New energy aluminum alloy part manufacturing and processing quality monitoring system and method

Abstract

The invention relates to the technical field of quality monitoring, and discloses a new energy aluminum alloy part manufacturing and processing quality monitoring system and method. According to the system and method, multi-dimensional parameters in the machining process are obtained in real time through the data collection module, denoising, standardization and other processing are conducted through the data preprocessing module, the reliability of data is ensured, the preprocessed parameters are deeply analyzed through the data analysis module, important machining quality indexes are automatically calculated, and the machining quality is improved. The quality monitoring module is combined with an artificial intelligence technology to intelligently analyze processing quality indexes and generate a detailed quality monitoring report, the result reporting module transmits the monitoring report to the industrial quality monitoring large screen to realize real-time monitoring, and the implementation of the system not only can reduce the dependence on manual sampling inspection and improve the automation degree of the manufacturing process, but also can realize real-time monitoring. And the production cost can be effectively reduced, and the consistency and reliability of products are improved, so that the requirements of the modern manufacturing industry on high precision and high efficiency are better met.

Description

Technical Field

[0001] This invention relates to the field of quality monitoring technology, specifically to a quality monitoring system and method for the manufacturing and processing of new energy aluminum alloy parts. Background Technology

[0002] In modern manufacturing, aluminum alloys are widely used in aerospace, automobile manufacturing, and electronic equipment due to their excellent lightweight properties and strength. With the development of new energy technologies, the demand for aluminum alloy parts has increased dramatically. However, the manufacturing process of aluminum alloy parts is complex, involving the real-time monitoring and analysis of multiple parameters. Any minor processing defect may lead to product failure and performance degradation. Therefore, how to effectively monitor and control these processing quality indicators has become an important challenge for aluminum alloy manufacturing enterprises.

[0003] Currently, traditional methods for monitoring the quality of aluminum alloy parts mostly rely on manual sampling and experience-based judgment, which is difficult to meet the requirements of modern manufacturing for high precision and high efficiency. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a quality monitoring system and method for the manufacturing and processing of new energy aluminum alloy parts. The system utilizes a data acquisition module to acquire multi-dimensional parameters during the processing in real time. A data preprocessing module performs noise reduction and standardization to ensure data reliability. A data analysis module conducts in-depth analysis of the preprocessed parameters, automatically calculating key processing quality indicators such as surface roughness, dimensional tolerances, and processing pass rate, enabling timely detection of potential quality problems. A quality monitoring module, combined with artificial intelligence technology, intelligently analyzes the processing quality indicators and generates detailed quality monitoring reports, ensuring timely adjustments and optimizations by the enterprise. A results reporting module transmits the monitoring reports to an industrial quality monitoring screen for real-time monitoring. The implementation of this system not only reduces reliance on manual sampling and increases the automation level of the manufacturing process but also effectively reduces production costs and improves product consistency and reliability, thereby better meeting the high precision and efficiency requirements of modern manufacturing.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides the following technical solution: a quality monitoring system for the manufacturing and processing of new energy aluminum alloy parts, comprising a data acquisition module, a data preprocessing module, a data analysis module, a quality monitoring module, and a result reporting module;

[0008] The data acquisition module is used to acquire processing parameters, environmental parameters, equipment parameters, and material parameters during the manufacturing and processing of new energy aluminum alloy parts;

[0009] The data preprocessing module is used to preprocess processing parameters, environmental parameters, equipment parameters and material parameters, including parameter denoising, duplicate parameter removal and parameter standardization. The preprocessed parameter data is stored in the system database.

[0010] The data analysis module acquires parameter data from the system database and performs monitoring and calculation of processing quality indicators, including calculating the surface roughness of parts, dimensional tolerances of parts, processing efficiency, failure rate of processing equipment, and pass rate of parts processing. Once the processing quality indicators are calculated, they are sent to the quality monitoring module.

[0011] The quality monitoring module uses artificial intelligence to analyze the processing quality indicators and generates a quality monitoring report for the manufacturing of aluminum alloy parts, which is then sent to the results reporting module.

[0012] The results reporting module transmits the aluminum alloy parts manufacturing and processing quality monitoring report to the industrial quality monitoring screen for real-time monitoring of the processing quality of aluminum alloy parts.

[0013] Preferably, the formula for parameter denoising is as follows:

[0014]

[0015] In the formula, S n X represents the parameter value after denoising. n-i This represents the i-th observation before the n-th time point, where N represents the number of observations used to calculate the average, and i represents the index subscript.

