Yarn Splice Quality Prediction Using Multi-Parameter Classification
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Solution Overview
Problem
Current quality control systems for textile yarn splices in textile machines lack standardized procedures for assessing splice quality, leading to potential breakages and production inefficiencies, as they primarily rely on geometric checks without considering essential parameters like strength and elasticity, resulting in suboptimal control and defects in the finished product.
Innovation Solution
A predictive quality control system that uses capacitive, optical, ultrasonic, or millimeter wave detection means to capture geometric characteristics and process parameters of yarn splices, associating them with qualitative classes in a database to classify and cut splices based on predefined quality thresholds, ensuring mechanical strength and reliability in subsequent textile processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If geometric checking only is used by the clearer, then the control procedure is simple and inexpensive, but the splice quality cannot be reliably predicted and may break in subsequent steps
Solution Approach 1:
The system performs preliminary detection of geometric characteristics and process parameters during the splicing operation, before the splice is completed. This allows prediction of splice quality (strength, elasticity) in advance, enabling preventive action to be taken before defects occur in subsequent textile processes
Solution Approach 2:
The control unit receives data from detection means about geometric characteristics and process parameters, compares this information with reference values, and provides feedback to determine whether to accept or reject the splice. This closed-loop feedback mechanism enables reliable quality assessment while maintaining operational simplicity
2Manufacturing precision
If the clearer cuts geometrically deformed splices, then geometric form limits are enforced, but splices with excellent strength performance may be unnecessarily rejected
Solution Approach 1:
The system transitions from controlling only geometric parameters to a multi-parameter assessment that includes process parameters (temperature, humidity, yarn tension, splicing speed). By considering multiple parameters together, the system can distinguish between splices that are geometrically imperfect but mechanically sound versus those that are truly defective, reducing unnecessary rejections
Solution Approach 2:
The system replaces the purely geometric/mechanical inspection approach of traditional clearers with a predictive model that uses detected geometric characteristics and process parameters to forecast splice quality. This substitution allows splices with acceptable strength performance to be accepted even if they have minor geometric deviations
3Ease of operation
If aesthetic acceptance is used without quality correlation, then visually acceptable splices pass, but splices with poor mechanical strength are not detected
Solution Approach 1:
The system introduces an intermediary predictive quality assessment between visual inspection and final splice acceptance. The control unit uses detected geometric characteristics and process parameters as intermediaries to predict mechanical strength and elasticity, providing a bridge between simple visual inspection and comprehensive quality evaluation without requiring complex mechanical testing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system effectively predicts and prevents splices with unacceptable quality characteristics from progressing, enhancing yarn reliability in processes like dyeing, warping, and knitting by cutting splices that may break or fail quality standards, thereby reducing machine stops and defects in the final product.
Implementation Method 1
capacitive means (46) for detecting the geometric characteristics of the splices (12)
Implementation Method 2
optical means (46) for detecting the geometric characteristics of the splices (12)
Implementation Method 3
ultrasonic means (46) for detecting the geometric characteristics of the splices (12)
Implementation Method 4
millimetre wave means (46) for detecting the geometric characteristics of the splices (12)
Data Source
Figure 1
Figure 2~3
AI summary
Predictive control system (4) of the quality of splices (12) of textile yarns (8) comprising means for detecting (16) the geometric characteristics of the splice (12) of threads of a textile yarn (8) and/or means for acquiring (18) splicing process parameters of said threads of textile yarn (8), a database (20) comprising at least one qualitative splice class (22), a control unit (28), operatively connected to said detection means (16) and/or to said acquisition means (18), and to said database (20), configured to: - associating the detected geometric characteristics and/or the process parameters acquired with at least one qualitative splice class (22) comprised in the database (20), so as to operate a classification of the splice (12) between said qualitative splice classes (22) present in the database (20), associating with the splice (12) a value of at least one qualitative parameter to be compared with a corresponding value chosen as a quality reference, - whether or not to cut the splice (12) on the basis of a comparison between the value of the at least one qualitative parameter of the qualitative splice class (22) and said corresponding value set as a qualitative reference for the splice (12)