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Exploring More Reliable Fabric Inspection Methods

Author: Site Editor     Publish Time: 2026-06-30      Origin: Site

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Exploring More Reliable Fabric Inspection Methods

In May 2026, the Gordon team from Serbia visited SUNTECH, touring the factory and engaging in technical exchanges. During the visit, both parties held in-depth discussions regarding current quality control challenges in production, focusing specifically on optimizing the fabric inspection process.

Gordon shared their specific requirements for fabric inspection. Unlike common minor defects, the client was particularly concerned with large-scale issues such as extensive color shading and large stains. Such defects not only directly impact product quality but also significantly affect customer acceptance and market feedback. Consequently, improving the reliability of detecting these types of anomalies became a key priority in their inspection management.

Based on the client's product characteristics and quality standards, the two parties discussed the application of AI-driven fabric inspection in actual production, covering various fabric types, quality benchmarks, and production workflows. The client sought a more stable and standardized inspection method to minimize inconsistencies caused by human judgment, thereby making quality management more transparent and controllable.

During the factory tour, the Gordon team observed the AI fabric inspection machine in operation and engaged in detailed discussions using their own fabric samples. After witnessing the inspection performance firsthand, the client endorsed the proposed solution, recognizing it as a step toward elevating their quality management capabilities.

This visit not only deepened mutual understanding regarding fabric quality control needs but also laid a solid foundation for future collaboration. Moving forward, SUNTECH will continue to provide intelligent solutions tailored to the client's specific operational needs, helping them further enhance product quality and production management standards.

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