Aluminum alloy material online detection and real-time analysis system

Abstract

The invention provides an aluminum alloy material online detection and real-time analysis system, relates to the technical field of aluminum material processing, and adopts a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and early warning system, an intelligent optimization algorithm and a remote monitoring and control system to form the whole system. According to the system, various data parameters in the aluminum alloy material machining process are monitored in real time through a multi-sensor data acquisition system, and rapid and accurate processing and analysis are carried out through a real-time data processing and analysis algorithm; the abnormity detection and early warning system can give an alarm in time and provide corresponding processing suggestions to help operators to quickly take measures, and the system can automatically adjust processing parameters through analysis and learning of historical processing data and real-time monitoring data by using an intelligent optimization algorithm, so that comprehensive monitoring and analysis of the processing process can be realized, and the processing efficiency is improved. Product quality and production efficiency are improved, and production cost and manual intervention are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of aluminum processing, and specifically to an on-line detection and real-time analysis system for aluminum alloy materials. Background Art

[0002] A defect detection method and system for aluminum alloy casting products disclosed in Chinese Patent No. "CN117233179A". The method includes: placing the aluminum alloy casting product to be detected on an X-ray detection device; collecting an X-Ray detection image of the aluminum alloy casting product to be detected through the X-ray detection device; and analyzing the X-Ray detection image to obtain a defect detection result. In this way, the defect detection of aluminum alloy casting products is realized.

[0003] The above patent document and the prior art have the following technical problems in use:

[0004] Problem 1: Only warnings are given for abnormal situations during the aluminum alloy processing, and corresponding countermeasures and suggestions cannot be given, and the degree of intelligence is not high;

[0005] Problem 2: During the aluminum alloy processing, the processing parameters cannot be adaptively adjusted while monitoring the processing, and manual intervention is required, which is not conducive to fully automated monitoring and analysis processing. Summary of the Invention

[0006] Technical Problems to be Solved

[0007] In view of the deficiencies of the prior art, the present invention provides an on-line detection and real-time analysis system for aluminum alloy materials, which solves the following problems:

[0008] 1. The problem that during the aluminum alloy processing, while giving warnings, no processing suggestions can be given, and the degree of intelligence is not high;

[0009] 2. The problem that during the aluminum alloy processing, the system can only be adjusted through manual intervention and cannot be self-adjusted according to the situation.

[0010] Technical Solutions

[0011] To achieve the above objectives, the present invention is realized through the following technical solutions: An on-line detection and real-time analysis system for aluminum alloy materials, the system is composed of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and warning system, an intelligent optimization algorithm, and a remote monitoring and control system, wherein:

[0012] Multi-sensor data acquisition system: Composed of multiple sensors, used to collect various data parameters during the aluminum alloy material processing;

[0013] Real-time data processing and analysis algorithm: Using the machine algorithm steps inside the system, quickly and accurately process and analyze the data collected by the multi-sensor data acquisition system, and monitor the data anomalies during the processing of aluminum alloy materials;

[0014] Anomaly detection and warning system: Monitor the entire aluminum alloy material processing process, organize and analyze through internal machine learning algorithms, and when the system detects anomalies during the processing, issue an alarm in a timely manner and provide corresponding processing suggestions;

[0015] Intelligent optimization algorithm: Automatically adjust the processing parameters and optimize the processing process by analyzing and learning historical processing data and real-time monitoring data;

[0016] Remote monitoring and control system: Provide remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, and can remotely control and adjust the processing equipment for remote management and production scheduling.

[0017] Preferably, the multi-sensor data acquisition system includes a laser measurement sensor, an image recognition sensor, a pressure sensor, and a temperature sensor.

[0018] Preferably, the detection types of the multi-sensor data acquisition system mainly include dimension detection, surface quality detection, pressure monitoring, and temperature monitoring.

[0019] Preferably, the real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is carried out in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization.

[0020] Preferably, the real-time data processing and analysis algorithm internally has at least a rule-based algorithm, a statistical method, a machine learning algorithm, and an adaptive control algorithm.

