A performance testing system for aluminum alloy forming

Abstract

The present invention discloses a performance detection system for aluminum alloy forming, which relates to the technical field of aluminum alloy performance detection, and comprises a data acquisition module, which is used to collect data from various sensors and monitoring probes in an environment, and also comprises a processing and analysis module, a control module, a forming module, a monitoring and feedback module, a performance detection module, a display and interaction module and a storage module. The advantages of the present invention are: by utilizing image processing and machine learning technology, the collected data is visualized, image preprocessed, feature extracted and defect identified, thereby improving the recognition accuracy and efficiency of aluminum alloy surface defects. At the same time, the control module intelligently generates equipment parameter adjustment instructions according to the analysis results, and realizes precise control through an actuator, thereby optimizing the aluminum alloy forming process, reducing material waste and the time cost of subsequent corrections, thereby improving the overall production efficiency and product quality.

Description

Technical Field

[0001] The invention relates to the technical field of aluminum alloy performance detection, and in particular to a performance detection system for aluminum alloy forming. Background Art

[0002] As a lightweight, high-strength metal material, aluminum alloy has been widely used in aerospace, automobile manufacturing, construction and other fields. However, the forming process of aluminum alloy is complex and sensitive to process parameters. Different forming conditions will directly affect the performance and quality of the product. Therefore, it is very important to accurately control the aluminum alloy forming process and perform performance testing.

[0003] The traditional aluminum alloy forming process mainly relies on manual experience and simple monitoring equipment, which has many shortcomings. Manual monitoring makes it difficult to achieve real-time monitoring and precise control of all parameters in the forming process, resulting in unstable product quality and serious material waste. Traditional detection methods mostly rely on manual visual inspection, which is not only inefficient but also easily affected by human factors, resulting in missed defects or misjudgment. In addition, with the continuous development of aluminum alloy forming technology, the complexity of product structure and the requirements for precision are getting higher and higher. Traditional detection methods can no longer meet the needs of modern production. For this reason, we propose a performance detection system for aluminum alloy forming. Summary of the invention

[0004] The purpose of the present invention is to provide a performance detection system for aluminum alloy forming.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a performance detection system for aluminum alloy forming, comprising a data acquisition module, the data acquisition module is used to collect data from various sensors and monitoring probes in the environment, and pre-process the collected data, and also includes the following modules:

[0006] A processing and analysis module, used to process and analyze the collected data;

[0007] The control module generates device parameter adjustment instructions based on the analysis results and controls the actuators to execute the corresponding instructions;

[0008] A forming module, comprising a forming simulation unit and a forming execution unit, wherein the forming simulation unit is used to simulate and optimize the forming process, and the forming execution unit is used to execute the forming operation of the aluminum alloy;

[0009] The monitoring and feedback module monitors the parameters and status of the molding process in real time and issues alarms for abnormal data;

[0010] The performance detection module can detect various parameters of the aluminum alloy after forming and determine the performance status of the aluminum alloy;

[0011] Display and interaction module, providing users with intuitive operation interface and data display;

[0012] The storage module stores collected data, analysis results, equipment parameters, and operation record information, and backs up and encrypts the data.

[0013] As a further solution of the present invention: the various types of sensors include temperature, humidity, pressure and displacement sensors, the preprocessing method is to clean and normalize the data, and the data acquisition module also includes a sensor scheduling unit, and the sensor scheduling unit is used to adjust the position of the sensor in the environment.

[0014] As a further solution of the present invention: the processing and analysis module includes a data visualization unit, an image preprocessing unit, a feature extraction unit and a defect recognition unit. The visualization unit is used to convert the collected sensor data into a numerical format, and clean and aggregate the data. The aggregation time interval is 24 hours. The image preprocessing unit is used to segment the image data collected by the monitoring probe. The feature extraction unit is used to analyze the texture and shape characteristics of the aluminum alloy surface and determine the type of the aluminum alloy. The defect recognition and classification unit is used to classify and recognize the extracted features. The specific method of feature classification is to establish a convolutional neural network model and use a labeled defect image data set for training. The specific method of feature recognition is to use the trained model to detect the image data segmented by the image preprocessing unit to determine the defects in the image.

