Research on mechanical parts precision evaluation based on machine learning

The machinery and equipment manufacturing industry are developing at an unprecedented speed, making the inspection of mechanical parts increasingly complex. In this work, we propose a neural network-based accuracy evaluation method for mechanical parts. This method involves the collection of a large number of machining and inspection data sets of mechanical parts, the removal of incomplete or erroneous data through data cleaning, the extraction of key parameters through the use of feature extraction techniques, and the implementation of standardized processing to ensure data consistency. A multi-layer feedforward neural network (MLP) was constructed, the ReLU activation function was employed to address the nonlinear transformation of the hidden layer, and the Adam optimization algorithm was utilized for training to enhance the convergence speed and stability of the model. The experimental results demonstrate that the proposed method is an effective and reliable approach for predicting the machining accuracy of mechanical parts, and it is well-suited to meet the needs of industrial processing sites.