In the field of modern industry, as an important basic material, forgings are widely used in key structures such as Bridges, high-rise buildings and water conservancy projects because of their excellent mechanical properties and corrosion resistance. With the development of science and technology, how to effectively classify and identify forgings to ensure their quality and safety has become an urgent problem to be solved. This paper aims to discuss the principle, method and application of manifold learning based forging classification and recognition technology, in order to provide useful reference for the research and practice in related fields.
Research method
The forging classification and recognition technology based on manifold learning is mainly composed of three key parts: data preprocessing, feature extraction and classifier design.
Data preprocessing: First of all, the collected forging data is cleaned and preprocessed, including removing outliers, filling in missing values, standardizing data, etc., to improve the accuracy and reliability of the data.
Feature extraction: manifold learning algorithm is used to extract features from the pre-processed data. Manifold learning is a nonlinear dimensionality reduction method that can map high-dimensional data to low-dimensional Spaces while preserving key features of the data. Through manifold learning algorithm, the features of forging data are extracted and represented to form a representative feature vector.
Classifier design: Based on extracted feature vectors, a classifier is designed to classify and identify forgings. Commonly used classifiers include support vector machines (SVM), neural networks, decision trees, etc. By training the classifier, it can automatically classify and recognize forgings according to feature vector.
Experimental results and analysis
Through experiments, we apply the manifold learning-based forging classification and recognition technique to the real data set and compare it with other related techniques. The experimental results show that the classification and recognition technology based on manifold learning has achieved good results in accuracy, recall rate and F1 value. Compared with traditional classification methods, the technology based on manifold learning can better capture the nonlinear characteristics of forging data and improve the classification performance.
Conclusion and prospect
This paper studies the forging classification and recognition technology based on manifold learning, and realizes the automatic classification and recognition of forging through three key links: data preprocessing, feature extraction and classifier design. The experimental results show that the technology has achieved good results in accuracy, recall rate and F1 value. The application of this technology is of great significance to improve the quality control level of forging production and reduce the misjudgment rate, and also provides a new idea and method for other related fields.
Looking forward to the future, the classification and recognition technology of forgings based on manifold learning still has many directions worthy of further research. Firstly, for different kinds of forgings, more refined feature representation methods need to be further explored to improve classification performance. Secondly, how to combine manifold learning with other deep learning algorithms to form a more powerful classification model is also worth studying. In addition, at the application level, how to better apply the technology in the actual production process, still need to carry out a lot of exploration and practice.