[0021] Preferably, the anomaly detection and warning system includes an anomaly detection algorithm, threshold setting, a warning mechanism, and anomaly record analysis.

[0022] Preferably, the intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function, and adaptive learning.

[0023] Preferably, the anomaly detection and warning system further includes the following:

[0024] Anomaly detection algorithm: Used to monitor the real-time collected data, identify and analyze anomalies during the processing, and these algorithms can be designed based on principles such as rules, statistical methods, and machine learning;

[0025] Threshold setting: For each monitored parameter, a corresponding abnormal threshold is set. When the monitored data exceeds the set threshold range, the system will determine that an abnormal situation has occurred.

[0026] Preferably, the abnormal detection and early warning system further includes the following:

[0027] Early warning mechanism: When an abnormal situation is detected, the system will immediately issue an alarm to notify relevant personnel for handling. The early warning can be carried out through means such as sound, vision, and mobile phone text messages;

[0028] Abnormal record and analysis: For each abnormal situation, the system will record detailed information, including the time of abnormal occurrence, specific parameter change conditions, etc., for subsequent analysis and processing

[0029] Beneficial effects

[0030] The present invention provides an on-line detection and real-time analysis system for aluminum alloy materials. It has the following beneficial effects:

[0031] 1. The present invention uses a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an abnormal detection and early warning system, an intelligent optimization algorithm, and a remote monitoring and control system to form the entire system. The system uses the multi-sensor data acquisition system to monitor various data parameters in the aluminum alloy material processing process in real time, and uses the real-time data processing and analysis algorithm for fast and accurate processing and analysis. Once the system detects an abnormal situation in the processing process, the abnormal detection and early warning system will promptly issue an alarm and provide corresponding handling suggestions to help the operator quickly take measures to avoid production accidents or quality problems and ensure the safety and smooth progress of production.

[0032] 2. The system of the present invention uses the intelligent optimization algorithm to automatically adjust the processing parameters and optimize the processing process by analyzing and learning the historical processing data and real-time monitoring data. This enables the system to achieve adaptive adjustment of the processing parameters according to different processing situations and requirements, improve production efficiency and product quality, and can realize comprehensive monitoring and analysis of the processing process, improve product quality and production efficiency, and reduce production costs and manual intervention. Description of the drawings

[0033] Figure 1 It is the system structure diagram of the present invention. Specific implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0036] As Figure 1 shown, an on-line detection and real-time analysis system for aluminum alloy materials, the system is composed of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and warning system, an intelligent optimization algorithm, and a remote monitoring and control system, where:

[0037] The multi-sensor data acquisition system: consists of multiple sensors and is used to collect various data parameters during the processing of aluminum alloy materials;

[0038] The real-time data processing and analysis algorithm: uses the machine algorithm steps inside the system to quickly and accurately process and analyze the data collected by the multi-sensor data acquisition system, and monitors the data anomalies during the processing of aluminum alloy materials;

[0039] The anomaly detection and warning system: monitors the entire aluminum alloy material processing process, organizes and analyzes through the internal machine learning algorithm, and when the system detects an anomaly during the processing, it issues an alarm in a timely manner and provides corresponding processing suggestions;

[0040] The intelligent optimization algorithm: automatically adjusts the processing parameters and optimizes the processing process by analyzing and learning the historical processing data and real-time monitoring data;

[0041] The remote monitoring and control system: provides remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, and can remotely control and adjust the processing equipment for remote management and production scheduling.