[0015] As a further solution of the present invention: the control module includes an instruction generation unit, an instruction transmission unit, an actuator control unit and a safety protection unit. The instruction generation unit is used to generate specific device parameter adjustment instructions according to the analysis results output by the processing and analysis module. The instruction transmission unit is used to transmit the generated device parameter adjustment instructions to the corresponding actuator. The actuator control unit is responsible for receiving the device parameter adjustment instructions sent by the instruction transmission unit and controlling the actuator to perform corresponding operations. The safety protection unit is used to monitor the status of the device during the device parameter adjustment process and execute the corresponding exception handling solution when an abnormal situation is detected.

[0016] As a further solution of the present invention: The forming simulation unit uses MAGMAsoft die-casting forming simulation software for simulation. The forming module further includes a parameter input unit and a defect prediction unit. The parameter input unit is used to receive the aluminum alloy material parameters, mold parameters, and forming process parameters input by the user, and transfer these parameters to the forming simulation unit for simulation calculation. The defect prediction unit is used to predict the types and positions of defects that occur during the aluminum alloy forming process, and give corresponding optimization solutions. The forming execution unit will generate specific equipment parameter adjustment instructions according to the optimization solution of the forming simulation unit, and transmit them to the actuator of the forming execution unit through the instruction transmission unit.

[0017] As a further solution of the present invention: The monitoring and feedback module includes a data analysis unit. The data analysis unit can process and analyze the sensor data received in real time, identify the abnormal states and trends of temperature, pressure, and displacement during the forming process, judge the types and severity of the abnormal data, and execute corresponding alarm mechanisms according to the severity. The severity is divided into minor abnormality, medium abnormality, and severe abnormality, and the severity is judged by a formula. The specific formula is as follows:

[0018]

[0019]

[0020]

[0021] Among them, K1, K2, and K3 are the temperature anomaly value, pressure anomaly value, and displacement anomaly value respectively. T0, P0, and L0 represent the preset temperature value, pressure value, and displacement value respectively. T1, P1, and L1 represent the real-time temperature value, pressure value, and displacement value respectively. If 0 < K1 ≤ 0.05, it is judged as a minor abnormality. If 0.05 < K1 ≤ 0.15, it is judged as a medium abnormality. If K1 > 0.15, it is judged as a severe abnormality. If 0 < K2 ≤ 0.05, it is judged as a minor abnormality. If 0.05 < K2 ≤ 0.15, it is judged as a medium abnormality. If K2 > 0.15, it is judged as a severe abnormality. If 0 < K3 ≤ 0.05, it is judged as a minor abnormality. If 0.05 < K3 ≤ 0.15, it is judged as a medium abnormality. If K3 > 0.15, it is judged as a severe abnormality. The alarm mechanism for minor abnormality is to send a text message warning notice to relevant personnel. The alarm mechanism for medium abnormality is to issue a flashing light alarm and stop the abnormal emergency equipment through the control module. The alarm mechanism for severe abnormality is to shut down the abnormal equipment through the control module and immediately notify the maintenance personnel for repair.

[0022] As a further solution of the present invention: the performance detection module includes a performance detection unit and a performance judgment unit, the performance detection unit is used to detect the material strength, material toughness and corrosion resistance data of the aluminum alloy, and the performance judgment unit can extract the data detected by the performance detection unit and calculate and judge the performance index of the aluminum alloy through a formula. The specific formula is as follows:

[0023]

[0024] Among them, ω1, ω2 and ω3 are the weight coefficients of tensile strength, fracture toughness and corrosion resistance, ω1+ω2+ω3=1, and ω1:ω2:ω3=5:3:2, T, K and V represent the tensile strength value, fracture toughness value and corrosion rate value respectively, T m , K m and V m They respectively represent the maximum tensile strength, maximum fracture toughness value and maximum corrosion rate within a given comparison range. C represents the performance value. The larger the C value is, the better the performance of the aluminum alloy is. If C<0.5, it means that the performance of the aluminum alloy is unqualified.

