment model meets the screening benchmark after any sequence is executed; the first adjustment model whose first adjustment cutting yield meets the screening benchmark is converted, traversed through the filtered first clustering model, and added to the screened first clustering model.

[0049] Specifically, we conduct a yield correlation analysis based on cutting correlation characteristics. We analyze the relationship between different cutting parameters and cutting yield, such as the impact of cutting speed, feed rate, and tool pressure on yield. We then calculate the correlation coefficient between each cutting parameter and yield to measure the degree of dependence.

[0050] Next, based on the cutting yield rate in the first cluster model, a cutting model from any batch or any round is randomly extracted to obtain a first control model. The first control model represents the standard cutting effect and yield rate under specific cutting conditions and is used as a reference for subsequent parameter adjustment of the first cluster model.

[0051] Then, a first adjustment model is randomly extracted from the filtered first cluster models. Next, the absolute value of the difference between the cutting-related characteristic parameters of the first adjustment model and the first control model is used as the first adjustment threshold. The adjustment threshold is set to control the amplitude of parameter changes during the model optimization process. For example, if the cutting speed of the first control model is 50 meters per minute, while the cutting speed of the first adjustment model is 40 meters per minute, then the absolute value of the difference of 10 meters per minute constitutes the adjustment threshold.

[0052] Based on the cutting-related features, the cutting-related feature parameters of the first adjustment model are extracted to obtain first feature parameters. The first feature parameters refer to cutting parameters that require attention when adjusting cutting control parameters in the first adjustment model, such as cutting speed and feed rate, and are the basis for ensuring the effectiveness of the optimization process.

[0053] Next, the first characteristic parameters are adjusted sequentially, using the characteristic yield correlation coefficient as the adjustment sequence, with the goal of bringing these parameters toward the standard values ​​of the first control model. During the analog control adjustment process, when the adjustment amplitude of the previous sequence meets the set adjustment threshold, the current cutting yield is analyzed. If the cutting yield still does not meet the set threshold, the next adjustment sequence will be executed to ultimately achieve the desired cutting yield.

[0054] Finally, when the cutting yield of the first adjustment model meets the screening benchmark, it is converted, and the first clustering model is traversed and filtered according to the converted cutting model, the control parameters are optimized, and the first clustering model is obtained and screened. This ensures that the obtained adjustment model can improve the final cutting effect through careful adjustment and optimization, so that the cutting quality meets the expected standards.

[0055] Through the updating of multiple clustering models, an efficient cutting model is finally formed which can be widely applied to different aluminum profiles and cutting requirements, ensuring that the cutting yield reaches the expected level, thereby improving production efficiency and cutting quality.

[0056] Furthermore, this application also includes:

[0057] Based on the cutting requirements and the differences in aluminum profiles, the matching and recombination clustering model is subjected to similarity matching of the target aluminum profile to obtain a similarity result; a first matching model is determined based on the similarity result; the target aluminum profile is input into the first matching model, and a first matching yield rate is output; it is determined whether the first matching yield rate meets a cutting yield rate threshold; if the first matching yield rate does not meet the cutting yield rate threshold, the target aluminum profile is input into the matching and recombination clustering model with the similarity results as a sequence until the matching yield rate meets the cutting yield rate threshold; if the first matching yield rate meets the cutting yield rate threshold, the target aluminum profile is automatically controlled for cutting according to the first matching parameter of the first matching model to obtain a target cutting result.

[0058] Specifically, based on the cutting requirements and profile differences, the matched recombined clustering models are matched against the target profile. This is done to calculate the similarity between the profile's characteristics and the cutting conditions of each clustering model. For example, if the target profile is thin and has low hardness, the clustering model most similar to it is calculated based on these characteristics to ensure the appropriate cutting parameters and avoid poor cutting results. Then, based on the calculated similarity, the first matching model is determined. This first matching model is the clustering model that most closely resembles the target profile and can provide the most appropriate cutting parameter combination, thereby maximizing cutting quality.

