CN114021286A - Simulation optimization method for hydraulic support structural member welding production line of digital factory - Google Patents

Abstract

The invention discloses a simulation optimization method for a welding production line of a hydraulic support structural part in a digital factory, which comprises the following steps: and (3) primarily planning the production line, establishing a corresponding simulation model by using plant simulation software, quantitatively verifying the aspects of layout of workshop equipment, production logistics design, capacity and the like, and finding and optimizing according to a simulation result. The optimization process comprises the following steps: adjusting the work load among the processes and the quantity of equipment of each process, obtaining a station distribution scheme under static calculation, and making an initialized logistics transfer strategy and a public resource distribution strategy as simulation model data input; and verifying the usability of the static production line layout scheme based on the simulation model, and performing iterative optimization of strategies and design planning. The invention can simulate the whole production flow before the production of the welding production line, continuously carry out strategy iterative optimization aiming at the simulation result, effectively verify the production capacity and planning of the production line and reduce the investment and research and development risks.

Description

Simulation optimization method for hydraulic support structural member welding production line of digital factory

Technical Field

The invention relates to the field of assembly of structural members of a digital factory and design of an automatic welding production line, in particular to a simulation optimization method for a hydraulic support structural member welding production line of the digital factory.

Background

The plant simulation technology is actually a discrete event simulation technology of a production system, and essentially is a process of establishing a system model for the real world and performing experimental research on the real system by using the established model.

The production planning link can perform pre-planning on production line layout, equipment configuration, production manufacturing process paths, logistics and the like of a factory by utilizing a virtual simulation technology, and performs analysis, evaluation and verification on the basis of 'preview' of a simulation model, so that problems and parts to be improved in system operation are quickly found, adjustment and optimization are performed in time, the change and rework times of a subsequent production execution link on an entity system are reduced, and therefore cost is effectively reduced, construction period is shortened, and efficiency is improved. Plant simulation techniques find wide application in the following scenarios: 1. before a real system is established, verifying system design and finding potential problems; 2. evaluating the influence caused by plan change in parallel with the existing production system; 3. the problem that cost and time are needed to be spent for correction in the production lifting process is discovered and eliminated; 4. the investment cost for the production line is minimized without damaging the production energy; 5 optimizing the performance of complex production systems containing multiple variables.

As a typical discrete production enterprise in the traditional coal mine machinery manufacturing industry, due to the factors of change of market operation environment, adoption of new materials, technology and production process and the like, the industry pattern is oscillating, the product manufacturing period must be continuously shortened, the production flexibility is improved, and the personalized requirements of users are met.

Virtual simulation is used as an important means for process planning and production line balancing of a workshop production line, a typical discrete system simulation model is constructed, key stations, line balancing, a production process, single-line production line layout and a logistics scheme of a welding workshop are analyzed, the workshop capacity is analyzed and optimized, and the method has important significance for stable production operation of enterprises and reduction of planning iteration cost.

Disclosure of Invention

The traditional welding production process of the hydraulic support structural part is influenced by various product types, large structure and welding process difference among different products, small product batch and short production change period, and the assembly welding production line has poor adaptability to different products. In the past, the mode of planning production lines and configuring equipment according to experience cannot quantitatively evaluate the operation effect of the production line in a dynamic production scene in advance, has long adjustment period and high trial production cost, and cannot meet the requirements of digital manufacturing.

In order to solve the problems and the defects, the invention provides a simulation optimization method for a welding production line of a hydraulic support structural part of a digital factory, which is based on digital factory simulation software, analyzes station utilization conditions and logistics equipment use conditions, optimizes equipment model selection and adjusts production line operation strategies (a driving calling strategy and a shared resource dynamic allocation strategy), can simulate the whole production flow before the welding production line is put into production, continuously performs strategy iterative optimization aiming at a simulation result, effectively verifies production line capacity and planning, and reduces investment and research and development risks.

In order to solve the technical problems, the invention adopts the following technical scheme:

a simulation optimization method for a welding production line of a hydraulic support structural part of a digital factory is designed, and comprises the following steps:

s1, model input preparation: aiming at an initialization distribution scheme of each procedure of a production line, calculating procedure capacity of each procedure of the production line, acquiring procedure time, performing static procedure capacity balance index calculation and adjustment, determining an adjusted equipment distribution scheme of each procedure and workpiece transfer logic among different procedures, and performing preliminary optimization adjustment on a planning scheme of the production line; the process comprises the steps of splicing a bottom plate, splicing, flat welding for one time, splicing and welding a base plate, splicing 1.5, vertical welding, splicing for two times, repair welding, flat welding for two times, vertical welding, splicing for three times, preheating and repair welding for three times;

s2, constructing a simulation model: according to the production line planning scheme obtained in the step S1 after the preliminary optimization and adjustment, corresponding data information and a distribution scheme are used as data input, and plant simulation factory simulation software is used for building a three-dimensional simulation model of the hydraulic support structure welding production line;

s3, simulation model verification and iterative optimization: verifying the availability of a static production line layout scheme, determining a workshop scheduling operation mode based on a virtual production line, focusing on the problems of station capability balance and shared resource dynamic allocation, and performing iterative optimization of strategies and design planning;

and S4, verifying the strategy effect after optimization.