[0016] Preferably, the formula used for removing duplicate parameters is as follows:

[0017] data_unique=drop_duplicates(x)

[0018] In the formula, data_unique represents the dataset that receives duplicate parameters, drop_duplicates represents the parameter deduplication function, and x represents the parameter dataset to be deduplicated.

[0019] Preferably, the formula for standardizing the parameters is as follows:

[0020]

[0021] In the formula, B ’ B represents the standardized parameters, and B represents the original parameters. min B represents the minimum value in the parameter dataset. max This represents the maximum value in the parameter dataset.

[0022] Preferably, the formula for calculating the surface roughness of the component is as follows:

[0023]

[0024] In the formula, Ra represents the surface roughness of the component, m represents the number of measurement points, and Z... i This represents the height deviation of the i-th measurement point, where i represents the index subscript.

[0025] Preferably, the formula for calculating the dimensional tolerances of the components is as follows:

[0026] T = |L max -L min |

[0027] In the formula, T represents the dimensional tolerance of the component, and L... max L represents the maximum permissible dimension during design. min Indicates the minimum allowable size during design.

[0028] Preferably, the formula for calculating the processing efficiency is as follows:

[0029]

[0030] In the formula, E represents processing efficiency, and N... c Tr represents the number of qualified parts completed per unit of time, and Tr represents the part production time.

[0031] Preferably, the formula for calculating the failure rate of the processing equipment is as follows:

[0032]

[0033] In the formula, F r N represents the failure rate of the processing equipment. f N represents the number of failures that occur per unit of time. t This indicates the total number of times the equipment operates per unit of time.

[0034] Preferably, the formula for calculating the pass rate of component processing is as follows:

[0035]

[0036] In the formula, C r N represents the pass rate of parts processing. c N represents the number of qualified parts completed per unit of time. p This indicates the total number of parts produced.

[0037] A method for monitoring the manufacturing and processing quality of aluminum alloy components for new energy sources includes the following steps:

[0038] S1. Obtain processing parameters, environmental parameters, equipment parameters, and material parameters during the manufacturing and processing of new energy aluminum alloy parts;

[0039] S2. Preprocessing parameters, environmental parameters, equipment parameters, and material parameters, including parameter denoising, removal of duplicate parameters, and parameter standardization, and storing the preprocessed parameter data in the system database;

[0040] S3. Obtain parameter data from the system database and perform monitoring and calculation of processing quality indicators;

[0041] S4. Use artificial intelligence to analyze processing quality indicators and generate a quality monitoring report for aluminum alloy parts manufacturing.

[0042] S5. Transmit the aluminum alloy parts manufacturing and processing quality monitoring report to the industrial quality monitoring screen for real-time monitoring of the aluminum alloy parts processing quality.

[0043] Compared with the prior art, the present invention provides a quality monitoring system and method for the manufacturing and processing of new energy aluminum alloy parts, which has the following beneficial effects:

[0044] This invention utilizes a data acquisition module to acquire multi-dimensional parameters during the processing in real time. A data preprocessing module performs noise reduction and standardization to ensure data reliability. A data analysis module conducts in-depth analysis of the preprocessed parameters, automatically calculating key processing quality indicators such as surface roughness, dimensional tolerances, and processing pass rate, promptly identifying potential quality problems. A quality monitoring module, combined with artificial intelligence technology, intelligently analyzes the processing quality indicators and generates detailed quality monitoring reports, ensuring timely adjustments and optimizations for the enterprise. A results reporting module transmits the monitoring reports to an industrial quality monitoring dashboard for real-time monitoring. The implementation of this system not only reduces reliance on manual sampling and increases the automation level of the manufacturing process but also effectively reduces production costs and improves product consistency and reliability, thereby better meeting the high precision and efficiency requirements of modern manufacturing. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the system flow of the present invention;

[0046] Figure 2 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Traditional methods for monitoring the quality of aluminum alloy components rely heavily on manual sampling and experience-based judgment, which is insufficient to meet the high precision and efficiency requirements of modern manufacturing. Therefore, a quality monitoring system and method for the manufacturing and processing of new energy aluminum alloy components is proposed. Please refer to [link / reference]. Figure 1-2 The system includes a data acquisition module, a data preprocessing module, a data analysis module, a quality monitoring module, and a results reporting module.

