Abstract:Product quality is an important part of enterprise competitiveness. Product processing is the key process of quality formation. In smart factories, the improvement of data acquisition and processing capability provides a basis for data-based quality control. In order to reduce the occurrence of product quality problems, we abstracted the product processing process as a data processing unit, abstracted the process of changing the product quality state as a process of the processing quality characteristics data,… Show more
“…In the manufacturing processing unit, represents the product state before the execution of the manufacturing processing unit; represents the product state after the execution of the manufacturing processing unit; From the perspective of quality data, refers to the resource processing data received by the manufacturing processing unit; represents the product quality state data before the manufacturing processing unit processes it; refers to the output product quality state data processed by the manufacturing processing unit; represents the difference between the actual qualified rate of the calculated output product and the qualified rate of the industrial product containing the predicted results, and is the threshold. When the value exceeds a certain threshold, the edge computing layer will generate corresponding process adjustment control instructions ,and send them to the relevant processing equipment, such as adjusting the spindle speed and feed rate 15 . Figure 5 Manufacturing processing unit.…”
Section: Active Control Methods For Quality Predictionmentioning
confidence: 99%
“…From the perspective of quality characteristics, the current quality characteristic is the result of the current process equipment processing the quality characteristic in the current environment 15 . The process of changing quality characteristics is the process of transforming input data into output data through its processing mechanism.…”
Section: Active Control Methods For Quality Predictionmentioning
confidence: 99%
“…In existing research 15 , divided equipment process parameters into static process data, direct dynamic process data, and indirect dynamic process data based on their impact on the quality characteristics of the processed products to facilitate the application of equipment process parameters. Among them, static equipment process data refers to the type of equipment process data that generally does not change during the product processing process; direct dynamic process data refers to the equipment process data that changes dynamically during the product processing process, and the numerical changes directly reflect the product quality characteristics; Indirect dynamic process data refers to the equipment process data that changes dynamically during the processing process, but its changes do not directly reflect the product quality characteristics.…”
“…Among them, static equipment process data refers to the type of equipment process data that generally does not change during the product processing process; direct dynamic process data refers to the equipment process data that changes dynamically during the product processing process, and the numerical changes directly reflect the product quality characteristics; Indirect dynamic process data refers to the equipment process data that changes dynamically during the processing process, but its changes do not directly reflect the product quality characteristics. Table 2 presents an example classification result of equipment process data 15 . Indirect dynamic equipment process data is the focus of this study.…”
“…The main approach to equipment fault diagnosis is to establish the mechanism of equipment faults, study the relationships between various causes of faults, fault characterizations, and fault signals, in order to diagnose equipment faults quickly when they occur. Quality prediction is an essential means to reduce the probability of product quality problems and improve the qualification rate by analyzing the operating parameters of the equipment, obtaining quality characteristics based on equipment parameters, and monitoring and controlling the parameters of the equipment processing process 15 . Figure 1 Research directions for equipment management.…”
With the emergence of intelligent manufacturing, new-generation information technologies such as big data and artificial intelligence are rapidly integrating with the manufacturing industry. One of the primary applications is to assist manufacturing plants in predicting product quality. Traditional predictive models primarily focus on establishing high-precision classification or regression models, with less emphasis on imbalanced data. This is a specific but common scenario in practical industrial environments concerning quality prediction. A SMOTE-XGboost quality prediction active control method based on joint optimization hyperparameters is proposed to address the problem of imbalanced data classification in product quality prediction. In addition, edge computing technology is introduced to address issues in industrial manufacturing, such as the large bandwidth load and resource limitations associated with traditional cloud computing models. Finally, the practicality and effectiveness of the proposed method are validated through a case study of the brake disc production line. Experimental results indicate that the proposed method outperforms other classification methods in brake disc quality prediction.