[0059] Next, the target aluminum profile is input into the first matching model to obtain the first matching yield rate. The first matching yield rate refers to the proportion of aluminum profiles that meet the quality requirements after cutting under the conditions of this model. Then, a determination is made as to whether the first matching yield rate meets the set cutting yield rate threshold. If the first matching yield rate does not reach the threshold, it means that the cutting effect is not ideal. There may be an ideal match, but there may be a mismatch due to environmental factors. Therefore, based on the similarity results, the target aluminum profile is input into the matching recombination clustering model, and the models are matched step by step until a matching model that meets the cutting yield rate threshold is found.

[0060] If the first matching yield rate meets the cutting yield rate threshold, the target aluminum profile can be automatically cut according to the cutting parameters in the first matching model. At this time, the cutting operation is performed according to the optimized parameters to ensure that the quality of the target aluminum profile after cutting meets the standard.

[0061] Through iterative matching and optimization, the cutting yield rate can be continuously improved, and ultimately efficient and precise automated cutting control can be achieved.

[0062] Furthermore, this application also includes:

[0063] The actual yield rate of the target cutting result is obtained by real-time monitoring of the visual monitoring unit; if the actual yield rate does not meet the actual yield threshold, the feedback relearning mechanism is triggered; the actual cutting model is obtained based on the feedback relearning mechanism, and automatic cutting control is performed according to the actual cutting model.

[0064] Specifically, the visual monitoring unit monitors and obtains the actual yield rate of the target cutting results in real time. The visual monitoring unit uses a camera or other sensor to inspect the cut aluminum profile, analyze its surface quality, and calculate the proportion of qualified cutting results that meet the standards.

[0065] Next, if the actual yield rate detected does not meet the preset yield rate threshold, a feedback relearning mechanism will be triggered. The yield rate threshold refers to the preset minimum passing standard during the cutting process. Cutting results that fall below this standard will trigger the relearning mechanism, which will improve the yield rate by adjusting certain cutting process parameters.

[0066] Once the feedback relearning mechanism is triggered, adjustments are made based on the actual cutting model. The actual cutting model is generated based on current cutting data and results, reflecting the performance of the cutting process under specific conditions. If the actual cutting result is lower than expected, the control parameters in the current model are analyzed to identify factors that may have contributed to substandard quality, such as excessive cutting speed or excessive tool wear. Adjustments are then made accordingly. After these adjustments, the new cutting model is used for the next cutting operation.

[0067] Finally, automatic cutting control will be performed based on the adjusted actual cutting model to ensure that a higher yield rate can be achieved in the subsequent cutting process and that the cutting quality is continuously optimized.

[0068] The visual monitoring system provides real-time feedback on the quality of the cutting results, and can dynamically adjust the cutting parameters to ensure that the yield rate meets the set standards.

[0069] Furthermore, this application also includes:

[0070] If the feedback re-learning mechanism is triggered, incremental learning is performed based on real-time failure samples, and the actual cutting model is obtained through adjustment.

[0071] Specifically, when the feedback re-learning mechanism is triggered, incremental learning is performed based on real-time failure samples. Real-time failure samples refer to those aluminum profile samples that fail to achieve the expected yield during the cutting process. For example, some cut aluminum profiles may have cracks, burrs, or do not meet the size requirements. These aluminum profiles are marked as failed samples. These failure samples are analyzed to identify the causes of the problems, such as excessive cutting speed, tool wear, uneven feed, etc. By performing incremental learning on failure samples, it is possible to continuously learn from new cutting data, thereby fine-tuning the original model to make the model better adapt to different situations that arise during the actual cutting process.

[0072] During the incremental learning process, the actual cutting model is adjusted. By analyzing the common characteristics of failed samples, key cutting parameters are adjusted, such as reducing cutting speed, increasing tool warm-up time, and adjusting tool pressure, to reduce the probability of cutting failure. The adjusted model can reflect new cutting experience, thereby providing higher precision and better quality assurance in the next cutting operation.

[0073] Through the feedback re-learning mechanism, the actual cutting model is continuously optimized to improve the cutting quality and achieve more precise and stable cutting effects.