[0049] The data acquisition module is responsible for acquiring and monitoring various types of parameters in real time to ensure the efficiency and stability of the production process. This module integrates a variety of sensors and instruments, including but not limited to temperature sensors, humidity sensors, pressure sensors and vibration sensors, to comprehensively capture various types of data related to processing.

[0050] The machining parameters include cutting speed, feed rate, depth of cut, and tool wear. These parameters are recorded in real time by high-precision CNC machine tools and cutting tool monitoring equipment. They are exchanged with the central control system through fieldbus technology (such as CAN bus or Modbus) to ensure real-time tracking and adjustment of these key parameters during the machining process, so as to optimize the machining process, reduce interference, and improve production efficiency.

[0051] Environmental parameters include temperature, humidity, and air quality in the production environment. By integrating environmental monitoring sensors with data acquisition systems, it is possible to monitor environmental changes in the production workshop in real time, thereby helping to identify environmental factors that may affect product quality. In addition, by connecting to an IoT (Internet of Things) platform, the collected environmental data can not only be stored but also used for big data analysis to improve the intelligence level of the entire production process.

[0052] The monitoring of equipment parameters mainly relies on the equipment self-monitoring system. By acquiring the equipment's operating status, fault logs, energy consumption data, etc. in real time, it ensures that all types of equipment operate under optimal conditions, greatly reducing the possibility of unexpected downtime and safety hazards. At the same time, by using predictive maintenance technology and training models based on equipment parameter data, the possibility of faults can be predicted, enabling early intervention and reducing maintenance costs.

[0053] Material parameters include the chemical composition, physical properties, hardness, and other key indicators of aluminum alloy materials. By using material testing instruments (such as spectrometers and hardness testers), the quality of raw materials put into production can be monitored in real time to ensure that they meet process requirements and guarantee the consistency and reliability of products from the source.

[0054] In summary, the data acquisition module not only covers information from multiple dimensions such as processing, environment, equipment and materials, but also provides a solid data foundation for production management and decision-making through real-time data transmission and integrated analysis. This significantly improves the processing quality and production efficiency of aluminum alloy parts for new energy vehicles, and promotes the transformation of the entire manufacturing process towards intelligence and digitalization.

[0055] The main task of the data preprocessing module is to comprehensively preprocess the collected processing parameters, environmental parameters, equipment parameters and material parameters to ensure that subsequent data analysis and decision-making are based on high-quality information. This module mainly includes three key steps: parameter denoising, duplicate parameter removal and parameter standardization.

[0056] First, in the process of parameter denoising, techniques such as moving average or median filtering can effectively eliminate random fluctuations and external noise in the data. For example, the formula for moving average is:

[0057]

[0058] This formula smooths the data by averaging the current data point and the N data points before it, reducing the impact of instantaneous fluctuations. In the manufacturing process, noise removal not only improves the signal-to-noise ratio of the data but also ensures the reliability of key parameters, thus providing a clear basis for subsequent quality control and optimization.

[0059] Secondly, removing duplicate parameters is another important step in ensuring data quality. The formula for parameter deduplication is:

[0060] data_unique=drop_duplicates(x)

[0061] Deduplication of parameters ensures that each observation in the dataset is unique, thereby improving the integrity and accuracy of the data and avoiding distortion caused by duplicate data in subsequent analysis. This process is particularly critical when storing data, because storing duplicate data wastes storage resources and introduces additional complexity in the analysis phase.

[0062] Finally, the parameter standardization process ensures the comparability of parameters with different dimensions on the same scale, thereby improving the performance of the prediction model. The standardization formula is as follows:

[0063]

[0064] This formula can transform the original data into a standard normal distribution, making the optimization process during model training more efficient. In addition, standardization is particularly important for machine learning algorithms because many algorithms are very sensitive to the scale of input features. Standardization will effectively improve the convergence speed and prediction accuracy of the model.

[0065] After the above processing, all preprocessed parameter data are stored in the system database for subsequent analysis and decision-making. The implementation of this preprocessing module not only improves the quality and consistency of the data, but also lays a solid foundation for subsequent quality monitoring and decision support systems, ensuring traceability and efficiency throughout the manufacturing process, and thus promoting the production of aluminum alloy parts for new energy vehicles to develop towards a higher level of intelligence and automation.

