A lean mes system for steel pipe whole-process tracing and intelligent scheduling

Abstract

The present application relates to a kind of steel pipe whole-process traceability and intelligent scheduling lean MES system, belong to steel pipe production technical field, including: unified identification and data acquisition module, for each steel billet / steel pipe is given unique identity code;Whole-process traceability management module, to unique identity code as index constructs "product digital twin archives";Intelligent scheduling and dynamic optimization module, for receiving production plan from ERP system generates process level scheduling instruction;Lean execution and visual monitoring module, for generating electronic job instruction according to process level scheduling instruction;System integration and closed-loop feedback module, for storing process level scheduling instruction and product quality in system knowledge base during production process, realize the continuous self-learning of system.The present application realizes material, process, quality data whole-process association and accurate traceability by constructing unified product identification system.

Description

Technical Field

[0001] This invention relates to the field of steel pipe production technology, specifically to a lean MES system for full-process traceability and intelligent scheduling of steel pipes. Background Technology

[0002] Steel pipe production is characterized by long processes, complex procedures, varied material specifications, and high-temperature, high-pressure production environments. Traditional production management methods generally suffer from several problems, including: difficulty in traceability (materials, process parameters, and quality data are scattered during production, making it difficult to quickly locate the problematic process, batch, and cause when quality issues arise, resulting in low traceability efficiency and high costs); reliance on experience in scheduling (production planning and scheduling largely depend on manual experience, making it difficult to respond in real time to abnormal events such as equipment failures, order changes, and material delays, leading to uneven production rhythms and high work-in-process inventory); lack of information transparency (severe information silos between processes, with production progress, material flow, and equipment status information unable to be shared and visualized in real time, resulting in insufficient management refinement); and disconnect from lean production concepts (traditional MES systems often focus on recording and reporting, lacking the ability to proactively optimize, reduce waste, and continuously improve lean management capabilities).

[0003] Therefore, there is an urgent need for an MES system that can achieve precise traceability of the entire process from raw materials to finished products, and integrate intelligent algorithms for dynamic scheduling and optimization, so as to truly support steel pipe companies in achieving lean production. Summary of the Invention

[0004] To address the aforementioned issues, the purpose of this invention is to provide a lean MES system for full-process traceability and intelligent scheduling of steel pipes.

[0005] A lean MES system for end-to-end traceability and intelligent scheduling of steel pipes includes:

[0006] The unified identification and data acquisition module is used to collect material data, process data, quality data, and resource status data, and assign a unique identification code to each steel billet / steel pipe;

[0007] The end-to-end traceability management module uses a unique identification code as an index to establish a spatiotemporal correlation database, automatically binding material data, process data, quality data, and resource status data according to the production sequence to form a "digital twin file of the product".

[0008] The intelligent scheduling and dynamic optimization module is used to receive production plans from the ERP system and generate process-level scheduling instructions.

[0009] The lean execution and visualization monitoring module is used to generate electronic work instructions based on process-level scheduling instructions, and to monitor abnormal situations in the production process based on the electronic work instructions;

[0010] The system integration and closed-loop feedback module is used to store process-level scheduling instructions and product quality data in the system knowledge base to enable the system to continuously learn itself.

[0011] Preferably, in the unified identification and data acquisition module, the material data includes: identification code, specifications, steel type, weight, and location; the process data includes: temperature, pressure, speed, processing time, and operators for each process; the quality data includes: dimensions, surface defects, online ultrasonic testing results, mechanical property test data, and metallographic test data; and the resource status data includes: equipment operating status, personnel on-duty status, and material consumption and inventory.

[0012] Preferably, in the unified identification and data acquisition module, RFID electronic tags, QR codes, or laser marking are used to assign a unique identification code to each steel billet / steel pipe.

[0013] Preferably, in the intelligent scheduling and dynamic optimization module, a multi-objective optimization mathematical model is established with the objectives of "highest on-time delivery rate", "shortest production cycle", "lowest work-in-process inventory", "most balanced equipment utilization rate" and "optimal energy consumption". Heuristic algorithms or reinforcement learning algorithms are used to generate process-level scheduling instructions under the premise of meeting preset constraints.

[0014] Preferably, in the lean execution and visualization monitoring module, when the process parameters in the production process do not meet the conditions in the electronic work instructions, it is determined that the corresponding production process is abnormal.

