Dyeing/Finishing/Printing
According to a new study, machine learning can help reduce textile manufacturing waste by mapping more accurately how colors change during the dyeing process.
“We're a great fan of the world,” said Warren Jasper, a professor at Wilson University who is based in Raleigh, North Carolina. “The fabric is dyed while wet, but the target shade is when the fabric is dry and wearable. This means that if there is an error in the color, you don't know until the fabric is dry. More fabrics are dyed for a long time while you wait for that drying to occur.
The amount of color change from wet to dry is not uniform between different colors, and this nonlinear relationship means that the amount of color change between wet and dry is unique to each color, and data from a sample of one color cannot be easily transferred to another color.
To tackle this problem, Jasper has developed five machine learning models, including neural networks specifically designed to map this type of nonlinear relationship. He then trained the model by entering visual data from 763 fabric samples in various colors, both wet and dry. Each stain took several hours to complete, resulting in a significant effort on the collected data.
All of these models outperformed non-machine learning models in terms of accuracy, but neural networks stood out much more accurately than any other option. The neural network used Ciede2000, a standardized color difference equation, to show a low error of 0.01 and a median error of 0.7. Other machine learning models show the CIEDE2000 error range between 1.1 and 1.6, with the baseline at 13.8. In the textile industry, Ciede2000 values above 0.8-1.0 are generally considered outside the tolerance limits.
This neural network can significantly reduce waste caused by color errors, allowing fabric manufacturers to better predict the final outcome of the dyeing process before large amounts of fabrics are accidentally dyed.
Jasper hopes that similar machine learning tools will be adapted more widely in the textile industry.
“We're a little behind in the curves of textiles,” he says. “The industry has begun to move further towards machine learning models, but it's very slow. These types of models can provide powerful tools to reduce continuous dyeing waste, which accounts for more than 60% of dyed fabrics and increase productivity.”
www.textiles.ncsu.edu
