Intelligent management and control method for precision component mass production

Abstract

The present application relates to the technical field of precision manufacturing and intelligent management and control, and particularly refers to a kind of precision component scale production intelligent management and control method.The method comprises the following steps: deploying IoT sensing nodes to cover the whole production process, collecting multi-source data such as raw materials, processing operation, assembly, detection, material storage, logistics, equipment and personnel, and forming an encrypted storage whole-process production database through standardized processing such as outlier rejection, normalization and missing value supplementation, to provide support for management and control and energy optimization.Breaking the barriers of fragmented process, realizing whole-process information synchronization, improving production collaboration efficiency;optimizing process parameters, getting rid of artificial dependence, ensuring component quality consistency and processing precision;realizing real-time quality early warning and closed-loop control, reducing defective products and reducing rework loss;early warning of equipment failure, reducing downtime and invalid energy consumption, improving equipment utilization;optimizing resource scheduling, avoiding waste, improving resource utilization efficiency;whole-process energy-saving management and control, reducing overall energy consumption.

Description

Technical Field

[0001] This invention relates to the field of precision manufacturing and intelligent control technology, specifically to an intelligent control method for the large-scale production of precision components. Background Technology

[0002] The large-scale production of precision components suffers from problems such as fragmented processes, inaccurate process control, delayed quality warnings, passive equipment operation and maintenance, low resource utilization, and serious energy waste. Existing control methods lack full-process coverage and systematic energy-saving solutions, and cannot meet the needs of green and efficient production.

[0003] Therefore, there is a need to develop an intelligent control method that is comprehensive, highly accurate, and energy-efficient. Summary of the Invention

[0004] To address the pain points in the large-scale production management of existing precision components, an intelligent management method is provided to achieve full-process digital integration, process optimization, quality early warning, equipment operation and maintenance, resource scheduling and energy-saving management, thereby improving production efficiency and reducing costs, risks and energy consumption.

[0005] Technical solution

[0006] A method for intelligent control of large-scale production of precision components, characterized by the following steps:

[0007] Step 1: Construct a full-process data acquisition system

[0008] Deploying IoT sensing nodes covers the entire production process, collecting multi-source data such as raw materials, processing operations, assembly, testing, material storage, logistics, equipment, and personnel. After standardized processing such as outlier removal, normalization, and missing value supplementation, an encrypted and stored full-process production database is formed, providing support for control and energy-saving optimization.

[0009] Step 2: Construct a digital twin management and control model

[0010] Based on component CAD models, production processes, equipment parameters, and workshop layout, a digital twin model is constructed, comprising five sub-models: components, equipment, processes, workshop, and energy-saving management. This enables real-time mapping between the physical system and the virtual model (accuracy ≤ ±0.005mm), achieving full-process visualized management and control, as well as pre-simulation optimization of production and energy-saving solutions.

[0011] Step 3: Adaptive Optimization and Control of Process Parameters

[0012] A process parameter optimization model is constructed with the objectives of component accuracy, quality, efficiency, and energy consumption. It integrates genetic and reinforcement learning algorithms, uses historical data for training and iteration, dynamically optimizes processing parameters, and sends them to the equipment for automatic execution. The actual and optimized values ​​of parameters and energy consumption are compared in real time, and adjustments are made automatically when deviations exceed the standards.

[0013] Step 4: Intelligent Early Warning and Closed-Loop Management of Quality Throughout the Entire Process

[0014] By integrating multiple detection technologies, real-time quality detection of the processing process is achieved (accuracy ≤ ±0.001mm). A neural network quality early warning model is constructed to trigger graded early warnings and push processing solutions and energy-saving suggestions. Intervention and rectification are carried out for abnormalities, and a non-conforming product analysis and traceability system is established to reduce rework energy consumption and material waste.

[0015] Step 5: Intelligent Equipment Operation and Maintenance and Dynamic Collaborative Scheduling of Resources

[0016] Based on equipment data, we build equipment failure prediction models (accuracy ≥ 95%), formulate personalized operation and maintenance plans including energy consumption optimization, reduce downtime and ineffective energy consumption; and build resource scheduling models to prioritize the scheduling of low-energy-consuming equipment, optimize material delivery routes, dynamically adjust scheduling schemes, and balance production continuity and energy saving.

