Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems.

Analysis of Static Pressure in Area between Back Plate and Cylinder of a Carding Machine with CFD

To analyze static pressure between back plate and cylinder in an A186 carding machine,a fluid model is established. The model takes into account static pressure of airflow near back plate with the numerical simulation method of Computational Fluid Dynamics (CFD) in FLUENT software. The result of the simulation in the model shows that static pressure in this area quickly increases to its maximum then rapidly decreases to a lower fixed value from inlet to outlet along a zone between back plate and cylinder. Both rotating speeds of the cylinder and the taker-in affect static pressure from the inlet to the outlet,of which the cylinder rotating speed has more influence than that of taker-in. Numerical simulations reveal that static pressure on surface of back plate are in good agreement with the former result of experimental analysis.

Impact of the Speed of Flat of a Typical Carding Machine on the Quality of Carded Sliver and 40 Ne Yarn

The carding cycle affects the sliver quality and the subsequent yarn attributes since it is the main sliver formation step. Processing parameters assume a significant part in affecting the nature of the eventual outcome in any sorts of production. In the case of carding machine, a higher production rate makes the operation more sensitive. And this will cause degradation in product quality. So optimization of speed is the talk of the town in spinning field [1]. Extreme higher speed can prompt fiber harm and unnecessary neps generation will corrupt the end result. Again lower speed will lessen the production rate which isn’t reasonable. So we need to discover the ideal speed which will be advantageous to both product quality and production rate. In carding machine, real operational activity happens between flats and cards [1]. From an ordinary perspective, high produce able cards generates higher level of speed. Speed of the cards impacts the carding cycle and the nature of the yarn and in practical point of view, flat’s level of speed is advanced and optimized. The aim of the project was to find out the optimum flat speed in the context of yarn quality. 40 Ne cotton yarns were produced with the slivers manufactured at different flat speeds such as 240, 260, 280, 300 and 320 mm/min. The quality parameters of slivers and yarns were tested and analyzed.

Credit Card Fraud Detection Using Weighted Support Vector Machine

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.

Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.

Weighted Adaptive Generalized Predictive Control for Carding Autoleveler

In textile industry, carding process has decisive influence on produced yarn quality. From the system theoretic point of view, it is marked by stochastic disturbance, long delays, and parameter variation. So, a carding process is difficult to control with traditional control algorithms (such as PID). In this paper, a weighted adaptive generalized predictive control (GPC) law was developed to control such a process. The experimental results show that GPC autoleveller controller could greatly reduce the sliver’s standard deviation and reject disturbance.

棉纺精梳机锡林不同梳理区梳理效果分析

为了研究棉纺精梳机锡林不同梳理区的精梳落棉率、棉结及纤维排除率的变化规律,使用5分区的整体锡林,并分别采用5个、4个、3个、2个、1个梳理区进行精梳试验,得到了5种不同试验方案的精梳落棉率、落棉中的棉结含量及纤维长度分布,并利用导出的公式计算得出锡林不同梳理区的落棉率、棉结排除率及不同长度纤维排除率的变化规律。结果表明:整体锡林的5个不同梳理区的落棉率、棉结排除率及不同长度纤维排除率的变化规律基本相同,即梳理区排序从大到小依次为第2梳理区、第3梳理区、第1梳理区、第4梳理区、第5梳理区。在锡林的同一梳理区内,当纤维长度小于钳板钳口外纤维丛长度时,纤维长度越短时排除率越高;当纤维长度大于钳板钳口外纤维丛长度时,随着纤维长度的增大纤维排除率有减小的趋势,并具一定的波动性。梳理隔距和锡林针齿深度是影响梳理效果的重要参数。

