6 results about "Manufacturing intelligence" patented technology

The application relates to the field of spring manufacturing intelligent control, and discloses a spring horizontal processing and single heat treatment coordinated intelligent regulation and control system, which comprises the following steps: a disturbance characteristic online coding module, which is used for online acquisition and coding of physical disturbance generated by each spring in horizontal processing procedures such as coiling and welding, and generates a unique disturbance characteristic spectrum; an online control law dynamic calibration module, which receives the disturbance characteristic spectrum and inversely solves a compensatory individualized heat treatment instruction sequence for the spring; an individualized heat treatment execution module, which executes accurate single heat treatment on a single spring according to the instruction sequence; and a quality feedback and model self-optimization module, which optimizes a control model in a long period according to the final measured performance of the spring. Through construction of a direct mapping and feedback optimization closed loop from horizontal processing disturbance to single heat treatment control, the application realizes deep coordination between the two, effectively compensates for process fluctuation, and significantly improves the consistency of spring product quality.

The application provides a product manufacturing intelligent scheduling method and device, and relates to the technical field of intelligent manufacturing. The method comprises the following steps: acquiring an original workpiece set and a newly-arrived workpiece set, wherein the original workpiece set comprises original workpieces, the newly-arrived workpiece set comprises newly-arrived workpieces, and there is an initial optimal scheduling scheme corresponding to the original workpiece set; setting an optional processing strategy for each newly-arrived workpiece; limiting the absolute value of the difference between the completion time of the original workpiece in a new scheduling scheme and the completion time of the original workpiece in the initial optimal scheduling scheme to be not more than a preset threshold value, and obtaining a maximum completion time deviation constraint; constructing a target function according to a weighted total completion time, a maximum completion time deviation cost, a compression cost and a rejection cost; and based on the target function and the maximum completion time deviation constraint, a mixed integer programming model is established, so that the global optimal or approximate optimal total cost is realized while the new order is accepted and the stability of the original plan is ensured.

The application relates to the technical field of intelligent equipment manufacturing, and specifically discloses an AI-based equipment manufacturing process energy consumption optimization and carbon footprint tracking system, which comprises a central collaborative scheduler, and the central collaborative scheduler is communicatively connected with the following modules: a data self-checking reconstruction module, a wavelet neural network optimized by using an improved particle swarm algorithm is used to construct a data self-checking model, and time sequence analysis and correlation comparison are carried out on multi-source heterogeneous sensing data of an equipment manufacturing process; the application can dynamically identify and correct inherent errors of sensing data by deploying a multi-source heterogeneous sensor network, combining a precise time synchronization protocol, constructing a time sequence alignment sequence, using a wavelet neural network optimized by using an improved particle swarm algorithm to construct a data self-checking model, outputting a high-fidelity data stream, and solving the problem of insufficient reliability of carbon footprint accounting data in a traditional manufacturing process from the source, thereby providing a data basis for energy consumption optimization and carbon emission tracking, and ensuring the authenticity and effectiveness of accounting results.

The application relates to the technical field of intelligent manufacturing and industrial automation, in particular to a manufacturing intelligent production scheduling and dispatching method based on an AI edge computing terminal, which comprises the following steps: generating a theoretical production scheduling reference time axis based on a work order BOM and a standard process route SOP issued based on ERP / MES and equipment OEE time sequence slices, workstation flow transfer beats and edge buffer pool inventory water levels collected in real time by the edge computing terminal; performing parameterized disturbance simulation on the theoretical production scheduling reference time axis to generate a theoretical residual time sequence set; generating a real residual time sequence according to actual workstation completion times; determining a disturbance type by using dynamic time warping distance calculation and similarity calculation based on a process route topology; generating a local work order translation dispatching instruction according to the disturbance type, and performing dynamic calibration to optimize the scheduling accuracy of subsequent production plans.

This invention discloses an intelligent control system for electrode foil formation process parameters that integrates diffusion models and ensemble learning. The system comprises five modules: multi-source data preprocessing and feature extraction, anomaly identification, time-series data generation, multi-objective performance prediction, and a front-end interface. Feature extraction employs a multi-scale comparative learning mechanism to extract time-series and batch features. Anomaly identification utilizes principal component analysis and weighted voting for real-time detection. Time-series data generation is based on a conditional diffusion model, learning the process and performance distribution mapping to generate controllable data. Performance prediction integrates multiple models and wavelet transform decomposition to jointly predict indicators such as specific capacitance and bending. The front-end is built on a responsive platform based on Grado, supporting asynchronous high concurrency. The system uses a diffusion model as its core, reconstructing full-cycle time-series data to reveal the control direction, and heatmaps enhance interpretability. Experimental results show that the system combines real-time performance with accuracy, identifying anomalies, predicting performance, and providing process suggestions, thereby improving the level of manufacturing intelligence.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to a manufacturing intelligent scheduling and dispatching method based on AI edge computing terminals. The method includes: generating a theoretical scheduling baseline timeline based on work order BOMs and standard process routes (SOPs) issued by ERP / MES, as well as real-time data collected by the edge computing terminal on equipment OEE time-series slices, workstation cycle times, and edge buffer pool inventory levels; performing parametric disturbance simulation on this data to generate a theoretical residual time-series set; generating a real residual time-series based on actual workstation completion times; determining the disturbance type using dynamic time warping distance calculation and similarity calculation based on process route topology; and generating local work order translation scheduling instructions accordingly, followed by dynamic calibration to optimize the scheduling accuracy of subsequent production plans.