Artificial Intelligence in the Textile Industry Concept and Technology Review

We often get pop-up advertisements in our social media apps about products and services that we have just been thinking of! This is how artificial intelligence (AI) has revolutionized today’s information world. It is so intense nowadays that human minds are being read. This technology is changing the concepts and creating scopes of innovative developments to solve technical and managerial problems. This article attempts to shed light on the concepts of AI application in the textile industry and the review of available technologies.

Artificial Intelligence (AI) Explained

Intelligence, the capability to acquire knowledge and apply them as skills, has been the sole supremacy of human beings. Intelligence can be characterized by the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. If this ability can be produced artificially, it may be termed artificial intelligence or AI in short. This is what happened, and today, AI is one of the most influential breakthrough inventions in the scientific world. So, AI is the simulation of human intelligence in machines in a way that it can mimic one or some of the human intelligence actions. Specifically, a machine that can learn and solve problems at its own capacity is considered a machine with AI technology.

Concept of AI in the Textile Industry

The application of AI technologies in the textile industry is becoming visible as solving complex problems quickly and accurately is becoming key to competitiveness. The textile industry is a labor-intensive industry where many operations, including production and quality control, are predominantly handled by human hands. With AI innovation, attempts are made to develop technologies to improve efficiency and effectiveness in such operations. The concept of AI technology is based on basic actions like detection, identification, inspection, grading, machine vision, prediction, etc. All these actions are primarily done by humans, often considered tedious and often with ambiguous outcomes. AI can augment these basic actions’ efficiency and accuracy with technologies categorized as expert systems, artificial neural networks, fuzzy logic, algorithms, and natural language.  The concepts used in developing AI solutions in the textile industry can be explained in table-1.

Table-1: AI Logics behind the basic actions in potential application areas in textile industry

 AI Technology Review in the Textile Industry

The use of AI in textile technology is not widespread yet. The complexity, the operating skill requirement and ROI are the probable reasons behind the limited diffusion of such technologies. However, rigorous research and development are ongoing in different labs and technology companies to develop AI technologies for the textile industry. Reliable technology forecast suggests that in the next 5-10 years, many textile manufacturing processes will adopt AI-based solutions to enhance efficiency. Table-2 demonstrates the current and potential future AI technologies and their reviews in the textile industry. These technologies are fundamentally based on the basic actions described in table-1.

Table-2: Review of AI technologies for the textile industry

Among the three technologies, Datacolor is already in operation in different textile industries in Bangladesh. The other two technologies are not yet been introduced but carry huge potentiality to deliver benefits. Some other technologies developed (or in developing stage) target the textile industry in addition to the three AI-based technologies described in this article. For example, an AI-based system for virtual modeling of yarn from fiber properties has been developed at Cornell University, USA. This technology will allow predicting probable yarn quality parameters accurately from yarn property data. Similar research has also been done in the Textile Department, Amirkabir University, Iran, which involved grading yarn appearances from fiber data. In Fraunhofer Institute, Germany simulation of mechanical textile properties has been done using their I solutions named TexMath software. Another process of fabric wrinkle measurement has been developed by Zhejiang University, China, where image processing method is used. Also, 3-D modelling of articles based on technical data can come in handy for high tech technical textile industries. There are other AI-based solutions that can benefit the textile business and decision making. These technologies are based on big data and artificial neural networking technique that tries to establish or identify critical co-relationships between different supply chain players including production, quality, retailing, marketing, costing, consumer feedback etc. Most of the Ai based technologies are only being adopted by tech-savvy textile industries predominantly in the developed countries who produce very high end products or have a very sophisticated operation process. However, solutions can also be designed and adopted in the mass manufacturing textile industries, especially in Bangladesh and other South Asian countries, to improve their production efficiency, design, and quality control beyond the level achievable by only human intervention.

Conclusion

The application of AI in textile technology is not still widespread, even in developed countries. However, technologies are being developed especially in Germany, the USA, and China, to cater to the complex problems prevailing in their textile supply chain. Yet, potential AI technologies have great scopes to benefit textile industries in developing countries where production and quality control problems persist. The key here is to get our industries ready to adopt such technologies. If we want to design the industry of the future, we have to look into things seriously and work collaboratively to develop our skill base and technology management ability.