名优茶连续式理条机参数优化设计与试验

针对传统茶叶理条做形装备主要依靠经验法设计,存在做形质量不佳的问题,该研究运用数值法对连续式茶叶理条机结构与作业参数进行优化,以改善连续式理条机的理条作业性能。通过对连续式理条机进行运动学分析,得到锅槽速度、加速度表达式,并探究锅槽运动与茶叶理条状态之间的关系;通过研究茶叶在连续式理条机锅槽内的运动规律和受力情况,建立茶叶-正U形槽动力学模型,分析了茶叶在锅槽运动周期内各阶段的受力特点及对茶叶成形的作用;运用EDEM软件建立茶叶理条仿真模型,对影响茶叶成形的曲柄转速、锅槽振幅、锅槽倾角、凸棱个数和凸棱高度等关键设计参数进行仿真模拟,得到上述因素作用下茶叶颗粒的平均速度、平均受力与做形时间的关系曲线,并以仿真结果为依据设计三因素三水平正交试验,以成条率、碎茶率为评价指标,利用Design-Expert软件进行数据处理与回归分析,并对优化结果进行验证试验。仿真结果表明:曲柄转速、锅槽振幅和凸棱个数等对理条质量影响较大,茶叶纵向占比率随理条时间增加而升高,促使茶叶主茎脉轴线方向受力形成条状外形;以曲柄转速为195 r/min、锅槽振幅为99 mm、凸棱个数为2的最优参数组合进行验证,成条率为87.39%,碎茶率为1.85%,与优化结果基本一致。研究结果可为连续式茶叶理条机优化设计提供理论参考。

冒用花呗行为定性之争:问题、本质及解释

互联网金融领域的刑事犯罪治理难题表现出消极的点面效应。围绕冒用花呗的行为定性,至少存在花呗的法律属性定位、机器能否被骗、机器如何被骗的分析难题。其一,对于花呗的法律属性,研究论证的瑕疵在于客观解释立场的缺失。根据客观解释立场,“其他金融机构”中的“其他”意表除了商业银行以外的可以发行信用卡的金融机构,花呗属于刑法意义上的信用卡。其二,关于机器能否被骗。机器不具有自我意识的认识桎梏不能说明机器不可以被骗,否则只会固化人机关系“二元认识论”的旧观念,故机器不能被骗的立场应当被摒弃。其三,关于机器如何被骗。在探讨人工智能作为诈骗对象时引入预设同意理论已成为学界共识,然而该理论的运用现状过于粗简,其不仅可以说明机器的处分意识来源,更能说明人机关系的一体化。冒用花呗的行为定性中,关键特征是“人机交互的一体关系”,机器是自然人的电子代理人,人所排斥之事项即为机器所排斥之事项。第三方支付对于冒用者的身份要素陷入了错误认识,进而导致被害人财产受损。冒用花呗的行为应当定性为信用卡诈骗罪。

从ITMA ASIA+CITME 2022看纺纱机械的技术进步

总结2022中国国际纺织机械展览会暨ITMA亚洲展览会上纺纱机械的最新技术进展。以展出的具有代表性的纺纱设备为例,重点介绍了国内外知名企业推出的新产品和创新技术,包括梳棉机、精梳机、并条机、粗纱机、细纱机、络筒机、倍捻机和新型纺纱机等。指出:展会聚焦于数字变革、绿色发展以及新兴产业跨界融合等主题,彰显了纺纱机械行业在全球经济逐步复苏的环境下对可持续和智能解决方案的关切。认为:纺纱设备的发展趋势是高效、节能、数字化、自动化和智能化。

基于CFD的JWF1217型梳棉机滤尘管道气流分析

为了探究JWF1217型梳棉机滤尘管道内风量风压分布的合理性,建立了管道流体域模型,基于计算流体动力学(CFD)理论,采用标准k-ε湍流模型对其进行数值模拟,并根据计算结果分析管道内速度场和压力场分布。模拟结果表明:在主管道吸口800 Pa负压吸风条件下,管道内气流流动顺畅,仅在梳棉机拐角处和管道连接处出现少量涡流;管道内压力分布合理,能量消耗少,负压利用率高;各吸风点的风量风压分配符合实际生产要求。认为:JWF1217型梳棉机滤尘管道结构能合理分配气流,主管道拐角处采用大圆弧平滑过渡气流,主、支管道连接处采用主管道尾端渐宽结构,有效减小压力损失和能量消耗,有效提高了滤尘管道的吸尘能力。

基于机器视觉技术的红外与可见光人脸图像配准

为解决某些环境图像中人脸轮廓模糊,人脸面部特征单一问题,提出基于机器视觉技术的红外与可见光人脸图像配准方法。采集红外与可见光人脸图像,对其进行直方图均衡化处理,采用Canny边缘算子方法提取人脸图像轮廓,并依据轮廓的梯度大小以及方向特征实现红外与可见光人脸图像配准。实结果表明:该方法可有效提升红外与可见光人脸图像的配准度,在无遮挡时配准度数值为0.961,在遮挡10%和20%时,配准度数值分别为0.949和0.944,由此说明该方法应用效果较为显著。

