A simulation optimization method for hydraulic support structure welding production line of digital factory

Abstract

The application discloses a kind of digital factory's hydraulic support structural member welding production line simulation optimization method, comprising the following steps: production line preliminary planning, corresponding simulation model is established using factory simulation software PlantSimulation, quantitative verification is carried out to workshop equipment layout, production logistics design, capacity and the like, and optimization is found according to simulation result.Optimization process includes: adjusting inter-process work load and the number of each process equipment, obtain the work station distribution scheme under static calculation, and make initialization logistics transfer strategy and public resource allocation strategy as simulation model data input;Based on simulation model, the availability of static production line layout scheme is verified, and iterative optimization of strategy and design planning is carried out.The application can simulate the whole production process before the welding production line is put into production, continue to carry out strategy iterative optimization according to the simulation result, effectively verify the production line capacity and planning, and reduce investment and research and development risk.

Description

Technical Field

[0001] This invention relates to the field of design of assembly and automated welding production lines for structural components in digital factories, and specifically to a simulation optimization method for a welding production line for hydraulic support structural components in a digital factory. Background Technology

[0002] Factory simulation technology is actually a discrete event simulation technology for production systems. Its essence is the process of establishing a system model of the real world and using the established model to conduct experimental research on the actual system.

[0003] Production system simulation focuses on the production planning stage. By utilizing virtual simulation technology, the production planning stage allows for the pre-planning of factory production line layout, equipment configuration, manufacturing process paths, and logistics. Based on the simulation model's "pre-run," analysis, evaluation, and verification are conducted to quickly identify problems and areas for improvement in system operation. Timely adjustments and optimizations are then made, reducing the number of changes and rework required in subsequent production execution, thereby effectively reducing costs, shortening lead times, and improving efficiency. Factory simulation technology is widely used in the following scenarios: 1. Verifying system design and identifying potential problems before establishing a real system; 2. Evaluating the impact of plan changes by operating in parallel with existing production systems; 3. Identifying and eliminating problems that require costly and time-consuming corrections during production improvement; 4. Minimizing investment costs for production lines without disrupting production capacity; 5. Optimizing the performance of complex production systems with multiple variables.

[0004] As a typical discrete manufacturing enterprise, the traditional coal mining machinery manufacturing industry is undergoing a transformation due to changes in the market environment, the adoption of new materials, technologies, and production processes. It must continuously shorten product manufacturing cycles, improve production flexibility, and meet the personalized needs of users.

[0005] Virtual simulation, as an important means of process planning and production line balancing in workshops, analyzes key workstations, line balancing, production processes, single-line production line layouts and logistics schemes in welding workshops by constructing typical discrete system simulation models. It analyzes and optimizes workshop capacity, which is of great significance for enterprises to stabilize production operations and reduce planning iteration costs. Summary of the Invention

[0006] Traditional hydraulic support structural component welding production processes are constrained by the diversity of product types, significant differences in structure and welding processes between different products, small production batches, and short changeover cycles. This results in poor adaptability of assembly and welding production lines to different products. The previous approach of planning production lines and configuring equipment based on experience cannot quantitatively assess the operational effectiveness of production lines in dynamic production scenarios in advance. This leads to long adjustment cycles and high trial production costs, making it unsuitable for the demands of digital manufacturing.

[0007] To address the aforementioned problems and shortcomings, this invention proposes a simulation optimization method for a welding production line of hydraulic support structural components in a digital factory. This simulation method, based on digital factory simulation software, analyzes the utilization of workstations and the usage of logistics equipment to optimize equipment selection and adjust production line operation strategies (crane dispatching strategies and dynamic allocation strategies for shared resources). It can simulate the entire production process before the welding production line is put into operation, and continue to iterate and optimize strategies based on the simulation results, effectively verifying the production line capacity and planning, and reducing investment and R&D risks.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] Design a simulation optimization method for a hydraulic support structure welding production line in a digital factory, including the following steps:

[0010] S1. Model Input Preparation: For the initial allocation scheme of each process in the production line, calculate the process capacity of each process, obtain the process time, perform static process capacity balance index calculation and adjustment, determine the equipment allocation scheme of each process after adjustment, and the workpiece transfer logic between different processes, and perform preliminary optimization and adjustment of the production line planning scheme; the process includes bottom plate assembly, first assembly, first flat welding, welding pad, 1.5 assembly, vertical welding, second assembly, supplementary welding, second flat welding, vertical welding, third assembly, preheating, and third supplementary welding;

[0011] S2. Simulation Model Construction: Based on the preliminary optimized and adjusted production line planning scheme obtained in step S1, the corresponding data information and allocation scheme are used as data inputs. The PlantSimulation software is used to construct a three-dimensional simulation model of the hydraulic support structural component welding production line.

[0012] S3. Simulation Model Verification and Iterative Optimization: Verify the usability of the static production line layout scheme, determine the workshop scheduling operation mode based on the virtual assembly line, focus on the problem of workstation capacity balance and dynamic allocation of shared resources, and carry out iterative optimization of strategy and design planning.

[0013] S4. Verification of the effect of the optimized strategy.

