This article is written by Shatarupa Majumdar
Introduction
The advent of Artificial Intelligence (AI) has revolutionized the production process, transforming every stage from blow room to carding, drawing, lap formation, combing, speed framework, ring spinning, winding, packing, and conditioning. With minimal human intervention, AI-powered control panels now recognize and finalize the required production parameters, streamlining the entire process.
AI has transformed the production process, automating every stage from blow room to conditioning, with AI-powered control panels optimizing production parameters and minimizing human involvement.
Benefits of AI
The implementation of AI in the textile industry has yielded numerous benefits, including:
Cost reduction: Due to the use of AI there is a saving in time and money. Hence most manufacturing sectors saw a cost saving of 20% over 4% companies and lowering in cost upto 10% for 28% companies.
Improved quality: The use of automation and artificial intelligence has also improved the quality of the material for manifolds.
Significant reduction (up to 60%) in yarn grading errors, leading to superior textile grading: The utilization of AI in spinning also helped in better grading of the materials and the Industry have seen a steady reduction of upto 60% in the grading errors.
Accurate measurement of fabric physical characteristics: AI reached the limits where humans could and hence pushing it’s benefits further and thus resulting in accurate measurement of fabric physical characteristics.
AI has transformed the textile industry by increasing accuracy, reducing errors, and providing a more precise understanding of fabric properties, ultimately leading to improved product quality and customer satisfaction.
Related Article: Use of Generative AI in the Fashion Industry
The Lead Advantages
The Integration of Artificial Intelligence in Textile Production: A Game-Changer for the Industry
1. Automation of Tedious Tasks
AI-powered robots take over tedious tasks like material handling and cutting – As the AI robots take up the tedious tasks it become less time-consuming and also a high rise of less errors can be noticed in the recent times in the companies implementing AI thus increasing in productivity as a whole.
Improves precision and accuracy - Due to the transfer of Tedious Tasks it likely gives more advantages like Improved precision and the accuracy of the material.
Increases productivity and reduces errors - AI-powered robots also help in increasing productivity and reduction of the error as a whole.
Frees up human resources for more strategic tasks – It improves the type of work a human is given to and hence now the workforce can have more time for strategic work.
2. Optimized Production Schedules
AI algorithms analyze vast amounts of data from textile production – AI tools analyze the vast amounts of data from textile productions and hence helps in optimizing the production schedules.
Enables mills to optimize production schedules – Due to a routine schedule, it enables the mills to optimize the productions and also keep a check in them.
Reduces downtime and increases overall efficiency – AI-operated machines face less stoppage time and have a resulting reduction in downtime and thus increase the overall efficiency.
3. Improved Quality Control and Defect Detection
AI-powered sensors, cameras, and Machine Learning algorithms enhance quality control processes – Due to the advent of the newer technologies we have access to AI powered sensors , cameras and Machine Learning algorithms thus enhancing the quality control processes.
Identifies defects in real-time – There is a facility of identification of the faults in real time and further measures can be taken to improve the same if feasible.
Reduces errors and improves overall product quality – As the faults are being identified in real time there is scope of improvement in the same stage of manufacturing thus it reduces errors and improves the overall product quality.
4. Minimized Supply Chain Risks
AI-based analytics predict demand and optimize production schedules – AI carefully recognizes and uses analytics to predict the demand and optimization of production schedules.
Manages inventory levels in real-time – Not only predictions AI is also capable of managing the inventories in real time.
Identifies and mitigates supply chain risks – AI further identifies and mitigates the supply chain risks.
5. Boosted Energy Efficiency
AI identifies areas of energy waste and reduces overconsumption – AI well identifies the areas of energy waste and thus helps to reduce the over consumption with the help of thorough monitoring.
Lowers energy costs and minimizes environmental impact – It also complies with energy regulations and hence lower the energy costs and thus minimizing the environmental impacts.
6. Virtual Factory Twins and Showrooms
AI-powered virtual and augmented reality technologies visualize immersive virtual factory twins and showrooms and hence it simulates the entire production process this also enhances transparency and tests new ideas
7. Optimized Inventory Management
AI analyzes sales data and predicts future demand – AI has highly effective analytical functions thus it can effectively analyze the sales data and thus predict the future demand.
Makes informed decisions about inventory level – It Identifies slow-moving items and adjusts inventory and distribution strategies, and thus making informed decisions about inventory levels.
Conclusion
Despite its potential, the adoption of AI in textile technologies remains limited, even in industrialized countries. However, emerging nations can greatly benefit from AI solutions to address production and quality control challenges in the textile sector. The industry is increasingly recognizing the value of AI in enhancing competitiveness by efficiently and accurately addressing complex problems.
Currently, many tasks in the labor-intensive textile industry, including production and quality control, rely heavily on manual labor. AI innovation aims to revolutionize these processes by developing solutions that improve efficiency and effectiveness. The core AI technologies, such as detection, identification, inspection, grading, machine vision, and prediction, form the foundation for these advancements.
About the Author: Shatarupa Majumdar is an avid learner and aspiring professional in the realm of textile technology. She is committed to contributing innovative solutions and pushing the boundaries of this ever-evolving industry. With a profound interest in the intricate world of textiles, she is dedicated to exploring the diverse facets of this field, aiming to combine technological advancements with creativity.