Quality Guard Pro Manufacturing Defect and Production Intelligence Platform
Kasturi, K and Surya, J (2026) Quality Guard Pro Manufacturing Defect and Production Intelligence Platform. Quality Guard Pro Manufacturing Defect and Production Intelligence Platform, 6 (14). ISSN 2581-9429
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Abstract
QualityGuard Pro is an advanced AI-driven Manufacturing Defect and Production
Intelligence Platform designed to improve product quality, reduce defects, and enhance overall
production efficiency in modern manufacturing industries. Traditional quality inspection methods rely
heavily on manual checking, which is time-consuming, error-prone, and unable to keep up with high
speed production environments. To overcome these limitations, QualityGuard Pro integrates artificial
intelligence, machine learning, computer vision, and real-time data analytics to automate the entire
quality control process. The system continuously collects data from industrial cameras, IoT sensors, and
production machines on the manufacturing line. This data is processed in real time to detect defects such
as surface scratches, misalignment, cracks, or missing components. Deep learning-based models are
used to ensure high accuracy in defect identification, while predictive analytics helps in forecasting
potential production failures before they occur. In addition to defect detection, the platform provides a
centralized dashboard that displays key performance indicators such as production rate, defect
percentage, machine efficiency, and quality scores. It also includes alert systems that notify supervisors
instantly when abnormal patterns or critical defects are detected. Overall, QualityGuard Pro transforms
traditional manufacturing systems into smart, data driven environments. It enhances decision-making,
reduces operational costs, minimizes waste, and ensures consistent product quality. This makes it a
powerful solution for Industry 4.0 smart factories aiming for automation and intelligent production
management.
| Item Type: | Article |
|---|---|
| Subjects: | Computer Applications > Artificial Intelligence |
| Domains: | Computer Science |
| Depositing User: | IR Admin |
| Date Deposited: | 02 Sep 2026 11:36 |
| Last Modified: | 02 Sep 2026 11:38 |
| URI: | https://ir.vistas.ac.in/id/eprint/21175 |