[0014] Preferably, the hydraulic support structural component welding production line includes an assembly area 1 for the first and third assembly processes, an assembly area 2 for the 1.5 and second assembly processes, a repair welding area 1, a repair welding area 2, an automatic welding interaction area 1, an automatic welding interaction area 2, a robot automatic welding area, and an AGV material handling area.

[0015] The hydraulic support structural component welding production line also includes logistics transfer equipment, which includes manual cranes, intelligent cranes, AGV material transport vehicles, and stepping assembly platforms.

[0016] Preferably, step S1 specifically includes:

[0017] S11. Initialize the equipment allocation scheme for each process, and calculate the theoretical cycle time for each process based on the initialized workstations, equipment allocation schemes, and process time data. The calculation method for the theoretical cycle time of each process is as follows:

[0018]

[0019] S12. Perform static process capability balance index calculation and adjust the workstation allocation scheme; the process capability balance index for each process is calculated as follows:

[0020]

[0021] in,

[0022] S13. Based on the process capability balance index calculated in step S12, classify each process into different process types:

[0023] If the process capability balance index is greater than 1, it is classified as a type A process, i.e., a bottleneck process.

[0024] If the process capacity balance index is less than 1, it is classified as a type B process, i.e., a process with excess capacity.

[0025] Based on production line scale, equipment cost, and equipment function, different processes are further subdivided into subtypes:

[0026] Process subtype 1: Only the workload is adjustable; if the production line area or equipment cost is a constraint, equipment cannot be added or removed.

[0027] Process Subtype 2: Only the number of equipment is adjustable. If the equipment has special characteristics, the process content cannot be handled by other equipment.

[0028] Process subtype 3: Both the number of equipment and the workload are adjustable;

[0029] Process subtype 4: The number of equipment and the operation are both non-adjustable;

[0030] The adjustment rules for different process subtypes are as follows:

[0031] Rule 1: Increase or decrease the number of workstations, applicable to situations where the number of equipment can be adjusted in process subtypes 2 and 3;

[0032] Rule 2: Adjusting the workload to processes with similar equipment functions and a smaller process capacity balance index applies to situations where the workload is adjustable in process subtypes 1 and 3.

[0033] Rule 3: From a design perspective, re-plan the equipment functions or change the equipment selection; from an operational perspective, try to extend the daily effective working time or change the material handling strategy. This applies to situations in sub-type 4 of the process where the number of equipment and workload cannot be adjusted.

[0034] S14. Based on the adjustment rules in step S13 and the cycle time of each process under the initialization scheme in step S11, subtype identification is performed for type A processes, and corresponding adjustments are made according to the above rules. The adjusted process capacity balance index is recalculated, and the index is satisfied to be less than 1. Based on this, the specific equipment adjustment amount or workload adjustment amount is determined. For type B processes, the excessive output is limited by reducing the number of equipment or increasing the workload. After completing the above steps, the process time of each process is updated, and the updated equipment allocation scheme and process time data of each process are used as the model input data in step S3.

[0035] S15. Develop an initialization scheme for the inter-process transfer logic and the public resource allocation logic;

[0036] ① Interactive bit allocation principle:

[0037] Interaction zones are set up between the robotic automatic welding area and assembly areas 1 and 2 for workpiece buffering and transfer. The number of interaction points is determined based on the site area and common dimensions of the structural components. This structural component welding production line has a total of X interaction points, of which X1 interaction points are set between assembly area 1 and the robotic automatic welding area, and X-X1 interaction points are set between assembly area 2 and the robotic automatic welding area. All interaction points can accept workpiece transfer when they are idle and are shared interaction points.

[0038] ②Train dispatching rules:

[0039] The crane operation is executed according to the "first-in, first-out" rule. After the workstation is completed, the transfer task is sent to the crane operation task. The crane will execute the transfer tasks in the order of task generation time.

[0040] Preferably, step S3 specifically includes:

[0041] S31. Assuming that there is sufficient material in the upstream process of the production line, the equipment allocation scheme, process time data, interaction position allocation rules and crane scheduling rules obtained in steps S12 and S13 are used as model input data to continuously simulate the three-dimensional simulation model.

[0042] The model simulation time is calculated as 30 days. Steady-state simulation data after 3 days of model operation is extracted for analysis to obtain simulation data on production station utilization, logistics equipment utilization, buffer / interaction station utilization, and production line output. The average equipment utilization rate, congestion rate, and idle rate in the production line model are also calculated.

[0043] S32. Using the virtual pipeline as a guiding principle and aiming to reduce workstation blockage rate and improve workstation utilization rate as the optimization direction, construct a workstation blockage / idle cause analysis model based on the simulation data obtained in step S31.

[0044] S33. For the issues of workstation capacity balancing and dynamic allocation of shared resources, iterative optimization of strategies and design planning is carried out, including:

[0045] ① Configure shared logistics resources: Intelligent driving priority strategy;

[0046] ② Configure shared cache resources: Interaction area location allocation strategy;

[0047] ③Configure shared processing resource allocation: Preheating furnace feeding priority strategy based on the predicted status of automatic welding stations.