Preferably, the hydraulic support structure welding production line comprises an assembling area 1 for a first assembling process and a third assembling process, an assembling area 2 for a 1.5 assembling process and a second assembling process, 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 taking area;

hydraulic support structure welding production line still includes the commodity circulation transportation equipment, the commodity circulation transportation equipment includes manual driving, intelligent driving, AGV material transport vehicle, marching type assembly platform.

Preferably, the step S1 specifically includes:

s11, initializing the equipment distribution scheme of each process, and respectively calculating theoretical takt time of each process based on the initialized stations, the equipment distribution scheme and the process time data; the theoretical beat time of each procedure is calculated in the following mode:

s12, calculating a static process capacity balance index, and adjusting a station distribution scheme; the process capacity balance index of each process is calculated in the following manner:

wherein the content of the first and second substances,

s13, dividing each process into different process types according to the process capability balance index calculated in the step S12:

if the process capacity balance index is more than 1, dividing the process capacity balance index into A type processes, namely bottleneck processes;

if the process capacity balance index is less than 1, dividing the process into B type processes, namely capacity-rich processes;

the different processes are further sub-classified according to production line size, equipment cost and equipment function:

procedure subtype 1: only the operation load is adjustable, if the operation load is limited by the area of a production line or the cost of equipment, the equipment cannot be increased or decreased;

procedure subtype 2: only the number of the devices is adjustable, if the devices have particularity, the process content of the devices cannot be borne by other devices;

procedure subtype 3: the number of devices and the operation load are adjustable;

procedure subtype 4: the number of the devices is not adjustable according with the operation;

the adjustment rules for the different process subtypes are as follows:

rule 1: the number of stations is increased or decreased, and the method is suitable for the condition that the number of equipment in the procedure subtype 2 and the procedure subtype 3 can be adjusted;

rule 2: adjusting the operation load to the operation with similar equipment function and smaller operation capacity balance index, which is suitable for the condition that the operation load in the operation subtype 1 and the operation subtype 3 can be adjusted;

rule 3: from the design perspective, the functions of the equipment are re-planned, or the types of the equipment are replaced and selected; from the operation perspective, an attempt is made to prolong the effective operation time per day or change the material access strategy, which is applicable to the situation that the equipment number and the operation load in the process subtype 4 are not adjustable:

s14, according to the adjustment rule in the step S13 and the beat time of each process under the initialization scheme in the step S11, performing subtype identification on the A-type process, performing corresponding adjustment according to the rule, recalculating the adjusted process capacity balance index, and determining the specific equipment adjustment amount or the operation load adjustment amount according to the result that the index is less than 1; for the type B procedure, the excessive production is limited by reducing the number of equipment or increasing the workload; after the above steps are completed, updating the process time of each process, and using the updated equipment allocation plan of each process and the process time data as the model input data in the step S3 for standby;

s15, establishing an initialization scheme of inter-process transfer logic and common resource allocation logic;

the principle of interactive bit allocation is as follows:

interaction areas are respectively arranged between the robot automatic welding area and the assembling area 1 and between the robot automatic welding area and the assembling area 2 and are used for workpiece caching and transferring and handing over, and the number of the interaction positions is determined according to the area of a field and the common size of a structural part; the structural member welding production line is provided with X interaction positions, wherein X1 interaction positions are arranged between the assembly area 1 and the robot automatic welding area, and X-X1 interaction positions are arranged between the assembly area 2 and the robot automatic welding area; all the interaction positions can accept the workpiece to be transferred when being idle and are common interaction positions;

driving scheduling rules:

the driving tasks are executed according to a first-in first-out rule, the transfer tasks are sent to the driving tasks after the stations are completed, and the driving sequentially executes the transfer tasks according to the sequence of the task generation time.

Preferably, the step S3 specifically includes:

s31, under the condition that the incoming material of the upstream process of the production line is enough, continuously simulating the three-dimensional simulation model by using the distribution scheme of each process equipment, the process time data, the interaction bit distribution rule and the running scheduling rule obtained in the steps S12 and S13 as model input data;

the simulation time of the model is counted by 30 days, and steady-state simulation data of the model after 3 days of operation are intercepted and analyzed to obtain simulation data of the utilization condition of production stations, the utilization condition of logistics equipment, the utilization condition of cache bits/interaction bits and the output quantity of a production line; calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the production line model;

s32, constructing a station blocking/idle cause analysis model according to the simulation data acquired in the step S31 by taking a virtual assembly line as a guiding principle to reduce the station blocking rate and improve the station utilization rate as an optimization direction;

s33, aiming at the problems of station capability balance and shared resource dynamic allocation, carrying out iterative optimization of strategy and design planning, wherein the iterative optimization comprises the following steps:

allocating shared logistics resources: an intelligent driving priority strategy;

secondly, configuring shared cache resources: an interaction area position distribution strategy;

thirdly, allocating shared processing resources: and (4) a preheating furnace feeding priority strategy based on the automatic welding station prediction state.