[0066] The data analysis module aims to extract and analyze various parameter data from the system database to monitor processing quality indicators and ensure that products meet strict quality standards. The module calculates key processing quality indicators, including component surface roughness, component dimensional tolerance, processing efficiency, processing equipment failure rate, and component processing pass rate, through a series of mathematical models and statistical methods. These calculation results are then sent to the quality monitoring module to provide decision support for subsequent quality control and improvement measures.

[0067] First, calculating the surface roughness of a component is a crucial step in evaluating its machining quality, and it is typically measured using the formula for arithmetic mean roughness (Ra):

[0068]

[0069] Among them, Z i The height deviation of the i-th measurement point is represented by m, and the number of measurement points is m. Through this formula, we can quantify the smoothness of the surface of the component. The smaller the surface roughness, the better the surface treatment, which is suitable for the requirements of precision components.

[0070] Secondly, the calculation of component dimensional tolerances is equally crucial to ensure that the parts are within the design dimensional range. The calculation formula is as follows:

[0071] T = |L max -L min |

[0072] Here, L max L represents the maximum permissible size of the design. min As the minimum permissible dimension for design, this indicator can clearly define the dimensional tolerance range of the product, improving the interchangeability of parts and assembly accuracy;

[0073] Next, the processing efficiency is evaluated using the following formula:

[0074]

[0075] In which, N c This represents the number of qualified parts completed per unit of time, while Tr is the total production time. Improved processing efficiency means increased production capacity per unit of time, which in turn affects production costs and resource utilization efficiency.

[0076] Meanwhile, the failure rate of processing equipment can be calculated using the following formula:

[0077]

[0078] Where, N f N represents the number of failures occurring per unit of time. t The total number of equipment operations; a low failure rate reflects the reliability of the equipment, reduces the risk of unplanned downtime, and optimizes the production process.

[0079] Finally, the calculation of the parts processing pass rate measures the effectiveness of quality control in the production process. The formula is:

[0080]

[0081] Here, N c It is the number of qualified parts, while N p It represents the total number of parts produced. A high pass rate indicates a large proportion of qualified products in the production process, which helps to improve customer satisfaction and market competitiveness.

[0082] Once the above quality indicators are calculated, the data will be organized and transmitted to the quality monitoring module to form a comprehensive quality monitoring system. Through these precise processing quality indicators, production managers can quickly identify potential quality problems and take corresponding improvement measures, which not only improves production efficiency but also ensures the high quality standards of aluminum alloy parts. The implementation of this data analysis process gives the entire manufacturing system powerful intelligent analysis capabilities, promoting technological progress and competitiveness enhancement in the new energy vehicle industry.

[0083] The quality monitoring module plays a core role in the manufacturing and processing of new energy aluminum alloy parts. It uses advanced artificial intelligence (AI) technology to conduct in-depth analysis of processing quality indicators, providing real-time feedback and improvement suggestions for the manufacturing process. Through machine learning algorithms, especially supervised and unsupervised learning methods, the module comprehensively evaluates key quality indicators (such as surface roughness, dimensional tolerance, processing efficiency, failure rate, and pass rate) obtained from the data analysis module. Using these algorithms, the system can identify potential quality trends and deviations, thereby helping engineers quickly reveal potential problems in the production process.

[0084] In terms of data processing, the quality monitoring module first uses data cleaning technology to ensure the accuracy and consistency of input indicators. These well-constructed models can process monitoring data from multiple production lines in real time and quickly generate manufacturing quality monitoring reports for aluminum alloy parts. The reports not only cover the evaluation results of various quality indicators, but also include trend analysis, anomaly detection, and recommended measures.

[0085] The generated quality monitoring report will then be automatically transmitted to the results reporting module, which is responsible for integrating the report and transmitting it to the industrial quality monitoring screen through the network system. This screen not only displays real-time quality data, but also uses visualization technology to transform complex information into intuitive charts and graphs, enabling managers and operators to grasp the current production quality status at a glance.

[0086] To ensure the accuracy and effectiveness of real-time monitoring, the industrial quality monitoring screen adopts a multi-layered display system that combines charts, monitoring views, and real-time alarm functions. For example, when a certain processing parameter deviates from the preset standard, the system will issue an alarm immediately to help operators react quickly and avoid potential quality problems. This efficient information transmission and feedback mechanism ensures the visualization and controllability of the processing quality of aluminum alloy parts throughout the entire production process, significantly improving the level of intelligence and response speed of quality management.