[0048] Preferably, in step S33, the intelligent driving priority strategy includes:

[0049] Priority generation rules: Outgoing tasks have higher priority than incoming tasks; for tasks of the same type, tasks in the subsequent process have higher priority.

[0050] The specific settings principles are as follows:

[0051] First, determine the task type. Tasks that transfer work-in-process from the robot's automatic welding station to the unloading area have higher priority.

[0052] If the task types are the same, then determine the robot automatic welding station involved. Transfer tasks related to bottleneck processing equipment within the current product process route have higher priority.

[0053] If all transfer tasks involve or do not involve bottleneck equipment, the priority is determined by the process in which the transfer task is located, with transfer tasks in later processes having higher priority.

[0054] Preferably, in step S33, the interaction area location allocation strategy includes: dividing the interaction area into a common interaction position and a dedicated interaction position, and setting a dedicated interaction position for the subsequent process according to the principle of prioritizing the output of the subsequent process to ensure that the workpiece is transferred out.

[0055] Preferably, in step S33, the preheating furnace feeding priority strategy based on the predicted state of the automatic welding station includes: there is a preheating furnace process between the interaction station and the automatic welding station as an intermediate shared resource, and the preheating furnace feeds in advance according to the required time of the automatic welding station, which can realize the connection between the preheated workpiece and the automatic welding station and reduce the waiting time between the two.

[0056] Preferably, step S4 specifically includes:

[0057] The strategies for shared logistics resources, shared cache resources, and shared processing resources in step S3 are input into the production line simulation model constructed in step S2 to obtain a further optimized production line simulation model. The simulation time is calculated as 30 days, and steady-state simulation data after 3 days of model operation is extracted for analysis. The average utilization rate, blockage rate, and idle rate of the equipment in the model are calculated and compared with the corresponding data in step S31 to determine whether the optimization requirements of the hydraulic support welding production line scheme are met. If the preset optimization target is not met, steps S3 and S4 are repeated based on the optimized production line model until the optimization requirements of the hydraulic support welding production line scheme are met.

[0058] The beneficial effects of this invention are as follows:

[0059] This invention involves preliminary production line planning and the use of PlantSimulation software to establish a simulation model based on the actual process flow of the planned production line. This model quantitatively verifies aspects such as workshop equipment layout, production logistics design, and capacity, and optimizes these aspects based on the simulation results. During optimization, the workstation allocation scheme under static calculation is obtained by adjusting the workload between processes and the number of equipment in each process. Initial logistics transfer strategies and public resource allocation strategies are then formulated as inputs to the simulation model. Based on these inputs, a simulation model of a structural component welding workshop is constructed. The usability of the static production line layout scheme is verified, and a workshop scheduling operation mode based on a virtual assembly line is determined. Focusing on the balance of workstation capacity and the dynamic allocation of shared resources, iterative optimization of strategies and design planning is performed. Through multiple iterations, manpower requirements and equipment utilization are optimized, effective logistics and shared resource allocation strategies are determined, and the impact of different technical solutions is evaluated, thereby comprehensively assessing the production line's capacity performance. This allows for simulation of the entire production process before the welding production line is put into operation, and further iterative optimization of strategies based on the simulation results, effectively verifying production line capacity and planning, and reducing investment and R&D risks.

[0060] This invention provides a simulation optimization of the welding production line for hydraulic support structural components. It enables the overall planning, layout, simulation operation, and optimization adjustment of the production line before it is put into production, ultimately forming an optimized solution that meets the preset requirements. Compared with traditional production line planning and layout methods, it can effectively shorten the adjustment cycle, reduce trial production costs, and save production resources. Attached Figure Description

[0061] Figure 1 This is a flowchart of the simulation optimization method for the welding production line of hydraulic support structural components in the digital factory of the present invention;

[0062] Figure 2 This is a flowchart of the manufacturing process for a typical component of a hydraulic support structure.

[0063] Figure 3 This is a schematic diagram of the main functional areas of the hydraulic support structural component welding production line;

[0064] Figure 4 It is a statistical chart of the cycle time of each process in the initialization scheme;

[0065] Figure 5 It is a statistical chart of the cycle time of each process after static calculation and adjustment;

[0066] Figure 6 This is a schematic diagram of the interaction bit allocation in the initialization scheme;

[0067] Figure 7 This is a schematic diagram of a three-dimensional simulation model of a hydraulic support structural component welding production line;

[0068] Figure 8 This is a statistical chart of the utilization rate of automatic welding workstation equipment;

[0069] Figure 9 This is a schematic diagram of the workstation blockage / idleness cause analysis model;

[0070] Figure 10 This is a schematic diagram illustrating the scheduling priority of intelligent trolley transfer tasks within the automated welding area;

[0071] Figure 11 This is a diagram showing the optimized location allocation of the interaction area;

[0072] Figure 12 It is a flowchart for generating the feeding priority of the preheating furnace based on the predicted status of the automatic welding station;

[0073] Figure 13 This is a diagram showing the output comparison before and after optimization;

[0074] Figure 14 This is a statistical chart showing the utilization rate of the optimized automatic welding workstation equipment. Detailed Implementation

[0075] The specific embodiments of the present invention are described below with reference to examples. However, these examples are only used to illustrate the present invention in detail and do not limit the scope of the present invention in any way. Unless otherwise specified, the equipment components involved in the following examples are all conventional equipment components.

