Yarn Feeding Device Learning Procedure for High-Speed Weaving
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Solution Overview
Problem
Existing yarn feeding devices for weaving machines, particularly those used for high-speed weaving of flat or tape yarns without twist, face challenges in achieving precise control during the start-up process, leading to potential malfunctions and inaccuracies due to insufficient knowledge of system components and behavior.
Innovation Solution
A yarn feeding arrangement with a learning procedure that gathers control data on system components before operation, using sensors and controllers to determine a motion model for precise yarn feeding, allowing for accurate control of the motor-driven bobbin and loop buffer device, and incorporating a slow-motion learning phase to capture dynamic properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the weaving machine operates at high speed, then productivity is improved, but the control precision during start-up deteriorates due to insufficient knowledge of system components and behavior
Solution Approach 1:
The system performs a learning procedure before actual high-speed weaving operation to gather data about system components and behavior. During this preliminary phase, the controller operates the bobbin and loop buffer device to determine motion models and geometric parameters, storing this information in memory for use during subsequent high-speed operation, thereby resolving the contradiction between high productivity and control precision during start-up
2Device complexity
If the controller operates without pre-learning system characteristics, then device complexity is reduced, but reliability deteriorates due to potential malfunctions and inaccuracies
Solution Approach 1:
The learning procedure enables the control system to automatically determine its own motion models and geometric parameters by operating the bobbin and loop buffer device under controlled conditions. The system self-calibrates by measuring actual yarn motion and comparing it with expected motion, storing the determined characteristics in memory for reliable operation without requiring external intervention or complex pre-programming
3Measurement precision
If the learning procedure operates at slow motion, then measurement precision of yarn motion is improved, but productivity is reduced during the learning phase
Solution Approach 1:
The learning procedure is performed as a preliminary phase before actual production, operating at slow motion to accurately measure yarn motion and determine system characteristics. Once the motion models and geometric data are stored in memory during this preliminary learning phase, the system can switch to high-speed operation without needing to maintain slow-motion conditions during production, thereby resolving the contradiction between measurement precision and productivity
Data Source
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AI summary
Described are, among other things,methods and devices for providing a learning procedure in a yarn feeding arrangement (12). The learning procedure aims at providing control data to the controller (32) about system components and behavior of system components of the yarn feeding arrangement beforehand, such that the controller has knowledge of the system components before the weaving machine (10) is started to operate at full operational speed.