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0016] This invention relates to a lean MES system for full-process traceability and intelligent scheduling of steel pipes. Compared with the prior art, this invention achieves full-process association and accurate traceability of material, process and quality data by constructing a unified product identification system; and integrates intelligent scheduling algorithms to achieve dynamic optimization of production resources and rapid response to anomalies, thereby shortening the production cycle, reducing work-in-process inventory, improving quality control level, and helping steel pipe enterprises achieve lean and digital production.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The present invention provides a flowchart of a lean MES system for full-process traceability and intelligent scheduling of steel pipes. Detailed Implementation

[0020] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0023] Please see Figure 1 A lean MES system for end-to-end traceability and intelligent scheduling of steel pipes, comprising:

[0024] 1. Unified Identification and Data Acquisition Module

[0025] Identification system: Each steel billet / pipe is assigned a unique identification code (such as based on RFID electronic tags, QR codes or laser markings). This identification code follows the material flow in all subsequent processes and serves as the core carrier for data association.

[0026] Multi-source data acquisition: Deploy data acquisition terminals (including RFID readers, barcode scanners, sensors, PLCs, smart meters, and manual terminals) at key nodes throughout the entire process, such as steelmaking, continuous casting, heating, piercing, rolling, heat treatment, straightening, inspection, and warehousing, to collect the following four types of data in real-time or near real-time:

[0027] Material data: identification code, specifications, steel type, weight, location.

[0028] Process data: Equipment parameters (temperature, pressure, speed, etc.), processing time, and operators for each process.

[0029] Quality data: online inspection results (dimensions, surface defects, ultrasonic testing, etc.) and offline laboratory data (mechanical properties, metallography).

[0030] Resource status data: equipment operating status (running, shutdown, fault), personnel on-duty status, material consumption and inventory.

[0031] 2. End-to-end traceability management module

[0032] Data association and storage: Using the product's unique identifier as an index, a spatiotemporal associated database is established, automatically binding the material, process, quality, and resource data collected from each process according to the production sequence to form a complete "product digital twin archive".

[0033] Forward traceability: Enter the identification code of any finished or semi-finished product to immediately query its complete production history, including the processes, time, process parameters, operation records, quality inspection results and batches of raw materials used.

[0034] Reverse tracing: When a quality defect is discovered, inputting the defect characteristics or process can quickly locate all affected product batches and their specific locations, and trace back to the process in which the problem occurred, the process parameter settings at the time, the equipment status, and the operating team, assisting in root cause analysis.

[0035] Genealogy management: Supports many-to-many relationship traceability between raw material batches and finished product batches, meeting quality recall and compliance requirements.

[0036] 3. Intelligent scheduling and dynamic optimization module

[0037] Advanced Planning and Scheduling (APS) Engine: Its core is a library of intelligent scheduling algorithms based on constraint theory. This engine receives master production schedules and order information from the ERP system, and obtains real-time data on equipment status, material availability, personnel availability, and other constraints from the field.

[0038] Multi-objective optimization scheduling: A multi-objective optimization mathematical model is established with objectives such as "highest on-time delivery rate," "shortest production cycle," "lowest work-in-process inventory," "most balanced equipment utilization," and "optimal energy consumption." Heuristic algorithms (such as genetic algorithms and simulated annealing) or reinforcement learning algorithms are employed to continuously generate or adjust detailed production operation plans and process-level scheduling instructions while satisfying process constraints, resource constraints, and time constraints.

[0039] Anomaly Response and Rescheduling: When the system detects abnormal events such as equipment failure, order insertion, quality scrap, and material delay, the scheduling engine can quickly assess the impact, trigger automatic rescheduling within minutes, generate response solutions (such as process substitution, path adjustment, and plan rearrangement), and issue the adjusted instructions to the relevant workstations.

[0040] 4. Lean Execution and Visual Monitoring Module

[0041] Electronic work instructions: Based on scheduling instructions and product files, the system automatically pushes illustrated electronic work instructions to each workstation terminal. These instructions include accurate process parameter requirements, quality checkpoints, and operating procedures to ensure consistent execution.

[0042] Andon system integration: Linked with the workshop Andon system to enable rapid invocation, response, and closed-loop processing and tracking of production anomalies (quality, equipment, materials, schedule).