[0076] Example 1: A simulation optimization method for a hydraulic support structural component welding production line in a digital factory. (Flowchart shown) Figure 1 As shown, it includes the following steps:

[0077] S1. Model Input Preparation: Based on the initial allocation scheme for each process in the production line, calculate the process capacity of each process, obtain the process time, perform static process capacity balance index calculation and adjustment, determine the adjusted equipment allocation scheme for each process, and the workpiece transfer logic between different processes, and conduct preliminary optimization and adjustment of the production line planning scheme. The processes include: base plate assembly, first assembly, first flat welding, welding pad, 1.5 assembly, vertical welding, second assembly, repair welding, second flat welding, vertical welding, third assembly, preheating, and third repair welding. See the typical component production process flow diagram in this hydraulic support structural welding production line. Figure 2 As shown.

[0078] See the schematic diagram of the main functional areas of the hydraulic support structural component welding production line in this embodiment. Figure 3 As shown, the assembly line includes assembly area 1 for the first and third assembly processes, assembly area 2 for the 1.5 and second assembly processes, welding repair area 1, welding repair area 2, automatic welding interaction area 1, automatic welding interaction area 2, robot automatic welding area, and AGV material handling area; the hydraulic support structure welding production line also includes logistics transfer equipment, which includes manual cranes, intelligent cranes, AGV material transport vehicles, and stepping assembly platforms.

[0079] Step S1 specifically includes:

[0080] S11. Initialize the equipment allocation scheme for each process, and calculate the theoretical cycle time for each process based on the initialized workstations, equipment allocation schemes, and process time data. The calculation method for the theoretical cycle time of each process is as follows:

[0081]

[0082] In this embodiment, the expected capacity of the production line is 21 units / day. Based on a daily shift of 21.5 hours, the theoretical cycle time is 61.43 minutes / unit.

[0083] In the initialization scheme of this embodiment, the process time data of each process of the structural component are shown in Table 1 below; the initialization allocation scheme of the robot automatic welding area station is shown in Table 2 below; and the initialization allocation scheme of the assembly area station is shown in Table 3 below.

[0084] Table 1. Process time data for each step of the structural component in the initialization scheme.

[0085]

[0086] Table 2 Initialization Allocation Scheme for Robot Automated Welding Area Workstations

[0087]

[0088] Table 3 Initial Allocation Scheme for Assembly Area Workstations

[0089]

[0090] As shown in Table 1, under the current equipment allocation scheme, the average cycle time for one flat weld, one vertical weld, and three supplementary welds is significantly longer than the cycle time required for a daily production capacity of 21 pieces. This has become the decisive factor restricting output.

[0091] S12. Perform static process capacity balance index calculation and adjust the workstation allocation scheme; based on the processing time of each process equipment for typical products, conduct static data analysis and prioritize the adjustment of process capacity. Taking the hydraulic support structural component welding production line as an example, consider it as a "virtual assembly line" and adjust the process capacity balance.

[0092] The process capacity balance index for each process is calculated as follows:

[0093]

[0094] in,

[0095] In this example, the expected daily output is 21 aircraft / day, and the effective daily operating time is 21.5 hours / day. The cycle time statistics for each process in the initialization scheme of this embodiment are shown in the following chart. Figure 4 As shown.

[0096] S13. Based on the process capability balance index calculated in step S12, classify each process into different process types:

[0097] If the process capability balance index is greater than 1, it is classified as a type A process, i.e., a bottleneck process.

[0098] If the process capacity balance index is less than 1, it is classified as a type B process, which is a process with surplus capacity. This type of process has a strong output capacity per unit time and has the potential risk of occupying shared resources and causing production line blockage.

[0099] Due to limitations in production line scale, equipment cost, and equipment functionality, the following situations may arise during production line balancing adjustments, necessitating further subdivision of different processes into subtypes:

[0100] Process subtype 1: Only the workload is adjustable; if the production line area or equipment cost is a constraint, equipment cannot be added or removed.

[0101] Process Subtype 2: Only the number of equipment is adjustable. If the equipment has special characteristics, the process content cannot be handled by other equipment.

[0102] Process subtype 3: Both the number of equipment and the workload are adjustable;

[0103] Process subtype 4: The number of equipment and the operation are both non-adjustable;

[0104] The applicable adjustment rules are as follows, depending on the different process subtypes:

[0105] Rule 1: Increase or decrease the number of workstations, applicable to situations where the number of equipment can be adjusted in process subtypes 2 and 3;

[0106] Rule 2: Adjusting the workload to processes with similar equipment functions and a smaller process capacity balance index applies to situations where the workload is adjustable in process subtypes 1 and 3.

[0107] Rule 3: From a design perspective, re-plan the equipment functions or change the equipment selection; from an operational perspective, try to extend the daily effective working time or change the material handling strategy. This applies to situations in sub-type 4 of the process where the number of equipment and workload cannot be adjusted.