[0025] As a further solution of the present invention: the display and interaction module includes a display screen unit and a voice prompt unit, the display screen unit is used to display in real time the image data aggregated by the visualization unit in the processing and analysis module and the current operating status of the system, and the voice prompt unit can broadcast abnormal situations in the system.

[0026] As a further solution of the present invention: the storage module includes a data storage unit, a data backup unit and an encryption unit. The data storage unit can classify and store data according to data types. The data backup unit can regularly copy the stored data to a backup storage medium. The encryption unit can encrypt the stored data. Only authorized users can access the stored data.

[0027] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. The present invention uses image processing and machine learning technology to visualize the collected data, perform image preprocessing, feature extraction and defect identification, thereby improving the recognition accuracy and efficiency of aluminum alloy surface defects. At the same time, the control module intelligently generates equipment parameter adjustment instructions based on the analysis results, and realizes precise control through the actuator, thereby optimizing the aluminum alloy forming process, reducing material waste and the time cost of subsequent corrections, thereby improving overall production efficiency and product quality;

[0029] 2. The present invention can quickly identify abnormal conditions that occur during the forming process through the real-time data analysis function of the monitoring and feedback module, and execute a corresponding alarm mechanism according to the severity, thereby improving the safety of the aluminum alloy forming process and reducing the risk of equipment failure and unqualified products, thereby improving the qualified rate and overall quality of the products;

[0030] 3. The present invention detects key parameters such as material strength, material toughness, corrosion resistance and high-temperature strength of aluminum alloy through a performance detection unit, and comprehensively analyzes the detection results through a performance judgment unit to calculate the performance index, thereby achieving a comprehensive evaluation of the performance of the aluminum alloy, enabling production enterprises to deal with unqualified products in a timely manner, and at the same time, optimizing the production process according to the evaluation results to further improve product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process of the performance detection system for aluminum alloy forming in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiment 1:

[0034] Please refer to the attached Figure 1 The present invention provides a performance detection system for aluminum alloy forming, including a data acquisition module, which is used to collect data from various sensors and monitoring probes in the environment and pre-process the collected data, and also includes the following modules:

[0035] A processing and analysis module, used to process and analyze the collected data;

[0036] The control module generates device parameter adjustment instructions based on the analysis results and controls the actuators to execute the corresponding instructions;

[0037] A forming module, including a forming simulation unit and a forming execution unit, wherein the forming simulation unit is used to simulate and optimize the forming process, and the forming execution unit is used to execute the forming operation of the aluminum alloy;

[0038] The monitoring and feedback module monitors the parameters and status of the molding process in real time and issues alarms for abnormal data;

[0039] The performance detection module can detect various parameters of the aluminum alloy after forming and determine the performance status of the aluminum alloy;

[0040] Display and interaction module, providing users with intuitive operation interface and data display;

[0041] Storage module, which stores collected data, analysis results, equipment parameters, and operation record information, and backs up and encrypts data;

[0042] Various sensors include temperature, humidity, pressure and displacement sensors. The preprocessing method is to clean and normalize the data. The data acquisition module also includes a sensor scheduling unit, which is used to adjust the position of the sensor in the environment;

[0043] The processing and analysis module includes a data visualization unit, an image preprocessing unit, a feature extraction unit and a defect recognition unit. The visualization unit is used to convert the collected sensor data into a numerical format, and clean and aggregate the data. The aggregation time interval is 24 hours. The image preprocessing unit is used to segment the image data collected by the monitoring probe. The feature extraction unit is used to analyze the texture and shape characteristics of the aluminum alloy surface and determine the type of aluminum alloy. The defect recognition and classification unit is used to classify and recognize the extracted features. The specific method of feature classification is to establish a convolutional neural network model and use a labeled defect image data set for training. The specific method of feature recognition is to use the trained model to detect the image data segmented by the image preprocessing unit and determine the defects in the image.

[0044] Specific work flow: In an automobile parts manufacturing enterprise, the aluminum alloy forming performance detection system of the present invention was introduced;

[0045] The data acquisition module collects data in the production environment in real time through temperature, pressure, displacement and other sensors and monitoring probes. For example, during the die-casting process, the temperature sensor monitors that the mold temperature is around 650°C, and the pressure sensor shows that the die-casting pressure is 120MPa. The sensor scheduling unit adjusts the sensor position according to the production progress to obtain more accurate data.

