Productivity graphical configuration modeling implementation

Abstract

The system is applied to the field of intelligent and digital operation management of industrial manufacturing. According to the method, digital simulation modeling is visually and accurately carried out on product manufacturing capabilities of productivity units such as working centers and intelligent production lines of an intelligent factory in a software graphical mode, and a unique manufacturing digital control execution system of each product is generated according to an established product productivity model. The graphical configuration model highly abstracts production elements such as human, machine, material, method, measurement and the like in the manufacturing capability and a combination mechanism, and provides the modeling capability of quickly constructing a product capacity model in a graph dragging mode. The problem that intelligent manufacturing core software MESMOM is difficult to adapt to products in different industries, different manufacturing processes and different management and control events is solved, manufacturing capacity digital models of all the products of the enterprises are changed from nothing to existence, the demand difference of intelligent manufacturing of all the enterprises is undertaken through model difference, the difficulty of digital manufacturing is reduced, and the production efficiency is improved. And the method has a wide market popularization value.

Description

Technical Field

[0001] This invention patent is applied in the field of intelligent and digital operation management of industrial manufacturing. It realizes the establishment of digital models of the manufacturing capability requirements of various products of an enterprise through graphical configuration, and produces a unique manufacturing execution system for the product based on the product manufacturing model. It belongs to the field of intelligent manufacturing and digital transformation of manufacturing. Background Technology

[0002] Smart manufacturing has been embraced by manufacturing enterprises across various industries. Many companies have already upgraded their existing factories to smart factory standards, and are directly planning and constructing new factories in accordance with smart factory requirements. The core software supporting smart factories includes Manufacturing Execution Systems (MES) and Manufacturing Operations Management (MOM). However, based on the actual construction process and implementation results of smart factories, the core software for smart manufacturing exhibits the following typical problems and shortcomings: 1. Low reusability.

[0003] The characteristics of products vary greatly across industries, as do the technologies and processes used in their manufacturing. In the early stages of MES and MOM software development, the focus was primarily on a few specific industry sectors. This inevitably led to customized upgrades and iterations to meet the specific industry characteristics and technological requirements, resulting in software products bearing a distinct industry-specific imprint and exhibiting low reusability. A broader perspective is needed to analyze industry manufacturing models to create a unified model that accommodates the diverse manufacturing processes across various industries.

[0004] 2. Weak quantitative capabilities.

[0005] The primary value of a smart factory lies in enabling data-driven manufacturing operations. This dictates that manufacturing digitalization cannot merely be limited to data recording and analysis; its more crucial role is to dynamically quantify the resources and capabilities of people, machines, materials, methods, and measurement at each stage of manufacturing, driving the dynamic operation of these resources. The organization and quantification of production capacity utilization are relatively complex, resulting in many smart factory systems having only a calibrated capacity but lacking the ability to quantify dynamic capacity, making it difficult to achieve effective production planning and scheduling within limited capacity.

[0006] 3. Insufficient configuration capabilities.

[0007] Over the past decade of development in China's intelligent manufacturing sector, MES and MOM software have primarily focused on project-based customized development. Business models in the manufacturing field have not been thoroughly abstracted, configurability is low, and they rarely reach the maturity of standard software. The existence of over 2,000 MES and MOM vendors in the market indicates that this software field is still in its early stages of development. Future development must focus on enhancing configurability, improving software maturity, and reducing the technical difficulties of implementation.

[0008] Based on the analysis of the main problems in the construction of smart factory projects in various industries, the core software of smart factories needs simpler and more intuitive new technologies, with substantial improvements in reusability, quantification, and configuration capabilities, forming a unified manufacturing digital model that can be adapted to the unique control requirements of different product manufacturing. Summary of the Invention

[0009] This invention patent utilizes a new technology of graphical configuration modeling for product production capacity. This model adapts to the differentiated needs of various industries and manufacturing processes for intelligent manufacturing, effectively solving the following core technical problems: 1) Create a unified digital model for intelligent manufacturing product capacity that is not limited by industry or manufacturing process. Promote the standardization of intelligent manufacturing software.

[0010] 2) Construct product manufacturing processes and production material relationships using a graphical, drag-and-drop modeling method. This reduces the technical complexity of project construction.

[0011] 3) Dynamically quantify and analyze capacity fluctuations of production units based on workpiece production cycle time. Quantified capacity promotes data-driven operations.

[0012] 4) Automatically generate a smart factory process-level executable event system based on the product capacity configuration model. Create a one-product-one-manufacturing execution system.

[0013] The product capacity graphical configuration model allows for intuitive online editing of product manufacturing capability models, aiming to simplify the modeling difficulty of digital manufacturing models in smart factories and generate manufacturing execution systems for various industries through product manufacturing capability models.

