The performance prediction and optimization of automobile forgings based on big data analysis is a process of predicting and optimizing the performance of automobile forgings by using big data technology and methods. The specific steps are as follows: Data collection and collation: collect and collate a large number of data related to automotive forgings, including the physical properties of materials, forging process parameters, processing conditions, etc. This data can be obtained through sensors, monitoring devices, laboratory tests, and more. Data preprocessing: The original data collected is cleaned, denoised, and the missing value is completed to ensure the quality and integrity of the data. At the same time, the format of data from different sources and different formats is unified and integrated for subsequent analysis and modeling. Feature extraction and selection: Meaningful features are extracted from pre-processed data through feature engineering methods. These characteristics may be physical quantities, material parameters, process parameters, etc. closely related to forging properties. In the process of feature extraction, it is also necessary to consider the importance of features, carry out feature selection, and eliminate irrelevant or redundant features for performance prediction. Model building and training: According to the extracted feature data, build a mathematical model suitable for predicting and optimizing the performance of automobile forgings. Common methods include regression analysis, machine learning, neural networks, etc. After the model is established, the historical data is used for training and parameter adjustment to optimize the accuracy and prediction ability of the model. Performance prediction and optimization: Use the trained model to predict the performance of new automobile forgings. By inputting corresponding characteristic values, the model can predict the performance index of forging, such as strength, hardness, toughness, etc. Based on the predicted results, the material formula and process parameters of the forgings are optimized and adjusted to achieve better performance requirements. Feedback and update: the performance data of forging obtained in actual production is fed back to the prediction model, and the model is updated and iteratively improved. Through the continuous collection and analysis of actual data, the model can be continuously optimized to improve the accuracy and reliability of predictions. The performance prediction and optimization of automobile forgings based on big data analysis can provide a scientific and efficient method to help optimize the design and production process of forgings, improve product quality and performance, and reduce production costs and human resource input.
