一种基于工艺大数据的电缆成缆控制方法

By using a cable cabling control method based on big data of processes, process parameters are collected and optimized in real time, achieving coupled quantitative control of tension and arrangement offset. This solves the consistency and stability problems in traditional cable cabling processes, and improves production efficiency and quality.

CN121483760BActive Publication Date: 2026-07-17QILU CABLE CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
QILU CABLE CO LTD
Filing Date
2025-12-31
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional cable assembly processes suffer from isolated production data, delayed parameter adjustments, high reliance on manual labor, poor production consistency, and high scrap rates, making it difficult to meet the precision requirements of high-performance cables. Furthermore, they cannot provide real-time coordinated control over uneven tension and core alignment deviations.

Method used

By collecting process parameters in real time, a simulated coupling factor is constructed. A numerical optimization algorithm is used to iteratively search for the amplification coefficient and sensitivity coefficient. Combined with the coupling control factor used in real-time control, the take-up speed and stranding torque are adjusted in real time, reducing tension instability and arrangement deviation, and improving production stability and consistency.

Benefits of technology

It significantly reduced the variability of quality indicators, improved the accuracy and stability of regulation, reduced the scrap rate and energy consumption, and enhanced the intelligence level and economic benefits of the production process.

✦ Generated by Eureka AI based on patent content.

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Abstract

本发明公开了一种基于工艺大数据的电缆成缆控制方法,属于电缆成缆控制技术领域;包括以下步骤,S1、实时采集参数并进行预处理,调用历史批次记录,计算张力不稳定度和排列偏移趋势速率,构建模拟耦合因子,得到优化后的系数组合;S2、分别计算当前的张力不稳定度和排列偏移变化率,得到调节后的收线速度和调节后的绞合扭距;通过调用同规格电缆历史批次记录并计算张力不稳定度和排列偏移趋势速率,并采用数值优化算法迭代搜索放大系数和敏感系数,使工艺导向的优化目标函数最小化,获得了优化后的系数组合,从而实现了基于历史数据的自适应参数优化,提升了控制策略对不同批次工艺波动的高适应性,显著降低了质量指标的变异性。
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