Beyond AOI: An AI-driven revolution in visual inspection
In the relentless pursuit of quality and yield in manufacturing, visual inspection plays a crucial role. As industries strive to produce flawless products, the need for accurate, efficient, and adaptable inspection processes has never been more pressing. In response to this challenge, Averroes.ai seeks to redefine visual inspection in manufacturing, which will ultimately replace outdated AOI inspection methods.
•The Quality Imperative: Quality control isn’t merely a buzzword; it’s a financial imperative.
According to a study by the American Society of Quality, the cost of poor quality in manufacturing organizations amounts to a staggering 15-20% of total sales revenue. This statistic underscores the critical importance of effective inspection processes across various manufacturing sectors, including electronics, semiconductors, and automotive industries. The impact of poor quality on a company’s bottom line highlights the need for advanced inspection techniques that can significantly reduce defects and improve overall product quality.
The evolution of visual inspection
Visual inspection has come a long way from its humble beginnings, but the rapid pace of technological advancement has outstripped traditional inspection methods.
Manual Inspection: The Beginning
Initially, manufacturers relied heavily on manual inspection processes, where human workers visually examined each product for defects. While this method allowed for detailed inspection, it was time-consuming and prone to human error, especially over long periods of repetitive work.
Semi-Automated Inspection: A Hybrid Approach
As technology advanced, semi-automated inspection methods were introduced. These provided workers with specialized tools to make more informed decisions and accelerate the inspection process. This hybrid approach combined the discernment of human inspectors with the efficiency of technological aids.
Automated Optical Inspection (AOI)
The next significant leap came with the introduction of Automated Optical Inspection (AOI) systems in the early 2000s. These fully automated systems use cameras and algorithms to detect defects without human intervention. AOI represented a major step forward in terms of speed and consistency, allowing for high-volume inspection in industries where manual methods were no longer feasible due to the scale and complexity of production.
The limitations of traditional AOI in modern manufacturing
However, the manufacturing landscape has changed since AOI was first introduced. Products are becoming increasingly smaller, more compact, and more complex. Defects are now harder to catch and often more subtle. The AOI systems designed for the technology of the early 2000s are struggling to keep up with these changes. What worked two decades ago is no longer sufficient for today’s manufacturing challenges.
The Need for a New Paradigm
As everything in manufacturing gets smaller and more intricate, the limitations of traditional AOI become more apparent. Complex defects that were once rare are now common, and the systems built for earlier technologies are proving inadequate.
This gap between inspection capabilities and manufacturing realities is driving the need for a new approach to visual inspection – one that can adapt to the increasing complexity and miniaturization of modern products.
How Does AOI Systems Work?
Traditional AOI systems typically consist of three key elements: the camera, the algorithm, and the actuator. While each component is crucial, the algorithm stands out as the most critical element, serving as the brain of the system.
Why traditional AOI Falls short in today’s manufacturing landscape
Accuracy: One of the primary issues with traditional AOI systems is accuracy. These systems often produce a high rate of false positives, flagging defects where none exist. This oversensitivity can significantly lower production yield, as perfectly good products may be unnecessarily rejected or subjected to additional inspection. The problem of false positives not only impacts efficiency but can also lead to increased costs and reduced overall productivity.
Flexibility: Adapting the algorithm to accommodate new product designs or detect new types of defects often requires a complete overhaul of the system.
This inflexibility can be a major drawback in industries where product designs evolve rapidly or where new types of defects may emerge due to changes in manufacturing processes.
Changes in the inspection environment: Changes in the inspection environment, such as variations in brightness or increased noise levels, can severely impact the algorithm’s effectiveness.
Even small changes in product design or positioning can lead to unreliable results, necessitating frequent recalibration and adjustment of the system.
Domain expertise required & resource intensive:
Implementing and maintaining traditional AOI systems is a resource-intensive process requiring significant domain expertise. Unlike AI-based systems, traditional AOI relies on programmed rules and parameters that need to be manually set and calibrated for each specific inspection task.
This process demands:
• Extensive knowledge of the manufacturing process and potential defects.
• Programming skills to create and adjust detection algorithms.
•Time-consuming calibration to ensure accurate detection.
• Ongoing adjustments to account for even minor changes in products or production processes.
The time-intensive nature of setup and recalibration can lead to production delays and increased costs, especially in industries with rapidly evolving products or frequent changes in manufacturing processes.