[0042] The entire system consists of a multi-sensor data acquisition system, real-time data processing and analysis algorithms, anomaly detection and warning systems, intelligent optimization algorithms, and remote monitoring and control systems. The system uses the multi-sensor data acquisition system to monitor various data parameters during the processing of aluminum alloy materials in real time, and uses real-time data processing and analysis algorithms for fast and accurate processing and analysis. Once the system detects an abnormal situation during the processing, the anomaly detection and warning system will issue an alarm in a timely manner and provide corresponding processing suggestions to help operators take measures quickly to avoid production accidents or quality problems, ensuring the safety and smooth progress of production. The system uses intelligent optimization algorithms to automatically adjust processing parameters and optimize the processing process by analyzing and learning historical processing data and real-time monitoring data. This enables the system to achieve adaptive adjustment of processing parameters according to different processing conditions and requirements, improving production efficiency and product quality, and being able to achieve comprehensive monitoring and analysis of the processing process, improving product quality and production efficiency, and reducing production costs and manual intervention. Specific Embodiment Two:

[0044] As Figure 1 shown, the multi-sensor data acquisition system includes a laser measurement sensor, an image recognition sensor, a pressure sensor, and a temperature sensor. The specific details are as follows:

[0045] Laser measurement sensor: Used to measure the size and shape of aluminum alloy materials, including parameters such as length, width, and thickness. Through the laser measurement sensor, real-time monitoring of the size of aluminum alloy materials during the processing can be achieved;

[0046] Image recognition sensor: Used to detect and analyze the surface quality of aluminum alloy materials, including surface defects, foreign objects, bubbles, etc. The image recognition sensor can take surface images of aluminum alloy materials and analyze them through image processing technology to achieve real-time monitoring of surface quality;

[0047] Pressure sensor: Used to monitor the pressure applied to aluminum alloy materials during the processing to ensure the stability and quality of the processing. The pressure sensor can collect the pressure changes during the processing in real time and transmit the data to the data acquisition system for processing and analysis;

[0048] Temperature sensor: Used to monitor the temperature of the processing equipment and aluminum alloy materials to prevent the influence of too high or too low temperature on the processing process. The temperature sensor can monitor the temperature changes of the processing equipment and aluminum alloy materials in real time and issue an alarm in a timely manner to ensure the stability of the processing process.

[0049] The detection types of the multi-sensor data acquisition system mainly include size detection, surface quality detection, pressure monitoring, and temperature monitoring.

[0050] Dimensional inspection: Use a laser measurement sensor to monitor the dimensional changes of aluminum alloy materials in real time, including parameters such as length, width, and thickness, to ensure dimensional accuracy and consistency during the processing;

[0051] Surface quality inspection: Use an image recognition sensor to detect and analyze the surface quality of aluminum alloy materials, including surface defects, foreign objects, bubbles, etc., to ensure that the surface quality of the product meets the requirements;

[0052] Pressure monitoring: Use a pressure sensor to monitor the pressure changes applied to aluminum alloy materials during the processing to ensure the stability and quality of the processing;

[0053] Temperature monitoring: Use a temperature sensor to monitor the temperature changes of the processing equipment and aluminum alloy materials to prevent adverse effects on the processing caused by too high or too low temperatures;

[0054] Through the setting of the multi-sensor data acquisition system, comprehensive monitoring and real-time analysis of the aluminum alloy material processing process can be achieved, ensuring the improvement of product quality and production efficiency. Specific Embodiment Three:

[0056] As Figure 1 shown, the real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is carried out in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization. The specific content of each step is as follows:

[0057] Data preprocessing: Preprocess the raw data collected from multiple sensors, including noise removal, smoothing, data normalization, etc., to ensure data quality and reliability;

[0058] Feature extraction: Extract meaningful features from the preprocessed data to describe the features and laws in the aluminum alloy material processing process. Common features include mean, variance, gradient, etc.;

[0059] Real-time monitoring and analysis: Based on the extracted features, design a real-time monitoring and analysis algorithm to detect abnormal situations and problems in the processing process and make timely responses. Common algorithms include rule-based algorithms, statistical methods, machine learning methods, etc.;

[0060] Adaptive optimization: According to the results of real-time monitoring and analysis, adaptively adjust the processing parameters or control strategies to optimize the processing process and improve product quality and production efficiency.

[0061] The real-time data processing and analysis algorithm internally has at least rule-based algorithms, statistical methods, machine learning algorithms, and adaptive control algorithms. The specific content is as follows:

[0062] Rule-based algorithms: Make judgments and decisions based on pre-set rules or conditions.