Preferably, in step S33, the intelligent driving priority policy includes:

priority generation rules: the priority of the discharging task is greater than that of the feeding task; tasks of the same type and post-process tasks are prior;

the specific setting principle is as follows:

firstly, judging the task type, and transferring the work-in-process in the automatic welding area station of the robot to the discharging task of the interaction area with higher priority;

if the task types are the same, the related automatic welding area stations of the robot are judged, and the priority of the transfer tasks related to the bottleneck processing equipment in the current product process route is higher;

if the transfer tasks all relate to or do not relate to bottleneck equipment, the priority is judged according to the process where the transfer tasks are located, and the priority of the transfer tasks behind the process is higher.

Preferably, in step S33, the interaction zone location allocation strategy includes: and dividing the interaction area into a public interaction position and a special interaction position, and setting the special interaction position for the subsequent process according to the principle of the discharging priority of the subsequent process to ensure that the workpiece is rolled out.

Preferably, in step S33, the preheating furnace feeding priority strategy based on the predicted status of the automatic welding station includes: a preheating furnace process is arranged between the interaction position and the automatic welding position and serves as a middle shared resource, the preheating furnace feeds materials in advance according to the time required by the automatic welding position, the connection between the preheated workpiece and the automatic welding position can be realized, and the waiting time of the preheated workpiece and the automatic welding position is reduced;

preferably, the step S4 specifically includes:

inputting and configuring strategies of sharing logistics resources, sharing cache resources and sharing processing resources in the step S3 into the production line simulation model constructed in the step S2 to obtain a further optimized production line simulation model; the simulation time is measured in 30 days, and the steady-state simulation data of the model after running for 3 days is intercepted and analyzed; and calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the model, comparing the data with the corresponding data in the step S31, judging whether the optimization requirement of the hydraulic support welding production line scheme is met, if the optimization requirement of the hydraulic support welding production line scheme is not met, repeating the steps S3 and S4 again on the basis of the optimized production line model until the optimization requirement of the hydraulic support welding production line scheme is met.

The invention has the beneficial effects that:

according to the invention, a corresponding simulation model is established according to the planned actual process flow of the production line by the primary planning of the production line and by using plant simulation software, so that the aspects of layout of workshop equipment, design of production logistics, capacity and the like are quantitatively verified, and optimization is found according to a simulation result. In the optimization process, a station distribution scheme under static calculation is obtained by adjusting the workload among the processes and the quantity of equipment in each process, and an initialized logistics transfer strategy and a public resource distribution strategy are formulated as simulation model input; constructing a simulation model of the structural member welding workshop based on the input; verifying the availability of a static production line layout scheme, determining a workshop scheduling operation mode based on a virtual production line, focusing on the problems of station capability balance and shared resource dynamic allocation, and performing iterative optimization of strategies and design planning. And optimizing the manpower demand and the equipment utilization rate in multiple iterations, determining an effective logistics and common resource allocation strategy, and evaluating the selection influence of different technical schemes, thereby integrally evaluating the productivity performance of the production line. The method can simulate the whole production flow before the production of the welding production line, continuously carry out strategy iterative optimization aiming at the simulation result, effectively verify the production capacity and planning of the production line, and reduce the investment and research and development risks.

The method provided by the invention can be used for simulating and optimizing the welding production line of the hydraulic support structural part, can be used for carrying out overall planning layout, simulation operation and optimization adjustment on the production line between production and finally forming an optimization scheme meeting the preset requirement.

Drawings

FIG. 1 is a flow chart of a simulation optimization method for a hydraulic support structure welding production line of a digital factory according to the present invention;

FIG. 2 is a flow chart of a typical part manufacturing process for a hydraulic bracket structural member;

FIG. 3 is a schematic diagram of the main functional areas of a hydraulic support structure welding line;

FIG. 4 is a beat time statistical chart of each process in the initialization scheme;

FIG. 5 is a statistical chart of the tempo times of the respective processes after adjustment of the static calculation;

FIG. 6 is a schematic diagram of the interaction bit allocation in an initialization scheme;

FIG. 7 is a schematic diagram of a three-dimensional simulation model of a hydraulic support structure welding line;

FIG. 8 is a statistical chart of equipment utilization for an automated welding workstation;

FIG. 9 is a schematic diagram of a station blocked/idle cause analysis model;

FIG. 10 is a schematic illustration of scheduling priorities of intelligent vehicle transfer tasks within an automated welding area;

FIG. 11 is a schematic diagram of the location allocation of the optimized interactive region;

FIG. 12 is a flow chart of preheat furnace feed priority generation based on the predicted status of the automated welding stations;

FIG. 13 is a schematic graph comparing throughput before and after optimization;

FIG. 14 is a statistical chart of optimized automated welding workstation equipment utilization.

Detailed Description

The following examples are given to illustrate specific embodiments of the present invention, but are not intended to limit the scope of the present invention in any way. The elements of the apparatus referred to in the following examples are conventional elements of the apparatus unless otherwise specified.

