Artificial intelligence has become one of the most overused phrases in manufacturing, yet its potential in nonwovens is beginning to attract serious attention. From R&D labs experimenting with autonomous discovery to production lines adopting vision systems and predictive maintenance, the industry is edging closer to practical deployments. The promise is real, but so are the gaps.

AI in materials science: a glimpse of what’s possible
At the research frontier, organizations like the National Institute of Standards and Technology (NIST) are developing autonomous laboratories where AI and robotics design, execute, and analyze experiments without human intervention. Microscopy and X-ray images that once required painstaking manual review are now interpreted by machine-learning models capable of flagging defects or structural features in real time.

For nonwovens, this hints at a future where AI could accelerate development of biodegradable fibers, PFAS-free treatments, or antimicrobial finishes, speeding R&D cycles from years to months. In essence, it offers a window into how materials innovation may be reimagined. But for now, most producers are not running self-driving labs; their AI journey tends to begin closer to the line.

AI is not new to the conversation. 

In 2018, Suominen introduced Intelligent Nonwovens, embedding digital patterns into wipes that could be recognized by smartphones and interpreted using AI. The goal was traceability, counterfeit protection, and consumer engagement. It was one of the sector’s first public attempts to link AI directly to nonwoven substrates. Yet the concept has not visibly expanded since, serving as a reminder that bold digital ideas can be floated, but scaling them to industrial reality is another matter.

Setting the groundwork for AI
AI is most visible today in factory automation and inspection, though in varying degrees of maturity. ANDRITZ’s Metris Copilot, built on Microsoft Azure’s OpenAI Service, integrates anomaly detection and operator assistance directly into line controls. “ANDRITZ is helping to shape the future by using Microsoft Azure to further enhance its autonomous factory solutions,” says Ralph Haupter, President of Microsoft EMEA. “The deep integration of their products with Azure cloud services is the type of technological innovation that drives sustainable and efficient change in the industry.” 

In parallel, Trützschler’s T-ONE provides what it calls a digital work environment for recipe management, process monitoring, and data analysis. In practice, it delivers measurable gains in speed and waste reduction, but the improvements stem from digitalization and process discipline rather than genuine artificial intelligence. It is better understood as a stepping stone towards AI-enabled nonwovens production lines. It has been shown to increase production speed by more than 50% and reduce waste by up to 30%.

Similar stories play out with Uster, ISRA Vision, and Mahlo, which supply vision and sensor systems capable of catching defects such as holes, thin spots, or contamination and adjusting parameters in real time. These remain rule-based systems with closed-loop control. They provide tangible value, but the real leap will come when such platforms evolve into self-learning models that predict and prevent defects before they occur. For now, they too should be seen as stepping stones on the path to AI-enabled production.

AI in inspection
Conversation around AI application is gaining visibility at industry events. At Cinte Techtextil China 2025, AiDLab hosted an AI in Automated Textile Material Inspection panel. Speakers included Prof. Calvin Wong (CEO, AiDLab), Cheng Yik Hung (Hong Kong Polytechnic University), Eric Sham (AiDLab), and Dorothy Yeung (AiDLab).

AiDLab’s track record is more than academic: WiseEye 2.0 achieves over 90 percent defect detection accuracy at 60 m/min across complex textile structures. The AiTIS system, deployed with Banitore, inspects masks at 500 units per minute with accuracy exceeding 99 percent, a successful commercial deployment in nonwoven healthcare manufacturing (PolyU).

For nonwovens converters, these projects demonstrate that AI-powered inspection is no longer a lab demo; it is crossing into high-speed, consumer-critical production environments.

Questions that impact the bottom line
1. Can AI predict bond uniformity before it fails a quality audit?

AiDLab’s WiseEye 2.0 shows that high accuracy in textiles is possible, but applying that precision to spunbond or meltblown bond uniformity remains a challenge.

2. Will algorithms be able to optimize energy consumption on spunbond and meltblown lines at a time of volatile power costs?
Metris Copilot offers efficiency insights, yet the shift from operator guidance to autonomous energy optimization has not happened.

3. Who will own the torrents of data generated by AI-enabled platforms, the OEM, the converter, or the brand?
Suominen’s Intelligent Nonwovens hinted at the potential and the risks of materials linked directly to data streams, a debate that remains unsettled.

For now, these questions matter less for the answers they lack than for how they frame the path ahead: AI in nonwovens will advance not through hype, but by resolving the practical tensions between accuracy, efficiency, and ownership.

What converters should consider
For nonwovens producers, the pragmatic path is to treat today’s tools as enablers rather than endpoints. Deploy vision systems to cut waste, digitalize recipe management for consistency, and pilot predictive maintenance for downtime reduction. At the same time, keep an eye on how materials scientists are reshaping the foundations of discovery. The next wave of breakthroughs may arrive not from the line, but from algorithms running in a lab.

In the world of nonwovens, hype tends to travel faster than adoption. The challenge for converters will be to balance experimentation with pragmatism, and to prepare for a future where the real intelligence is not in the buzzwords, but in the execution. In other words, the race is less about adopting AI quickly and more about proving where it actually delivers value.