[0063] Statistical methods: Use statistical principles for data analysis and pattern recognition. Common methods include mean, variance, covariance, correlation coefficient, etc. For example:

[0064]

[0065] Machine learning methods: Utilize machine learning algorithms to learn patterns and rules from data and apply them to tasks such as prediction, classification, or clustering. Common machine learning algorithms include support vector machines (SVM), neural networks, decision trees, etc.

[0066] y = f(x)

[0067] Adaptive control algorithms: According to real-time monitoring data and feedback information, adaptively adjust control parameters to achieve the optimal performance of the system.

[0068] In practical applications, multiple algorithms and technologies may be combined and selected and combined according to specific requirements and situations to achieve real-time monitoring and analysis of the aluminum alloy material processing process. Specific embodiment four:

[0070] Such as Figure 1 As shown, the anomaly detection and warning system includes anomaly detection algorithms, threshold setting, warning mechanisms, and anomaly record analysis. The specific contents are as follows:

[0071] Anomaly detection algorithms: Used to monitor real-time collected data, identify and analyze abnormal situations in the processing process. These algorithms can be designed based on principles such as rules, statistical methods, and machine learning;

[0072] Threshold setting: For each monitored parameter, set the corresponding anomaly threshold. When the monitored data exceeds the set threshold range, the system will determine that an abnormal situation has occurred;

[0073] Warning mechanism: When an abnormal situation is detected, the system will immediately issue an alarm to notify relevant personnel for handling. The warning can be carried out through means such as sound, vision, and mobile phone text messages;

[0074] Anomaly record and analysis: For each abnormal situation, the system will record detailed information, including the time of anomaly occurrence, specific parameter change conditions, etc., for subsequent analysis and processing.

[0075] The specific method is as follows:

[0076] Rule setting: Based on the experience and professional knowledge of the processing process, set a set of anomaly detection rules. For example, set rules such as the temperature exceeding a certain range, abnormal increase in pressure, and deviation of dimensions from the standard;

[0077] Statistical methods: Use statistical methods to analyze the data collected in real time, identify abnormal situations. For example, use indicators such as mean, variance, and standard deviation to determine whether the data is abnormal;

[0078] Machine learning methods: Utilize machine learning algorithms to learn normal patterns from historical data, and compare the newly collected data with the learned patterns to identify abnormal situations. Commonly used algorithms include support vector machines, neural networks, random forests, etc.;

[0079] Real-time monitoring: The system monitors various parameters during the processing in real time, and continuously analyzes the data. Once an abnormal situation is detected, the warning mechanism is triggered immediately.

[0080] Through the above methods, the anomaly detection and warning system can timely detect and handle abnormal situations during the processing, ensuring product quality and production efficiency. Specific Embodiment Five:

[0082] As Figure 1 shown, the intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function, and adaptive learning. The specific content is as follows:

[0083] Parameter adjustment strategy: Automatically adjust the key parameters during the processing, such as temperature, pressure, speed, etc., according to the data and feedback information monitored in real time to optimize the processing;

[0084] Optimization objective function: Design an optimization objective function to measure the efficiency and quality of the processing. The optimization objective can be to maximize the production speed, minimize the processing cost, maximize the product quality, etc.;

[0085] Adaptive learning: The system has the ability of adaptive learning, and can continuously adjust the parameters and strategies of the optimization algorithm according to historical data and real-time monitoring data to adapt to different processing scenarios and workpiece requirements.

[0086] The specific method is as follows:

[0087] Rule-based optimization: Set a set of rule-based optimization strategies, and select appropriate parameter adjustment schemes according to different processing situations and product requirements;

[0088] Statistical methods: Use statistical methods to analyze and model the data during the processing, and adjust the processing parameters according to the modeling results to achieve the optimization objective;

[0089] Machine learning method: Using machine learning algorithms to learn the patterns and rules of the processing process from historical data, and automatically adjusting the processing parameters according to the learned patterns. Commonly used algorithms include genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, etc.

