# Bionic Design of a Winding Roller and Experiments for Cleaning Long Foreign Matter from Raw Cotton
## 1 Introduction  
As a natural fiber, cotton has been used in a wide variety of fields, such as textiles, pharmaceuticals, chemistry, printing, and other national industries. As one of the most important cotton-growing countries, nearly 30% of the cotton fibers in the world are produced in China [1, 2]. As we all know, cotton fiber is unique in its strong, durable, safe, healthy, and breathable characteristics, which may be not be replaced by other unnatural synthetic fabrics. However, contamination matter is inevitably mixed into the cotton fibers during the whole process of cotton production. This may dramatically affect the performance of textile products. Many existing investigations have indicated that the contaminated contents of machine-harvested cotton in Xinjiang province, China, account for 8% to 27% of the total content [3]. In practice, long foreign matters (LFM) such as long hair, binding ropes, plastic films, chemical fibers, polypropylene, and twines inevitably become mixed into the cotton during the planting, picking, and treatment processes. This greatly deteriorates the quality of the textile and decreases the monetary value of cotton products. Moreover, the price of hand-cleaned cotton is higher (about $USD 120/ton) than UN-cleaned under standard commercial conditions. In this case, cotton processing enterprises have to hire many people to clean the contaminants, which in turn intensifies the economic burden [4, 5]. Furthermore, all Chinese cotton enterprises are facing critical challenges in the international cotton market. Generally, microtrash or coarse trash such as sticks, leaves, dust, and metal particles can be removed by a centrifugal fan or other operations. However, the LFM in rawcotton is mostly longer than 4 cm, and a small amount of LFM may be crushed or torn during ginning, which makes it more numerous and more difficult to clean. Therefore, the LFM in raw cotton must be completely removed before ginning [6]. Over the past decades, the manufacturing system of the cotton industry has changed a great deal; smart agriculture is booming, and smart manufacturing technology is the mainstream today [7]. Recently, different varieties of automatic detection technologies have been proposed based on differences of LFM from cotton in color, shape, or other properties. Moreover, the machine vision method is the most important technology in realizing the identification of LFM [8,9]. Many methods, such as CT X-rays [10,11], ultraviolet [12], multi-band image fusion, and more have been proposed [13]. However, the majority of the above methods mainly focus on the lint cotton stage, which can be identified as the second processing procedure in the ginning of raw cotton. In this case, even the highest contamination removal efficiency may be limited to only 85% owing to a larger shattered LFM quantity in many systems [14]. At present, Chinese enterprises usually adopt a manual picking process for the pre-cleaning of raw cotton LFM. However, this process has several disadvantages. The efficiency of manually picking long fibers is about 1 ton/day, the cost is CNY 200-300 per person, and the cost of manual picking for enterprises is about 2-3 million CNY/year; moreover, due to such factors as visual fatigue and physical fatigue of workers, it is difficult to ensure the efficiency and precision of workers in picking LFM. Unfortunately, although there are many disadvantages in the manual picking process, it remains a widely used sorting method. LFM-scavenging technology in raw cotton needs to be developed. Designing automatic LFM scavenging equipment has become one of the top priorities in China's cotton industry [15]. To date, little automatic mechanical equipment has been developed to remove LFMs mixed in raw cotton. The reasons for this can be summarized as follows:  
1. The environment of raw cotton processing is seriously polluted, which is difficult for optical devices;
2. Raw cotton is compressible in bundles and has non-uniform thickness, which results in low control precision;
3.