Example 1: a simulation optimization method for a welding production line of a hydraulic support structural part in a digital factory is disclosed, a flow chart is shown in figure 1, and the simulation optimization method comprises the following steps:

s1, model input preparation: aiming at an initialization distribution scheme of each procedure of a production line, calculating procedure capacity of each procedure of the production line, acquiring procedure time, performing static procedure capacity balance index calculation and adjustment, determining an adjusted equipment distribution scheme of each procedure and workpiece transfer logic among different procedures, and performing preliminary optimization adjustment on a planning scheme of the production line; the process comprises the steps of splicing a bottom plate, splicing, flat welding for one time, splicing and welding a base plate, splicing 1.5, vertical welding, splicing for two times, repair welding, flat welding for two times, vertical welding, splicing for three times, preheating and repair welding for three times. A typical process flow diagram for producing components in the hydraulic support structure welding line is shown in fig. 2.

The main functional area schematic diagram of the hydraulic support structure member welding production line in the embodiment is shown in fig. 3, and includes an assembling area 1 for a first assembling process and a third assembling process, an assembling area 2 for a 1.5 assembling process and a second assembling process, 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 taking area; hydraulic support structure welding production line still includes commodity circulation transportation equipment, and commodity circulation transportation equipment includes manual driving, intelligent driving, AGV material transport vehicle, marching type assembly platform.

Step S1 specifically includes:

s11, initializing the equipment distribution scheme of each process, and respectively calculating theoretical takt time of each process based on the initialized stations, the equipment distribution scheme and the process time data; the theoretical beat time of each procedure is calculated in the following mode:

in this embodiment, the expected capacity of the production line is 21 pieces/day, calculated as the shift time of 21.5h per day, and the theoretical takt time is 61.43 min/piece.

In the initialization scheme of the embodiment, the process time data of each procedure of the structural member is shown in the following table 1; the station initialization allocation scheme of the automatic welding area of the robot is shown in the following table 2; the initial allocation scheme for the splice area stations is shown in table 3 below.

Table 1 process time data for each process of the structural member in the initialization scheme

TABLE 2 robot automatic weld zone station initialization assignment scheme

TABLE 3 initial assignment of splice zone stations

As can be seen from Table 1, under the current equipment distribution scheme, the average takt time of one horizontal welding, one vertical welding and three times of repair welding is obviously longer than the takt time under the requirement of 21 pieces per day of productivity. Becomes a decisive factor for restricting the output.

S12, calculating a static process capacity balance index, and adjusting a station distribution scheme; and (4) according to the processing time of each process device on a typical product, performing static data analysis and preferentially adjusting the process capacity. Taking a hydraulic support structural member welding production line as an example, a virtual production line is taken into consideration to perform process capacity balance adjustment.

The process capacity balance index of each process is calculated in the following manner:

wherein the content of the first and second substances,

in this example, the expected production amount is 21 frames/day, and the effective daily working time is 21.5 h/day. A statistical chart of the beat time of each process in the initialization scheme of this embodiment is shown in fig. 4.

S13, dividing each process into different process types according to the process capability balance index calculated in the step S12:

if the process capacity balance index is more than 1, dividing the process capacity balance index into A type processes, namely bottleneck processes;

if the process capacity balance index is less than 1, dividing the process into B type processes, namely capacity-rich processes; the process has strong output capacity in unit time, and has the potential risk of blocking a production line due to the occupation of shared resources.

Due to the scale of the production line, the cost of the equipment and the function of the equipment, there are several situations in the process of balancing the production line, and different processes need to be further classified into sub-types:

procedure subtype 1: only the operation load is adjustable, if the operation load is limited by the area of a production line or the cost of equipment, the equipment cannot be increased or decreased;

procedure subtype 2: only the number of the devices is adjustable, if the devices have particularity, the process content of the devices cannot be borne by other devices;

procedure subtype 3: the number of devices and the operation load are adjustable;

procedure subtype 4: the number of the devices is not adjustable according with the operation;

for different process subtypes, the applicable adjustment rules are as follows:

rule 1: the number of stations is increased or decreased, and the method is suitable for the condition that the number of equipment in the procedure subtype 2 and the procedure subtype 3 can be adjusted;

rule 2: adjusting the operation load to the operation with similar equipment function and smaller operation capacity balance index, which is suitable for the condition that the operation load in the operation subtype 1 and the operation subtype 3 can be adjusted;

rule 3: from the design perspective, the functions of the equipment are re-planned, or the types of the equipment are replaced and selected; from the operation perspective, an attempt is made to prolong the effective operation time per day or change the material access strategy, which is applicable to the situation that the equipment number and the operation load in the process subtype 4 are not adjustable:

s14, according to the adjustment rule in the step S13, combining the beat time of each process under the initialization scheme in the step S11, performing subtype identification and corresponding adjustment on the A-type process:

a primary flat welding procedure: type a3, bottleneck process, and adjustable equipment quantity and workload;

a primary vertical welding process: type a3, bottleneck process, and adjustable equipment quantity and workload;

repair welding: type a2, bottleneck process, and adjustable equipment count.

