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.

A study led by Warren Jasper shows machine learning can reduce textile dyeing waste by accurately predicting dry fabric colours from wet samples.
A neural network model trained on 763 samples achieved near-perfect accuracy, helping avoid costly errors.
Jasper urges wider adoption to boost sustainability and efficiency in continuous dyeing.

Fibre2Fashion News Desk (HU)

German Chancellor Friedrich Merz called Tuesday for “more flexibility” from the EU in the transition to electric cars as resistance grows over plans to phase out combustion-engine vehicle sales by 2035.

Merz made the appeal at the opening of the IAA motor show in Munich, as Europe’s struggling automakers line up to plead for the bloc to reconsider the plan aimed at combating climate change.

German titans VW, BMW and Mercedes-Benz as well as US-European giant Stellantis have voiced concerns as they struggle to build up competitive electric vehicles (EV) against Chinese rivals like BYD.

While he did not openly criticize the EU’s deadline to halt sales of new combustion engine cars in a decade, Merz said he wanted to see “more flexibility” in European regulation.

“We are of course committed to the transition to e-mobility,” the conservative leader, who took office in May, told the show.

But he added that “we need smart, reliable and flexible European regulation—it is more necessary than ever”.

“We want to achieve climate protection as cost-effectively as possible through technological openness. Unilateral political commitments to specific technologies are fundamentally the wrong economic policy approach.”

Markus Soeder, the leader of Bavaria state where the motor show is taking place and a political ally of Merz, was more blunt.

“This combustion engine ban is wrong,” he told the show.

“We need other options because, to be perfectly honest, combustion engines still have a future. Electric mobility will prevail in the long term but we need significantly more time to organize the whole thing in Europe.”

‘Supportive’ speeches

Jan Vlasak, who works in software for a German carmaker, praised the speeches as “really supportive of the automotive industry”.

The 35-year-old agreed the 2035 ban should be reviewed, calling for it to be pushed back by five to 10 years.

A stuttering shift to EVs is one of the major challenges facing Europe’s auto sector. Manufacturers have invested huge sums in the transition, but sales have grown far more slowly than anticipated.

On top of that, the industry has faced rising production costs at home along with fierce competition in China from BYD and other EV makers that has eroded sales for foreign manufacturers in the world’s biggest auto market.

In Germany, the auto sector has already shed more than 50,000 jobs over the past year, according to EY.

Volkswagen is planning 35,000 layoffs between now and 2030 and taking the unprecedented step of halting production at two of its sites in Germany.

Plans for redundancies have been coming thick and fast at Porsche, Audi, and at hundreds of German auto sector suppliers.

Merz is planning to host meetings with key auto sector players soon to chart a way forward.

“Our goal is for Germany to remain one of the world’s leading places for automotive and manufacturing in the future—we want to shape the transformation of the automotive industry,” he said.

Chinese competition

On the other side of the argument, more than 150 businesses in the EV sector wrote an open letter to EU chief Ursula von der Leyen on Monday urging her to “not row back” on the 2035 target.

When the IAA winds down on Friday, carmakers are expected to have a meeting with von der Leyen in Brussels to discuss how to save the sector.

Underlining the competition the German car sector faces, more Chinese carmakers are expected at the fair than ever before.

Fourteen Chinese carmakers—as opposed to just 10 European ones—are displaying new models.

Around 100 of the 700 firms taking part overall in the IAA will be from China, up 40% from the last show in 2023.

Chinese carmakers there range from BYD, whose sales in Europe rose dramatically in the first half of this year, to GAC, which is taking its first steps in the European market.

BYD on Monday presented its compact Dolphin Surf model, which has been on sale in Europe since May for around 20,000 euros ($23,500).

From later this year it will be produced in a new facility in Hungary, with the company hoping to avoid EU tariffs on Chinese imports.

Volkswagen is trying to fight back with its own models at the more affordable end of the market.

© 2025 AFP

Birds flock in order to forage and move more efficiently. Fish school to avoid predators. And bees swarm to reproduce. Recent advances in artificial intelligence have sought to mimic these natural behaviors as a way to potentially improve search-and-rescue operations or to identify areas of wildfire spread over vast areas—largely through coordinated drone or robotic movements. However, developing a means to control and utilize this type of AI—or “swarm intelligence”—has proved challenging.

In a Proceedings of the National Academy of Sciences paper, an international team of scientists describes a framework designed to advance swarm intelligence—by controlling flocking and swarming in ways that are akin to what occurs in nature.

“One of the great challenges of designing robotic swarms is finding a decentralized control mechanism,” explains Matan Yah Ben Zion, an assistant professor at the Donders Center for Cognition at the Netherlands’ Radboud University and one of the authors of the paper.

“Fish, bees, and birds do this very well—they form magnificent structures and function without a singular leader or a directive. By contrast, synthetic swarms are nowhere near as agile—and controlling them for large-scale purposes is not yet possible.”

The research team, which included NYU scientists Mathias Casiulis and Stefano Martiniani, addressed these challenges by developing geometric design rules for the clustering of self-propelled particles. These rules are modeled using natural computation—similar to the “positive” or “negative” charges in protons and electrons that are foundational to the formation of matter.

Under these rules, active particles moving in response to external forces have an intrinsic property that causes them to curve—a quantity the researchers call “curvity.”

“This curvature drives the collective behavior of the swarm, which points to a means to potentially control whether the swarm flocks, flows, or clusters,” explains NYU’s Martiniani, an assistant professor of physics, chemistry, and mathematics.

Their conclusion was supported by a series of experiments in which the scientists showed that the curvature-based criterion controls robot-pair attraction and naturally extends to thousands of robots. Each robot was treated as having a positive or negative curvity, and similar to electric charge, this curvity controls the robots’ mutual interactions.

“This charge-like quantity, which we call ‘curvity,” can take positive or negative values and can be directly encoded into the mechanical structure of the robot,” explains Ben Zion.

“As with particle charges, the value of the curvity determines how robots become attracted to one another in order to cluster or deflect from one another in order to flock.”

Ben Zion, who, as an NYU student, previously developed microscopic swimmers, added, “Finding a design rule of geometric nature, such as curvature, makes it applicable to industrial or delivery robots or to cellular-sized microscopic robots that have the potential to improve drug delivery and other medical treatments.”

“The best part is that these rules are based on elementary mechanics, making their implementation in a physical robot straightforward,” adds Casiulis, a postdoctoral researcher at New York University’s Center for Soft Matter Research and NYU’s Simons Center for Computational Physical Chemistry.

“More broadly, this work transforms the challenge of controlling swarms into an exercise in materials science, offering a simple design rule to inform future swarm engineering.”