With the continuous development of science and technology, machine learning technology has been widely used in many fields. In the manufacturing industry, quality prediction and control technology based on machine learning provides a new solution for improving product quality and reducing production costs. This paper will discuss how to apply machine learning to quality prediction and control of Marine rudder system forgings to improve product reliability and reduce production risk.
Machine learning is an artificial intelligence technology that realizes tasks such as prediction and classification of new data by learning and extracting rules from large amounts of data. In manufacturing, machine learning can be applied to quality prediction and control by analyzing information such as historical data and process parameters to predict product quality and optimize process parameters, thereby improving product quality and reducing production costs.
Forging quality prediction of Marine rudder system based on machine learning
Data collection and feature extraction: In order to make quality prediction, it is necessary to collect data related to the quality of Marine rudder system forgings, including raw material attributes, process parameters, production environmental conditions, etc. From this data are extracted features that have an impact on quality, such as temperature, pressure, time, etc.
Model selection and training: Select suitable machine learning algorithms, such as support vector machines, neural networks, decision trees, etc., train the model and optimize it according to the extracted features and corresponding quality data. Common optimization methods include cross validation, grid search and so on.
Model evaluation and prediction: Use test data to evaluate the trained model, calculate the accuracy, accuracy, recall rate and other indicators of the model. According to the evaluation results, the model is optimized to improve the prediction accuracy. Finally, the trained model is used to predict the forging quality of the new Marine rudder system.
Quality control of forgings for Marine rudder system based on machine learning
Process parameter optimization: Through the analysis of historical production data by machine learning algorithm, find out the process parameters that have a significant impact on the quality of Marine rudder forging, such as temperature, pressure, time, etc. Then according to the quality prediction results, the process parameters are adjusted to optimize the product quality.
Online monitoring and early warning: The use of machine learning technology for real-time monitoring of the production process, timely detection of abnormal situations and early warning. By analyzing historical data, anomaly detection model is established, threshold is set and real-time monitoring is carried out. Once abnormal data is found to exceed the threshold, an alarm is immediately issued and appropriate measures are taken to adjust and correct.
Continuous improvement and iterative optimization: Quality control based on machine learning is a process of continuous improvement. By collecting new production data and adjusting process parameters, the quality and reliability of Marine rudder forging can be gradually improved. At the same time, we can combine the actual production demand and market feedback, and constantly optimize the quality control strategy and adjust the model parameters to achieve more efficient quality control and reduce production risks.
The quality prediction and control technology of Marine rudder forging based on machine learning brings new opportunities and challenges to the manufacturing industry. By applying machine learning to quality prediction and control, product quality can be improved and production costs reduced. In practical applications, attention should be paid to the accuracy of data collection and feature extraction, the optimization of model selection and training, and the effectiveness of real-time monitoring and early warning systems. At the same time, strengthening industry-university-research cooperation and technical exchange is also an important way to promote the development of machine learning-based Marine forging quality prediction and control technology.