[0017] Step 6: Establishing a comprehensive energy-saving management and control system

[0018] Real-time monitoring of energy consumption data at each stage, visualization of energy consumption through digital twin models, identification of waste points and establishment of energy consumption benchmarks; implementation of energy-saving optimization in processing, material storage, logistics and testing stages, including parameter adjustment, equipment modification and path optimization, to achieve green and low-carbon production.

[0019] The beneficial effects of this invention are:

[0020] Break down the barriers of fragmented processes, achieve full-process information synchronization, and improve production collaboration efficiency;

[0021] Optimize process parameters, reduce reliance on manual labor, and ensure consistent component quality and processing accuracy;

[0022] To achieve real-time quality early warning and closed-loop management, reduce defective products and rework losses;

[0023] Early warning of equipment failures reduces downtime and wasted energy consumption, improving equipment utilization.

[0024] Optimize resource allocation, avoid waste, and improve resource utilization efficiency;

[0025] Energy-saving management throughout the entire process reduces overall energy consumption and meets the needs of green production.

[0026] It comprehensively improves production efficiency and pass rate, reduces production costs, and adapts to the needs of multiple fields.

[0027] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0028] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the process structure of the present invention. Detailed Implementation

[0030] The present invention is illustrated below with specific embodiments, but these are not intended to limit the scope of the invention. Figure 1 As shown, a method for intelligent control of large-scale production of precision components is presented.

[0031] Taking the mass production of precision aluminum alloy components (tolerance ±0.008mm, surface roughness Ra≤0.8μm) in the aerospace field as an example, this method is applied as follows: IoT sensing nodes are deployed to collect data from the entire process and process it in a standardized manner; a digital twin model with a mapping accuracy of ±0.003mm is constructed to achieve visualization and simulation optimization; process parameters are optimized through algorithms to ensure that quality and energy consumption meet the standards; real-time quality detection and graded early warning are implemented to achieve closed-loop management; intelligent operation and maintenance equipment and dynamic resource scheduling are combined with energy-saving measures in each stage, ultimately improving production efficiency by 25%, the pass rate to 99.6%, and reducing downtime by 35%, cost by 18%, and energy consumption.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0033] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent control of large-scale production of precision components, characterized in that, Includes the following steps: Step 1: Build a full-process data acquisition system, collect multi-source data and standardize the processing to form an encrypted production database; Step 2: Construct a digital twin control model containing five sub-models to achieve real-time mapping between physical and virtual systems, full-process visualization, and scheme pre-simulation; Step 3: Construct a process parameter optimization model to achieve adaptive optimization and precise control of parameters, taking into account both quality and energy consumption; Step 4: Construct a quality early warning and closed-loop management system to achieve real-time detection, tiered early warning, rectification, and quality traceability; Step 5: Achieve intelligent operation and maintenance of equipment and dynamic collaborative scheduling of resources to reduce energy consumption and downtime losses; Step 6: Establish a full-process energy-saving management and control system, implement energy-saving optimization in each stage, and achieve green production.

2. The method according to claim 1, characterized in that, In step 1, data preprocessing uses the 3σ principle to remove outliers, interpolation to supplement missing data, and simultaneous encryption of sensitive data.

3. The method according to claim 1, characterized in that, In step 2, the mapping accuracy of the digital twin model is ≤ ±0.005mm, which can be used for pre-simulation to optimize production and energy-saving solutions.

4. The method according to claim 1, characterized in that, In step 3, the process parameter optimization model integrates genetic and reinforcement learning algorithms, and the constraints include energy consumption thresholds.

5. The method according to claim 1, characterized in that, In step 4, the quality inspection accuracy is ≤ ±0.001mm, and the early warning is divided into three levels, with energy-saving suggestions pushed out simultaneously.

6. The method according to claim 1, characterized in that, In step 5, the equipment failure prediction uses the LSTM algorithm with an accuracy of ≥95%, and the logistics delivery uses AGV with an on-time rate of ≥98%.

7. The method according to claim 1, characterized in that, Step 6 involves targeted energy-saving optimizations in the processing, material storage, logistics, and testing stages.

8. The method according to any one of claims 1-7, characterized in that, It is applicable to aerospace, new energy vehicles, and medical device fields, and its control parameters can be flexibly adjusted.

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