[0043] Real-time visual dashboards: Dynamically display key performance indicators (KPIs) such as production progress (planned vs. actual), material flow, overall equipment efficiency (OEE), work-in-process distribution, and first-pass yield rate at the factory, production line, and process levels through workshop screens, PCs, and mobile devices.

[0044] Lean Indicator Analysis: Automatically calculates and analyzes core lean production indicators such as production cycle time, changeover time, and value stream mapping, and identifies wasteful processes such as waiting, handling, and over-processing in the production process.

[0045] 5. System Integration and Closed-Loop Feedback Module

[0046] Vertical integration: Upward integration with the ERP system through standard interfaces to obtain order and material master data; downward integration with the equipment layer (DCS / PLC / SCADA) to realize instruction issuance and data acquisition.

[0047] Horizontal collaboration: It can exchange data with warehouse management systems (WMS), laboratory information management systems (LIMS), quality management systems (QMS), etc.

[0048] Knowledge Accumulation and Optimization Loop: The results of traceability analysis (such as a database of process causes for quality problems) and experience in scheduling optimization (such as optimal production sequencing rules for different product specifications) are accumulated in the system knowledge base. This knowledge can be fed back into the scheduling engine's rule base and optimization model, and can also be used to update the standards of electronic work instructions, enabling the system to continuously learn and improve in a lean cycle.

[0049] The following section uses a typical hot rolling production line of a medium-sized seamless steel pipe enterprise to illustrate the specific deployment, workflow, and interaction process of the system of this invention. The main processes of this production line include: billet acceptance, ring furnace heating, piercing, continuous rolling, sizing, cooling bed cooling, straightening, non-destructive testing, manual inspection, oiling, and warehousing.

[0050] 1 System Deployment and Initialization

[0051] Hardware deployment:

[0052] Identification carrier: Before loading the continuously cast billet or rolled round tube billet, a high-temperature resistant (>900℃) ceramic-encapsulated RFID tag is fixed to the end of the billet by welding or snap-fit. Each tag is written with a globally unique EPC code and is bound to the material number, steel type, specification, and furnace number information in the ERP system.

[0053] Data acquisition points: Fixed UHF RFID readers are deployed at key locations such as the ring furnace inlet roller conveyor, piercing mill inlet, continuous rolling mill inlet, cooling bed input / output roller conveyor, straightener inlet, inspection line inlet, and finished product warehouse inlet. Data acquisition gateways are installed on the PLC side of the main equipment (heating furnace, rolling mill, straightener) to read core process parameters of the equipment in real time (such as furnace temperature, rolling force, rolling speed, and straightener roll reduction). Industrial tablet computers are equipped at the inspection stations for manual entry or confirmation of inspection results.

[0054] Network and Servers: An industrial gigabit Ethernet ring network is built within the workshop, connecting all readers, data gateways, and workstation terminals. Application servers, database servers, and real-time data servers are deployed in the computer room to ensure high system availability.

[0055] Software configuration and model training:

[0056] Maintain a complete factory model in the system, including equipment resources, process routes, product BOM, and quality judgment criteria.

[0057] The APS model of the intelligent scheduling engine needs to be initialized and trained. Import production order data, equipment failure records, and actual working hours data from the past six months or more. Through simulation, the key parameters in the algorithm (such as the standard changeover time for each process and the theoretical processing rate of different specifications of products on each machine) are initially calibrated to form a baseline scheduling rule base.

[0058] 2. Detailed description of typical workflow

[0059] Scenario: Receiving an order for 50 tons of 20CrMo material with specifications of Φ114×6mm.

[0060] Planned Receiving and Intelligent Scheduling:

[0061] After receiving the sales order from the ERP system, the system automatically converts it into a production work order. The APS engine then starts to perform "capacity check" and "detailed scheduling".

[0062] Capacity check: The engine checks whether the required billet inventory is complete and assesses the load of key bottleneck equipment (such as continuous rolling mills) in the future planning period.

[0063] Detailed scheduling: Based on multi-objective optimization (such as prioritizing delivery dates while minimizing the number of specification changes on the continuous rolling mill), and considering constraints such as the current production schedule, equipment preventative maintenance calendar, and shift personnel arrangements, a genetic algorithm is used for calculation. Within minutes, the recommended production window (suggested start heating time), process timetable (estimated start and end times for each process), and optimal rolling sequence are output for this work order (it is recommended to produce this batch of orders adjacent to another batch of Φ121×7mm orders with similar specifications to reduce roll changeover time). After the planner reviews and confirms this plan, the schedule is officially issued.