[0108] S14. Based on the adjustment rules in step S13 and combined with the cycle time of each process under the initialization scheme in step S11, perform subtype identification and corresponding adjustments for type A processes:

[0109] One-time flat welding process: Type A3, bottleneck process, and the number of equipment and workload can be adjusted;

[0110] One-time vertical welding process: Type A3, bottleneck process, and the number of equipment and workload can be adjusted;

[0111] Repair welding process: Type A2, bottleneck process, and the number of equipment can be adjusted.

[0112] The corresponding adjustment strategy is as follows:

[0113] For a single flat welding process, rules 1 and 2 apply. The number of equipment is increased from 2 to 3. At the same time, the workload is adjusted, and some operations are moved to the assembly and welding of backing plates.

[0114] For a single vertical welding process, rules 1 and 2 apply, and the number of equipment units increases from 3 to 4.

[0115] For the repair welding process, rule 1 applies, and the number of workstations is adjusted from 4 to 5.

[0116] Subtype identification is performed for type B processes (i.e., processes with a process capability balance index of less than 1):

[0117] Three-stage process: Type B1, only the workload is adjustable;

[0118] Secondary flat welding process: Type B3, the number of equipment and the workload can be adjusted.

[0119] The corresponding adjustment strategy is as follows:

[0120] For the three-piece assembly process, rule 2 applies: the workload of the three-piece assembly process is adjusted to the one-piece assembly process, and a workstation is allocated from the original one-piece assembly workstation to simultaneously handle both the one-piece and three-piece assembly operations.

[0121] For the secondary flat welding process, rules 1 and 2 apply, and the number of equipment is reduced from 4 to 2.

[0122] For type A processes, the adjusted process capacity balance index is recalculated. If the index is less than 1, the specific equipment adjustment amount or workload adjustment amount can be determined. For type B processes, excessive output is limited by reducing the number of equipment or increasing the workload. After completing the above steps, the process time of each process is updated, and the updated equipment allocation scheme and process time data for each process are used as input data in S31.

[0123] The adjusted process time data for each step after static calculation are shown in Table 4 below; the adjusted station allocation scheme for the robot's automatic welding area is shown in Table 5 below; the adjusted station allocation scheme for the assembly area is shown in Table 6 below. The cycle time statistics for each step after static calculation are shown in [the table below]. Figure 5 As shown.

[0124] Table 4. Process time data for each process after static calculation adjustment.

[0125]

[0126]

[0127] Table 5. Robotic Automatic Welding Area Station Allocation Scheme After Static Calculation Adjustment

[0128]

[0129] Table 6. Assembly Area Workstation Allocation Scheme After Static Calculation Adjustment

[0130]

[0131] S15. Develop an initialization scheme for the inter-process transfer logic and the public resource allocation logic;

[0132] ① Interactive bit allocation principle:

[0133] Interaction zones are set up between the robotic automatic welding area and assembly areas 1 and 2 for workpiece buffering and transfer. The number of interaction points is determined based on the site area and common dimensions of the structural components. This structural component welding production line has a total of X interaction points: X1 interaction points between assembly area 1 and the robotic automatic welding area, and X-X1 interaction points between assembly area 2 and the robotic automatic welding area. All interaction points can accept workpiece transfers when idle and are shared. See the schematic diagram of the interaction point allocation scheme. Figure 6 As shown.

[0134] In this embodiment, the structural component welding production line has a total of 12 interaction positions, of which 6 interaction positions are set in the automatic welding interaction area 1 and 6 interaction positions are set in the automatic welding exchange area 2. All interaction positions can accept workpiece transfers when they are idle, and all are shared interaction positions.

[0135] ②Train dispatching rules:

[0136] The crane operation is executed according to the "first-in, first-out" rule. After the workstation is completed, the transfer task is sent to the crane operation task. The crane will execute the transfer task in the order of the task generation time.

[0137] S2. Simulation Model Construction: Based on the preliminary optimized production line planning scheme obtained in step S1, the equipment allocation scheme for each process, process time data, interaction position allocation rules, and crane scheduling rules are used as data inputs. A three-dimensional simulation model of the hydraulic support structural component welding production line is constructed using PlantSimulation factory simulation software. See the schematic diagram of the three-dimensional simulation effect of this structural component welding production line. Figure 7 .

[0138] S3. Simulation Model Verification and Iterative Optimization: Verify the usability of the static production line layout scheme, determine the workshop scheduling operation mode based on the virtual assembly line, focus on the problem of workstation capacity balance and dynamic allocation of shared resources, and carry out iterative optimization of strategy and design planning.

[0139] Step S3 specifically includes:

[0140] S31. Assuming that there is sufficient material in the upstream process of the production line, the equipment allocation scheme, process time data, interaction position allocation rules and crane scheduling rules obtained in steps S12 and S13 are used as model input data to continuously simulate the three-dimensional simulation model.

[0141] The model simulation time is calculated to be 30 days. To ensure the reliability of the data, steady-state simulation data after 3 days of model operation is extracted for analysis to obtain simulation data on the utilization of production workstations, logistics equipment, buffer / interaction positions, and production line output. The average utilization rate, congestion rate, and idle rate of equipment in the production line model are also calculated.