“…In the manufacturing processing unit, represents the product state before the execution of the manufacturing processing unit; represents the product state after the execution of the manufacturing processing unit; From the perspective of quality data, refers to the resource processing data received by the manufacturing processing unit; represents the product quality state data before the manufacturing processing unit processes it; refers to the output product quality state data processed by the manufacturing processing unit; represents the difference between the actual qualified rate of the calculated output product and the qualified rate of the industrial product containing the predicted results, and is the threshold. When the value exceeds a certain threshold, the edge computing layer will generate corresponding process adjustment control instructions ,and send them to the relevant processing equipment, such as adjusting the spindle speed and feed rate 15 . Figure 5 Manufacturing processing unit.…”
Section: Active Control Methods For Quality Predictionmentioning
confidence: 99%
“…From the perspective of quality characteristics, the current quality characteristic is the result of the current process equipment processing the quality characteristic in the current environment 15 . The process of changing quality characteristics is the process of transforming input data into output data through its processing mechanism.…”
Section: Active Control Methods For Quality Predictionmentioning
confidence: 99%
“…In existing research 15 , divided equipment process parameters into static process data, direct dynamic process data, and indirect dynamic process data based on their impact on the quality characteristics of the processed products to facilitate the application of equipment process parameters. Among them, static equipment process data refers to the type of equipment process data that generally does not change during the product processing process; direct dynamic process data refers to the equipment process data that changes dynamically during the product processing process, and the numerical changes directly reflect the product quality characteristics; Indirect dynamic process data refers to the equipment process data that changes dynamically during the processing process, but its changes do not directly reflect the product quality characteristics.…”
“…Among them, static equipment process data refers to the type of equipment process data that generally does not change during the product processing process; direct dynamic process data refers to the equipment process data that changes dynamically during the product processing process, and the numerical changes directly reflect the product quality characteristics; Indirect dynamic process data refers to the equipment process data that changes dynamically during the processing process, but its changes do not directly reflect the product quality characteristics. Table 2 presents an example classification result of equipment process data 15 . Indirect dynamic equipment process data is the focus of this study.…”
“…The main approach to equipment fault diagnosis is to establish the mechanism of equipment faults, study the relationships between various causes of faults, fault characterizations, and fault signals, in order to diagnose equipment faults quickly when they occur. Quality prediction is an essential means to reduce the probability of product quality problems and improve the qualification rate by analyzing the operating parameters of the equipment, obtaining quality characteristics based on equipment parameters, and monitoring and controlling the parameters of the equipment processing process 15 . Figure 1 Research directions for equipment management.…”
With the emergence of intelligent manufacturing, new-generation information technologies such as big data and artificial intelligence are rapidly integrating with the manufacturing industry. One of the primary applications is to assist manufacturing plants in predicting product quality. Traditional predictive models primarily focus on establishing high-precision classification or regression models, with less emphasis on imbalanced data. This is a specific but common scenario in practical industrial environments concerning quality prediction. A SMOTE-XGboost quality prediction active control method based on joint optimization hyperparameters is proposed to address the problem of imbalanced data classification in product quality prediction. In addition, edge computing technology is introduced to address issues in industrial manufacturing, such as the large bandwidth load and resource limitations associated with traditional cloud computing models. Finally, the practicality and effectiveness of the proposed method are validated through a case study of the brake disc production line. Experimental results indicate that the proposed method outperforms other classification methods in brake disc quality prediction.
Introducción: En el refinado de petróleo crudo, el proceso de craqueo catalítico fluido (FCC) convierte el petróleo crudo en productos petroquímicos de alta calidad. Comprender las interacciones moleculares en FCC es crucial para fines de optimización, eficiencia y calidad. Este estudio cuantitativo y descriptivo analiza las coordenadas cartesianas de compuestos clave, empleando química computacional para este propósito. Metodología: Cuantitativa y descriptiva. A través de una revisión de la literatura, se identificaron compuestos químicos típicos que alimentan el proceso de FCC, incluidas parafinas, olefinas, aromáticos y naftenos, entre otros. Estos compuestos se procesaron mediante química computacional para obtener sus coordenadas 3D, optimizando su geometría molecular para representar la estructura real, garantizando una precisión confiable de los datos en simulaciones y análisis posteriores. Análisis y discusión de resultados: Las coordenadas cartesianas ayudan a comprender e identificar las condiciones operativas óptimas, mejorando la comprensión de las interacciones moleculares en tiempo real y facilitando la predicción de comportamientos de separación. Estas coordenadas están previstas para optimizar los procesos de refino de crudo en FCC, mediante la modelización y visualización de movimientos y colisiones a nivel atómico. Conclusiones: La optimización de la geometría molecular utilizando el campo de fuerza apropiado es crucial para obtener coordenadas cartesianas precisas. Estas coordenadas permiten la simulación de interacciones moleculares a nivel atómico, el diseño de catalizadores más eficientes y la optimización de procesos de refinado. Además, el monitoreo en tiempo real con datos moleculares precisos podría garantizar una calidad constante del producto en FCC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
