Textile Material Flow Prediction for AGV Spinning Logistics
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Solution Overview
Problem
Current textile machine management systems fail to seamlessly track and trace material flow, quality, and characteristics across production steps, and do not effectively predict raw material needs or optimize logistics in spinning mills, leading to inefficiencies and increased labor intensity.
Innovation Solution
A textile machine management system that includes a material flow database, prediction module, and disposition module to track and predict material carrier changes, combined with automated guided transportation vehicles for efficient logistics, and unique identifiers for material carriers to enhance tracking and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current textile machine management systems are used to track material flow, then basic monitoring is provided, but seamless tracking and tracing of material flow, quality, and characteristics across production steps is not achieved
Solution Approach 1:
The system divides material flow tracking into discrete segments by assigning unique identifiers to individual material carriers (bobbins, cans, cubic cans) and tracking them through each production step separately. This segmentation enables comprehensive tracking without requiring a monolithic complex system, as each carrier is tracked independently through the production chain.
Solution Approach 2:
The patent introduces intermediate tracking devices such as RFID tags, barcodes, or QR codes as mediators between the material carriers and the management system. These intermediaries carry identification information that bridges the physical material flow and the digital tracking system, enabling seamless information transfer without direct complex system integration at every point.
2Productivity
If more automated guided transportation vehicles are deployed to transport material carriers, then material transport capacity is improved, but investment and operational costs increase
Solution Approach 1:
The prediction module performs preliminary actions by forecasting when material carriers will need to be transported between production steps. By predicting future material carrier changes based on current production status and historical data, the system prepares transportation schedules in advance, optimizing vehicle utilization and reducing the total number of vehicles needed.
Solution Approach 2:
The disposition module uses feedback from the prediction module and real-time production status to dynamically adjust transportation schedules and vehicle allocation. This closed-loop feedback system optimizes the use of existing transportation vehicles by rerouting and rescheduling based on actual material flow patterns, thereby reducing the quantity of vehicles required while maintaining productivity.
3Device complexity
If manual tracking and sorting of material carriers is performed, then system complexity is reduced, but labor intensity increases
Solution Approach 1:
The system implements self-service tracking where material carriers automatically provide their own identification information through embedded tags or codes. As carriers move through production steps, reading devices automatically capture their identifiers without requiring manual intervention. This self-service mechanism eliminates labor-intensive manual tracking while keeping the system relatively simple.
Solution Approach 2:
The patent replaces manual mechanical tracking operations with automated optical or electromagnetic reading systems. Instead of workers physically tracking and recording carrier positions, the system uses automated readers to detect and record carrier identifiers, substituting mechanical human labor with automated detection technology.
4Productivity
If prediction of material carrier changes is not implemented, then system simplicity is maintained, but logistics optimization is reduced
Solution Approach 1:
The prediction module performs preliminary analysis of material flow patterns to forecast future material carrier changes before they occur. By analyzing current production status, machine speeds, and historical data, the system predicts when carriers will need to be transported or changed, enabling proactive logistics planning that optimizes vehicle scheduling and reduces waiting time.
Data Source
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AI summary
It is disclosed a textile machine management system (3) comprising a plurality of textile machines (2, 21, 22, 23), said plurality of textile machines (2) comprising end spinning machines; a plurality of automated guided transportation vehicles (8) in order to transport material carriers (9) between the plurality of textile machines (2) and a logistic control system (81) for controlling movement of the plurality of automated guided transportation vehicles (8). Furthermore, it comprises a material management apparatus (6) configured to manage the material flow between the plurality of textile machines (2) comprising a material flow database (61), a prediction module (62) for predicting of the timing of expected required material carrier (9) changes at the plurality of textile machines (2) based on the information in the material flow database (61); and a disposition module (63) for calculation disposition information for the logistic control system (81).