If you are into weaving which naturally entails dyeing of fabrics, then this news is for you. Research from Wilson College of Textiles at North Carolina State University is using machine learning to tackle the mismatch of shades when a fabric is being dyed. This could help reduce textile manufacturing waste by more accurately mapping how colours will change during the dyeing process.
- Professor Warren Jasper, lead researcher, has worked out a neural network that has the potential to cut down significantly on waste caused by colour errors, as it would allow fabric manufacturers to better predict the end result of the dyeing process before large amounts of fabric has been incorrectly dyed.
- Professor Warren Jasper puts forward his study in the paper, ‘A Controlled Study on Machine Learning Applications to Predict Dry Fabric Color from Wet Samples: Influences of Dye Concentration and Squeeze Pressure,’ published in Fibers with Samuel Jasper as co-author.
THE ISSUE: Fabrics are typically dyed while wet, and their colours change as they dry. This can make it difficult to know what a piece of fabric will end up looking like in its finished state, said
- The fabric is dyed while wet, but the target shade is when it's dry and wearable. That means that, if you have an error in colouration, you aren't going to know until the fabric is dry. While you wait for that drying to happen, more fabric is being dyed the entire time. That leads to a lot of waste, because you just can't catch the error until late in the process.
- The amount of colour changes from wet to dry states is not uniform between different colours. This non-linear relationship means that the amount of colour changes between wet and dry is unique to each colour, and data from one color sample cannot be easily transferred to another.
RESOLUTION WITH MACHINE LEARNING: Five machine learning models, including a neural network was designed specifically to map this type of non-linear relationship, and then trained by inputting visual data from 763 fabric samples of various colours, both wet and dry. Each dyeing took several hours to complete, which made collecting data a significant undertaking.
- While all of these models outperformed non-machine learning models in terms of accuracy, the neural network stood out as significantly more accurate than any other option.
- The neural network showed an error as low as .01 and a median error of 0.7 using CIEDE2000, a standardised colour difference formula. The other machine learning models showed CIEDE2000 error ranges of anywhere between 1.1 to 1.6, while the baseline went as high as 13.8. In the textile industry, CIEDE2000 values exceeding 0.8 to 1.0 are generally considered outside of acceptable limits.
WHAT THEY SAID
We're a bit behind the curve in textiles. The industry has started to move more toward machine learning models, but it's been very slow. These types of models can offer powerful tools in cutting down on waste and improving productivity in continuous dyeing, which accounts for over 60% of dyed fabrics. I hope to see similar machine learning tools adapted more broadly in the textile industry.
— Professor Warren Jasper
Department of Textile Engineering, Chemistry & Science
North Carolina State University
