This paper introduces the technology and method of quality detection and classification recognition of aluminum alloy forgings based on artificial intelligence. Firstly, the importance of artificial intelligence in the quality inspection of aluminum alloy forgings is outlined, and then the principles and steps of quality inspection and defect identification of aluminum alloy forgings by using deep learning, computer vision and other technologies are described in detail. Finally, the advantages and future development direction of artificial intelligence in quality detection and classification of aluminum alloy forgings are summarized.
As the key basic material of high-end equipment manufacturing, the quality of aluminum alloy forgings directly affects the performance and life of equipment. The traditional manual inspection method has some problems, such as subjectivity and low efficiency, which can not meet the needs of large-scale and high-precision quality inspection. Therefore, the quality detection and classification recognition technology of aluminum alloy forgings based on artificial intelligence is of great significance.
Deep learning is an important branch of the field of artificial intelligence, through the construction of deep neural network model, the input data can be abstracted and learned layer by layer. In the quality inspection of aluminum alloy forgings, deep learning technology can be used to analyze the X-ray, ultrasonic and other inspection images of forgings, and automatically learn and extract the features in the images to achieve accurate identification of defects. This method overcomes the shortcomings of traditional manual detection such as subjectivity and fatigue, and improves the accuracy and efficiency of detection.
Computer vision is another important technology of artificial intelligence, through the processing and analysis of images or videos, to achieve the recognition, tracking and understanding of the target. Computer vision technology can be used to detect and identify surface defects in aluminum alloy forgings. The automatic recognition and location of surface cracks, pores, inclusions and other defects are realized by preprocessing, feature extraction and classifier design of forging images. This method greatly improves the speed and accuracy of defect identification, and provides strong support for the quality control of aluminum alloy forgings.
Pattern recognition and machine learning are the core technologies of artificial intelligence, which realize automatic recognition and classification of patterns through learning and training of data. In the classification and recognition of aluminum alloy forgings, these technologies can be used to learn and classify the shape, size, material and other features of the forgings to achieve automatic recognition and classification of different types of forgings. This method can greatly improve the production efficiency and product quality, and reduce the error and cost of manual classification.
The quality detection and classification recognition technology of aluminum alloy forgings based on artificial intelligence has brought many advantages to the production and application of aluminum alloy forgings, including improving the detection accuracy and efficiency, reducing the production cost, optimizing the production process and so on. In the future, with the continuous development and innovation of artificial intelligence technology, the quality detection and classification identification of aluminum alloy forgings will be more intelligent, automated and accurate. At the same time, these technologies are also expected to be applied to a wider range of fields and promote the high-quality development of manufacturing.