[0142] The utilization rate of the preheating furnace equipment is shown in Table 7 below; the utilization rate of the automatic welding workstation equipment is shown in... Figure 8 As shown.

[0143] Table 7. Utilization Status of Preheating Furnace Equipment

[0144] Preheating furnace 1-1 25.46％ 3.18％ 71.36％ Preheating furnace 1-2 26.54％ 3.16％ 70.3％ Preheating furnace 2-1 54.32％ 21.12％ 24.56％ Preheating furnace 2-2 49.51％ 24.24％ 26.25％

[0145] S32. Using the virtual pipeline as a guiding principle, and optimizing for reducing workstation blockage rate and improving workstation utilization, construct a workstation blockage / idleness cause analysis model based on the simulation data obtained in step S31. (See appendix) Figure 9 .

[0146] Workstations with strong process capabilities produce quickly, with frequent material feeding and discharging demands, resulting in frequent occupation of shared logistics resources. Consequently, workstations with weaker process capabilities cannot feed or discharge materials in a timely manner.

[0147] Workpieces produced by workstations with strong process capabilities cannot be processed by subsequent processes in a timely manner and quickly occupy limited shared buffer resources, causing workpieces produced by other processes to be unable to be transferred out normally.

[0148] Interruptions in incoming materials to downstream processes lead to increased idle rates on downstream equipment. Ultimately, this causes production line blockages, preventing normal operation and impacting the release of production line capacity.

[0149] This model shows that it is necessary to configure the calling strategy for shared logistics resources, shared cache resources, and shared processing resources to limit the output of processes with shorter cycle times and ensure the smooth operation of the production line.

[0150] S33. For the issues of workstation capacity balancing and dynamic allocation of shared resources, iterative optimization of strategies and design planning is carried out, including:

[0151] ① Configure shared logistics resources: Intelligent driving priority strategy.

[0152] Priority generation rules: The priority of the discharge task is higher than that of the feed task; for the same type of task (feed / discharge), the task of the subsequent process takes priority.

[0153] The specific settings principles are as follows:

[0154] First, determine the task type. Tasks that transfer work-in-process from the robot's automatic welding station to the unloading area have higher priority.

[0155] If the task types are the same, then determine the robot automatic welding station involved. Transfer tasks related to bottleneck processing equipment within the current product process route have higher priority.

[0156] If all transfer tasks involve or do not involve bottleneck equipment, the priority is determined by the process in which the transfer task is located, with transfer tasks in later processes having higher priority.

[0157] In this embodiment, the welding process of this structural component and the scheduling priority of the intelligent crane transfer task within the automatic welding area are as follows: Figure 10 As shown in the figure, different processes and different types of crane transfer tasks are labeled as follows: A>B>C, A1>A2>A3.

[0158] ② Configure shared cache resources: Interaction area location allocation strategy.

[0159] The strategy for allocating the interaction area includes dividing the interaction area into public interaction positions and dedicated interaction positions. In accordance with the principle of prioritizing the output of subsequent processes, dedicated interaction positions are set for subsequent processes to ensure that the workpiece is transferred out.

[0160] Specific allocation details in this embodiment:

[0161] Automatic welding interaction area 1: 4 common interaction positions and 2 dedicated interaction positions.

[0162] Automatic welding interaction area 2: 3 common interaction positions and 3 dedicated interaction positions.

[0163] like Figure 11 As shown, the first priority for material output in the 3-weld preheating process is interaction bit 5, the second priority is the common interaction bit, and interaction bit 6 is the third priority; the first priority for material output in the secondary vertical welding process is interaction bit 5, the second priority is the common interaction bit; and the first batch of material output can only select the common interaction bit.

[0164] ③ Configure shared processing resource allocation: A preheating furnace feeding priority strategy based on the predicted status of the automatic welding station. This includes: The preheating furnace process exists between the interactive station and the automatic welding station, serving as an intermediate shared resource. The preheating furnace feeds materials in advance according to the automatic welding station's demand time (just-in-time production), enabling seamless connection between the preheated workpiece and the automatic welding station, reducing waiting time for both. See the flowchart for generating the preheating furnace feeding priority based on the predicted status of the automatic welding station. Figure 12 As shown.

[0165] In this embodiment, a preheating furnace process exists between the interaction area and the robot automatic welding area as a shared processing resource. After preheating, the workpiece needs to meet the feeding requirements of different processes. An unreasonable feeding sequence will increase the waiting time of the automatic welding equipment and greatly reduce the effectiveness of the interaction position as a feeding buffer for automatic welding.

[0166] S4. Verification of the effect of the optimized strategy.

[0167] The strategies for sharing logistics resources, shared cache resources, and shared processing resources from step S3 are re-inputted into the production line simulation model constructed in step S2 to obtain a further optimized production line simulation model. The simulation time is calculated as 30 days, and steady-state simulation data after 3 days of model operation is extracted for analysis. The average equipment utilization rate, blockage rate, and idle rate in the model are calculated and compared with the corresponding data in step S31 to determine whether the optimization requirements of the hydraulic support welding production line scheme are met. If the preset optimization target is not met, steps S3 and S4 are repeated based on the optimized production line model until the optimization requirements of the hydraulic support welding production line scheme are met.

