What is AI in the context of quality inspection? What are its advantages over manual inspection and existing types of automated inspection? When and why should I implement AI on my production line? Can AI really replace human inspectors with a more reliable quality control process?

In this blog series, we will start to answer some of the key questions you have on industrial AI, and chart a path for you to realize the power of AI visual inspection in your factory.

When Traditional Inspection Methods Don’t Measure Up

Manual inspection relies on the subjective skill of the human visual inspector, while traditional computer vision relies on stacking processing filters that isolate the feature of interest and apply masks or geometries to perform a quality measurement. These methods work well for fabricated or machined parts that are very consistent. The solution becomes very complicated, however, when parts under inspection are organic such as beef, pork, or lumber.  

Such products introduce a lot of variability into the accurate assessment of what “good” looks like, while the need to effectively grade them is critical for manufacturers to increase profitability.

Deep Learning For Industrial Applications

Deep learning has become increasingly popular in recent years due to industry and academia driving investment in GPU processing power and new AI network models.  Although AI and machine learning have been used for some time now, it is the public availability of large image datasets (such as ImageNet), affordable GPU hardware, and the open-source movement that has created a library of pre-trained network models other developers can use. This has made it possible to develop affordable AI-based solutions for a wide range of applications including industrial inspection. 

The Unique Benefits of AI For Quality Inspection

Industrial quality inspection is particularly suitable for AI due to its repetitive nature and high level of predictability. This makes it possible to collect a dataset of images that can be used to train a custom neural network for classification, object, or anomaly detection. 

While products that are nearly identical and need to be measured for tolerance or conformity are probably easier to solve using a stack of traditional algorithms, products that require a subjective evaluation, sometimes from an experienced visual inspector, are highly suitable for deep learning-based inspection systems.  

For example, medical device inspection benefits significantly from using AI inspection to detect a variety of defects including zipper lines, dirt, and gels across a variety of medical balloon types. Addressing this type of inspection with a traditional rule-based system, including solving the edge (or corner) cases that come along with multiple defect categories and inspection targets, would make this solution incredibly complex and time-consuming to implement. 

AI, on the other hand, can help determine if a solution is feasible within a matter of days and if possible provide a repeatable path to implementing a production system within weeks.

AI identifies 18 different defects including zipper lines, dirt, and gels in up to 50 different medical balloon types.

Stay tuned for our next post, where we chart a path to developing and deploying a custom FactorySmart® AI inspection system for your factory.