The corresponding adjustment strategy is:

one-time flat welding procedure, which is applicable to the rules 1 and 2, increases the number of equipment, and adjusts the number of equipment from 2 to 3, and simultaneously adjusts the workload, and adjusts part of the operation to the splicing and welding pad plate procedure;

one-time vertical welding procedure is adopted, rules 1 and 2 are applied, and the number of equipment is increased from 3 to 4;

and (4) repair welding procedures, wherein the rule 1 is applied, and the number of stations is adjusted to 5 from 4.

Subtype identification is performed on type B procedures (i.e., procedures with a procedure capacity balance index less than 1):

three-splicing process: type B1, only workload adjustable;

and (3) secondary flat welding: b3 type, equipment number and work load can be adjusted.

The corresponding adjustment strategy is:

and a third splicing process, wherein the rule 2 is applied, the operation load of the third splicing process is adjusted to the first splicing process, and the original first splicing station divides a station to simultaneously undertake the first splicing operation and the third splicing operation.

And (4) a secondary flat welding process, wherein rules 1 and 2 are applied, and the number of the devices is adjusted from 4 to 2.

And recalculating the adjusted process capacity balance index for the type A process, determining the specific equipment adjustment amount or the specific workload adjustment amount when the index is less than 1, and reducing the number of equipment or increasing the workload for the type B process to limit the over-production of the type B process. After the above steps are completed, the process time of each process is updated, and the updated equipment allocation plan and the updated process time data of each process are used as the input data in S31.

The process time data of each procedure after static calculation and adjustment are shown in the following table 4; the station allocation scheme of the robot automatic welding area after static calculation and adjustment is shown in the following table 5; the statically calculated adjusted splice region assignment scheme is shown in table 6 below. A statistical chart of the tact time of each step after the adjustment of the static calculation is shown in fig. 5.

TABLE 4 static calculation of adjusted process time data for each process

TABLE 5 static calculation adjusted robot automatic weld area station assignment scheme

TABLE 6 static calculation adjusted allocation scheme for stations in assembly area

S15, establishing an initialization scheme of inter-process transfer logic and common resource allocation logic;

the principle of interactive bit allocation is as follows:

interaction areas are respectively arranged between the robot automatic welding area and the assembling area 1 and between the robot automatic welding area and the assembling area 2 and are used for workpiece caching and transferring and handing over, and the number of the interaction positions is determined according to the area of a field and the common size of a structural part; the structural member welding production line is provided with X interaction positions, wherein X1 interaction positions are arranged between the assembly area 1 and the robot automatic welding area, and X-X1 interaction positions are arranged between the assembly area 2 and the robot automatic welding area; all the interaction positions can accept the workpiece to be transferred into when being idle, and all the interaction positions are the shared interaction positions. A schematic diagram of the interaction bit allocation scheme is shown in fig. 6.

In this embodiment, the structural member welding production line is provided with 12 interaction positions, wherein 6 interaction positions are provided in the automatic welding interaction area 1, and 6 interaction positions are provided in the automatic welding interaction area 2. All the interaction positions can accept the workpiece to be transferred into when being idle, and all the interaction positions are the shared interaction positions.

Driving scheduling rules:

the driving tasks are executed according to a first-in first-out rule, the transfer tasks are sent to the driving tasks after the stations are completed, and the driving sequentially executes the transfer tasks according to the sequence of the task generation time.

S2, constructing a simulation model: and (4) according to the production line planning scheme obtained in the step (S1) after the preliminary optimization and adjustment, inputting the distribution scheme of each process device, the process time data, the interaction position distribution rule and the driving scheduling rule as data, and constructing a three-dimensional simulation model of the hydraulic support structure welding production line by using plant simulation factory simulation software. The three-dimensional simulation effect schematic diagram of the structural member welding production line is shown in fig. 7.

S3, simulation model verification and iterative optimization: verifying the availability of a static production line layout scheme, determining a workshop scheduling operation mode based on a virtual production line, focusing on the problems of station capability balance and shared resource dynamic allocation, and performing iterative optimization of strategies and design planning.

Step S3 specifically includes:

s31, under the condition that the incoming material of the upstream process of the production line is enough, continuously simulating the three-dimensional simulation model by using the distribution scheme of each process equipment, the process time data, the interaction bit distribution rule and the running scheduling rule obtained in the steps S12 and S13 as model input data;

the simulation time of the model is counted by 30 days, and in order to ensure the reliability of data, steady-state simulation data of the model after 3 days of operation is intercepted and analyzed to obtain simulation data of the utilization condition of production stations, the utilization condition of logistics equipment, the utilization condition of cache bits/interaction bits and the output quantity of production lines; and calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the production line model.

The utilization of the preheating furnace equipment is shown in the following table 7; the automated welding station equipment utilization is shown in fig. 8.