[0168] In this embodiment, the average output of the optimized welding production line model increased from 12.37 pieces / day to 18.92 pieces / day. A comparison diagram of output before and after optimization is shown below. Figure 13 As shown in Table 8 below; the optimized utilization rate of the preheating furnace equipment is shown in Table 8 below; the optimized utilization rate of the automatic welding workstation equipment is shown in Table 8 below. Figure 14 As shown.

[0169] Table 8. Utilization Status of the Optimized Preheating Furnace Equipment

[0170] Preheating furnace 1-1 79.19％ 12.14％ 8.67％ Preheating furnace 1-2 53.24％ 39.88％ 6.87％ Preheating furnace 2-1 79.18％ 13.64％ 7.18％ Preheating furnace 2-2 79.14％ 14.73％ 6.14％

[0171] In this embodiment, the optimized automatic flat welding equipment achieved an average utilization rate of 79.53%, the vertical welding equipment (positioner) utilization rate of 86.30%, and the primary preheating furnace utilization rate of 78.97%. The blockage rate at each workstation was below 10%. Compared to the original scheme with an average flat welding utilization rate of 49.06% and an average vertical welding utilization rate of 53.09%, the optimization effect is significant. This meets the optimization requirements of the hydraulic support welding production line scheme and overcomes the drawback of traditional structural component welding production line planning methods that cannot quantitatively assess the operational effectiveness of dynamic production scenarios in advance.

[0172] The present invention has been described in detail above with reference to the embodiments. However, those skilled in the art will understand that, without departing from the spirit of the present invention, various specific parameters in the above embodiments can be changed to form multiple specific embodiments, all of which are common variations of the present invention, and will not be described in detail here.

Claims

1. A simulation optimization method for a hydraulic support structural component welding production line in a digital factory, characterized in that, Includes the following steps: S1. Model Input Preparation: For the initial allocation scheme of each process in the production line, calculate the process capacity of each process, obtain the process time, perform static process capacity balance index calculation and adjustment, determine the equipment allocation scheme of each process after adjustment, and the workpiece transfer logic between different processes, and perform preliminary optimization and adjustment of the production line planning scheme; the process includes bottom plate assembly, first assembly, first flat welding, welding pad, 1.5 assembly, first vertical welding, second assembly, supplementary welding, second flat welding, second vertical welding, third assembly, preheating, and third supplementary welding; S2. Simulation Model Construction: Based on the preliminary optimized and adjusted production line planning scheme obtained in step S1, the corresponding data information and allocation scheme are used as data inputs. The PlantSimulation software is used to construct a three-dimensional simulation model of the hydraulic support structural component welding production line. S3. Simulation Model Verification and Iterative Optimization: Verify the usability of the static production line layout scheme, determine the workshop scheduling operation mode based on the virtual assembly line, focus on the problem of workstation capacity balance and dynamic allocation of shared resources, and carry out iterative optimization of strategy and design planning. Specifically, iterative optimization of strategies and design planning is carried out to address the issues of workstation capacity balancing and dynamic allocation of shared resources, including: ① Configure shared logistics resources: Intelligent driving priority strategy; Intelligent driving priority strategies include: Priority generation rules: Outgoing tasks have higher priority than incoming tasks; for tasks of the same type, tasks in the subsequent process have higher priority. The specific settings principles are as follows: First, determine the task type. Tasks that transfer work-in-process from the robot's automatic welding station to the unloading area have higher priority. If the task types are the same, then determine the robot automatic welding station involved. Transfer tasks related to bottleneck processing equipment within the current product process route have higher priority. If all transfer tasks involve or do not involve bottleneck equipment, then determine the priority based on the process in which the transfer task is located. Transfer tasks in later processes have higher priority. ② Configure shared cache resources: Interaction area location allocation strategy; ③Configure shared processing resource allocation: Preheating furnace feeding priority strategy based on the predicted status of automatic welding stations; Among them, the preheating furnace feeding priority strategy based on the predicted status of the automatic welding station includes: there is a preheating furnace process between the interaction station and the automatic welding station as an intermediate shared resource. The preheating furnace feeds materials in advance according to the required time of the automatic welding station, which can realize the connection between the preheated workpiece and the automatic welding station and reduce the waiting time between the two. S4. Verification of the effect of the optimized strategy.

2. The simulation and optimization method for the welding production line of hydraulic support structural components in a digital factory according to claim 1, characterized in that, The hydraulic support structural component welding production line includes an assembly area 1 for the first and third assembly processes, an assembly area 2 for the 1.5 and second assembly processes, a repair welding area 1, a repair welding area 2, an automatic welding interaction area 1, an automatic welding interaction area 2, a robot automatic welding area, and an AGV material handling area. The hydraulic support structural component welding production line also includes logistics transfer equipment, which includes manual cranes, intelligent cranes, AGV material transport vehicles, and stepping assembly platforms.

3. The simulation optimization method for the welding production line of hydraulic support structural components in a digital factory according to claim 2, characterized in that, Step S1 specifically includes: S11. Initialize the equipment allocation scheme for each process, and calculate the theoretical cycle time for each process based on the initialized workstations, equipment allocation schemes, and process time data. The calculation method for the theoretical cycle time of each process is as follows: ; S12. Perform static process capability balance index calculation and adjust the workstation allocation scheme; the process capability balance index for each process is calculated as follows: in, ; S13. Based on the process capability balance index calculated in step S12, classify each process into different process types: If the process capability balance index is greater than 1, it is classified as a type A process, i.e., a bottleneck process. If the process capacity balance index is less than 1, it is classified as a type B process, i.e., a process with excess capacity. Based on production line scale, equipment cost, and equipment function, different processes are further subdivided into subtypes: Process subtype 1: Only the workload is adjustable; if the production line area or equipment cost is a constraint, equipment cannot be added or removed. Process Subtype 2: Only the number of equipment is adjustable. If the equipment has special characteristics, the process content cannot be handled by other equipment. Process subtype 3: Both the number of equipment and the workload are adjustable; Process subtype 4: The number of equipment and the operation are both non-adjustable; The adjustment rules for different process subtypes are as follows: Rule 1: Increase or decrease the number of workstations, applicable to situations where the number of equipment can be adjusted in process subtypes 2 and 3; Rule 2: Adjusting the workload to processes with similar equipment functions and a process capacity balance index <1 applies to situations where the workload is adjustable in process subtypes 1 and 3. Rule 3: From a design perspective, re-plan the equipment functions or change the equipment selection; from an operational perspective, try to extend the daily effective working time or change the material inbound / outbound strategy. This applies to the situation where the number of equipment and the workload cannot be adjusted in sub-type 4 of the process: S14. According to the adjustment rules in step S13, combined with the cycle time of each process under the initialization scheme in step S11, identify the sub-type of type A processes and make corresponding adjustments according to the above rules. Recalculate the adjusted process capacity balance index, which should be less than 1. Based on this, determine the specific equipment adjustment amount or workload adjustment amount; for type B processes, limit their over-output by reducing the number of equipment or increasing the workload; after completing the above steps, update the process time of each process, and use the updated equipment allocation scheme and process time data of each process as the model input data in step S3 for later use. S15. Develop an initialization scheme for the inter-process transfer logic and the public resource allocation logic; ① Interactive bit allocation principle: Interaction zones are set up between the robotic automatic welding area and assembly areas 1 and 2 for workpiece buffering and transfer. The number of interaction points is determined based on the site area and common dimensions of the structural components. This structural component welding production line has a total of X interaction points, of which X1 interaction points are set between assembly area 1 and the robotic automatic welding area, and X-X1 interaction points are set between assembly area 2 and the robotic automatic welding area. All interaction points can accept workpiece transfer when they are idle and are shared interaction points. ②Train dispatching rules: The crane operation is executed according to the "first-in, first-out" rule. After the workstation is completed, the transfer task is sent to the crane operation task. The crane will execute the transfer task in the order of the task generation time.

4. The simulation optimization method for the welding production line of hydraulic support structural components in a digital factory according to claim 3, characterized in that, Step S3 specifically includes: S31. Assuming sufficient material supply from the upstream processes of the production line, the equipment allocation schemes, process time data, interaction position allocation rules, and crane scheduling rules obtained in steps S12 and S13 are used as input data for the model. Continuous simulation is performed on the 3D simulation model. The model simulation time is calculated as 30 days. Steady-state simulation data after 3 days of model operation is extracted for analysis to obtain simulation data on production station utilization, logistics equipment utilization, buffer / interaction position utilization, and production line output. The average equipment utilization rate, congestion rate, and idle rate in the production line model are also calculated. S32. Using the virtual pipeline as a guiding principle and aiming to reduce workstation blockage rate and improve workstation utilization rate as the optimization direction, construct a workstation blockage / idle cause analysis model based on the simulation data obtained in step S31.

5. The simulation and optimization method for the welding production line of hydraulic support structural components in a digital factory according to claim 1, characterized in that, The strategy for allocating the interaction area includes dividing the interaction area into public interaction positions and dedicated interaction positions. In accordance with the principle of prioritizing the output of subsequent processes, dedicated interaction positions are set for subsequent processes to ensure that the workpiece is transferred out.

6. The simulation optimization method for the welding production line of hydraulic support structural components in a digital factory according to claim 4, characterized in that, Step S4 specifically includes: The strategies for shared logistics resources, shared cache resources, and shared processing resources in step S3 are input into the production line simulation model constructed in step S2 to obtain a further optimized production line simulation model. The simulation time is calculated as 30 days, and steady-state simulation data after 3 days of model operation is extracted for analysis. The average utilization rate, blockage rate, and idle rate of the equipment in the model are calculated and compared with the corresponding data in step S31 to determine whether the optimization requirements of the hydraulic support welding production line scheme are met. If the preset optimization target is not met, steps S3 and S4 are repeated based on the optimized production line model until the optimization requirements of the hydraulic support welding production line scheme are met.

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