[0046] The processing and analysis module processes the collected data. The data visualization unit converts the sensor data into numerical format and cleans and aggregates it. A data report is generated every 24 hours. The image preprocessing unit segments the images collected by the monitoring probe. The feature extraction unit analyzes the texture and shape characteristics of the surface of the aluminum alloy parts and determines that it is an aluminum alloy part of the automobile engine cylinder. The convolutional neural network model is used for defect recognition. The trained model detects that the probability of tiny pore defects on the surface is 0.5%.

[0047] The control module generates equipment parameter adjustment instructions based on the analysis results. For example, when it is found that the die-casting pressure is slightly low, the instruction generation unit generates an instruction to increase the pressure, which is transmitted to the actuator control unit through the instruction transmission unit to increase the die-casting pressure to 130MPa. The safety protection unit monitors the equipment status in real time during the entire adjustment process to ensure safety.

[0048] In the molding module, the molding simulation unit uses MAGMAsoft die-casting molding simulation software. The parameter input unit receives aluminum alloy material parameters (such as aluminum alloy grade, composition, etc.), mold parameters (mold size, cooling system, etc.) and molding process parameters (die-casting speed, pouring temperature, etc.). The defect prediction unit predicts that the defect type that may appear under the current parameters is shrinkage cavity, which is mainly located in the thick wall part of the component, and gives an optimization plan, such as adjusting the pouring temperature and die-casting speed. The molding execution unit performs production according to the optimization plan, effectively reducing the occurrence of shrinkage cavity defects.

[0049] The monitoring and feedback module monitors the production process in real time. When the temperature is abnormal, the real-time temperature reaches 680°C, exceeding the preset temperature value of 670°C. The data analysis unit calculates the temperature abnormality value to be 0.08, which is judged as a medium abnormality. A flashing light alarm is issued and related emergency equipment is stopped.

[0050] The performance detection module performs performance detection on the formed parts. The performance detection unit detects that the tensile strength is 350MPa, the fracture toughness is 25MPa·m^(1 / 2), and the corrosion resistance is tested in a simulated corrosion environment with a corrosion rate of 0.05mm / a. The performance judgment unit calculates the performance index C as 0.7 according to the formula, indicating that the performance of this batch of aluminum alloy parts is good.

[0051] The display unit of the display and interaction module displays production data and system operation status in real time, and the voice prompt unit promptly broadcasts when abnormal conditions occur;

[0052] The storage module stores collected data, analysis results, equipment parameters, and operation record information in a classified manner, and regularly backs them up to external storage media. The data is encrypted so that only authorized personnel can access it.

[0053] By using this system, the company's automotive aluminum alloy parts production efficiency increased by 30%, and the defective product rate was reduced from the original 5% to 1%.

[0054] Furthermore, by utilizing image processing and machine learning techniques, the collected data is visualized, image preprocessed, feature extracted, and defect identified, thereby improving the recognition accuracy and efficiency of aluminum alloy surface defects. At the same time, the control module intelligently generates equipment parameter adjustment instructions based on the analysis results, and achieves precise control through the actuator, thereby optimizing the aluminum alloy forming process, reducing material waste and the time cost of subsequent corrections, and thus improving overall production efficiency and product quality.

[0055] Embodiment 2:

[0056] Based on the first embodiment, the control module includes an instruction generation unit, an instruction transmission unit, an actuator control unit and a safety protection unit. The instruction generation unit is used to generate a specific device parameter adjustment instruction according to the analysis result output by the processing and analysis module. The instruction transmission unit is used to transmit the generated device parameter adjustment instruction to the corresponding actuator. The actuator control unit is responsible for receiving the device parameter adjustment instruction sent by the instruction transmission unit and controlling the actuator to perform the corresponding operation. The safety protection unit is used to monitor the state of the device during the device parameter adjustment process and execute the corresponding abnormality handling plan when an abnormality is detected.

