Comparative study of ring yarn properties spun with static and rotary grooved contact surfaces

Spinning with a static contact surface is an energy-saving method to reduce spun yarn hairiness; however, the spun yarn irregularity and tensile properties are deteriorated. To prohibit the deteriorations, this paper introduces a rotary grooved surface contacting ring spinning strand within the yarn formation zone. In theory, the modeling analysis of spinning with contact surface is conducted to reveal the prohibition mechanism of yarn irregularity and tensile property deteriorations for a rotary grooved surface. Theoretical analysis results indicated that groove-yarn surface friction could wrap the concentrated hairs onto yarn stem while block inflowing twists to the spinning strangle zone; the rotary grooved surface could reduce twist blockage and hair wrapping concentrations to better the situation after a comparison with the static grooved surface. Then, two kinds of specially-designed grooved cylinders (one was rotatable while the other was static) were used to validate the theoretical analysis. The experimental results showed that, unlike the static grooved cylinder which significantly deteriorated the original yarn unevenness CVm, the rotary achieved significant hairiness reduction without any significant deterioration of other yarn properties. This might be due to the decreased friction and twist propagation for the rotary grooved cylinder contacting the spinning strand. In this case, spinning with a rotary grooved cylinder was preferably applied in the first step to control ring spun yarn hairiness

Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classification

Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag (OOB) predictions, Gini changes as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analysed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production feature importance and correlations by three various quantitative indicators including the unbiased feature importance (FI), gain improvement FI and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing