Machine learning can reduce textile dyeing waste: US Researchers

A new study, led by Warren Jasper, professor at the US' Wilson College of Textiles has demonstrated how machine learning can help reduce waste in textile manufacturing by improving the accuracy of colour prediction during the dyeing process.

The research, titled ‘A Controlled Study on Machine Learning Applications to Predict Dry Fabric Color from Wet Samples: Influences of Dye Concentration and Squeeze Pressure’, addresses one of the industry’s longstanding challenges: predicting what dyed fabric will look like once it dries.

Fabrics are typically dyed while wet, but their colours often change as they dry. This makes it difficult for manufacturers to determine the final appearance of the material during production. The issue is further complicated by the fact that colour changes from wet to dry are non-linear and vary across different shades, making it impossible to generalise data from one colour to another, according to the paper co-authored by Samuel Jasper.

“The fabric is dyed while wet, but the target shade is when its dry and wearable. That means that, if you have an error in coloration, 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,” said Warren Jasper.

To address this, Jasper developed five machine learning models, including a neural network specifically designed to handle the non-linear relationship between wet and dry colour states. The models were trained on visual data from 763 fabric samples dyed in various colours. Jasper noted that each dyeing process took several hours, making data collection a time-intensive task.

All five machine learning models outperformed traditional, non-ML approaches in predicting final fabric colour, but the neural network proved to be the most accurate. It achieved a CIEDE2000 error as low as 0.01 and a median error of 0.7. In comparison, the other machine learning models showed error ranges from 1.1 to 1.6, while the baseline model recorded errors as high as 13.8.

The CIEDE2000 formula is a standard metric for measuring colour difference, and in the textile industry, values above 0.8 to 1.0 are generally considered unacceptable.

By enabling more accurate predictions of final fabric colour, the neural network could help manufacturers avoid costly dyeing mistakes and reduce material waste. Jasper expressed hope that similar machine learning tools would be adopted more widely across the textile sector to support efficiency and sustainability.

“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 per cent of dyed fabrics,” stated Warren.

Fibre2Fashion News Desk (HU)