Search Results (7)

Cotton processing is the process of converting harvested seed cotton into lint by cleaning, ginning, and cleaning the lint. The real-time acquisition of lint parameters during processing is critical in improving cotton processing quality and efficiency. The existing online inspection system cannot realize quantitative sampling detection, resulting in large fluctuations in the detection of moisture rate, and the impurity content of lint can only be measured according to the number of impurity grains and the percentage of impurity areas. This research developed a quantitative sampling device for cotton lint processing that can collect the right number of cotton samples and obtain the weight of the samples, laying the foundation for the accurate detection of cotton lint dampness and impurity rates. This research aimed to develop an online quantitative sampling device with a sampling plate as its core. The quantitative sampling procedure, consisting of a gas–solid two-phase flow in a cotton pipeline, was numerically simulated and experimentally analyzed using computational fluid dynamics (CFD) and the discrete element method (DEM). According to the coupling results, the maximum pressure differential between the top and bottom regions of the sampling plate when conveying was 1024.45 Pa. This pressure is adequate to allow for cotton samples to accumulate on the sampling plate. Simultaneously, the steady conveying speed of lint is 59.31% of the unloaded conveying wind speed, providing a theoretical foundation for the sampling time of the quantitative sample device in the processing chain. The results from testing the prototype indicate that the quantitative sampling device in the cotton flow can effectively perform the quantitative sampling of cotton lint under uniform conditions, with a sampling pass rate of 84%. Full article

Developing cotton ginning methods that improve fiber length uniformity index to levels that are compatible with newer and more efficient spinning technologies would expand market share and increase the demand for cotton products and give U.S. cotton a competitive edge to synthetic fibers. Older studies on lint cleaning machines showed that the most widely used feed mechanism that places fiber on the cleaning cylinder damages the fiber and reduces uniformity. The present study evaluates how conventional and experimental feed mechanisms affect uniformity. The lint cleaners were used with both saw and roller gin stands. Four diverse cotton cultivars from the Far West, Southwest, and Mid-South were used in the test. Statistical analysis used a random effects modeling approach which included constructing a 95% confidence interval for each ginning treatment around the predicted mean for the fiber property of interest, and then examining which treatments overlap (for comparison). Results show that the micro-saw gin with the direct-feed lint cleaner had the best uniformity at 85.8%. Prior research has shown that roller ginning is consistently higher in uniformity than any type of saw ginning. In this study, the roller ginning treatments had uniformities of 85.3 and 85.6%, so it is encouraging that the saw gin stand with the direct-feed lint cleaner had very high uniformity. This suggests that it may be beneficial to place fiber directly onto the lint cleaning saw without changing direction. Additionally, the saw gin-coupled lint cleaner had a uniformity of 84.3% which is also a respectable level of uniformity. These results indicate that the direct-feed lint cleaner and coupled lint cleaner warrant further testing under better controlled conditions. Full article

