DP Dyers is a company that consistently puts innovation at the forefront of commission piece dyeing. It is a known issue that mass piece dyeing can struggle to replicate lab dyeing results, leading to excessive repeat dyeing, and wasted resource built into its processes. In the spirit of streamlining efficiency and reducing waste, DP Dyers have collaborated with Future Fashion Factory in a R&D project.
About the Project
Led by the University of Leeds’ Professor Stephen Westland, the Call 6 DP Dyers project employs the use of computational modelling to analyse inconsistencies between lab and mass dyed fabrics to improve results. The process has analysed the areas in which lab dyeing results do not translate to mass dyed fabrics and has posed innovative solutions to eradicate any inconsistencies. It goes without saying that streamlining this process has had significant impacts in terms of reduced waste, reduced energy, and water consumption.
Issues with Traditional Piece Dyeing
Traditional piece dyeing is a process whereby woven lengths of white fabric are dyed to a specific colour using a dye recipe. Typically, DP Dyers have been commissioned to create new shades on new fabrics with complex fibre compositions. This means continually testing dye recipes and techniques in their lab to perfect the required shade before upscaling this process for mass dyeing. In theory, anything that is produced in the lab should be able to be scaled up to bulk without any degree of error. In practice, mass dyed fabrics can show significant and varying inconsistencies compared to their lab produced counterparts. If inconsistencies do arise, the whole process must be redone.
Conditions in the lab are very tightly controlled, with variables at an absolute minimum. The purpose of the lab process is to establish good matches prior to bulk dyeing, and as the lab is a fully controlled single unit it is inherently better at producing and repeating results.
Machine dyeing is often performed in the opposite environment, and thus often results in discrepancies due to inconsistencies in the capabilities of the machines, complications of upscaling the dye recipe and human errors in the process, leading to a series of iterations based on trial and error; this produces a lot of waste – especially in terms of water, gas, electricity, fabric, and human resources.
This project has helped drive us on a path of improvement, to be able to compete and to meet the demands of our customers. We cannot stand still, and the history of WTJ shows we will use every new technology available to make those improvements.Alan Dolley, Technical Manager at DP Dyers and WT Johnson
The precision possible in the lab is therefore the only way to ensure the perfect colour match for the end fabric in a sustainable and efficient manner. It has become essential that the transfer from lab to bulk is seamless and maintains the precision of the lab process: this has not been possible without substantial reliance on the skill of the dyer to adjust any variation that arises in the first bulk dyeing, which is both a lengthy, resource heavy and wasteful process.
The Computational Model
DP Dyers, part of the WT Johnson Group, have worked with Future Fashion Factory and the University of Leeds to adopt a process that has effectively digitally translated lab dye processes into mass manufacturing without the trial and error involved previously – this has consequently helped to yield significant sustainability and efficiency benefits.
The computational modelling technology – through its implementation of a matrix of complicated systems, built using a variety of mechanistic approaches and complex algorithms – has helped to reduce the need for repeat dyeing. A natural consequence is therefore the reduced amount of water, chemicals and energy needed in the dyeing process, thus saving time, money and increasing capacity for mass production.
For DP Dyers, the project was critical when helping the team to better understand their skills and tackle a future where dye rejects can cause real problems. The potential risks could carry a need for repeat work, or even result in unacceptable delays when it comes to delivery times.