TABLE 7 preheating furnace facility utilization

Equipment object	Work by	Wait for	Blocking up
				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％

And S32, constructing a station blocking/idle cause analysis model according to the simulation data acquired in the step S31 by taking the virtual assembly line as a guiding principle to reduce the station blocking rate and improve the station utilization rate as an optimization direction, and referring to the attached figure 9.

Stations with strong process capability can output quickly, the feeding and discharging requirements are frequent, and shared logistics resources are frequently occupied, so that the stations with weak process capability cannot feed and discharge materials in time;

workpieces output by stations with high process capability cannot be timely processed by the subsequent process and quickly occupy limited shared cache resources, so that workpieces output by other processes cannot be normally transferred out;

the incoming material of the post-process is interrupted, and the idle rate of the post-process equipment is increased. Finally, the production line is blocked and cannot normally circulate, and the release of the production capacity of the production line is influenced.

According to the model, the strategy configuration needs to be called through shared logistics resources, shared cache resources and shared processing resources, the output of the short procedure with short beat is limited, and the smooth circulation of a production line is guaranteed.

S33, aiming at the problems of station capability balance and shared resource dynamic allocation, carrying out iterative optimization of strategy and design planning, wherein the iterative optimization comprises the following steps:

allocating shared logistics resources: and (5) intelligent driving priority strategy.

Priority generation rules: the priority of the discharging task is greater than that of the feeding task; the same type (feeding/discharging) task, and the post-process task is prior.

The specific setting principle is as follows:

firstly, judging the task type, and transferring the work-in-process in the automatic welding area station of the robot to the discharging task of the interaction area with higher priority;

if the task types are the same, the related automatic welding area stations of the robot are judged, and the priority of the transfer tasks related to the bottleneck processing equipment in the current product process route is higher;

if the transfer tasks all relate to or do not relate to bottleneck equipment, the priority is judged according to the process where the transfer tasks are located, and the priority of the transfer tasks behind the process is higher.

In this embodiment, the tailor welding process flow of the partial structural member and the scheduling priority of the intelligent vehicle transportation task in the automatic welding area are shown in fig. 10, where different procedures and different types of vehicle transportation tasks are labeled: a > B > C, A1> A2 > A3.

Secondly, configuring shared cache resources: and (4) interaction zone position allocation strategy.

The strategy for distributing the position of the interactive zone comprises the following steps: and dividing the interaction area into a public interaction position and a special interaction position, and setting the special interaction position for the subsequent process according to the principle of the discharging priority of the subsequent process to ensure that the workpiece is rolled out.

The specific allocation case in this embodiment:

automatic welding interaction area 1: the number of public interaction bits is 4, and the number of special interaction bits is 2.

Automatic welding interaction area 2: the public interaction bits are 3, and the special interaction bits are 3.

As shown in fig. 11, the first priority of the 3-welding preheating process discharging is the interaction site 5, the public interaction site is selected in the suboptimal mode, and the interaction site 6 is selected in the third priority; the first priority of the secondary vertical welding discharging is an interaction bit 5, and the second priority is a public interaction bit; only public interaction bits can be selected for a single ingredient.

Thirdly, allocating shared processing resources: and (4) a preheating furnace feeding priority strategy based on the automatic welding station prediction state. The method comprises the following steps: a preheating furnace process is arranged between the interaction position and the automatic welding position and serves as a middle shared resource, the preheating furnace feeds materials in advance according to the time required by the automatic welding position (just-in-time production), the connection between the preheated workpiece and the automatic welding position can be realized, and the waiting time of the preheated workpiece and the automatic welding position is shortened. A flow chart for generating the feed priority of the preheat furnace based on the predicted status of the automated welding station is shown in fig. 12.

In this embodiment, a preheating furnace process is present between the interaction area and the robot automatic welding area as a common processing resource, the preheated workpieces need to meet the feeding requirements of different processes, and the unreasonable feeding sequence can increase the waiting time of the automatic welding equipment and greatly reduce the effect of the interaction position as an automatic welding feeding cache.

And S4, verifying the strategy effect after optimization.

Strategies of sharing logistics resources, sharing cache resources and sharing processing resources in the step S3 are input and configured into the production line simulation model constructed in the step S2 again, and a further optimized production line simulation model is obtained; the simulation time is measured in 30 days, and the steady-state simulation data of the model after running for 3 days is intercepted and analyzed; and calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the model, comparing the data with the corresponding data in the step S31, judging whether the optimization requirement of the hydraulic support welding production line scheme is met, if the optimization requirement of the hydraulic support welding production line scheme is not met, repeating the steps S3 and S4 again on the basis of the optimized production line model until the optimization requirement of the hydraulic support welding production line scheme is met.

In this embodiment, the average yield of the optimized welding line model is increased from 12.37 pieces/day to 18.92 pieces/day, and a comparison schematic diagram of the yields before and after optimization is shown in fig. 13; the optimized utilization condition of the preheating furnace equipment is shown in the following table 8; the optimized equipment utilization rate of the automatic welding workstation is shown in figure 14.

