Product perfection is now an expectation for consumers, which places far more pressure on manufacturers to always deliver. Surprisingly, a great deal of quality control still relies on the human eye and subjective decisions.

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When it comes to inspection tasks, humans do a pretty good job. We can rely on our senses to detect differences, and even taste and smell depending on the product. If something changes, we’re adaptable and can make a quick decision on our own. We’re also pretty easy to train, and we learn by example.

However, when we get tired, distracted or bored we start making mistakes. Traditional vision inspection has error rates as high as 30 percent. Often, what an inspector flags as an error is actually a false positive as they begin to doubt their own decisions.

The key advantage with technology – it can make decisions faster, consistently, and over and over without getting bored. Now with artificial intelligence (AI), machines can begin to learn and make their own decisions. Despite these machine advantages, there’s still a significant requirement for humans in inspection processes.

For manufacturers specializing in short-run, customized, seasonal, and regional products it’s often uneconomical to fully automate inspection processes. For these manufacturers, AI-based visual inspection tools can help add decision-support so an operator always makes the right, objective choice.

New AI-based tools for visual inspection take advantages of advances in edge processing and simplified algorithm development to help highlight product differences and deviations. For many manufacturers who have or are evaluating AI, model training and development pose significant cost and expertise barriers. In comparison, new tools use operator input to help train the AI model. Using a basic image compare function, the system visually highlights possible product differences for an operator as they evaluate products against a saved “golden reference” image. As the operator accepts or rejects initial possible errors, behind-the-scenes the AI model is transparently trained and it will quickly begin suggesting if it thinks a difference is an error.

In addition, automated and customizable reporting tools help manufacturers gain data on visual inspection processes. Often, visual inspection poses a “data black hole” for manufacturers, which makes it difficult to ensure end-to-end quality and time-consuming to resolve issues when errors occur. For example, with these reporting tools, operators can capture images of manual inspected products alongside their notes and save the data to their manufacturing planning system.

Making Subjective Decisions Consistent

One of the challenges with rules-based machine vision inspection is subjective decisions, especially where it may be a fine line between “good and bad”. Products with irregular grains and patterns, for example, are difficult to assess with machine vision. For example, automated inspection may have trouble distinguishing between a scratch on a piece of hardwood floor (“bad”) and the naturally occurring grain, knots and patterning desirable in high-end flooring products (“good”).

With an AI-based decision support tool, the AI model can be trained to match the capabilities of a manufacturer’s best inspectors. Once the quality manager is satisfied with the AI model’s decision support, they can stop training the model and share the app with other inspectors. In essence, AI replicates the decision making capabilities, experience, and expertise of their best inspector and shared it across the production facility.