[0074] In summary, the intelligent control method for automated aluminum profile cutting provided by this application has the following technical effects:

[0075] By extracting cutting-related feature parameters with cutting-related features as a control based on historical aluminum profile cutting data, and extracting the cutting yield, an initial cutting model is constructed based on the cutting-related feature parameters and the cutting yield; the initial cutting model is clustered and divided according to the cutting requirements and aluminum profile differences, and a plurality of clustering models are simulated for control parameter homogeneity according to the cutting yield, and integrated to obtain a recombinant clustering model, and the recombinant clustering model is cached; the target aluminum profile is scanned based on the cutting requirements and aluminum profile differences, and the recombinant clustering model is searched to obtain a matching recombinant clustering model; the matching recombinant clustering model is input according to the target aluminum profile for automatic cutting control to obtain the target cutting result. That is to say, by realizing the technical goal of dynamic adaptive control in the cutting process, the technical effect of improving cutting quality and production efficiency is achieved.

[0076] In the second embodiment, based on the same invention concept as the intelligent control method for automatic cutting of aluminum profiles in the above embodiment, this application also provides an intelligent control device for automatic cutting of aluminum profiles, see the attached Figure 2 ,include:

[0077] An initial cutting model construction module 11 is used to extract cutting-related feature parameters based on cutting-related features according to historical aluminum profile cutting data, and extract the cutting yield, and construct an initial cutting model based on the cutting-related feature parameters and the cutting yield; a recombinant clustering model acquisition module 12 is used to cluster the initial cutting model according to cutting requirements and aluminum profile differences, perform control parameter homogeneity simulation on multiple clustering models according to the cutting yield, integrate to obtain a recombinant clustering model, and cache the recombinant clustering model; a matching recombinant clustering model acquisition module 13 is used to scan the target aluminum profile based on the cutting requirements and aluminum profile differences, and search for the recombinant clustering model to obtain a matching recombinant clustering model; a target cutting result acquisition module 14 is used to input the matching recombinant clustering model according to the target aluminum profile for automatic cutting control to obtain a target cutting result.

[0078] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0079] The cutting-related features are extracted according to the historical aluminum profile cutting data; the historical aluminum profile cutting data is divided into batches based on the cutting requirements and aluminum profile differences to obtain multiple batches of aluminum profile cutting data; the first batch of aluminum profile cutting data is extracted from the multiple batches of aluminum profile cutting data; the first batch of aluminum profile cutting data is interacted with the cutting-related features to obtain first cutting-related feature parameters; the first batch of cutting yield is extracted from the first batch of aluminum profile cutting data; a first cutting model is constructed through the first cutting-related feature parameters and the first batch of cutting yield, and is added to the initial cutting model.

[0080] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0081] A preset clustering benchmark is configured for the cutting requirements and aluminum profile differences; the initial cutting model is clustered and divided according to the preset clustering benchmark to obtain the multiple clustering models; a first clustering model is extracted according to the multiple clustering models; the first clustering cutting yield of the first clustering model is screened according to the cutting yield threshold as the screening benchmark to obtain a screened first clustering model and a filtered first clustering model; the filtered first clustering model is subjected to a control parameter homogenization simulation tending to the screened first clustering model until the first clustering cutting yield of the filtered first clustering model meets the screening benchmark and is converted into the screened first clustering model; the screened first clustering model is reorganized to obtain a reorganized first clustering model, and is added to the reorganized clustering model.

[0082] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0083] A yield correlation analysis is performed based on the cutting association feature to obtain a feature yield correlation coefficient; a first control model is extracted from the screened first clustering model based on the first cluster cutting yield of the screened first clustering model; a first adjustment model is extracted based on the filtered first clustering model; the absolute value of the difference between the cutting association feature parameters of the first adjustment model and the first control model is used as a first adjustment threshold; the cutting association feature parameters of the first adjustment model are extracted based on the cutting association feature to obtain first feature parameters; the feature yield correlation coefficient is used as an adjustment sequence, and the first feature parameters are adjusted toward the first control model in sequence, and when the adjustment amplitude of the previous sequence in the adjustment sequence meets the adjustment threshold, the next adjustment sequence is executed until the first adjustment cutting yield of the first adjustment model meets the screening benchmark after any sequence is executed; the first adjustment model whose first adjustment cutting yield meets the screening benchmark is converted, traversed through the filtered first clustering model, and added to the screened first clustering model.

