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Design And Test Of Cotton Fiber Length Strength Fast Measuring Device In Southern Xinjiang

Posted on:2024-02-17Degree:MasterType:Thesis
Country:ChinaCandidate:X D XuFull Text:PDF
GTID:2531307115467654Subject:Agricultural engineering and information technology
Abstract/Summary:PDF Full Text Request
Xinjiang’s cotton output accounts for 90% of the total cotton output in the country,Xinjiang cotton in the country’s cotton textile industry occupies a very important position.In production practice,the indexes of cotton fiber length and strength directly affect the formulation of production plans of cotton spinning enterprises,and the demand for rapid detection of cotton fiber length and strength is increasing day by day in various industries.The length and strength of cotton fiber is a very important economic index of cotton.At present,cotton fiber testing devices on the market are still mainly imported equipment.The existing testing equipment has high measurement cost and long time cycle.In this paper,the standard cotton sample provided by the fiber inspection is used to simulate the natural climate in the constant temperature and humidity box according to the climatic data of Alar over the years.Weibull statistical model,neural network and multiple linear regression analysis are used for mathematical modeling of cotton fiber length strength detection,and the cotton fiber length strength and rapid detection device is built.1)In the process of studying the mechanical properties of cotton fiber bundles,a single fiber strength tester was used.Since the defects in the length direction of the fiber bundles showed random distribution,the mathematical model of the number of fiber roots and the breaking work and the mathematical relationship between the number of fiber roots and the strength of the fiber bundles were obtained by using fracture work analysis and Weibull regression analysis.At present,the cost of the bundle fiber strength tester is relatively high,which provides a theoretical basis for the rapid test of bundle fiber strength.2)In the study of rapid strength detection of cotton fiber length,BP neural network is used to predict the length and strength of cotton fiber,and the results calculated by BP neural network mathematical model are compared with the results obtained by HVI standard device.The correlation of length prediction R=0.70903,the correlation of strength model R=0.78196,The measurement data meet the accuracy requirements and achieve the purpose of rapid detection.Based on the theory of multiple linear regression analysis,the prediction model of cotton fiber length strength was obtained.The prediction accuracy of the model was verified by variance analysis and principal component analysis.3)In the study of cotton fiber length strength testing device,using solidworks three-dimensional modeling software,the design of the first generation of cotton fiber length strength testing device,the structure of the machine has the advantages of lightweight,modular integration.The structure innovation and improvement of the fast detection mechanism device model are carried out,and the three-dimensional model of the second generation of long strength detection device is designed to optimize the force measuring mechanism,which is theoretically more convenient than the traditional Stroh strength detection.Based on matlab visual interface,the operation interface of cotton fiber length strength detection is designed.Through the research,the designed detection device can reduce the detection cost,shorten the detection cycle and improve the detection efficiency,provide a new idea of detection methods,and reduce the input of human and material resources.In summary,the cotton fiber length strength rapid detector designed in this paper has the advantages of fast detection speed,wide detection range,low cost,test data in line with the standard,reliable and simple use,convenient maintenance,and so on,to achieve the design purpose.
Keywords/Search Tags:Rapid detection of cotton fiber length strength, Weibull statistical model, BP neural network, Multiple linear regression, Parameter optimization, Visual interface
PDF Full Text Request
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