Intelligent production line thermal error compensation strategy system based on IoT big data

This research focuses on mitigating thermal errors within intelligent manufacturing systems. By incorporating Internet of Things (IoT) technologies, big data analytics, and machine learning approaches, the objective is to counteract the influence of thermal inaccuracies in machinery on both production efficiency and product quality in the realm of precision manufacturing. A predictive model for thermal errors was developed by employing a Back Propagation (BP) neural network alongside multiple linear regression methods. Historical operational data from machinery and temperature records were utilized in this process. To guarantee the quality of the input data for modeling, rigorous preprocessing measures were implemented. For the training and validation phases, a leave-one-out cross-validation technique was employed to improve the model's generalization capabilities and enhance its predictive accuracy. In the case of the BP neural network, optimization of the network architecture and parameter weights was performed. Meanwhile, the focus for the multiple linear regression approach was on selecting appropriate independent variables and calibrating the coefficients of the model, ensuring it could accurately represent the intrinsic patterns of thermal errors in the equipment. During the experimental verification phase, a Computer Numerical Control (CNC) lathe was selected as the subject of study, with real-time temperature and machining error data collected via temperature and contact sensors. Following the training and testing of the models, the BP neural network and multiple linear regression methods demonstrated prediction accuracies of 94.3% and 90.2%, respectively. These results affirm the efficacy and dependability of the system in compensating for thermal errors, providing a viable solution for intelligent production lines.