[0064] Automatic construction of end-to-end traceability data:

[0065] Billet loading: When a billet with an RFID tag arrives at the loading area, the reader automatically identifies its EPC code, and the system creates a master traceability record for the billet in the traceability database and marks its status as "to be heated".

[0066] Process execution and data binding: The billet enters the ring furnace for heating. Upon exiting the furnace, the inlet reader identifies it again, and the system records a "furnace exit timestamp". At the same time, the data gateway automatically captures the actual process curves of the heating furnace during this time period (set values ​​and measured values ​​for each temperature zone), and binds them to the main traceability record of the billet through time correlation, forming the first process sub-record.

[0067] Step-by-step data transmission and information enrichment: After piercing, the billet becomes a rough tube. The reader identifies the original tag, and the system records the measured parameters of the piercing process (such as mandrel extension and roll speed). In the continuous rolling process, in addition to collecting rolling parameters, the system also associates the steel tube (which has now been cut to length) with the key process die numbers used in rolling (such as the continuous rolling roll ring number). In this way, as the steel tube passes through each station, its digital twin file automatically adds a new data dimension (time, process, quality, equipment, personnel).

[0068] Quality data integration: When the steel pipe arrives at the ultrasonic flaw detection station, the waveform data output by the testing equipment and the defect marking location information (such as "equivalent defect of Φ2mm flat bottom hole exists 3.5 meters from the pipe end") are automatically uploaded through the interface and accurately associated with the steel pipe's identification code. Inspectors re-evaluate suspected defects on a flat plate, and their judgment results ("qualified", "repaired", "scraped") and operator's employee number are also recorded.

[0069] Dynamic scheduling and exception response:

[0070] Preset anomaly: Suppose that during the rolling of this batch of steel pipes, the main motor of the continuous rolling mill suddenly experiences a temperature rise alarm, and the DCS system issues an "unplanned shutdown" signal.

[0071] Event Capture and Impact Analysis: The MES system's monitoring module captures the event in real time. The intelligent scheduling engine immediately initiates "impact analysis":

[0072] Identify the steel pipes currently being rolled and those planned to be produced on the mill (including subsequent steel pipes from this batch and other batches).

[0073] Assess the estimated repair time for the fault (based on the initial diagnosis input by the maintenance department).

[0074] Check the possibility of parallel or alternative production paths (in this case, if there is only a single rolling line, there are no alternative paths).

[0075] Automatic rescheduling and decision support: The engine initiates the rescheduling algorithm within seconds. Considering that a failure could cause a 2-hour delay, the algorithm will attempt:

[0076] Reorder all subsequent production tasks awaiting rolling.

[0077] Assess whether to postpone some non-urgent orders in order to prioritize high-priority orders.

[0078] Calculate the estimated completion time for each new order.

[0079] Ultimately, the system generates a rescheduling suggestion and pushes an alert via Kanban and mobile devices: "Continuous rolling mill malfunction, estimated delay of 2 hours. Suggested solution: Swap 'Order B' with subsequent 'Order C' to prioritize the delivery of 'Order A' (customer with the tightest deadline). After the adjustment, 'Order A' is expected to be delayed by 1.5 hours." After confirmation by the production supervisor, the system automatically sends the updated scheduling instructions to the relevant workstation terminals.

[0080] Lean Execution and Visual Monitoring:

[0081] Electronic work instructions: During the straightening process, the terminal screen in front of the operator will automatically display the optimized straightening parameter preset values ​​(such as straightening roller angle and pressure) and the key quality inspection points for the upcoming steel pipe specification (Φ114×6mm), replacing the traditional paper process cards.

[0082] Real-time visualization: The value stream map dynamically flows in real time on the central dashboard of the workshop, clearly displaying the current quantity of work-in-process at the annular furnace, rolling mill, cooling bed, etc. The equipment status matrix uses colors to distinguish between equipment running (green), stopped (yellow), and faulty (red). The order progress bar intuitively shows that "Order A" is 60% complete (has passed continuous rolling).

[0083] Andon lights up: If a steel pipe is determined to have surface cracks requiring grinding at the manual inspection station, the inspector clicks the "Quality Andon" button on the tablet. The system immediately pushes this information to the grinding process and flashes a notification on the dashboard. At the same time, the steel pipe is marked as "Pending Grinding" in the traceability record, and the status is only updated in a closed loop after grinding is completed and the re-inspection is passed.

