Informed efficiency: AI in nonwovens manufacturing

Efficiency in the production of nonwovens is mainly determined by two resources: fiber and energy. To increase both efficiency and sustainability of their operations, manufacturers can now draw upon the power of artificial intelligence (AI).

author: Marco Eichelkraut, Marketing Manager

Industry Insight by:

Manufacturers of machinery used in the textiles-manufacturing industry confront many challenges – new materials, new products and applications, stricter tolerances for product quality and process stability, and increasingly high requirements for resource efficiency all drive the need for innovative production solutions.

With the Smart Machine Assistant, we want to increase process stability and product quality and make targeted recommendations for increasing energy efficiency”. Rebekka Dilo, Head of the Technical Research Center, DiloGroup.
With the Smart Machine Assistant, we want to increase process stability and product quality and make targeted recommendations for increasing energy efficiency”. Rebekka Dilo, Head of the Technical Research Center, DiloGroup.

At the same time, the textiles industry is a highly competitive market and any production machine must be able to demonstrate its value to justify investment – and that is especially true of machines for nonwovens. “We want to continuously improve on the value we provide to our users, in terms of machine and line performance but also in terms of usability” – says Rebekka Dilo, Head of DiloGroup’s Technical Research Center. She continues: “One area where we definitely see a growing awareness in the market is resource efficiency, especially energy and fiber efficiency”. The company is a leading manufacturer of needlefelt production lines. It serves customers in more than 80 countries worldwide and is constantly working to provide solutions that help users optimize their entire production process.

Boosting reliability and cost efficiency in the textile industry

 

Assisting users with artificial intelligence

What makes such optimization so challenging is that nonwovens production involves several complex processes that are sensitive to changing production conditions. Identifying correlations and reacting properly to changing production conditions can be difficult.

As many companies will see many of their experienced machine operators retire, transferring all of that know-how will soon become a challenge, “which is why we are currently exploring how we can preserve the experience that operators have as a technical solution to better support our customers,” says Dilo. The solution DiloGroup is currently evaluating for this purpose is the Siemens Cloud application Smart Machine Assistant, a self-learning application that uses machine learning capabilities to determine the optimal settings of an industrial machine in a complex environment. “Product quality of nonwovens depends on many parameters, with time lags between cause and effect and multiple interdependencies” – explains Dilo. This makes formalizing machine and process behavior very complex, and any optimization requires large amounts of data, “which is why having a self-learning algorithm was a big help” – says Dilo.

The application uses machine learning capabilities to identify unknown relationships among machine parameters and key performance indicators, which helps increase overall product quality and machine efficiency.

Leveraging the power of production data

Introducing Smart Machine Assistant is the latest of several Internet of Things (IoT) projects that DiloGroup has executed with Siemens. The flexible architecture of the IoT ecosystem within the Xcelerator digital business platform ensures that different applications and solutions can be easily integrated so that users can get the most out of their data: “By integrating the data and the application into the Siemens IoT ecosystem, we also have a uniform database for our process data and can also reuse them in other applications” – says Dilo. With a broad range of automation and drive systems complemented with software and hardware solutions for IoT applications, Siemens is able to support DiloGroup in many aspects of the company’s research and design processes.

“With the Smart Machine Assistant, we want to increase process stability and product quality, and enable any operator to choose the optimum settings for their product so they can reduce setup times and reduce scrap. Plus, we want to make targeted recommendations for increasing energy efficiency which helps cut overall power consumption – an important benchmark for our users and for us” – says Dilo. Working with Siemens not only has helped DiloGroup leverage the power of its production data, but also has helped it increase machine availability and performance. DiloGroup’s automation and drives portfolio is largely standardized on Siemens components and systems, and the company also cooperates with Siemens for repair services. This combination of IoT, textile and service expertise is another big asset according to Dilo: “Support from Siemens was always spot on” – she says.

 

Smart Machine Assistant will help operators fine-tune the process parameters of their DiloGroup lines.

 

 

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