Automating a manual quality inspection task can bring huge benefits: Improved consistency, reduced labor costs, and higher throughput. However, transitioning from human judgment to a machine vision system isn’t always straightforward. Quality control often relies on subjective, experience-based decisions that can be difficult to replicate in an automated system.
In this article, we’ll explore some of the key challenges in automating an inspection task and how to address them.
The complexity of human judgment
A skilled inspector doesn’t just follow a simple set of rules. They intuitively evaluate multiple factors at once - perhaps one type of surface scratch is acceptable, while another justifies rejecting the product. This judgment is based on experience and an understanding of the production context, but it’s not always easy to explain why one defect is critical, and another isn’t.
For machine vision to take over, this knowledge needs to be converted into precise, measurable criteria. The system must be trained on a well-defined set of defects, ensuring it can identify and classify them just as a human would.
Ensuring a uniform presentation of parts
A human inspector can pick up a part, rotate it under a light, and make small adjustments to get a clear view. An automated system doesn’t have that flexibility—it requires a controlled, repeatable process.
For successful automation, items need to be presented consistently. This can be achieved by:
- Maintaining a stable flow on a conveyor system
- Using robotic handling to position parts precisely
- Standardizing lighting and camera angles
Without this, variations in presentation could lead to unreliable inspection results.
The challenge of freak defects
A machine vision system is programmed to detect specific defects, but what happens when an entirely new type of issue appears? Humans are naturally good at spotting anomalies—if a product is suddenly flipped upside down or has an unexpected flaw, an inspector will immediately recognize something is off.
An automated system, however, only sees what it has been trained to see. This is where anomaly detection comes into play. By integrating artificial intelligence, a vision system can learn what a “normal” product looks like and flag anything that deviates from the expected pattern, even if it’s an unfamiliar defect.
When automation reveals the truth about your quality standards
One challenge that many companies don’t anticipate is that automating quality control makes defects measurable. And sometimes the results come as a surprise.
We’ve seen cases where a company sets up a vision system to detect defects based on their stated quality tolerances, only to realize that a large percentage of their products don’t actually meet those standards. What they thought was an acceptable level of defects turns out to be much higher when measured objectively.
This can lead to two paths forward:
- Use data to improve production processes – Identifying the root causes of defects and optimizing manufacturing to reduce waste.
- Reassess quality standards - If a certain level of variation has always been acceptable, specifications may need to be adjusted.
Not just a question of replacing people
Automating a manual inspection task isn’t just about replacing human inspectors with a machine. It requires defining quality in measurable terms, ensuring consistent part presentation, handling unexpected defects, and sometimes confronting hard truths about actual production quality.
Despite the challenges, the benefits in terms of higher efficiency, reduced costs, and better data-driven quality control make automation a worthwhile investment.