[0084] Traceability analysis and closed-loop optimization:

[0085] Quality Analysis: At the end of the month, the quality department discovered an increase in the rate of a certain type of internal defect in Φ114×6mm steel pipes. Reverse tracing was initiated in the system: by inputting the defect characteristics, the system quickly filtered out all steel pipes with the same type of defect and automatically analyzed their common characteristics—80% occurred during "Night Shift B Group" production, and the corresponding heating furnace's "Zone 3 Temperature" fluctuated below the lower limit during the corresponding time period. Based on this, a preliminary analysis report was generated, identifying areas for improvement.

[0086] Knowledge Accumulation: Based on this analysis, the process department decided to include "temperature control stability in the third zone" as a key focus of process monitoring for this specification. This experience has been added as a new rule to the system's "quality early warning rule base." In future production, once the system detects similar fluctuations, it will automatically send an early warning to the furnace control personnel to prevent the recurrence of batch problems.

[0087] As can be seen from the above specific and detailed implementation process, the system of the present invention is not just a recording tool, but a lean production hub that integrates automatic identification, intelligent decision-making, real-time linkage and continuous learning, realizing a fundamental transformation of steel pipe production from "experience-driven" to "data-driven".

[0088] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0089] 1. Improved traceability efficiency: Quality traceability time is reduced from hours or even days to minutes, the recall scope is more accurate, and losses are reduced.

[0090] 2. Intelligent scheduling: Improved production plan compliance rate, increased on-time order delivery rate, reduced work-in-process inventory by 20%-30%, and more balanced equipment utilization.

[0091] 3. Management transparency: Achieve visualization of the entire production process, and shift management decisions from experience-driven to data-driven.

[0092] 4. Supporting Lean Improvement: It provides quantitative data and analysis tools to reduce waste, shorten cycles, and achieve continuous improvement, effectively supporting the implementation of the enterprise's lean production system.

[0093] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A lean MES system for full-process traceability and intelligent scheduling of steel pipes, characterized in that, include: The unified identification and data acquisition module is used to collect material data, process data, quality data, and resource status data, and assign a unique identification code to each steel billet / steel pipe; The end-to-end traceability management module uses a unique identification code as an index to establish a spatiotemporal correlation database, binding material data, process data, quality data, and resource status data according to the production sequence to form a "product digital twin file". The intelligent scheduling and dynamic optimization module is used to receive production plans from the ERP system and generate process-level scheduling instructions. The lean execution and visualization monitoring module is used to generate electronic work instructions based on process-level scheduling instructions, and to monitor abnormal situations in the production process based on the electronic work instructions; The system integration and closed-loop feedback module is used to store process-level scheduling instructions and product quality data in the system knowledge base to enable the system to continuously learn itself.

2. The lean MES system for full-process traceability and intelligent scheduling of steel pipes according to claim 1, characterized in that, In the unified identification and data acquisition module, material data includes: identification code, specifications, steel type, weight, and location; process data includes: temperature, pressure, speed, processing time, and operators for each process; quality data includes: dimensions, surface defects, online ultrasonic testing results, mechanical property test data, and metallographic test data; resource status data includes: equipment operating status, personnel on-duty status, and material consumption and inventory.

3. The lean MES system for full-process traceability and intelligent scheduling of steel pipes according to claim 2, characterized in that, In the unified identification and data collection module, RFID electronic tags, QR codes, or laser markings are used to assign a unique identification code to each steel billet / steel pipe.

4. The lean MES system for full-process traceability and intelligent scheduling of steel pipes according to claim 3, characterized in that, In the intelligent scheduling and dynamic optimization module, a multi-objective optimization mathematical model is established with the goals of "highest on-time delivery rate", "shortest production cycle", "lowest work-in-process inventory", "most balanced equipment utilization" and "optimal energy consumption". Heuristic algorithms or reinforcement learning algorithms are used to generate process-level scheduling instructions under the premise of meeting preset constraints.

5. The lean MES system for full-process traceability and intelligent scheduling of steel pipes according to claim 4, characterized in that, In the lean execution and visual monitoring module, when the process parameters in the production process do not meet the conditions in the electronic work instructions, it is determined that the corresponding production process is abnormal.

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