[0090] When the intelligent optimization algorithm is actually used, one or several of the following algorithms can be selected:

[0091] Genetic algorithm: The genetic algorithm is a bionic optimization algorithm that searches for the optimal solution by simulating the evolutionary process in nature. Its basic formulas include operations such as selection, crossover, and mutation;

[0092] Particle swarm optimization algorithm: The particle swarm optimization algorithm simulates the behavior of bird flocks or fish schools, and searches for the optimal solution by continuously adjusting the positions and velocities of particles. Its basic formulas include the formulas for updating the positions and velocities of particles;

[0093] Simulated annealing algorithm: The simulated annealing algorithm simulates the temperature change during the annealing process of metals, and searches for the optimal solution through random perturbations and acceptance probabilities. Its basic formulas include the calculation formula for the acceptance probability and the rules for state transition.

[0094] Through the above methods and algorithms, the intelligent optimization algorithm can achieve real-time optimization of the processing process, improve product quality and production efficiency, and reduce production costs and energy consumption.

[0095] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0096] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An on-line detection and real-time analysis system for aluminum alloy materials, characterized in that: The system consists of a multi-sensor data acquisition system, a real-time data processing and analysis algorithm, an anomaly detection and warning system, an intelligent optimization algorithm, and a remote monitoring and control system, where: Multi-sensor data acquisition system: Composed of multiple sensors, it is used to collect various data parameters during the processing of aluminum alloy materials; Real-time data processing and analysis algorithm: Using the machine algorithm steps inside the system, it quickly and accurately processes and analyzes the data collected by the multi-sensor data acquisition system, and monitors data anomalies during the processing of aluminum alloy materials; Anomaly detection and warning system: Monitors the entire aluminum alloy material processing process, organizes and analyzes it through internal machine learning algorithms. When the system detects anomalies during the processing, it issues an alarm in a timely manner and provides corresponding handling suggestions; Intelligent optimization algorithm: By analyzing and learning historical processing data and real-time monitoring data, it automatically adjusts processing parameters and optimizes the processing process; Remote monitoring and control system: Provides remote monitoring and control functions, allowing operators to remotely monitor the processing process through the network, and can remotely control and adjust processing equipment for remote management and production scheduling.

2. The on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: The multi-sensor data acquisition system includes a laser measurement sensor, an image recognition sensor, a pressure sensor, and a temperature sensor.

3. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: The detection types of the multi-sensor data acquisition system mainly include dimension detection, surface quality detection, pressure monitoring, and temperature monitoring.

4. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: The real-time data processing and analysis algorithm mainly includes data preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization, and each step is carried out in the order of preprocessing, feature extraction, real-time monitoring and analysis, and adaptive optimization.

5. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: Inside the real-time data processing and analysis algorithm, there are at least a rule-based algorithm, a statistical method, a machine learning algorithm, and an adaptive control algorithm.

6. The on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: The anomaly detection and warning system includes an anomaly detection algorithm, threshold setting, a warning mechanism, and anomaly record analysis.

7. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 1, characterized in that: The intelligent optimization algorithm includes three steps: parameter adjustment strategy, optimization objective function, and adaptive learning.

8. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 6, characterized in that: The anomaly detection and warning system further includes the following: Anomaly detection algorithm: Used to monitor the real-time collected data, identify and analyze anomalies during the processing. These algorithms can be designed based on principles such as rules, statistical methods, and machine learning; Threshold setting: For each monitored parameter, set the corresponding anomaly threshold. When the monitored data exceeds the set threshold range, the system will determine that an anomaly has occurred.

9. An on-line detection and real-time analysis system for an aluminum alloy material according to claim 6, characterized in that: The anomaly detection and warning system further includes the following: Warning mechanism: When an anomaly is detected, the system will immediately issue an alarm to notify relevant personnel for handling. The warning can be carried out through means such as sound, vision, and mobile phone text messages; Anomaly record and analysis: For each anomaly, the system will record detailed information, including the anomaly occurrence time, specific parameter change conditions, etc., for subsequent analysis and processing.

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