Machine learning can reduce textile dyeing waste

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.