With the continuous progress of science and technology, machine learning technology has been widely used in various fields. In the petrochemical industry, as one of the key equipment, the quality control of petrochemical forgings is of great significance to the safe and stable operation of the entire production line. The traditional quality control methods of petrochemical forgings mainly rely on manual inspection and experience judgment, and there are problems of low efficiency and high misjudgment rate. In order to solve these problems, the quality control method of petrochemical forgings based on machine learning has been proposed and widely used. This paper will discuss the principle, application and advantages of the quality control method of petrochemical forgings based on machine learning.
The quality control method of petrochemical forgings based on machine learning mainly uses machine learning algorithm to learn and model the manufacturing process and quality data of petrochemical forgings to realize automatic detection and control of forging quality. The specific process includes the following steps:
Data collection: Collect manufacturing process data and quality data of petrochemical forgings, including material composition, manufacturing process parameters, dimensional measurement data, etc.
Data preprocessing: Pre-processing operations such as cleaning, normalization and feature extraction are carried out on the collected data for subsequent processing by machine learning algorithms.
Model training: Select a suitable machine learning algorithm, such as support vector machine (SVM), neural network, etc., and use the pre-processed data for model training to get a model that can predict the quality of forging.
Quality inspection and control: Input the new manufacturing process data into the trained model to obtain the corresponding forging quality prediction results. According to the comparison between the predicted result and the actual quality standard, the automatic detection and control of forging quality can be realized.
The quality control method of petrochemical forgings based on machine learning has achieved good results in practical application. Here are some specific application cases:
Defect detection: The model is trained to identify defects on the forging surface, such as cracks, pores, etc. Through automatic defect detection, quality problems can be found and dealt with in time, and product quality and production efficiency can be improved.
Size control: Machine learning algorithm is used to predict and control the size of the forging to ensure that the product size meets the design requirements. By monitoring dimensional data in real time and making adjustments, the rejection rate can be reduced and the product qualification rate can be improved.
Process optimization: By learning and optimizing the process parameters in the manufacturing process, the best process combination can be found to improve product quality and production efficiency. Through real-time monitoring and adjustment of process parameters, the production process can be automated and intelligent.
Quality prediction and early warning: By learning and modeling historical quality data, future quality trends can be predicted and early warning can be made. By discovering potential quality problems in time and taking corresponding measures, quality accidents can be avoided and production safety can be improved.
Compared with traditional methods, the quality control method of petrochemical forgings based on machine learning has the following advantages:
Automation and intelligence: Through the introduction of machine learning algorithms, automatic detection and control of forging quality can be achieved, reducing manual intervention and improving production efficiency.
Accuracy and reliability: By learning and modeling large amounts of manufacturing process and quality data, you can get more accurate and reliable predictions, reduce misjudgments and improve product quality.
Scalability and flexibility: By choosing the right machine learning algorithm and model structure, it can adapt to different production environments and demand changes, and achieve the scalability and flexibility of quality control methods.
However, the quality control method of petrochemical forgings based on machine learning also faces some challenges and problems:
Data acquisition and processing difficulty: The manufacturing process and quality data of petrochemical forgings are usually characterized by complexity, high dimension and imbalance, which brings certain difficulties to data acquisition and processing.
Model selection and parameter adjustment: Selecting the right machine learning algorithm and model structure and adjusting model parameters are one of the key factors affecting the prediction results, which requires a lot of experiments and verification.
Real-time and robust requirements: The manufacturing process of petrochemical forgings usually requires real-time monitoring and control, which puts forward higher requirements for the real-time and robust quality control methods.
The quality control method of petrochemical forgings based on machine learning provides a new solution for the quality control of petrochemical industry. Through the introduction of machine learning algorithms and models, the automatic detection and control of forging quality can be realized, and the product quality and production efficiency can be improved. However, the method still faces some challenges and problems in practical application, which need further research and improvement. In the future, with the continuous development and improvement of machine learning technology and the emergence of more advanced quality control methods, it is believed that the quality control of petrochemical forgings will be more intelligent, efficient and reliable.
