In the modern manufacturing industry, product quality inspection has always been an important link. As the basis of mechanical parts, the quality of forgings directly affects the performance and life of machinery. With the development of science and technology, more and more new technologies are applied to the field of quality inspection. Among them, the development of machine learning technology provides a new solution for forging quality automatic detection. This article will introduce how to automatically detect forging quality based on machine learning method.
The traditional forging quality inspection mainly relies on manual inspection and simple instruments and equipment, which has certain subjectivity and error. With the development of Industry 4.0, the accuracy and efficiency of forging quality inspection are increasingly required, and automatic inspection has become an inevitable trend. The rapid development of machine learning technology provides the possibility of automatic detection, which can be realized with high precision by training a large number of data models.
The application of machine learning in forging quality automatic detection mainly includes the following steps:
Data collection: Collect a large number of forging quality data, including qualified products and unqualified products, while recording the parameters of each forging, such as size, weight, hardness, etc.
Data preprocessing: The collected data is cleaned, denoised and normalized to facilitate model training.
Model training: Select suitable machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) and train the pre-processed data to obtain a quality prediction model.
Model evaluation: Use test data set to evaluate the model, calculate the accuracy of the model, recall rate and other indicators, and adjust the model parameters according to the evaluation results.
Model application: The trained model is applied to the actual production, real-time monitoring and prediction of forging quality, timely detection of unqualified products, improve production efficiency.
A large machinery manufacturing enterprise uses neural network algorithm to train forging quality prediction model. First of all, 5000 forging sample data were collected, including 2000 qualified products and 3000 unqualified products. Then, these data are preprocessed to normalize the data to the same range. Next, the model is trained using the neural network algorithm, and after 100 iterations, the model converges. Finally, the model is applied to actual production, and more than 90% accuracy and 85% recall rate are achieved. Compared with the traditional detection method, this method greatly improves the detection efficiency and accuracy, and reduces the labor cost.
This paper introduces an automatic forging quality detection method based on machine learning. Through the steps of data collection, preprocessing, model training, evaluation and application, the high precision forging quality automatic detection is realized. Compared with traditional inspection methods, machine learning technology has higher efficiency and accuracy, which can find quality problems in time and improve production efficiency. However, machine learning technology also has certain limitations, such as high data quality and quantity requirements, requiring professionals to train and adjust the model. Therefore, future research directions should include optimizing data preprocessing methods, exploring more efficient algorithms, and improving the interpretability of models.