[0057] The forming simulation unit uses MAGMAsoft die-casting forming simulation software for simulation. The forming module also includes a parameter input unit and a defect prediction unit. The parameter input unit is used to receive the aluminum alloy material parameters, mold parameters and forming process parameters input by the user, and pass these parameters to the forming simulation unit for simulation calculation. The defect prediction unit is used to predict the defect type and location that appear in the aluminum alloy forming process and give the corresponding optimization plan. The forming execution unit will generate specific equipment parameter adjustment instructions according to the optimization plan of the forming simulation unit, and transmit them to the actuator of the forming execution unit through the instruction transmission unit;

[0058] The monitoring and feedback module includes a data analysis unit, which can process and analyze the sensor data received in real time, identify the abnormal state and trend of temperature, pressure and displacement during the molding process, determine the type and severity of the abnormal data, and execute the corresponding alarm mechanism according to the severity. The severity is divided into slight abnormality, medium abnormality and severe abnormality, and the severity is determined by the formula. The specific formula is as follows:

[0059]

[0060]

[0061]

[0062] Among them, K1, K2, and K3 are the temperature anomaly value, pressure anomaly value, and displacement anomaly value respectively. T0, P0, and L0 represent the preset temperature value, pressure value, and displacement value respectively. T1, P1, and L1 represent the real-time temperature value, pressure value, and displacement value respectively. If 0 < K1 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K1 ≤ 0.15, it is judged as a medium anomaly. If K1 > 0.15, it is judged as a severe anomaly. If 0 < K2 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K2 ≤ 0.15, it is judged as a medium anomaly. If K2 > 0.15, it is judged as a severe anomaly. If 0 < K3 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K3 ≤ 0.15, it is judged as a medium anomaly. If K3 > 0.15, it is judged as a severe anomaly. The alarm mechanism for minor anomalies is to send a text message warning notice to relevant personnel. The alarm mechanism for medium anomalies is to issue a flashing light alarm and stop the abnormal emergency equipment through the control module. The alarm mechanism for severe anomalies is to shut down the abnormal equipment through the control module and immediately notify the maintenance personnel for repair.

[0063] Specific working process: In an aviation manufacturing enterprise, the performance detection system of the present invention is applied to produce aviation aluminum alloy components.

[0064] The data acquisition module collects various data in the production environment. The temperature sensor monitors the temperature during the heat treatment process at 450 °C. The humidity sensor shows that the environmental humidity is 40% RH. The pressure sensor detects a pressure of 150 MPa during the pressure processing. The sensor scheduling unit adjusts the sensor position according to different production processes.

[0065] The processing and analysis module processes the data. The visualization unit converts the data into a numerical format and performs cleaning and aggregation. The image preprocessing unit segments the images collected by the monitoring probes. The feature extraction unit analyzes the texture and shape features of the aluminum alloy components and determines that they are aluminum alloy components for aircraft wings. The defect recognition unit uses a convolutional neural network model to detect that the probability of surface scratch defects is 0.3%.

[0066] The control module generates equipment parameter adjustment instructions. For example, when it is found that the pressure is unstable during the pressure processing, the instruction generation unit generates an instruction to adjust the pressure, which is transmitted to the actuator control unit through the instruction transmission unit to make the pressure stable within the range of 150 MPa ± 5 MPa. The safety protection unit ensures the safety during the equipment adjustment process.

[0067] In the forming module, the forming simulation unit uses MAGMAsoft software for simulation. The parameter input unit receives aluminum alloy material parameters (specific composition of high-strength aluminum alloy, etc.), mold parameters (complex wing mold parameters) and forming process parameters (forging temperature, deformation rate, etc.). The defect prediction unit predicts that the possible defect type is internal cracks, located at the stress concentration part of the wing, and gives optimization solutions, such as adjusting the forging temperature and deformation rate. The forming execution unit produces according to the optimization solution, reducing the occurrence of internal cracks.

[0068] The monitoring and feedback module monitors the production process in real time. When the pressure is abnormal, the real-time pressure reaches 160MPa, exceeding the preset pressure value of 150MPa. The data analysis unit calculates the pressure abnormality value as 0.067, which is judged as a medium abnormality. A flashing light alarm is issued and related emergency equipment is stopped.