In order to solve the problem of the poor seed-cleaning performance of the inside-filling pneumatic cotton precision seed-metering device, a double-sided seed-cleaning mechanism combining a seed-cleaning scraper and seed-disturbing air nozzle was designed which can realize alternate seed cleaning on both sides of the suction hole’s end surface. By constructing a mechanical model of the seed-cleaning process, the influence law of the seed-cleaning mechanism on the critical adsorption performance of cotton seed was clarified, and the key structure and parameters of the seed-cleaning mechanism were decided by combining a theoretical analysis with operational requirements. So as to explore the seed-cleaning performance, some relevant bench tests were carried out, with E’kangmian-10 coated de-linted cotton seeds with a moisture content of 8.92% (wet basis) serving as the test objects; and single seed rate, excessive cleaning rate, and missing cleaning rate were taken for test indicators. First of all, a single-factor comparison test was employed with the shape of the seed-cleaning scraper as the impact factor to choose the optimal one. The results of the comparison test showed that, no matter what shape of seed-cleaning scraper was adopted for the seed-metering device, the missing cleaning rates under the corresponding optimal seed-cleaning effect were greater than 5%, and the sharp scraper gave a better seed-cleaning performance than the flat scraper. Next, combining the sharp seed-cleaning scraper with the seed-disturbing air nozzle to form combined seed-cleaning mechanism of the seed-metering device, the Box–Behnken Design test was executed to explore the influence law about seed-cleaning distance, seed-disturbing distance, and seed-disturbing pressure on the seed-cleaning performance. Then the parameter optimization module was applied to achieve the best combination of operating parameters for the test factors. The test results indicated that the test factors influencing test indicators were in the following order: seed-cleaning distance, seed-disturbing pressure, and seed-disturbing distance. The optimal combination of parameters was a seed-cleaning distance of 3.1 mm, seed-disturbing distance of 6.2 mm, and seed-disturbing pressure of 2165 Pa. Lastly, based on the optimal combination, a verification test of seed-cleaning performance was performed, and the corresponding evaluation indexes were a single seed rate of 98.03%, missing cleaning rate of 1.42%, and excessive cleaning rate of 0.55%. In comparison with the optimal seed-cleaning effects under the single-sided seed-cleaning scrapers of flat and sharp shape, respectively, the combined double-sided seed-cleaning mechanism reduced 3.90 and 3.61 percentage points in missing cleaning rate, reduced 2.02 and 1.17 percentage points in excessive cleaning rate, and increased 5.92 and 4.78 percentage points in single seed rate, thus indicating that the combined double-sided seed-cleaning mechanism can effectively enhance the inside-filling pneumatic precision seed-metering device seed-cleaning performance. This study provides a reference for the design and parameter optimization of the seed-cleaning mechanism of a precision seed-metering device. Full article

The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%. Full article

Plastic contamination in US lint bales has increased with the adoption of new cotton harvesters that form cylindrical or round modules on the machine. It is of significant interest to the US cotton industry to reduce this contamination to preserve grower profitability and the reputation of the US as a reliable source of clean cotton fiber. The objective of this work is to describe the design and operation of a system for use on cotton gin module feeders that provides monitoring of plastic accumulation on the dispersing cylinders and video data to help document the module wrap condition and unloading/unwrapping procedures that may have caused the potential contamination event on the dispersing cylinders. In 2020, an integrated plastic contamination monitoring system was installed on module feeders at two commercial cotton gins in Texas. The system is comprised of sub-systems that provide images of plastic accumulation on the dispersing cylinders, a log of the processing sequence for round modules, video data of the unloading/unwrapping process for each module and a software program that integrates the data from the two sub-systems. The system was developed to operate on one computer, store the data in a common location, and simplify the process of extracting module specific data for a given event when plastic accumulates on the module feeder dispersing cylinders. The data provided by the system can be useful to manufacturers in comparing performance among module wrap products as well as to gin managers in training gin employees on module handling procedures to mitigate plastic contamination and improve worker safety. Full article

Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter. Full article

One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU) as an alternative to thePC’s traditional use of the central processing unit (CPU). The use of the GPU, as analternative computation platform, allowed for the machine vision system to gain asignificant improvement in processing time. By improving the processing time, thisresearch seeks to address the lack of availability of rapid trash sensing systems and thusalleviate a situation in which the current systems view the cotton lint either well before, orafter, the cotton is cleaned. This extended lag/lead time that is currently imposed on thecotton trash cleaning control systems, is what is responsible for system operators utilizing avery large dead-band safety buffer in order to ensure that the cotton lint is not undercleaned.Unfortunately, the utilization of a large dead-band buffer results in the majority ofthe cotton lint being over-cleaned which in turn causes lint fiber-damage as well assignificant losses of the valuable lint due to the excessive use of cleaning machinery. Thisresearch estimates that upwards of a 30% reduction in lint loss could be gained through theuse of a tightly coupled trash sensor to the cleaning machinery control systems. Thisresearch seeks to improve processing times through the development of a new algorithm forcotton trash sensing that allows for implementation on a highly parallel architecture.Additionally, by moving the new parallel algorithm onto an alternative computing platform,the graphic processing unit “GPU”, for processing of the cotton trash images, a speed up ofover 6.5 times, over optimized code running on the PC’s central processing unit “CPU”, wasgained. The new parallel algorithm operating on the GPU was able to process a 1024x1024image in less than 17ms. At this improved speed, the image processing system’s performance should now be sufficient to provide a system that would be capable of realtimefeed-back control that is in tight cooperation with the cleaning equipment. Full article