Serialized gradient chromatography for the digital blending of colored fibers and spinning of gradient colored yarn

2021 ◽  
pp. 004051752110257
Author(s):  
Xianqiang Sun ◽  
Yuan Xue ◽  
Peng Cui ◽  
Zhiwu Xu ◽  
Dejun Zeng
Keyword(s):  
Time Series ◽  
Color Difference ◽  
Processing Parameters ◽  
Numerical Control ◽  
Natural Gradient ◽  
Knitted Fabrics ◽  
Spun Yarns ◽  
Mixing Matrix ◽  
Color Gradient ◽  
Color Mixing

Gradient colored yarns are manufactured by controlling the blending ratios of three-primary-colored fibers in a slight distribution of gradients along the yarn length, thereby resulting in a continuous natural variation in mixed colors of the fibers throughout the whole yarn. The spinning of gradient colored yarns still remains a challenge, which requires the reliance on digital blending theory of colored fibers and the supporting of multi-channel computer numerical control (CNC) spinning technique. This paper constructed a three-primary-colored fiber gridded color mixing model and its mass mixing matrix and color mixing chromatography matrix by mass discretization and coupling pairing with a 10% gradient for the three-primary-colored fibers. With the aim of continuous natural gradient of mixed colors, the blending ratio gradient path of three-primary-colored fibers was planned based on the mass mixing matrix, and a method of regulating the gradient of color difference between adjacent color segments was proposed. In order to realize the natural gradient of color of the forming yarn, the spinning mechanism of gradient colored yarn was established based on three-channel CNC spinning mechanism and the time-series yarn simulation model, and the time-series spinning processing parameters of three-channel CNC spinning machine were devised. Four gradient colored yarns with different gradient paths were designed and prepared, the linear density, twist, unevenness, surface hairiness, and tensile strength of the spun yarns were measured, compared, and analyzed, and knitted fabrics with color gradient effect were fabricated by small circular knitting machine to obtain continuous and natural color transition with a dreamy and mysterious color effect.

ARIMA-Based Time Series Model of Cutting Temperature in Facing Process

2019 ◽  
Vol 18 (03) ◽  
pp. 395-411
Author(s):  
Samya Dahbi ◽  
Latifa Ezzine ◽  
Haj El Moussami
Keyword(s):  
Time Series ◽  
Cutting Temperature ◽  
Numerical Control ◽  
Performance Criteria ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Machining Processes ◽  
Adequate Model ◽  
Average Percentage

During machining processes, cutting temperature directly affects cutting performances, such as surface quality, dimensional precision, tool life, etc. Thus, evaluation of cutting temperature rise in the tool–chip interface by reliable techniques can lead to improved cutting performances. In this paper, we present the modeling of cutting temperature during facing process by using time series approach. The experimental data were collected by conducting facing experiments on a Computer Numerical Control lathe and by measuring the cutting temperature by an infrared camera. The collected data were used to test several Autoregressive Integrated Moving Average (ARIMA) models by using Box–Jenkins time series procedure. Then, the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 1, 1) and it was tested by conducting a new facing operation under the same cutting conditions (spindle speed, feed rate, depth of cut, and nose radius). It was clearly seen that there is a good agreement between experimental and simulated temperatures, which reveals that this approach simulates the evolution of cutting temperature in facing process with high accuracy (average percentage error [Formula: see text] 0.57%).

scholarly journals Open-End Yarn Properties Prediction Using HVI Fibre Properties and Process Parameters

2017 ◽  
Vol 17 (1) ◽  
pp. 6-11 ◽  
Author(s):  
Hanen Ghanmi ◽  
Adel Ghith ◽  
Tarek Benameur
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Processing Parameters ◽  
Raw Material ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Spun Yarns ◽  
Yarn Count ◽  
Input Variables ◽  
Rotor Spun Yarn

AbstractThis article provides three models to predict rotor spun yarn characteristics which are breaking strength, breaking elongation and unevenness. These models used noncorrelated raw material characteristics and some processing parameters. For this purpose, five different cotton blends were processed into rotor spun yarns having different metric numbers (Nm10, Nm15, Nm18, Nm22, Nm30 and Nm37). Each count was spun at different twist levels. Response surface method was used to estimate yarn quality characteristics and to study variable effects on these characteristics. In this study, predicting models are given by the analysis of response surface after many iterations in which nonsignificant terms are excluded for more accuracy and precision. It was shown that yarn count, twist and sliver properties had considerable effects on the open-end rotor spun yarn properties. This study can help industrial application since it allows a quality management-prediction based on input variables such as fibre characteristics and process parameters.

Comfort properties of bamboo/cotton blended knitted fabrics produced from rotor spun yarns

2015 ◽  
Vol 106 (12) ◽  
pp. 1371-1376 ◽  
Author(s):  
G. Ramakrishnan ◽  
P. Umapathy ◽  
C. Prakash
Keyword(s):  
Knitted Fabrics ◽  
Spun Yarns

scholarly journals The Possibilities of Improving the Fatigue Durability of the Ship Propeller Shaft by Burnishing Process

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 63
Author(s):  
Stefan Dzionk ◽  
Włodzimierz Przybylski ◽  
Bogdan Ścibiorski
Keyword(s):  
Fatigue Strength ◽  
Cold Rolling ◽  
Processing Parameters ◽  
Numerical Control ◽  
Automatic Process ◽  
Structural Elements ◽  
Fatigue Durability ◽  
Burnishing Process ◽  
Seawater Environment ◽  
Ship Propeller