Table 8 optimized utilization condition table of preheating furnace equipment

Equipment object	Work by	Wait for	Blocking up
				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％

In the embodiment, the average utilization rate of the optimized automatic flat welding equipment is 79.53%, the utilization rate of the vertical welding equipment (positioner) is 86.30%, the utilization rate of the primary preheating furnace is 78.97%, and the blocking rate of each work station is below 10%. Compared with the average utilization rate of horizontal welding of 49.06% in the original scheme and the average utilization rate of vertical welding of 53.09%, the data optimization effect is better, the requirement for optimizing the scheme of the hydraulic support welding production line can be met, and the defect that the operation effect of a dynamic production scene cannot be quantitatively evaluated in advance by means of planning of the traditional structural member welding production line is overcome.

While the present invention has been described in detail with reference to the embodiments, those skilled in the art will appreciate that various changes can be made in the specific parameters of the embodiments without departing from the spirit of the present invention, and that various specific embodiments can be made, which are common variations of the present invention and will not be described in detail herein.

Claims (8)

1. A simulation optimization method for a welding production line of a hydraulic support structural member of a digital factory is characterized by comprising the following steps:

s1, model input preparation: aiming at an initialization distribution scheme of each procedure of a production line, calculating procedure capacity of each procedure of the production line, acquiring procedure time, performing static procedure capacity balance index calculation and adjustment, determining an adjusted equipment distribution scheme of each procedure and workpiece transfer logic among different procedures, and performing preliminary optimization adjustment on a planning scheme of the production line; the process comprises the steps of splicing a bottom plate, splicing, flat welding for one time, splicing and welding a base plate, splicing 1.5, vertical welding, splicing for two times, repair welding, flat welding for two times, vertical welding, splicing for three times, preheating and repair welding for three times;

s2, constructing a simulation model: according to the production line planning scheme obtained in the step S1 after the preliminary optimization and adjustment, corresponding data information and a distribution scheme are used as data input, and plant simulation factory simulation software is used for building a three-dimensional simulation model of the hydraulic support structure welding production line;

s3, simulation model verification and iterative optimization: verifying the availability of a static production line layout scheme, determining a workshop scheduling operation mode based on a virtual production line, focusing on the problems of station capability balance and shared resource dynamic allocation, and performing iterative optimization of strategies and design planning;

and S4, verifying the strategy effect after optimization.

2. The simulation optimization method for the welding production line of the hydraulic support structural part of the digital factory as claimed in claim 1, wherein the welding production line of the hydraulic support structural part comprises an assembling area 1 for a first assembling process and a third assembling process, an assembling area 2 for a 1.5 assembling process and a second assembling process, a repair welding area 1, a repair welding area 2, an automatic welding interaction area 1, an automatic welding interaction area 2, an automatic robot welding area and an AGV material taking area;

hydraulic support structure welding production line still includes the commodity circulation transportation equipment, the commodity circulation transportation equipment includes manual driving, intelligent driving, AGV material transport vehicle, marching type assembly platform.

3. The simulation optimization method for the hydraulic support structure welding production line of the digital factory as claimed in claim 2, wherein the step S1 specifically comprises:

s11, initializing the equipment distribution scheme of each process, and respectively calculating theoretical takt time of each process based on the initialized stations, the equipment distribution scheme and the process time data; the theoretical beat time of each procedure is calculated in the following mode:

s12, calculating a static process capacity balance index, and adjusting a station distribution scheme; the process capacity balance index of each process is calculated in the following manner:

wherein the content of the first and second substances,

s13, dividing each process into different process types according to the process capability balance index calculated in the step S12:

if the process capacity balance index is more than 1, dividing the process capacity balance index into A type processes, namely bottleneck processes;

if the process capacity balance index is less than 1, dividing the process into B type processes, namely capacity-rich processes;

the different processes are further sub-classified according to production line size, equipment cost and equipment function:

procedure subtype 1: only the operation load is adjustable, if the operation load is limited by the area of a production line or the cost of equipment, the equipment cannot be increased or decreased;

procedure subtype 2: only the number of the devices is adjustable, if the devices have particularity, the process content of the devices cannot be borne by other devices;

procedure subtype 3: the number of devices and the operation load are adjustable;

procedure subtype 4: the number of the devices is not adjustable according with the operation;

the adjustment rules for the different process subtypes are as follows:

rule 1: the number of stations is increased or decreased, and the method is suitable for the condition that the number of equipment in the procedure subtype 2 and the procedure subtype 3 can be adjusted;

rule 2: adjusting the operation load to the operation with similar equipment function and smaller operation capacity balance index, which is suitable for the condition that the operation load in the operation subtype 1 and the operation subtype 3 can be adjusted;

rule 3: from the design perspective, the functions of the equipment are re-planned, or the types of the equipment are replaced and selected; from the operation perspective, an attempt is made to prolong the effective operation time per day or change the material access strategy, which is applicable to the situation that the equipment number and the operation load in the process subtype 4 are not adjustable:

