3results about How to "Realize automatic warning" patented technology

The utility model discloses an automatic material storage equipment, including storage area, auxiliary material processing work area, set up in the transfer robot between storage area and auxiliary material processing work area, and the transfer robot is used to snatch auxiliary material coiled material, the auxiliary material processing work area includes: work table, and is provided with the material pulling manipulator, folding mechanism, cutting knife and material pivot on the work table, and the work table unrolls auxiliary material coiled material and carries out cutting, and folds after cutting auxiliary material, label printer, label printer sets up in work table one end and is connected to work table through transmission line, is used to mark after folding auxiliary material. The utility model can realize the automatic management material inventory, and high -efficiently saves manpower cost, site cost, material cost, through the construction small -size intelligent storage system, realizes the goal of intelligent production scheduling finally.

The present application belongs to the technical field of 3D printing, and specifically relates to a printing head wear detection method based on machine learning, which solves the problems of relying on manual judgment, low detection accuracy, insufficient prediction ability and the like in the prior art. By collecting current, temperature and working time data, and extracting key features to construct a regression model, real-time evaluation and remaining life prediction of the printing head wear state are realized. Compared with the traditional method, this method fuses multi-source data, improves detection accuracy, and can identify wear trends earlier; a weighted calculation method is used to establish a quantitative relationship, enhancing the scientific nature of life prediction; combined with the control system, automatic warning is realized, reducing unnecessary replacement and sudden failure risk, thereby improving equipment operation efficiency, reducing maintenance cost, and improving the intelligent level and stability of the 3D printing system.

This invention relates to the field of mold price prediction technology, and in particular to a mold price prediction method based on multi-dimensional data fusion. A hybrid neural network model combining Transformer and LSTM is constructed. The global attention mechanism of the Transformer module is used to deeply mine the complex relationships between high-dimensional static features, while the LSTM module captures the temporal evolution patterns of historical prices. The two types of features are effectively fused, significantly improving the accuracy and stability of mold price prediction. Through a pre-processing layer and attention heads that process features of specific categories, the model is guided to perform structured modeling based on feature categories in the early stages of learning. This effectively distinguishes and focuses on the dependencies within different categories of features, such as static attributes, process attributes, and temporal costs, reducing noise interference between irrelevant features and improving the interpretability, efficiency, and generalization ability of the model's feature association learning.