Yarn Spindle Quality Control Using a Knowledge Graph
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Solution Overview
Problem
In chemical fiber production, identifying and addressing quality issues in yarn spindles is challenging due to complex processes and multiple affecting factors, requiring significant manpower and time to adjust production parameters manually.
Innovation Solution
A yarn spindle quality control method and apparatus utilizing a knowledge graph to determine abnormal parameters and provide adjustment manners based on quality inspection results, enabling automated optimization of production processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of production process is used to address quality issues, then product quality can be corrected, but significant manpower and time are required
Solution Approach 1:
The knowledge graph pre-stores adjustment manners for various abnormal parameters before quality issues occur. When quality problems are detected, the system can immediately retrieve pre-prepared solutions without requiring manual analysis or trial-and-error adjustments, thus reducing the time needed to correct product quality issues.
Solution Approach 2:
The knowledge graph acts as an intermediary between quality inspection results and production process adjustments. It stores structured relationships between abnormal parameters and their corresponding adjustment manners, enabling automated retrieval of correct adjustments without requiring manual expert intervention, thereby reducing both manpower and time requirements.
2Manufacturing precision
If manual adjustment of production process is used to address quality issues, then product quality can be corrected, but significant manpower is required
Solution Approach 1:
The system enables self-service quality control by automatically retrieving adjustment manners from the knowledge graph based on detected abnormal parameters. The production process can be adjusted without requiring manual expert intervention, as the system independently queries and applies the appropriate correction methods stored in the knowledge graph, thereby reducing manpower requirements.
Solution Approach 2:
The knowledge graph serves as an intermediary that automates the decision-making process between quality inspection and production adjustment. It contains pre-established knowledge about parameter relationships and adjustment strategies, allowing the system to automatically determine correct adjustments without requiring manual expert analysis, thus reducing the manpower needed for quality correction.
3Manufacturing precision
If complex production process monitoring is implemented to identify quality issues, then product quality can be maintained, but the complexity of the system increases
Solution Approach 1:
The knowledge graph acts as an intermediary layer that simplifies the complexity of production process monitoring. Instead of requiring complex real-time analysis of multiple interrelated parameters, the system stores pre-established relationships between parameters and their optimal settings in the knowledge graph. This allows quality maintenance through simple parameter queries rather than complex process analysis, reducing system complexity while maintaining product quality.
Data Source
AI summary
Provided is a yarn spindle quality control method based on a knowledge graph, an electronic device, and a storage medium. The method includes determining whether there is an abnormal parameter in a production parameter and/or a process parameter of a group of yarn spindles based on a quality inspection result, the production parameter and the process parameter of the group of yarn spindles; searching an adjustment manner corresponding to the production parameter and/or the process parameter from a knowledge graph for yarn spindle production and management, in a case where there is the abnormal parameter; and sending the adjustment manner corresponding to the production parameter and/or the process parameter to a related device of the group of yarn spindles.