基于机器视觉的自动贴标系统设计

针对传统的贴标流程人力成本高、效率低、精度差的问题,开发一套采用运动控制卡为基础的彩盒自动化贴标系统。设计可调流水线机构、载具交互机构、翻转机构、机械手机构、打印机机构、控制系统、视觉系统等部分,由工控机通过EtherCAT现场总线发送指令控制机构的动作,解决系统的稳定性、联动性问题。运行效率提高,20轮评价用时缩短23 s,稳定性识别率上升1.1%。研究为物料的生产、封装提供较新的解决方案。

精梳质量控制与减少成纱纱疵的技术措施

为减少细小纱疵和偶发性纱疵,总结了精梳质量控制与减少成纱纱疵的技术措施。影响细小纱疵的主要因素是原料品质和精梳质量。重点分析了精梳锡林齿密、精梳梳理隔距、锡林针布状态、顶梳针齿嵌花等对成纱质量的影响。试验结果表明:采用加密新型锡林,特细号精梳纱纱疵明显减少;锡林、顶梳严重嵌花会使精梳落棉率大幅增加;适当缩小锡林梳理隔距,保证纤维分离、伸直和平行,可以改善成纱质量。同时,总结了减少偶发性纱疵的技术措施。认为:减少细小纱疵的关键在于精梳的梳理质量,核心在锡林针布;提高精梳设备和运转操作技术水平,严格执行清洁操作标准,加强车间温湿度调控,是减少突发性纱疵的有效方法。

再生纤维原料纺纱相关装备及其技术特点

总结加工再生纤维原料的纺纱装备及其技术特点。简述了废旧纺织品的概况。对废旧纺织品再利用的相关技术进行了梳理,包括物理回收法、化学回收法、热能回收法和机械开松法。重点总结了瑞士立达公司和卓郎公司在2022中国国际纺织机械展览会暨ITMA亚洲展览会上展出的关于加工再生纤维原料的纺纱系统、纺纱装备及其技术特点。认为:科学有序地引导和发展废旧纺织品再利用符合我国法律、法规和发展规划;经机械开松后的废旧纺织品可再次进入纺纱流程,实现其高值化利用。

基于改进SMOTE算法和深度学习集成框架的信用卡欺诈检测

当前机器学习(ML)算法已经被广泛用于信用卡欺诈检测。然而持卡人线上购物的动态性,以及正常和欺诈交易数据严重不平衡问题,影响了分类器的检测精度。为此,提出了基于深度学习集成框架的信用卡欺诈检测方法。首先,通过改进的合成少数类过采样(SMOTE)算法,解决信用卡数据集中欺诈交易和正常交易数量严重不平衡问题。其次,构建堆栈式深度学习集成框架,使用双向长短时记忆网络(Bi-LSTM)和门控循环单元(GRU)作为基础分类器,并通过多层感知机(MLP)作为元分类器,结合集成学习和深度学习的优点提高信用卡欺诈检测率。在公开数据集上的实验结果表明,所提深度学习集成方法与改进SMOTE算法相结合,分别实现了99.57%和99.82%的灵敏度和特异性结果,优于其他先进的信用卡欺诈检测算法。

多目标意象驱动的梳棉机造型设计研究

目的 为满足用户对梳棉机的多维感性需求,多层次提升纺织机械的造型意象满意度和用户满意度,提出一种多目标意象驱动的梳棉机造型设计方法,并展开设计实践与实验研究。方法 首先,设计感性评价实验,通过焦点小组讨论和问卷调研获取用户对梳棉机的感性意象评价均值;其次,采用形态分析法,对梳棉机造型设计要素进行划分,并对代表性样本的造型类目特征进行编码;基于反向传播神经网络(Back Propagation Neural Network,BP-NN),建立梳棉机产品造型要素与用户感性意象评价均值之间的关联映射模型,建立用于造型推荐的样本库,获取单意象维度下的梳棉机造型设计策略;再次,应用层次分析法(Analytic Hierarchy Process,AHP)得到各目标意象维度的权重值,输出多目标意象下梳棉机造型设计策略;最后,结合梳棉机的造型设计实践及感性评价,进一步验证该方法的可靠性和有效性。结果 基于该方法展开设计实践,依据推荐的梳棉机造型设计策略得到的设计方案,在目标感性意象的整体评价得分优于对照样本。结论 该方法有较好的可靠性和有效性,能为企业的新产品开发及设计师的设计实践输出指向具体、操作性强的梳棉机多目标意象设计策略。