3results about How to "Waste minimization" patented technology

The application relates to the dispensing technical field, in particular to a barrier-avoiding dispensing method based on machine vision, which comprises the following steps: step 1, visual identification generates a dispensing model, an analysis unit determines the angle of a workpiece, and the dispensing model is corrected; step 2, dispensing parameters are determined according to the measured value of a balancing machine; step 3, single-process conditions of a dispensing mechanism are determined, a new dispensing process is generated, and dispensing is carried out; step 4, the dispensing mechanism is adjusted to enter the next dispensing process; step 5, the dispensing mechanism is controlled to carry out dispensing of the quality-minimal dispersion correction point, and the dispensing process of the dispensing mechanism is determined again; step 6, the dispensing mechanism is corrected according to the deviation quality condition of any dispensing process; and step 7, all dispensing processes are completed, the dispensing cylinder is reset, and the workpiece generates a dispensing finished product after a certain curing time. The application improves the qualified rate of dispensing finished products by carrying out barrier-avoiding algorithm processing on the dispensing process.

This invention relates to a molding process. In particular, this invention relates to an improved two-layer molding process that allows for the selective and localized deposition of an additional cellulose layer only at points detected as critical, with the aim of enhancing its mechanical and aesthetic features.

The application discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: acquiring and integrating the working states of various types of equipment in the factory, the inventory location states of different materials, manual data, production order states, process route requirements, the plan and completion time of each process, and the delivery period requirements of customers; combining the supply chain material data, the factory storage space, the equipment switching cost, the production rules, the order process dependency relationship, and the production target, and generating an initial production scheduling plan through an AI optimization algorithm; the application generates an initial scheduling plan by acquiring and integrating multiple types of production data, monitors dynamic events, evaluates multiple schemes, selects an optimal scheme through economic model evaluation, and realizes production refinement, dynamic response, and benefit optimization.