Heavily loaded structural elements operating in a corrosive environment are usually quickly destroyed. An example of such an element is a ship propeller operating in a seawater environment. This research presents a fatigue resistance test performed on elements operating in seawater. Different processing parameters applied on the samples in particular were compared with the specimens whose surface had been burnished differently and they were compared to specimens with a grinded surface. The research shows that the structural elements whose surface has been burnished can have up to 30% higher fatigue strength in a seawater environment than the elements whose surface has been grinded. During burnishing, an important feature of the process is the degree of cold rolling of the material. The resistance of the component to fatigue loads increases only to a certain level with increasing the degree of the cold rolling. Further increasing the degree of cold rolling reduces the fatigue strength. Introducing additional stresses in the components (e.g., assembly stresses) reduces the fatigue strength of this component in operation and these additional stresses should be accounted for while planning the degree of the cold rolling value. A device that allows for simultaneous turning and shaft burnishing with high slenderness is presented in the appendix of this article. This device can be connected to the computerized numerical control system and executed automatic process according to the machining program; this solution reduces the number of operations and cost in the process.

scholarly journals Theoretical and Practical Analysis of Fiber Blend Model in Gray Spun Yarn

2017 ◽  
Vol 12 (2) ◽  
pp. 155892501701200
Author(s):  
Chongqi Ma ◽  
Yujuan Wang ◽  
Junli Li ◽  
Lu Cheng ◽  
Jinlian Yang
Keyword(s):  
Mixing Model ◽  
Color Match ◽  
Reflectance Curve ◽  
Spun Yarn ◽  
Spun Yarns ◽  
Practical Analysis ◽  
Color Mixing ◽  
The Relationship ◽  
Final Color

The final color of blend yarns is dependent on the proportion of the various colored fibers used in colored spun yarns. How to scientifically and effectively quantify the relationship between the proportion of different colored fibers and the color of the yarn is a major problem hindering the development of color spinning technology. This paper suggests three methods for analyzing and comparing the color of gray spun yarns. A color match software, Datacolor Match, was used to calculate a recipe simulating the match of dyes; the a predicting formula based on color mixing model of Kubelka-Munk (K-M) double-constant theory for fiber blends was established. Finally, the colored fiber formula based on the known reflectance curve was calculated according to the relationship between the reflectance value of gray spun yarn and proportion of colored fiber present. This work provides an understanding of colored spun yarn fabrication.

A study of multi-quality processing parameter optimization for sueded fabric

2016 ◽  
Vol 87 (4) ◽  
pp. 389-398 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Wen-Tsung Lin
Keyword(s):  
Taguchi Method ◽  
Color Difference ◽  
Processing Parameters ◽  
Quality Characteristics ◽  
Gray Relational Analysis ◽  
Processing Parameter ◽  
Optimum Processing ◽  
Multiple Quality Characteristics ◽  
Response Table ◽  
Fabric Surface

Sueded fabric quality control depends on the processing parameter settings. The quality characteristics considered in this study are surface softness and color difference. The Taguchi method was combined with gray relational analysis (GRA) to optimize the multi-quality sueding processing parameter combinations. First, an orthogonal array is designed by using the design of experiments of the Taguchi method for the major processing parameters of the sueding machine. The signal/noise ratio and analysis of variance are calculated from the measured fabric surface softness and color difference data, significant factors influencing the quality characteristics obtained, and GRA used to remedy the deficiency in the Taguchi method, which is only applicable to single-quality characteristics. The optimum processing parameters of multiple-quality characteristics are obtained from the response table and response diagram of GRA. The quality of suede fabric can be controlled effectively by using the optimum processing parameters to set the processing parameters, and the 95% confidence interval validates the reliability and reproducibility of the experiment.

Research on performance of twin-core spun yarn and fabric

2019 ◽  
Vol 32 (3) ◽  
pp. 338-355
Author(s):  
Xuzhong Su ◽  
Xuzhong Su ◽  
Xinjin Liu
Keyword(s):  
High Temperature ◽  
Fiber Composite ◽  
Woven Fabrics ◽  
Numerical Control ◽  
Content Type ◽  
The Core ◽  
Spun Yarn ◽  
Finishing Process ◽  
Spun Yarns ◽  
Elastic Filament

Purpose As one kind of filament/staple fiber composite yarn, core spun yarn has been widely used, especially on Jeans. However, there is only one filament in the commonly used core spun yarn, such as spandex, and the performance of the one filament often is influenced during dyeing and finishing. Therefore, in the paper, twin-core spun yarns with two different filaments feeding simultaneously were spun on ring spinning frame modified by one kind of filament feeding numerical control device. The paper aims to discuss these issues. Design/methodology/approach Four kinds of twin-core spun yarns, cotton/spandex/PBT, cotton/spandex/CM800, cotton/spandex/T400, cotton/spandex/SPH with linear density 36.4tex/40D/50D were spun. For improving the covering effect of the two filaments, the filament feeding position, filament pre-drafting multiple, distance between two staple roving, designed twist factor of the core spun yarn were optimized. Findings It is shown that comparing with the core spun yarn, the breaking strength and elongation of the twin-core spun yarns are improved since the addition of another elastic filament, while the evenness is a little worse. Originality/value By using the twin-core spun yarns, corresponding knitted and woven fabrics are produced. Meanwhile, for simulating the dyeing and finishing process, the knitted fabrics were treated during the 150°C high temperature. It is shown that comparing with the fabrics produced by cotton/spandex yarn, addition of another elastic filament can improve the fabric strength and resistant and has positive effect on worsen prevention for high temperature treated fabric elastic recovery, and on change prevention during the dyeing and finishing process for fabric handle properties, and improves the fabric stability.

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

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