[0069] The performance detection module performs performance detection on the formed parts. The performance detection unit detects that the tensile strength is 500MPa, the fracture toughness is 30MPa·m^(1 / 2), and the corrosion resistance is tested by the salt spray test, and the corrosion rate is 0.03mm / a. The performance judgment unit calculates the performance index C as 0.8 according to the formula, indicating that the performance of this batch of aviation aluminum alloy parts is excellent.

[0070] The display unit of the display and interaction module displays production data and system operation status in real time, the voice prompt unit broadcasts in time when abnormal situations occur, and the storage module classifies, stores, backs up and encrypts data;

[0071] By using this system, the company's aviation aluminum alloy parts production efficiency increased by 25%, and the defective product rate was reduced from the original 3% to 0.5%.

[0072] Furthermore, through the real-time data analysis function of the monitoring and feedback module, abnormal conditions occurring during the forming process can be quickly identified, and the corresponding alarm mechanism can be executed according to the severity, thereby improving the safety of the aluminum alloy forming process and reducing the risk of equipment failure and unqualified products, thereby improving the product qualification rate and overall quality.

[0073] Embodiment three:

[0074] Based on the second embodiment, the performance detection module includes a performance detection unit and a performance judgment unit. The performance detection unit is used to detect the material strength, material toughness and corrosion resistance data of the aluminum alloy. The performance judgment unit can extract the data detected by the performance detection unit and calculate and judge the performance index of the aluminum alloy through a formula. The specific formula is as follows:

[0075]

[0076] Among them, ω1, ω2 and ω3 are the weight coefficients of tensile strength, fracture toughness and corrosion resistance, ω1+ω2+ω3=1, and ω1:ω2:ω3=5:3:2, T, K and V represent the tensile strength value, fracture toughness value and corrosion rate value respectively, T m , K m and V m They represent the maximum tensile strength, maximum fracture toughness value and maximum corrosion rate within a given comparison range respectively. C represents the performance value. The larger the C value is, the better the performance of the aluminum alloy is. If C<0.5, it means that the performance of the aluminum alloy is unqualified.

[0077] The display and interaction module includes a display unit and a voice prompt unit. The display unit is used to display the image data aggregated by the visualization unit in the processing and analysis module and the current operating status of the system in real time. The voice prompt unit can broadcast abnormal conditions in the system.

[0078] The storage module includes a data storage unit, a data backup unit and an encryption unit. The data storage unit can classify and store data according to data types. The data backup unit can regularly copy the stored data to the backup storage medium. The encryption unit can encrypt the stored data. Only authorized users can access the stored data.

[0079] Specific work flow: In an electronic equipment manufacturing enterprise, the performance detection system of the present invention is used to produce aluminum alloy housings of electronic equipment;

[0080] The data acquisition module collects data in the production environment. The temperature sensor monitors that the temperature during the injection molding process is 250°C. The displacement sensor detects that the opening and closing displacement of the mold is 50mm. The sensor scheduling unit adjusts the sensor position according to the production schedule.

[0081] The processing and analysis module processes the data, the visualization unit converts the data into numerical format and performs cleaning and aggregation, the image preprocessing unit segments the images collected by the monitoring probe, the feature extraction unit analyzes the texture and shape characteristics of the aluminum alloy shell and determines that it is an aluminum alloy shell of a certain brand of mobile phone, and the defect recognition unit uses the convolutional neural network model to detect that the probability of surface defects is 0.2%;

[0082] The control module generates equipment parameter adjustment instructions. For example, when the injection temperature is found to be slightly high, the instruction generation unit generates an instruction to lower the temperature, which is transmitted to the actuator control unit through the instruction transmission unit to lower the injection temperature to 240°C. The safety protection unit ensures equipment safety during the adjustment process.

[0083] In the molding module, the molding simulation unit uses MAGMAsoft software for simulation. The parameter input unit receives aluminum alloy material parameters (specific composition of thin and light aluminum alloy, etc.), mold parameters (high-precision mold parameters) and molding process parameters (injection speed, holding time, etc.). The defect prediction unit predicts that the possible defect type is flow mark, which is located at a specific part of the shell surface, and gives an optimization plan, such as adjusting the injection speed and holding time. The molding execution unit produces according to the optimization plan, reducing the occurrence of flow mark defects.

[0084] The monitoring and feedback module monitors the production process in real time. When the temperature is slightly abnormal, the real-time temperature is 252°C, which exceeds the preset temperature value of 250°C. The data analysis unit calculates the temperature abnormality value as 0.008, which is judged as a slight abnormality, and sends a text message warning notification to relevant personnel;

[0085] The performance detection module performs performance detection on the formed shell. The performance detection unit detects that the tensile strength is 200MPa, the fracture toughness is 15MPa·m^(1 / 2), and the corrosion resistance is tested by simulating seawater corrosion environment, and the corrosion rate is 0.04mm / a. The performance judgment unit calculates the performance index C as 0.65 according to the formula, indicating that the performance of this batch of electronic equipment aluminum alloy shells is good.

[0086] The display unit of the display and interaction module displays production data and system operation status in real time, the voice prompt unit broadcasts in time when abnormal situations occur, and the storage module classifies, stores, backs up and encrypts data;

[0087] By using this system, the company's production efficiency of aluminum alloy shells for electronic equipment has increased by 20%, and the rate of unqualified products has been reduced from the original 4% to 1.5%.

[0088] Furthermore, the key parameters of the aluminum alloy, such as material strength, material toughness, corrosion resistance and high-temperature strength, are tested through the performance testing unit, and the test results are comprehensively analyzed through the performance judgment unit to calculate the performance index, thereby achieving a comprehensive evaluation of the performance of the aluminum alloy, so that production enterprises can deal with unqualified products in a timely manner. At the same time, the production process can be optimized according to the evaluation results to further improve product quality.

[0089] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A performance detection system for aluminum alloy forming, comprising a data acquisition module, wherein the data acquisition module is used to collect data from various sensors and monitoring probes in the environment and pre-process the collected data, characterized in that: Also includes the following modules: A processing and analysis module, used to process and analyze the collected data; The control module generates device parameter adjustment instructions based on the analysis results and controls the actuators to execute the corresponding instructions; A forming module, comprising a forming simulation unit and a forming execution unit, wherein the forming simulation unit is used to simulate and optimize the forming process, and the forming execution unit is used to execute the forming operation of the aluminum alloy; The monitoring and feedback module monitors the parameters and status of the molding process in real time and issues alarms for abnormal data; The performance detection module can detect various parameters of the aluminum alloy after forming and determine the performance status of the aluminum alloy; Display and interaction module, providing users with intuitive operation interface and data display; The storage module stores collected data, analysis results, equipment parameters, and operation record information, and backs up and encrypts the data.

2. A performance detection system for aluminum alloy forming according to claim 1, characterized in that: The various sensors include temperature, humidity, pressure and displacement sensors. The preprocessing method is to clean and normalize the data. The data acquisition module also includes a sensor scheduling unit, which is used to adjust the position of the sensor in the environment.

3. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The processing and analysis module includes a data visualization unit, an image preprocessing unit, a feature extraction unit and a defect recognition unit. The visualization unit is used to convert the collected sensor data into a numerical format, and clean and aggregate the data. The aggregation time interval is 24 hours. The image preprocessing unit is used to segment the image data collected by the monitoring probe. The feature extraction unit is used to analyze the texture and shape characteristics of the aluminum alloy surface and determine the type of aluminum alloy. The defect recognition and classification unit is used to classify and recognize the extracted features. The specific method of feature classification is to establish a convolutional neural network model and use a labeled defect image data set for training. The specific method of feature recognition is to use the trained model to detect the image data segmented by the image preprocessing unit to determine the defects in the image.

4. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The control module includes an instruction generation unit, an instruction transmission unit, an actuator control unit and a safety protection unit. The instruction generation unit is used to generate specific device parameter adjustment instructions according to the analysis results output by the processing and analysis module. The instruction transmission unit is used to transmit the generated device parameter adjustment instructions to the corresponding actuator. The actuator control unit is responsible for receiving the device parameter adjustment instructions sent by the instruction transmission unit and controlling the actuator to perform corresponding operations. The safety protection unit is used to monitor the status of the device during the device parameter adjustment process and execute the corresponding exception handling plan when an abnormal situation is detected.

5. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The forming simulation unit uses MAGMAsoft die-casting forming simulation software for simulation. The forming module further includes a parameter input unit and a defect prediction unit. The parameter input unit is used to receive the aluminum alloy material parameters, mold parameters, and forming process parameters input by the user, and transmit these parameters to the forming simulation unit for simulation calculation. The defect prediction unit is used to predict the types and positions of defects occurring during the aluminum alloy forming process, and give corresponding optimization solutions. The forming execution unit will generate specific equipment parameter adjustment instructions according to the optimization solutions of the forming simulation unit, and transmit them to the actuator of the forming execution unit through the instruction transmission unit.

6. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The monitoring and feedback module includes a data analysis unit. The data analysis unit can process and analyze the sensor data received in real time, identify the abnormal states and trends of temperature, pressure, and displacement during the forming process, judge the types and severity levels of abnormal data, and execute corresponding alarm mechanisms according to the severity levels. The severity levels are divided into minor anomalies, medium anomalies, and severe anomalies, and the severity level is judged by a formula. The specific formula is as follows: Among them, K1, K2, and K3 are the temperature anomaly value, pressure anomaly value, and displacement anomaly value respectively. T0, P0, and L0 represent the preset temperature value, pressure value, and displacement value respectively. T1, P1, and L1 represent the real-time temperature value, pressure value, and displacement value respectively. If 0 < K1 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K1 ≤ 0.15, it is judged as a medium anomaly. If K1 > 0.15, it is judged as a severe anomaly. If 0 < K2 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K2 ≤ 0.15, it is judged as a medium anomaly. If K2 > 0.15, it is judged as a severe anomaly. If 0 < K3 ≤ 0.05, it is judged as a minor anomaly. If 0.05 < K3 ≤ 0.15, it is judged as a medium anomaly. If K3 > 0.15, it is judged as a severe anomaly. The alarm mechanism for minor anomalies is to send a text message warning notice to relevant personnel. The alarm mechanism for medium anomalies is to issue a flashing light alarm and stop the abnormal emergency equipment through the control module. The alarm mechanism for severe anomalies is to shut down the abnormal equipment through the control module and immediately notify the maintenance personnel for repair.

7. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The performance detection module includes a performance detection unit and a performance judgment unit. The performance detection unit is used to detect the material strength, material toughness, and corrosion resistance data of the aluminum alloy. The performance judgment unit can extract the data detected by the performance detection unit and calculate and judge the performance index of the aluminum alloy through a formula. The specific formula is as follows: Among them, ω1, ω2 and ω3 are the weight coefficients of tensile strength, fracture toughness and corrosion resistance, ω1+ω2+ω3=1, and ω1:ω2:ω3=5:3:2, T, K and V represent the tensile strength value, fracture toughness value and corrosion rate value respectively, T m , K m and V m They respectively represent the maximum tensile strength, maximum fracture toughness value and maximum corrosion rate within a given comparison range. C represents the performance value. The larger the C value is, the better the performance of the aluminum alloy is. If C<0.5, it means that the performance of the aluminum alloy is unqualified.

8. The aluminum alloy forming performance detection system according to claim 3, characterized in that: The display and interaction module includes a display screen unit and a voice prompt unit. The display screen unit is used to display in real time the image data aggregated by the visualization unit in the processing and analysis module and the current operating state of the system. The voice prompt unit can broadcast the abnormal situations in the system.

9. The aluminum alloy forming performance detection system according to claim 1, characterized in that: The storage module includes a data storage unit, a data backup unit and an encryption unit. The data storage unit can classify and store data according to data types. The data backup unit can regularly copy the stored data to a backup storage medium. The encryption unit can encrypt the stored data. Only authorized users can access the stored data.

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