[0084] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0085] Based on the cutting requirements and the differences in aluminum profiles, the matching and recombination clustering model is subjected to similarity matching of the target aluminum profile to obtain a similarity result; a first matching model is determined based on the similarity result; the target aluminum profile is input into the first matching model, and a first matching yield rate is output; it is determined whether the first matching yield rate meets a cutting yield rate threshold; if the first matching yield rate does not meet the cutting yield rate threshold, the target aluminum profile is input into the matching and recombination clustering model with the similarity results as a sequence until the matching yield rate meets the cutting yield rate threshold; if the first matching yield rate meets the cutting yield rate threshold, the target aluminum profile is automatically controlled for cutting according to the first matching parameter of the first matching model to obtain a target cutting result.

[0086] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0087] The actual yield rate of the target cutting result is obtained by real-time monitoring of the visual monitoring unit; if the actual yield rate does not meet the actual yield threshold, the feedback relearning mechanism is triggered; the actual cutting model is obtained based on the feedback relearning mechanism, and automatic cutting control is performed according to the actual cutting model.

[0088] Furthermore, the intelligent control device for automatic cutting of aluminum profiles is also used for:

[0089] If the feedback re-learning mechanism is triggered, incremental learning is performed based on real-time failure samples, and the actual cutting model is obtained through adjustment.

[0090] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The intelligent control method for automatic cutting of aluminum profiles and the specific examples in the aforementioned embodiment one are also applicable to an intelligent control device for automatic cutting of aluminum profiles in this embodiment. Through the aforementioned detailed description of the intelligent control method for automatic cutting of aluminum profiles, those skilled in the art can clearly understand the intelligent control device for automatic cutting of aluminum profiles in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0091] 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.

[0092] 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. An intelligent control method for automatic cutting of aluminum profiles, characterized in that: include: Extracting cutting-related feature parameters with cutting-related features as a control based on historical aluminum profile cutting data, extracting a cutting yield rate, and constructing an initial cutting model based on the cutting-related feature parameters and the cutting yield rate; Clustering the initial cutting model based on cutting requirements and aluminum profile differences, performing control parameter homogenization simulation on multiple clustering models based on the cutting yield, integrating and obtaining a recombined clustering model, and caching the recombined clustering model, including: Configuring a preset clustering benchmark for the cutting requirements and aluminum profile differences; Clustering the initial cutting model based on the preset clustering benchmark to obtain the multiple clustering models; extracting a first clustering model according to the multiple clustering models; Taking the cutting yield rate threshold as a screening benchmark, screening the first cluster cutting yield rate of the first cluster model to obtain a screened first cluster model and a filtered first cluster model; Performing a control parameter homogeneity simulation on the filtered first cluster model toward the screened first cluster model until the first cluster cutting yield of the filtered first cluster model meets the screening benchmark, and converting the model into the screened first cluster model; Recombining the screened first cluster model to obtain a recombined first cluster model, and adding the recombined first cluster model to the recombined cluster model; Scanning the target aluminum profile based on the cutting requirements and the aluminum profile differences, and searching the recombinant clustering model to obtain a matching recombinant clustering model; The matching and recombinant clustering model is input according to the target aluminum profile to perform automatic cutting control to obtain a target cutting result.

2. The intelligent control method for automatic cutting of aluminum profiles according to claim 1, characterized in that: The method comprises extracting cutting-related feature parameters based on cutting-related features according to historical aluminum profile cutting data, extracting a cutting yield rate, and constructing an initial cutting model based on the cutting-related feature parameters and the cutting yield rate, including: Extracting the cutting-related features according to the historical aluminum profile cutting data; Dividing the historical aluminum profile cutting data into batches based on the cutting requirements and aluminum profile differences to obtain multiple batches of aluminum profile cutting data; Extracting the first batch of aluminum profile cutting data from the multiple batches of aluminum profile cutting data; Interacting the first batch of aluminum profile cutting data with the cutting-related feature to obtain first cutting-related feature parameters; Extracting the first batch cutting yield rate from the first batch of aluminum profile cutting data; A first cutting model is constructed using the first cutting-associated characteristic parameters and the first batch cutting yield, and is added to the initial cutting model.