s14, according to the adjustment rule in the step S13 and the beat time of each process under the initialization scheme in the step S11, performing subtype identification on the A-type process, performing corresponding adjustment according to the rule, recalculating the adjusted process capacity balance index, and determining the specific equipment adjustment amount or the operation load adjustment amount according to the result that the index is less than 1; for the type B procedure, the excessive production is limited by reducing the number of equipment or increasing the workload; after the above steps are completed, updating the process time of each process, and using the updated equipment allocation plan of each process and the process time data as the model input data in the step S3 for standby;

s15, establishing an initialization scheme of inter-process transfer logic and common resource allocation logic;

the principle of interactive bit allocation is as follows:

interaction areas are respectively arranged between the robot automatic welding area and the assembling area 1 and between the robot automatic welding area and the assembling area 2 and are used for workpiece caching and transferring and handing over, and the number of the interaction positions is determined according to the area of a field and the common size of a structural part; the structural member welding production line is provided with X interaction positions, wherein X1 interaction positions are arranged between the assembly area 1 and the robot automatic welding area, and X-X1 interaction positions are arranged between the assembly area 2 and the robot automatic welding area; all the interaction positions can accept the workpiece to be transferred when being idle and are common interaction positions;

driving scheduling rules:

the driving tasks are executed according to a first-in first-out rule, the transfer tasks are sent to the driving tasks after the stations are completed, and the driving sequentially executes the transfer tasks according to the sequence of the task generation time.

4. The simulation optimization method for the hydraulic support structure welding production line of the digital factory as claimed in claim 3, wherein the step S3 specifically comprises:

s31, under the condition that the incoming material of the upstream process of the production line is enough, continuously simulating the three-dimensional simulation model by using the distribution scheme of each process equipment, the process time data, the interaction bit distribution rule and the running scheduling rule obtained in the steps S12 and S13 as model input data;

the simulation time of the model is counted by 30 days, and steady-state simulation data of the model after 3 days of operation are intercepted and analyzed to obtain simulation data of the utilization condition of production stations, the utilization condition of logistics equipment, the utilization condition of cache bits/interaction bits and the output quantity of a production line; calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the production line model;

s32, constructing a station blocking/idle cause analysis model according to the simulation data acquired in the step S31 by taking a virtual assembly line as a guiding principle to reduce the station blocking rate and improve the station utilization rate as an optimization direction;

s33, aiming at the problems of station capability balance and shared resource dynamic allocation, carrying out iterative optimization of strategy and design planning, wherein the iterative optimization comprises the following steps:

allocating shared logistics resources: an intelligent driving priority strategy;

secondly, configuring shared cache resources: an interaction area position distribution strategy;

thirdly, allocating shared processing resources: and (4) a preheating furnace feeding priority strategy based on the automatic welding station prediction state.

5. The simulation optimization method for the hydraulic support structure welding production line of the digital factory as claimed in claim 4, wherein in the step S33, the intelligent driving priority strategy comprises:

priority generation rules: the priority of the discharging task is greater than that of the feeding task; tasks of the same type and post-process tasks are prior;

the specific setting principle is as follows:

firstly, judging the task type, and transferring the work-in-process in the automatic welding area station of the robot to the discharging task of the interaction area with higher priority;

if the task types are the same, the related automatic welding area stations of the robot are judged, and the priority of the transfer tasks related to the bottleneck processing equipment in the current product process route is higher;

if the transfer tasks all relate to or do not relate to bottleneck equipment, the priority is judged according to the process where the transfer tasks are located, and the priority of the transfer tasks behind the process is higher.

6. The simulation optimization method for the welding production line of the hydraulic support structure members in the digital factory as claimed in claim 4, wherein in the step S33, the interaction zone position allocation strategy comprises: and dividing the interaction area into a public interaction position and a special interaction position, and setting the special interaction position for the subsequent process according to the principle of the discharging priority of the subsequent process to ensure that the workpiece is rolled out.

7. The simulation optimization method for the welding production line of the hydraulic support structure members in the digital factory as claimed in claim 4, wherein in the step S33, the preheating furnace feeding priority strategy based on the predicted state of the automatic welding station comprises: a preheating furnace process is arranged between the interaction position and the automatic welding position and serves as a middle shared resource, the preheating furnace feeds materials in advance according to the time required by the automatic welding position, the workpiece can be connected with the automatic welding position after being preheated, and the waiting time of the workpiece and the automatic welding position is shortened.

8. The simulation optimization method for the hydraulic support structure welding production line of the digital factory as claimed in claim 4, wherein the step S4 specifically comprises:

inputting and configuring strategies of sharing logistics resources, sharing cache resources and sharing processing resources in the step S3 into the production line simulation model constructed in the step S2 to obtain a further optimized production line simulation model; the simulation time is measured in 30 days, and the steady-state simulation data of the model after running for 3 days is intercepted and analyzed; and calculating the average utilization rate, the blocking rate and the idle rate of the equipment in the model, comparing the data with the corresponding data in the step S31, judging whether the optimization requirement of the hydraulic support welding production line scheme is met, if the optimization requirement of the hydraulic support welding production line scheme is not met, repeating the steps S3 and S4 again on the basis of the optimized production line model until the optimization requirement of the hydraulic support welding production line scheme is met.

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