Transform Manufacturing Quality Control with Computer Vision.
In the fast-paced competitive world of manufacturing ensuring product quality while maintaining efficiency is paramount. To maintain stringent quality standards, the traditional quality control methods often fall short due to their reliance on manual inspection processes. As the production process proceeds through various stages toward completion, delayed defects identification can significantly increase the cost of defects. The “Rule of Ten” suggests that the cost of correcting a defect increases tenfold at each subsequent stage of the production development cycle. Thus, it’s extremely important to identify defects in the early production cycles to reduce overall cost impact and, in extreme cases, total product recall.
To overcome challenges, improve productivity, efficiency, and customer satisfaction, an automated solution with machine learning (ML) models is required to detect anomalies in the manufacturing production line. Using Amazon Web Services’ (AWS) computer vision at the edge, companies can detect defects thus improving product quality and reducing costs.
Computer vision and ML-based solutions can revolutionize manufacturing inspection by automating defect detection, leading to improved inspection process, reduced errors, and visibility into the manufacturing chain. These technologies automate extraction, analysis, classification, and understanding of useful information from images and videos. To achieve optimal performance, deep learning models require extensive datasets of images and videos for training.
Insight and visibility into the manufacturing process can be further improved by implementing dashboards, notifications, and alarm systems at different stages. Let’s explore the architecture and components of these solutions.