[0014] 1) Capacity Model Structure Product capacity configuration modeling is the process of building product manufacturing capabilities. This model focuses on constructing the logical relationships between elements such as capacity, processes, events, and production materials required for workpiece manufacturing, rather than a three-dimensional model. Product capacity configuration selects the necessary elements from the "capacity library," "process library," "event library," and "resource library" of the smart factory's digital system to build a complete manufacturing capability model that meets the workpiece's requirements.

[0015] The model's role: First, there's the product capacity model, then the manufacturing execution system (MES). Based on the logical relationships of the elements configured for each workpiece, an executable system for digital control of its manufacturing is generated, ensuring that each workpiece has its own unique logical MES. Figure 1 illustrates the structure from the abstract model to the concrete system. The blue section on the left represents the configuration for product capacity modeling, and the yellow section on the right represents the corresponding execution system generated based on the manufacturing business model configured on the left.

[0016] 2) Mechanism of capacity model construction The product capacity model uses the workpiece as the modeling object. A self-made workpiece must have a corresponding manufacturing capability model to match it, regardless of whether the capability is the internal manufacturing capability or the external manufacturing capability.

[0017] The product capacity model mainly consists of four parts: capacity unit, capacity swimlane, process event, and process resource. I. Production Capacity Unit A capacity unit is an abstraction of a manufacturing capability unit. A factory's capacity is managed using a structured tree approach. For factories with large capacities, this can be broken down into multiple independent capacity structured trees for separate management based on work sections. When configuring product capacity, the required capacity group or capacity unit is selected from the capacity structured tree. The selected capacity model is managed hierarchically, with capacity groups further subdivided into capacity groups down to capacity units.

[0018] Capacity unit occupancy accounting: Based on the product capacity model, calculate the capacity required by each capacity unit to support the specific products of the manufacturing order, and the remaining capacity per shift, etc., as well as other numerical operational indicators.

[0019] Methods for measuring the capacity of a production unit: Capacity Unit Capacity = Tavailable: Available time; ηefficiency: Production efficiency; ηyield: pass rate; Tcycle: Production cycle for a single product; Formula for measuring production line capacity: Production line capacity = min(capacity of capacity unit 1, capacity of capacity unit 2, ..., capacity of capacity unit n) II. Capacity Swimlane A capacity swimlane is a management unit in the production control process, also known as a "process swimlane," and will be referred to as a capacity swimlane below. Each capacity unit in the model corresponds to one capacity swimlane. The capacity swimlane can be configured with the corresponding process and the events to be processed by that process. Multiple capacity swimlanes have a left-right order, representing the process flow of workpiece production utilizing the capacity unit, equivalent to the definition of the workpiece manufacturing process flow.

[0020] III. Process Events Process events are specific business operations that a particular process needs to handle (such as reporting work, reporting inspection, calling AGVs, laser printing, etc.), and are uniformly abstracted into events. To access specific swimlanes, simply select the desired event from the event library and drag it to the corresponding swimlane. A swimlane can be configured with multiple, non-repeating events. Events also support enterprise-defined events.

[0021] IV. Process Resources Process resources are the production materials required to complete a workpiece within a specific process unit. Production materials include personnel, machinery, and materials. Personnel include the skills possessed by the personnel working on that process; machinery includes hardware requirements (equipment, molds, fixtures, cutting tools) and software requirements (issued process parameters and equipment-collected parameters); materials include the materials supplied to the production line and the corresponding delivery supplies.

[0022] 3) Graphical modeling delivery method The interactive process for graphical capacity configuration modeling can be summarized as "three selections, two drags, and one edit," or simply "3+2+1." Modeling delivery method: Three options: Select the required capacity units from the capacity library (capacity tree) to form swimlanes; select the required processes for the workpiece from the process library to define swimlanes; select the next level of materials for the workpiece from the product structure tree to the line-side shops of the workstations and define the material distribution strategy.

[0023] Two drag-and-drop operations: drag the required events from the event library to the specific capacity swimlane to configure the events; drag the capacity swimlanes to define the manufacturing process by sorting them before and after.

[0024] An editor can edit and set relevant auxiliary parameters for attributes such as process (cycle time) and production resources (molds, fixtures, line shops) on the attribute page.

[0025] 4) Modeling and generating an executable system When a production order is placed, the production capacity model corresponding to each workpiece under the product is retrieved, and the manufacturing execution system for the production of that product is generated. Beneficial effects

[0026] The new technology based on graphical capacity configuration modeling directly brings the following beneficial effects: 1) Graphical configuration modeling of product capacity elements, supporting configuration of various industries and manufacturing processes.

[0027] The graphical modeling of product capacity has abstracted the operational mechanisms of manufacturing elements such as people, machines, materials, methods, measurement, and environment in various industries. It allows for the flexible construction of product manufacturing capacity models for various industries and manufacturing processes in a graphical and drag-and-drop manner.

[0028] 2) Dynamic quantification and scheduling of production capacity units to support the application of production capacity in the digital operations layer.

[0029] Streamline which production capacity units should be used for product components, which components each production capacity unit can support, quickly and dynamically assess the remaining available capacity of each production capacity unit per day and per shift, and provide the decision-making data required for digital operations management based on limited capacity.

