CS18 - FIBER OPTIC MANUFACTURING USING AI IN QUALITY ASSURANCE
SCURS Disciplines
Business
Document Type
General Poster
Invited Presentation Choice
Not Applicable
Abstract
Traditional quality assurance (QA) in manufacturing has been reliant on manual inspection and post-production testing. While these methods once supported stable production environments, they are becoming difficult to sustain. With production speeds increasing and performance expectations rising, these high-precision environments require high-speed, impeccable performance, and cost-efficient production at the same time.
Artificial intelligence (AI) in quality assurance allows facilities to move toward a proactive quality management through smart detection of product deviations, while maintaining consistency, enabling faster production cycles, and ensuring operational safety. Rather than identifying defects after production, these AI-driven tools independently adjust production equipment to specific requirements using analytical data for predictive maintenance across every production stage. As technology evolves, it will become essential for manufacturing facilities to operate with high-efficiency smart systems. In fiber optic manufacturing, this need is critical, where the most minute defect can compromise signal transmission and network reliability.
This research evaluates how AI, when integrated with advanced manufacturing technologies, enhances quality control in fiber optic production and delivers higher-quality products at a rapid pace. Drawing from published research, industry literature, and real-world industry applications, this analysis highlights the importance of improved real-time defect detection through AI-assisted quality assurance. Industry leaders such as AFL and Corning illustrate how AI-enabled systems are modernizing legacy inspection practices, strengthening operational stability, and supporting workforce training.
These findings suggest that AI-enabled smart systems are not merely a technological upgrade but a structural evolution in a manufacturing strategy in which humans and machines work together. As global demand increases and production quality standards continue to advance, these smart, adaptive systems will play a vital role in positioning production facilities to meet the emerging expectations of Industry 5.0.
Keywords
Artificial Intelligence; Fiber Optic Manufacturing; Predictive Maintenance; Quality Assurance
Start Date
10-4-2026 9:30 AM
Location
University Readiness Center Greatroom
End Date
10-4-2026 11:30 AM
CS18 - FIBER OPTIC MANUFACTURING USING AI IN QUALITY ASSURANCE
University Readiness Center Greatroom
Traditional quality assurance (QA) in manufacturing has been reliant on manual inspection and post-production testing. While these methods once supported stable production environments, they are becoming difficult to sustain. With production speeds increasing and performance expectations rising, these high-precision environments require high-speed, impeccable performance, and cost-efficient production at the same time.
Artificial intelligence (AI) in quality assurance allows facilities to move toward a proactive quality management through smart detection of product deviations, while maintaining consistency, enabling faster production cycles, and ensuring operational safety. Rather than identifying defects after production, these AI-driven tools independently adjust production equipment to specific requirements using analytical data for predictive maintenance across every production stage. As technology evolves, it will become essential for manufacturing facilities to operate with high-efficiency smart systems. In fiber optic manufacturing, this need is critical, where the most minute defect can compromise signal transmission and network reliability.
This research evaluates how AI, when integrated with advanced manufacturing technologies, enhances quality control in fiber optic production and delivers higher-quality products at a rapid pace. Drawing from published research, industry literature, and real-world industry applications, this analysis highlights the importance of improved real-time defect detection through AI-assisted quality assurance. Industry leaders such as AFL and Corning illustrate how AI-enabled systems are modernizing legacy inspection practices, strengthening operational stability, and supporting workforce training.
These findings suggest that AI-enabled smart systems are not merely a technological upgrade but a structural evolution in a manufacturing strategy in which humans and machines work together. As global demand increases and production quality standards continue to advance, these smart, adaptive systems will play a vital role in positioning production facilities to meet the emerging expectations of Industry 5.0.