3. The intelligent control method for automatic cutting of aluminum profiles according to claim 1, characterized in that: The step of performing a control parameter homogenization simulation on the filtered first cluster model to approach the screened first cluster model until the first cluster cutting yield of the filtered first cluster model meets the screening benchmark and converting the model into the screened first cluster model includes: Performing a yield rate correlation analysis based on the cutting correlation features to obtain a feature yield rate correlation coefficient; Extracting a first control model from the screened first cluster model according to the first cluster cutting yield of the screened first cluster model; extracting a first adjustment model based on the filtering of the first clustering model; The absolute value of the difference between the cutting-related characteristic parameters of the first adjustment model and the first control model is used as a first adjustment threshold; Extracting the cutting-related feature parameters from the first adjustment model based on the cutting-related feature to obtain first feature parameters; Using the characteristic yield correlation coefficient as an adjustment sequence, sequentially adjusting the first characteristic parameter toward the first control model, executing a subsequent adjustment sequence when the adjustment amplitude of a previous sequence in the adjustment sequence meets the first adjustment threshold, until the first adjustment cutting yield of the first adjustment model meets the screening benchmark after any sequence is executed; The first adjustment model whose first adjustment cutting yield meets the screening benchmark is converted, traversed through the filtered first clustering model, and added to the screened first clustering model.

4. The intelligent control method for automatic cutting of aluminum profiles according to claim 1, characterized in that: The step of inputting the matching and recombining clustering model according to the target aluminum profile to automatically control cutting and obtain a target cutting result includes: Based on the cutting requirements and the differences in aluminum profiles, the matching and recombinant clustering model is used to perform similarity matching on the target aluminum profile to obtain a similarity result; determining a first matching model based on the similarity result; Inputting the target aluminum profile into the first matching model and outputting a first matching yield rate; Determining whether the first matching yield rate meets a cutting yield rate threshold; If the first matching yield rate does not meet the cutting yield rate threshold, the target aluminum profile is input into the matching recombination clustering model using the similarity results as a sequence until the matching yield rate meets the cutting yield rate threshold; If the first matching yield rate meets the cutting yield rate threshold, the target aluminum profile is automatically cut and controlled according to the first matching parameter of the first matching model to obtain a target cutting result.

5. The intelligent control method for automatic cutting of aluminum profiles according to claim 1, characterized in that: Also includes: Obtaining the actual yield rate of the target cutting result through real-time monitoring by a visual monitoring unit; If the actual yield rate does not meet the actual yield threshold, a feedback re-learning mechanism is triggered; An actual cutting model is obtained based on the feedback relearning mechanism, and automatic cutting control is performed according to the actual cutting model.

6. The intelligent control method for automatic cutting of aluminum profiles according to claim 5, characterized in that: If the feedback re-learning mechanism is triggered, incremental learning is performed based on real-time failure samples, and the actual cutting model is obtained through adjustment.

7. An intelligent control device for automatic cutting of aluminum profiles, characterized in that: The steps for implementing the intelligent control method for automatic cutting of aluminum profiles according to any one of claims 1 to 6 include: An initial cutting model construction module is configured to extract cutting-related feature parameters based on cutting-related features according to historical aluminum profile cutting data, extract a cutting yield rate, and construct an initial cutting model based on the cutting-related feature parameters and the cutting yield rate; a recombinant clustering model acquisition module, the recombinant clustering model acquisition module being used to cluster the initial cutting model according to cutting requirements and aluminum profile differences, perform control parameter homogenization simulation on multiple clustering models according to the cutting yield, integrate and obtain a recombinant clustering model, and cache the recombinant clustering model; A matching and recombinant clustering model acquisition module, wherein the matching and recombinant clustering model acquisition module is used to scan the target aluminum profile based on the cutting requirements and the aluminum profile differences, and search for the recombinant clustering model to obtain a matching and recombinant clustering model; The target cutting result acquisition module is used to input the matching recombinant clustering model according to the target aluminum profile to perform automatic cutting control and obtain the target cutting result.

Images