We always get pop-up ads on goods and services that we have only thought of in our social media apps! That has revolutionized today’s world of information from artificial intelligence (AI). Today, human minds are read in such an intense way. This technology changes principles to solve technological and management problems and created fields of creative growth. This article aims to highlight AI applications ideas and reviews available innovations in the textile industry.
AI In the Textile Industry
The application of AI technology in the textile sector becomes noticeable when the rapid and precise resolution of complex issues is crucial for productivity. The textile industry is an intensive labor market where all activities, including manufacturing and quality control, are handled mostly by people. Attempts are made with AI innovation to implement systems to maximize the productivity and efficiency of such activities. The AI concept is based on fundamental activities such as detection, identification, inspection, classification, machine vision, forecasting, etc. These are all mostly human activities, which are often regarded as tedious and often have unclear effects. With the technology classified as expert systems, artificial neural networks, fugitive logic, algorithms, and natural language, AI can enhance the effectiveness and precision of these fundamental actions.
Future of AI in Textile
AI is still not widely used in textile technology. The sophistication, requirements for operating skills, and ROI are the likely factors for the limited diffusion of such technologies. Nevertheless, in various laboratories, and technology firms, rigorous research, and development are underway to develop textile AI technology. In the 5-10 next year, many textile production processes will embrace AI-based solutions for productivity improvement, according to a reliable technology prognosis.
AI technologies in Textile Industry:
- The device detects weaving textile defects.
- The machine vision in the loom can be installed, scanned continuously and 40 common fabric flaws can be identified with an accurate resolution of 0.1mm/pixel, reducing the likelihood of manufacturing a fabric that is below the standard by up to 90%.
- An electric motor is used to monitor the fabric as the machine produces a high-performance LED lighting bar equipped with a high resolution, load-coupled camera for devices mounted on a track. The images captured are preprocessed and transported into an AI system preloaded with hundreds of meters of fabric data.
At present, manufacturers inspect the random sides of the fabric with the eye and match a provided fault sheet by quality control for textile production. This method is unreliable and inconsistent with fatigue and simplistic human errors. Compared to traditional processes, Wise Eye has shown a 90% greater efficiency in fault detection.
- Technology adapted to the recognition of fabric patterns in the textile industry.
- The Cognex ViDi platform can check for weave, knitting, tanning, finishing, and printing fabric pattern aspects automatically.
- No development period is required for the platform to be integrated into a manufacturing system, and predeterminate images of what a good fabric sample looks like can be trained.
The system exceeds the best analyzers for human fabric patterns. No software development is necessary. The software algorithm trains on a set of known samples/patterns to develop its reference template is an extremely simple installation process.
- AI Fabric color matching tolerance, color measurement, formulation, laboratory coloration, and dispensing.
- It has a Pass/Fail (P/F) AI feature to improve instrumental tolerance accuracy and efficiency.
- The color of the data A platform for productivity and color measurement, spectrophotometers offer accuracy and accuracy.
- The system dispenser Data color contains concentrated dyes, liquid pigments, pastes, thickeners, and mediums. In less time for the thinning sectors, prepare the most precise solutions.
Data color’s high precision and effectiveness in comparison with manual or traditional color management and resetting dyeing systems in textile manufacture.
Data color is currently operational in various textile sectors in Bangladesh, among the three technologies. The other two systems have not been launched yet but have enormous advantages. Besides the three AI-based technologies identified in this article, several other technologies developed (or in the development stage) target the textile industry. For instance, Cornell University, USA, has developed an AI-based system for virtual yarn modeling of fiber characteristics. This technology will allow you to accurately predict likely yarn quality parameters from yarn data.
In the Department of Textile, Amir Kabir University, Iran, similar investigations were also carried out, including fiber data grading of yarn appearance. The Fraunhofer Institute in Germany used its I solutions known as TexMath software to simulate mechanical textile properties. Zhejiang University in China, where the picture processing method is used, has also introduced a procedure of cloth wrinkle measurement. In addition, high-tech textile industry 3-D modeling articles based upon technical data can prove useful.
The textile industry and decision-making will benefit from all AI solutions. These tools are based on the use of large data and artificial neural networking techniques to create or detect critical links between various players, including production, quality, retail, marketing, costs, consumer feedback, etc. Most Ai-based technologies are only used in advanced textiles industries which produce very high-quality products or have a very sophisticated operating process. In most developed countries. In mass textile industry manufacturing, however, solutions can also be developed and taken to improve their production efficiency, design, and quality monitoring beyond the level that only human intervention can achieve, particularly in Bangladesh and other South Asian countries.
Even in developed countries, the application of AI in textile technology is still not widely used. In Germany, the United States, and China, moreover, systems are being developed to address the complex problems predominant in their textile supply chain. However, in developed countries where production and quality control issues remain, prospective AI technologies may benefit the textile industry. The important thing here is to prepare our companies to embrace these innovations. If we want to create a future industry, we need to seriously examine things and work together to build our know-how and technology management skills.
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