Yarn Quality Sensor Adjustment via Intentional Defect Simulation
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Solution Overview
Problem
Existing yarn quality sensor systems struggle to detect defects, particularly those related to relative changes or workstation-specific parameters, as they rely on absolute or relative comparisons that may not accurately identify defects like yarn unevenness or hairiness, and can produce defective yarn during reference value determination.
Innovation Solution
A method where a reference value for yarn parameters is determined by intentionally producing defective yarn, allowing for better sensor adjustment and defect detection by comparing current values to these simulated defect values, enabling more accurate quality evaluation and adaptation of cleaning parameters to individual workstations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the absolute method is used to detect yarn defects by comparing current parameter values to a set defect level, then defects with known absolute values can be detected, but defects with unknown absolute values or relative changes (such as yarn unevenness and hairiness) cannot be detected
Solution Approach 1:
The patent changes the reference parameter from a fixed absolute value to a dynamic relative value that adapts to different workstations and defect types. By comparing current parameter values to reference values specific to each workstation rather than universal absolute thresholds, the system can detect both absolute defects and relative defects like yarn unevenness and hairiness that vary by production conditions.
2Measurement precision
If the relative group method is used to compare yarn parameter to mean value of a group of clearers, then defects with relative changes can be detected, but defects with broad value ranges at individual workstations cannot be distinguished from normal variation
Solution Approach 1:
The patent applies local quality by establishing reference values specific to each individual workstation rather than using a group average. Each workstation develops its own reference parameter based on its specific production conditions, allowing it to distinguish between normal local variations and actual defects. This localizes the quality assessment to match the local production characteristics of each workstation.
3Adaptability or versatility
If the relative individual method is used to compare yarn parameter to reference value of the particular workstation, then workstation-specific defects can be detected, but defective yarn may be produced during reference value determination since the system cannot identify defects without a reference
Solution Approach 1:
The patent applies preliminary action by establishing a preliminary reference value during an initialization phase before normal production begins. During this preliminary phase, the system collects parameter data and establishes a baseline reference value without the risk of producing defective yarn, since no defect detection decisions are made during reference establishment. This preliminary setup ensures reliable defect detection during subsequent production.
Solution Approach 2:
The system performs self-service by automatically establishing its own reference values during an initialization phase without external intervention. The clearer device autonomously collects parameter data during normal operation and uses this data to set its own reference values, eliminating the need for manual calibration and ensuring the reference values reflect actual workstation conditions.
4Measurement precision
If reference value is determined by measuring correct yarn at the particular workstation, then accurate reference for defect detection is established, but defective yarn may be produced during measurement since the system does not know what is defective
Solution Approach 1:
The system performs self-service by automatically establishing its own reference values during an initialization phase without external intervention. The clearer device autonomously collects parameter data during normal operation and uses this data to set its own reference values, eliminating the need for manual calibration and ensuring the reference values reflect actual workstation conditions.
Solution Approach 2:
The patent applies preliminary action by establishing a preliminary reference value during an initialization phase before normal production begins. During this preliminary phase, the system collects parameter data and establishes a baseline reference value without the risk of producing defective yarn, since no defect detection decisions are made during reference establishment.
Data Source
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AI summary
A method of adjustment of a workstation and a yarn clearer i.e. a yarn quality sensor (9) at a workstation of a textile machine, in which measurement and/or calculation creates a reference value (PR) of a particular parameter of yarn (1), to which the current value (PP) of the particular parameter measured is compared during the continuous production of yarn. The reference value of the particular yarn parameter is determined as the reference value of (PfR) of the particular yarn parameter of deliberately defective yarn (1) intentionally produced at the particular workstation and subsequently it is compared to the current and/or reference value (PP, P0, PPR, P0R) of the particular parameter of yarn (1) produced at the particular workstation with production quality or higher than production quality.