Machine Learning in Yarn Spinning Greatly Improves Quality and Reduces Rejects
The textile manufacturing industry is one sector where artificial intelligence and machine learning can work wonders to improve quality and production while reducing rejects. Textile is one area that benefits vastly by incorporating machine learning into its processes. Yarn spinning is one section of textile manufacturing that benefits when you put machine learning development to work here.
Spinning is A High Speed and Automatic
Spinning may make use of synthetic polymers or it can make use of natural fibers such as jute, cotton, and linen. Synthetic yarn may be spun using wet, dry, melt, and gel spinning in automated machinery with hundreds of spindles per machine running at hundreds of RPM. If it is a thermoplastic-based yarn then the process is different from one used for solvent-based synthetic polymer in which a fluid polymer is forced through a spinneret and cools even as it is drawn. For cotton the process is different. In short, where you have high-speed automatic processes that work according to fixed parameters then other variables influence the quality of yarn, its consistency, and strength that ultimately have a bearing on the quality of the woven fabric. The actual spinning, as in that of cotton yarn, maybe just one part of a sequence that includes carding, straightening, roving, and then spinning on mule or ring frames. Today’s modern spinning machines are automated and require minimum operator intervention. Yet, one notices variations in the quality of the yarn that the automatic process did not factor in.
Common Defects in Yarn
No machine is perfect and even automation cannot prevent defects from occurring such as:
Thick and thin places in the yarn
Soft and loose yarn at places
Slubs, sarl, and neps
Bad piercing and over half a dozen others
These defects lead to slow down the spinning process and reduce output. Plus, defects reduce the value of the yarn and fetch lower prices for the yarn and when such yarn is used, the fabric sells at a lower price.
Such defects may occur frequently or occasionally depending on variables such as quality of components that operate at high speed, quality of raw material such as cotton fiber or polymer, temperature, and humidity among others. The cleanliness of the equipment also plays a role as does settings. Some spinning units simply accept defects as part of the process and accept it as a way of production. However, these defects can be reduced and quality as well as production improved by opting in machine learning development services.
Machine Learning in Spinning
Yarn faults are classified as seldom, frequent, and special. Thin and thick neps are frequent occurrences. Stub fly happens infrequently and knots, snarls, and crackers are special faults. Imaging technology along with neural networks and machine learning may be used to identify causes that lead to defects and then find ways to minimize such variations to the absolute minimum possible and/or predict yarn quality.
Machine learning in yarn manufacturing may be implemented at the machine level by manufacturers of spinning equipment. It may also be possible for spinning units to install their own sensors and intelligent monitoring system incorporating artificial intelligence and machine learning to collect data from the existing machines and then identify shortcomings and implement predictive control techniques. The fault may not always be with the process or raw fiber; it could equally apply to spindles, aprons, and other moving parts that contribute to variations.
In the post coronavirus world, especially in developed countries, there will be an emphasis on better quality, reduced cost, and reduced waste in the textile industry. Even in developing nations where the inferior quality yarn is used to make cheap fabrics, machine learning can help yarn manufacturers to still lower their cost of production and yet guarantee better quality products at competitive prices. The implementation of artificial intelligence-based systems could help in predicting the yarn strength and consistency of other physical parameters.
While implementing ML into yarn manufacturing across the entire process may prove time consuming and expensive, a good place to start is to use ML to examine the consistency of fiber that is used as input for yarns.
Opportunities for experts in machine learning development are huge in textile manufacturing. Yarn is just one part of it. You could implement machine learning in fabric weaving, in printing, and in dyeing processes with positive gains.
On the other hand, machine learning may not be so easy to implement in manufacturing but the yarn industry can benefit by using machine learning development services to build predictive models to forecast demand, analyze existing markets and future trends and thus be future proof. Predictive machine maintenance of spinning equipment is another area where ML helps reduce downtime. It pays to engage in machine learning solution providers in any of these areas of textile manufacturing.