[0030] 3) The process execution system is generated from the product model, changing the approach of having the system first and then the model.

[0031] Based on the instantiation of the corresponding capacity model of the order product, an executable system at the process level is automatically created, which includes the manufacturing process and corresponding events of each workpiece of the order product, creating a new method of first having the model and then the execution system.

[0032] 4) Object-oriented and graphical modeling thinking is beneficial for AI learning, training, and application.

[0033] Effective methods for training and learning artificial intelligence (AI) involve graphical and object-oriented content. A graphical configuration model of production capacity helps AI learn the product manufacturing process in a smart factory, supporting order and product operations. Attached Figure Description

[0034] Figure 1 is a diagram of the capacity configuration structure of the present invention. Figure 2 is a configuration diagram of the production capacity model of the present invention. Figure 3 is an example diagram of constructing product capacity from the capacity tree according to the present invention. Figure 4 is an example diagram of the graphical configuration modeling of production capacity according to the present invention. Figure 5 is a schematic diagram of the production capacity lane layout from top to bottom according to the present invention. Figure 6 is a schematic diagram of the capacity lane layout from left to right according to the present invention. Figure 7 is a schematic diagram of the capacity lane layout from bottom to top according to the present invention. Figure 8 is a schematic diagram of the production capacity lane layout from right to left of the present invention. Figure 9 is a schematic diagram of the production capacity structure connection line of the present invention. Figure 10 is a comparison example of the capacity lane before and after dragging according to the present invention.

Claims

1. This invention provides a graphical configuration modeling implementation for production capacity, characterized in that, It comprises three parts: a highly abstract mechanism for combining manufacturing capacity elements, a precisely configured graphical digital model of product manufacturing, and an automatically generated product-specific manufacturing execution system. 1) Highly abstract mechanism of manufacturing capacity factor combination: This model abstracts different industries and manufacturing processes to construct a unified digital model of intelligent manufacturing product capacity. The model includes the elements required for manufacturing: 1) Product and component library: The manufacturing capability model describes the manufacturing process of a product and its components.

2. (ii) Capacity Unit Library: The digitalization of manufacturing plant capabilities, including the definition and quantification of equipment capabilities and personnel capabilities.

3. (iii) Process Event Library: Defines the events that need to be processed and controlled in each stage of manufacturing, and supports custom events.

4. (iv) Production resource library, containing production materials needed in the manufacturing process, such as molds, fixtures, cutting tools, equipment parameter definitions, electronic work instructions (SOPs), etc. 2) Precisely configure product manufacturing graphic digital models A drag-and-drop modeling method is used to construct product manufacturing capability requirements and production material demand models. The modeling interaction steps include: 1) Modeling object selection: Select the self-made part, also known as the workpiece, from the product BOM structure as the modeling object. If product A is assembled from self-produced parts B and C, then you can select three objects, A, B, and C, to model separately.

5. (ii) Production Capacity Unit Selection: Based on the manufacturing capacity requirements of the selected modeling object, select one or more required production capacity units from the smart factory digital production capacity unit library. For example, if the production of workpiece B requires stamping, deburring, and chamfering, then the corresponding production capacity unit should be selected. 6.3) Capacity swimlane generation: Automatically generate corresponding capacity swimlanes for each selected capacity unit. Multiple batches of different capacity units can be added and corresponding capacity swimlanes can be generated. The dragged graphic swimlanes can be sorted to meet the manufacturing sequence requirements.

7. (iv) Select process events. Drag the desired event from the event library to the specific capacity swimlane and configure the control events under that capacity unit. For example, if the stamping process of workpiece B requires scanning and loading, calling AGV, and quality inspection, then the corresponding event needs to be dragged to that swimlane. 8.5) Production resource allocation: Allocate the required production resources and their attributes in specific production capacity lanes, such as manufacturing cycle time setting, mold selection, line-side shop setting, and material distribution. 9.3) Automatically generate product-specific manufacturing execution systems Besides providing a clear visual representation of product capacity requirements, the more important function of building a graphical model is to generate a digital manufacturing control and execution system for each stage of the product's manufacturing process, thus creating a unique manufacturing execution system for each product. The content generated through this model-driven execution system includes: 1) Product manufacturing task generation: Based on the product quantity in the order and the production capacity model corresponding to the product and its components, generate the manufacturing task for the product, including the tasks for its components.

10. (ii) Production Capacity Unit Event Generation: Generate event instances for each production capacity swimlane according to the model event configuration. Events under a production capacity unit include both manual event interactions and business events automatically processed by the system.

11. (iii) Manufacturing sequence control: Complete the manufacturing tasks one by one according to the production capacity unit order, and ensure that the manufacturing is carried out strictly in accordance with the manufacturing process flow. Complete the manufacturing tasks level by level from bottom to top according to the hierarchical relationship of the product BOM.

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