Database system of wire and arc additive manufacturing based on BP neural network

Based on the classification of single pass weld experiments data of wire arc additive manufacturing, this paper analyzes the needs of users and uses them. Based on the Django framework under python programming language, a database system of wire and arc additive manufacturing is developed with B/S architecture. The system is developed by the combination of database and algorithm prediction model. It mainly sets three modules: user authority management, basic experiments data management and weld geometry prediction. It has the function of storing and expanding printing experiment data and predicting weld geometry under unknown welding process parameters. Different printing process methods introduce different BP neural network structures. When in use, the database system automatically reads the existing algorithm model in the database or trains a new model according to the existing experimental data. After entering the experimental data, the model will be retrained automatically, so as to realize the parameter prediction automatically adapted with the expansion or correction of the experimental data in the database, it can predict the weld geometry and size under unknown process parameters. Finally, a set of validation tests are designed based on MIG process to test the effect of the prediction function of the database. The prediction error of melt width is 1.3% and the prediction error of reinforcement is 1.5%, which shows the feasibility of the prediction function of the database system.Highlights: This paper combines the data classification and storage function of the traditional database with the prediction algorithm, gives full play to the value of the existing experimental data, integrates the data of different experimental materials and welding process methods, constructs the algorithm model, and realizes the prediction function of welding process parameters under various experimental conditions and welding process methods.