Minimizing Downtime: Intelligent Automation for Sewing Thread Breakage Analysis

Md. Shadman Shakil

Information technology and artificial intelligence (AI) have transformed several industries, including management, engineering, physics, medicine, and textiles. With an emphasis on achieving high quality, cost reduction, statistical process control, just-in-time manufacturing, and computer-integrated production, artificial intelligence (AI) uses mathematical models to solve problems and make decisions. AI is used extensively in the textile and clothing sector for supply chain management, design, sewing floors, production planning, pattern construction, and sales forecasting. Manufacturing automation is improved by a variety of AI technologies, including fuzzy logic, genetic algorithms, artificial neural networks (ANN), evolutionary techniques, and multi-agent systems.

Sewing thread breakage prediction is one crucial area in which AI significantly influences. Depending on the fabric type, stitch density, machine speed, needle size, and thread count, thread breakage impacts garment productivity. By employing machine learning, an intelligent system may anticipate and reduce thread breakage, increasing efficiency. Eleven features and nine supervised algorithms have been used to analyze data from the garment industry; models such as decision trees, random forests, and artificial neural networks (ANN) have demonstrated great accuracy (up to 100%). Many businesses ignore thread breakage-related production inefficiencies, which unskilled workers and skill shortages frequently bring on. This AI-powered system improves productivity and accelerates economic growth by anticipating and preventing problems. Industries can propel economic advancement by fusing AI with human intelligence and sensory capacities. In contrast to traditional computers, AI-powered systems facilitate learning, promoting ongoing innovation and productivity. The use of AI in the production of textiles and clothing will increase process optimization and boost industrial competitiveness as it develops.

Intelligent Automation’s Contribution to Reducing Downtime

Intelligent automation is an innovative way to reduce downtime caused by sewing thread breaking. Automated systems can forecast the likelihood of breakages by utilizing real-time data analytics and machine learning algorithms. This reduces unplanned downtime and maintains a steady flow of production by enabling preventive interventions and real-time changes. In addition to increasing operational efficiency, automation minimizes human error and maximizes resource use.

The garment industry’s current financial situation:

Expert sewers sew garments, and their quality is checked. Several elements, including lead time, product quality, production cost, and labor productivity, are critical to success in the apparel sector. Global issues have impacted exports; China leads with $116 billion, followed by Bangladesh with $33.16 billion. Production is interrupted, and repairs and quality problems affect the economy. One significant disadvantage is sewing thread breakage, which lowers productivity and product value. RMG, which employs more than 4 million people, provided 84% of Bangladesh’s foreign exchange during the fiscal year 2022–2023. Consumer demand is what keeps the apparel sector very competitive. By monitoring time and speed, work measurement techniques establish a connection between productivity manufacturing faults and staff performance. Improvements can lower losses even while stitching flaws cannot be removed entirely. Considering worker skills and equipment limits, factories use automation, sales forecasting, and supply chain management to improve output while analyzing sewing costs and rework concerns.

Sewing Thread Breakage Feature Factors:

Thread tension, machine speed, stitches per inch, fabric type and weight, thread count, tensile Strength, and thread elongation are the primary elements that affect sewing thread breaking. More thread breaks result in reduced quality, more rework, and extra expenses.

1. Sewing Tension (ST): Higher values on this scale, which runs from 0 to 9, indicate tighter seams.
2. Machine Speed (rpm): Depending on the type and thickness of the cloth, sewing machine speeds can range from 2400 to 4000 rpm.
3. Stitches Per Inch (SPI): Although the number of stitches varies depending on the cloth, 8, 10, and 12 SPI are typical figures.
4. Needle Number (NN): Needles are available in various sizes; heavier fabrics require larger needles.
5. Fabric Weight (GSM) and Thickness (mm) are evaluated based on ASTM standards to determine the fabric’s characteristics.
6. Sewing Thread Count (Tex): The study uses threads with 50, 60, and 100 tex counts.
7. Tensile Strength (N/tex): This factor influences fabric type and GSM and ranges from 4.50 CN/Tex to 18.25 CN/Tex or force per unit linear thread density.
8. Thread Elongation Percentage (El%): Higher elongation rates are typically found in threads with higher tex values.
9. Fabric Types: To obtain the best results, the research used a variety of fabrics, including jersey, denim, fleece, pique, Lacoste, and others.

Minimizing thread breakage and related downtime requires understanding these aspects and using intelligent technologies to monitor them.

