Teaching-learning-based optimization of ring and rotor spinning processes

Diyaley, Sunny; Chakraborty, Shankar

doi:10.1007/s00500-021-05990-0

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…In addition, because students lack correct evaluation ability, this information is not of great significance to themselves. According to reference [17], the field of motor skill learning serves as a vehicle for achieving the goals of other learning fields. Other learning fields will become trees without roots and water without a source if there is no sports skill learning field, and the characteristics of this course will be lost, as will the overall health goal of "body, mind, and society."…”

Section: Related Workmentioning

confidence: 99%

Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

Guo

Yang

2022

Wireless Communications and Mobile Computing

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In order to make students more interested in PE learning in PE teaching, it is necessary for PE educators to change the traditional way of thinking, take students as the main body of PE learning, be guided by teachers themselves, and use mobile edge computing and neural network technology as technologies. It enables students to carry out intelligent physical education independently. Whether the teaching is effective does not mean whether the teacher has completed the teaching content seriously, but whether the students have learned something, or whether the students have learned well. Based on the actual teaching practice of flipped sports classroom, this paper takes the “effectiveness” of sports classroom teaching as the breakthrough point, mainly adopts the research methods of comparative analysis, mobile edge computing, and neural network, and combines relevant domestic and foreign research results on the teaching of sports flipped classroom at home and abroad. An in-depth and comprehensive analysis of the effectiveness was carried out. The research results show that it is reliable to build a university sports flipped classroom with a neural network driven by motor skill learning and mobile edge computing as the carrier.

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Section: Related Workmentioning

confidence: 99%

Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

Guo

Yang

2022

Wireless Communications and Mobile Computing

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“…The results show that the air flow field and fiber movement are the main factors affecting the fabric structure. Diyaley and Chakraborty (2021) uses the teaching-learning optimization algorithm to determine the optimal parameter combination of the variables in the front and rear regions of the ring frame and the input variables in the spinning process of the rotor. The optimization performance is compared with firefly algorithm, differential evolution algorithm, Cuckoo search algorithm and quantum particle swarm optimization algorithm in terms of the accuracy and consistency of the solution, so as to obtain the best combination of different input variables and achieve the target quality characteristics.…”

Section: Jet Spinning Machine Basedmentioning

confidence: 99%

Structure analysis of double comb about jet spinning machine based on CFD

Haifeng,

Zhang,

Sun

et al. 2024

IJCST

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PurposeAs a new type of spinning machine, the jet spinning machine absorbs the carding system of the rotating cup spinning series and the nozzle part of the jet spinning. This paper aims to intends to introduce the double carding structure currently studied by the rotating cup spinning into the jet spinning machine, and analyze the influence of the nozzle characteristic number on the flow field in the double carding structure to verify the advantages of the double carding structure.Design/methodology/approachThe simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field. The influence of the nozzle characteristic number on the flow pattern in the four models is compared. The advantages and disadvantages of a conventional single comb and a double comb with a bypass channel on the longer side of the transport channel as an additional air supply channel are also evaluated.FindingsAt present, the double comb technology of rotary cup spinning is being studied at home and abroad to improve the spinning quality and improve the difficult problem of mixed yarn with large difference in processing fiber properties. At present, the jet spinning machine combines the advantages of rotary cup spinning and jet spinning, absorbing the comb system of rotary cup spinning series and the nozzle part of jet spinning. Therefore, it is found that the introduction of the double-split structure into the wool jet spinning has research value to improve the spinning quality.Originality/valueThe purpose of this paper is to refer to the previous research on the double comb structure in rotary spinning, and to apply the double comb structure in the new jet spinning machine to improve the spinning quality. The simulation is used to evaluate the performance of single/double split jet spinning and nozzle feature number, verify the technical advantages of double split jet spinning and evaluate the influence of nozzle feature number on flow field.

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“…Yang et al [4] used genetic algorithm to optimize the weight and threshold of gradient backpropagation neural network, and established a forward model of spinning process parameters, on which the genetic algorithm was used to reverse the process parameters of combed hair spinning production process. Based on the teaching optimization algorithm, Diyaley et al [5] determined the optimal parameter combination of the front and rear area variables and the input variables in the process of ring spinning, and produced the yarn with the quality index meeting the requirements. According to the performance characteristics of Ura ber, Sun et al [6] made the pretreatment of Ura ber, the mixing process of weighing raw materials, preferred the process parameters of each process, and selected the comb roller speed, spinning process and spinning tension in the cup spinning process.…”

Section: Introductionmentioning

confidence: 99%

“…In order to more accurately and conveniently evaluate the comprehensive quality of jute/cotton (J/C) blend, Zhong et al [22] proposed a comprehensive quality evaluation method of jute / cotton blend based on principal component analysis. Diyaley et al [23] determines the optimal parameter combination of the front and rear area variables of the ring ingot spinning machine and the input variables in the cup spinning process based on optimization algorithm for teaching and learning. Ghosh et al [24] aims to maximize yarn strength and minimize raw material quality, and obtains the optimal process parameter solution set through a non-dominated sorting genetic algorithm with elite strategy.…”

Section: Introductionmentioning

confidence: 99%

Quality parameter adaptive optimization for spinning process using dynamic non-dominated sorting algorithm

Wu,

Sheng

2024

Preprint

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Intelligent textile equipment can discover potential patterns in the production process through data mining, and utilize these patterns through intelligent optimization, ultimately achieving intelligent and automated textile production. This paper focuses on the spinning process parameters optimization under changing spinning conditions and proposes a dynamic non-dominant ranking parameter quality adaptive optimization algorithm. Firstly, the changing factors of spinning conditions is analyzed to explore the influence of the change of spinning process conditions on the quality optimization of spinning parameters. Then the factors of spinning process condition changes is transformed into mathematical dynamic constraints and constructing an adaptive optimization model for spinning parameter quality. Basis on this, the response mechanism of spinning environment is established to readjust the optimization direction according to the change of spinning conditions, and the DNSGA-II is used to solve the quality adaptive optimization model. A case study is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed method offers enhanced performance in terms of providing corresponding parameter optimization combinations for different spinning conditions.

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Teaching-learning-based optimization of ring and rotor spinning processes

Cited by 10 publications

References 15 publications

Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

Construction of University Sports Flipped Classroom Based on Sports Skills Learning and Mobile Edge Computing-Driven Neural Network

Structure analysis of double comb about jet spinning machine based on CFD

Quality parameter adaptive optimization for spinning process using dynamic non-dominated sorting algorithm

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