[0087] The implementation of this system can not only reduce reliance on manual sampling and improve the automation level of the manufacturing process, but also effectively reduce production costs and improve product consistency and reliability, thereby better meeting the requirements of modern manufacturing for high precision and high efficiency.

[0088] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A quality monitoring system for the manufacturing and processing of new energy aluminum alloy components, characterized in that: It includes a data acquisition module, a data preprocessing module, a data analysis module, a quality monitoring module, and a results reporting module; The data acquisition module is used to acquire processing parameters, environmental parameters, equipment parameters, and material parameters during the manufacturing and processing of new energy aluminum alloy parts; The data preprocessing module is used to preprocess processing parameters, environmental parameters, equipment parameters and material parameters, including parameter denoising, duplicate parameter removal and parameter standardization. The preprocessed parameter data is stored in the system database. The data analysis module acquires parameter data from the system database and performs monitoring and calculation of processing quality indicators, including calculating the surface roughness of parts, dimensional tolerances of parts, processing efficiency, failure rate of processing equipment, and pass rate of parts processing. Once the processing quality indicators are calculated, they are sent to the quality monitoring module. The quality monitoring module uses artificial intelligence to analyze the processing quality indicators and generates a quality monitoring report for the manufacturing of aluminum alloy parts, which is then sent to the results reporting module. The results reporting module transmits the aluminum alloy parts manufacturing and processing quality monitoring report to the industrial quality monitoring screen for real-time monitoring of the processing quality of aluminum alloy parts.

2. The new energy aluminum alloy component manufacturing and processing quality monitoring system according to claim 1, characterized in that: The formula for parameter denoising is as follows: In the formula, S n X represents the parameter value after denoising. n-i This represents the i-th observation before the n-th time point, where N represents the number of observations used to calculate the average, and i represents the index subscript.

3. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 2, characterized in that: The formula used for removing duplicate parameters is shown below: data_unique=drop_duplicates(x) In the formula, data_unique represents the dataset that receives duplicate parameters, drop_duplicates represents the parameter deduplication function, and x represents the parameter dataset to be deduplicated.

4. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 3, characterized in that: The formula for standardizing the parameters is as follows: In the formula, B′ represents the standardized parameters, B represents the original parameters, and B0 represents the parameters. min B represents the minimum value in the parameter dataset. max This represents the maximum value in the parameter dataset.

5. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 4, characterized in that: The formula for calculating the surface roughness of the component is as follows: In the formula, Ra represents the surface roughness of the component, m represents the number of measurement points, and Z... i This represents the height deviation of the i-th measurement point, where i represents the index subscript.

6. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 5, characterized in that: The formula for calculating the dimensional tolerances of the components is as follows: T=|L max -L min | In the formula, T represents the dimensional tolerance of the component, and L... max L represents the maximum permissible dimension during design. min Indicates the minimum allowable size during design.

7. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 6, characterized in that: The formula for calculating processing efficiency is as follows: In the formula, E represents processing efficiency, and N... c Tr represents the number of qualified parts completed per unit of time, and Tr represents the part production time.

8. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 7, characterized in that: The formula for calculating the failure rate of the processing equipment is as follows: In the formula, F r N represents the failure rate of the processing equipment. f N represents the number of failures that occur per unit of time. t This indicates the total number of times the equipment operates per unit of time.

9. The new energy aluminum alloy parts manufacturing and processing quality monitoring system according to claim 8, characterized in that: The formula for calculating the pass rate of component processing is as follows: In the formula, C r N represents the pass rate of parts processing. c N represents the number of qualified parts completed per unit of time. p This indicates the total number of parts produced.

10. A method for monitoring the manufacturing and processing quality of new energy aluminum alloy parts, characterized in that, Includes the following steps: S1. Obtain processing parameters, environmental parameters, equipment parameters, and material parameters during the manufacturing and processing of new energy aluminum alloy parts; S2. Preprocessing parameters, environmental parameters, equipment parameters, and material parameters, including parameter denoising, removal of duplicate parameters, and parameter standardization, and storing the preprocessed parameter data in the system database; S3. Obtain parameter data from the system database and perform monitoring and calculation of processing quality indicators; S4. Use artificial intelligence to analyze processing quality indicators and generate a quality monitoring report for aluminum alloy parts manufacturing. S5. Transmit the aluminum alloy parts manufacturing and processing quality monitoring report to the industrial quality monitoring screen for real-time monitoring of the aluminum alloy parts processing quality.

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