Methods of Machine Learning for Thread Breakage Prediction

With historical data, training machine learning models, such as support vector machines, decision trees, and artificial neural networks (ANNs), can predict sewing thread breakage. These algorithms can identify patterns that result in breakage by analyzing variables including fabric type, thread tension, and machine speed. Manufacturers can ensure smoother operations by implementing these forecasts into the production system and taking corrective action before thread breaking occurs.

Combining Up-to-Date Information for Efficient Breakage Analysis

For breakage analysis to be effective, real-time data collection is essential. IoT sensors integrated into the machine may continuously monitor machine speed, thread tension, and fabric quality. This data can be sent to centralized systems for analysis, giving quick insights into possible problems. Manufacturers can combine these systems with machine learning models to get real-time predictions, alerts, and recommendations. This enables them to make quick adjustments to avoid thread breakage.

Improving Machine Speed, Thread Tension, and Other Elements

Optimization entails adjusting factors like stitches per inch, machine speed, and thread tension to avoid breakage. Intelligent automation systems using real-time data can continuously monitor and adapt these characteristics. For instance, when the system detects a higher danger of breaking, it may immediately reduce machine speed or modify tension. By optimizing these variables, production can run more smoothly and quickly and significantly reduce downtime.

Methods for Gathering and Examining Data for Sewing Thread Breakage Research

Thread type, fabric characteristics, machine settings, and operating conditions are just a few variables that must be gathered for thread breakage investigations. Subsequently, this data is examined using statistical methods or machine learning algorithms to find patterns and correlations. Techniques like regression analysis, clustering, and predictive modeling can be used to comprehend how many elements interact and lead to thread breaking. Developing successful automation solutions requires careful data collection and analysis.

Case Studies: Effective Automated Solution Implementation

Several clothing manufacturers have successfully used intelligent automation to lower sewing thread breakage. One plant in Bangladesh, for instance, installed a predictive maintenance system based on machine learning, which significantly decreased downtime caused by thread breakage. The firm reduced thread breakage events by 30% by automatically altering machine parameters and monitoring real-time data from sewing machines, which increased productivity and efficiency. These case studies demonstrate the advantages of automation in practical settings.

The Financial Gains from Cutting Downtime in the Production of Clothing

Cutting downtime in the clothing production process has significant financial advantages. Factories can increase productivity, save labor costs, and improve product quality by reducing thread breakage and operational disruptions. Predictive maintenance and real-time modifications make costly rework and repairs less necessary. These advancements result from better resource use, increased profitability, and a more formidable competitive position in the market.

Implementing Intelligent Automation for Thread Breakage Presents Difficulties

Although intelligent automation has many benefits, implementing it can be difficult. Among these are the high upfront costs associated with automation technology, the requirement for qualified staff to run and maintain the systems, and the integration of new systems with preexisting equipment. Furthermore, gathering and evaluating data from various devices and procedures can be challenging. Careful planning, training expenditures, and a gradual automation shift are necessary to overcome these obstacles.

Trends for the Future: Machine Learning and Artificial Intelligence in Clothing Production

AI and machine learning in clothing manufacturing have a bright future as technology develops. Thanks to developments in automation, deep learning, and predictive analytics, more precise forecasts and improved production procedures will be possible. In addition to thread breakage, AI systems can detect more general production line inefficiencies, leading to significant gains in efficiency, quality, and cost control. AI will be essential to the next generation of smart factories as it is increasingly incorporated into routine manufacturing.

Increasing Efficiency with Automation and Predictive Maintenance

In conclusion, reducing sewing thread breakage-related downtime using intelligent automation and predictive maintenance is a potent tactic to boost output in the clothing industry. Manufacturers may increase production, decrease costs, and minimize thread breakages by utilizing machine learning models, real-time data, and continuous optimization. Long-term advantages include a more competitive, efficient, and sustainable manufacturing process, which eventually boosts the economy and industry.

References:

1. https://www.textileworld.com/textile-world/features/2020/03/automated-cutting-sewing-developments/
2. http://dspace.uiu.ac.bd/handle/52243/2064
3. https://www.fibre2fashion.com/industry-article/9744/automation-in-garment-making
4. https://www.fibre2fashion.com/industry-article/9797/revolution-in-garment-industry-advancement-in-cutting-and-sewing
5. https://www.amefird.com/wp-content/uploads/2010/01/MinimizingThread-BreakageSkips-2-5-10.pdf
6. https://www.scribd.com/document/498546497/artificial-intrlligence