The textile industry faces low manual quality inspection efficiency, high missed inspection rates, and rising labor costs. According to statistics, the detection rate of traditional cloth inspectors is only 70%, and the speed is less than 1/3 of the AI system. AI visual inspection technology has a defect detection rate of more than 85% and a detection speed of 60 meters/minute, becoming a key breakthrough for enterprises to reduce costs and increase efficiency. SUNTECH will combine real cases to analyze how AI visual inspection can reduce labor costs and improve efficiency.
Three Major Pain Points in Fabric Inspection
1.High Labor Cost
Fabric inspectors can detect up to 200 defects per hour, and their concentration can only last 20-30 minutes. Labor costs account for 15%-20% of the total production costs. The AI system can run 24 hours a day, and a single device can replace 3-5 workers.
2.Missed Detection of Minor Defects
Traditional manual labor makes it difficult to identify yarn-level defects (such as broken warp and weft, sparse and dense lines), and the missed detection rate is as high as 30%. Taking export orders as an example, a piece of cloth is returned due to minor snags, and the direct loss can reach tens of thousands of yuan.
3.Data Loss
Manual inspection cannot record the location and type of defects, making it difficult to trace the root cause of the problem. A lining cloth factory wasted more than 100,000 yuan in raw material costs per month due to failure to locate the yarn-breaking machine in time.
Advantages of AI Visual Inspection
1. High-Precision Defect :
Multispectral imaging technology: Through bright field and dark field dual-station inspection, surface dirt (such as color spots) and light-transmitting defects (such as thin warp and holes) can be identified simultaneously, covering 99% of common defect types.
Customer case: After an Indian factory introduced the AI inspection system, the missed inspection rate dropped from 30% to 0.2%, reducing the annual return loss by more than 2 million yuan.
2. Production Adaptation
The system can be seamlessly connected to packaging machines and other equipment, and the inspection width of 1.8-4 meters can be flexibly customized to meet the needs of different materials such as knitted fabrics and non-woven fabrics.
3. Full Process Data Recording
Defect report: The defect location, type, and image of each piece of cloth are recorded to help the factory locate the problem machine. For example, a company found through data analysis that 80% of weft breaks originated from the same loom, and the yield rate increased by 18% after repair.
Integration with ERP/MES system: The inspection data is synchronized to the production management system in real time to realize the dynamic adjustment of work order priority.
How to Choose AI Vision Inspection System?
Require suppliers to provide measured data of fabrics of the same material, such as the lining fabric detection speed must be ≥80 meters/minute and the comprehensive detection rate must be >95%. Be wary of "universal" solutions, and complex jacquard fabrics require customized algorithm support11. Pay attention to long-term service capabilities, choose a system that supports lifelong learning of defect models, and ensure that suppliers provide a localized operation and maintenance team with a response time of <4 hours. Calculate the comprehensive ROI: including dimensions such as manpower savings, reduced returns, and reduced energy consumption.
Conclusion
AI visual inspectionis by no means a simple tool to "replace manual labor", but a strategic investment to promote textile companies to shift from cost centers to quality centers. It is estimated that large-scale application companies can increase efficiency by 2-5 million yuan per year, and at the same time provide data endorsement for high-end order access (such as EU environmental certification). Contact our technical team immediately to obtain exclusive cost calculation models and benchmark factory inspection appointments, and start zero-risk intelligent upgrades!
