With the advent of the Industry 4.0 era, the application of big data technology in the manufacturing industry has gradually become prominent. This paper takes the production process of aluminum alloy forgings as the research object, discusses how to use big data technology to realize real-time monitoring and quality prediction of production process. Firstly, the production process and quality control requirements of aluminum alloy forgings are introduced. Secondly, the application of big data technology in production process monitoring and quality forecasting is analyzed. Finally, an example is given to verify the effectiveness of the proposed method.
As a kind of lightweight and high-strength structural material, aluminum alloy forgings are widely used in aerospace, automobile, machinery manufacturing and other fields. With the continuous growth of market demand, quality control in the production of aluminum alloy forgings has become the focus of attention of enterprises. Traditional quality control methods mainly rely on manual experience and regular inspection, it is difficult to realize real-time monitoring and forecasting of production process. Therefore, how to use big data technology to improve the quality control level of aluminum alloy forging production process has become an urgent problem to be solved.
The production process of aluminum alloy forgings mainly includes melting, forging, heat treatment and surface treatment. In each production link, the key parameters need to be strictly controlled to ensure the quality of the final product. For example, in the melting process, parameters such as alloy composition, melting temperature and melting time need to be controlled; In the forging process, parameters such as forging temperature, forging pressure and forging speed need to be controlled. In addition, for abnormal conditions in the production process, such as equipment failure, process parameters exceed the standard, etc., need to be discovered and dealt with in time to avoid adverse effects on product quality.
Application of big data technology in production process monitoring and quality prediction
Production process real-time monitoring
By deploying sensors and data acquisition devices on the production site, information such as process parameters, equipment status and environmental data can be collected in real time during the production process. Then, big data technology is used to analyze and process these data in real time to achieve real-time monitoring of the production process. Specifically, abnormal situations in the production process can be discovered in time by setting thresholds and establishing anomaly detection models, and the alarm system can be triggered to notify relevant personnel to deal with them. This can not only improve production efficiency, but also avoid product quality problems caused by abnormal conditions.
Quality prediction
Based on historical data and machine learning algorithm, a quality prediction model was established to predict the quality of aluminum alloy forgings. First, the historical data need to be cleaned and pre-processed to extract the features closely related to product quality; Then, select the appropriate machine learning algorithm (such as support vector machine, neural network, etc.) to build the quality prediction model. Finally, the model is evaluated and optimized by validation set. In practical application, the quality prediction model can be updated in real time and calculate the information of quality index and qualification rate of new batch products online. This can identify potential quality problems before products leave the factory, saving businesses costs and improving customer satisfaction.
Taking an aluminum alloy forging enterprise as an example, the big data technology proposed in this paper is used for real-time monitoring and quality prediction of the production process. The results show that the method can detect the abnormal situation in the production process and deal with it in time to ensure the stability of product quality. At the same time, the quality prediction model has high prediction accuracy and generalization ability, and can provide reliable quality prediction results for enterprises. Finally, after the implementation of the method proposed in this paper, the product quality of aluminum alloy forgings has been significantly improved, and the customer complaint rate has been reduced by 30%.
This paper discusses the application of big data technology in the production process monitoring and quality prediction of aluminum alloy forgings, and verifies the effectiveness of this method in improving product quality and production efficiency. With the continuous development and improvement of big data technology, it is believed that its application in the manufacturing industry will be more extensive and in-depth, and create more value for enterprises.
