AI Suited to Industrial Inspection

Why is AI Suited to industrial inspection?

The topic of AI for factories is trendy right now. This poses several questions for producers concerning AI and its potential effects on productivity, profitability, and product quality.

What does AI mean in terms of quality control? What distinguishes it from manual inspection and the other automated inspection methods now in use? When and why should I integrate AI into my manufacturing process? Can an improved quality control method using AI replace human inspectors?

In this blog series, we’ll begin by responding to some of your most pressing inquiries about industrial AI and laying out a plan for you to harness the potential of AI visual inspection in your plant.

When Conventional Examination Techniques Fall Short?

Whereas typical computer vision depends on stacking processing filters that isolate the feature of interest and apply masks or geometries to generate a quality measurement, manual inspection relies on the subjective skill of the human visual inspector. These techniques perform effectively for consistently manufactured or fabricated products. But, when the components are biological, like beef, pork, or lumber, the problem becomes complicated.

Such products significantly impact the accurate evaluation of what “good” looks, and manufacturers must grade them correctly to enhance revenue.

Industrial Deep Learning Applications

Due to commercial and academic investments in GPU processing power and novel AI network models, deep learning has grown in popularity over the past few years. Although AI and machine learning have been used for some time, it is only recently that a library of pre-trained network models has been made available to other developers thanks to the public availability of large image datasets (like ImageNet), reasonably priced GPU hardware, and the open-source movement. As a result, it is now possible to create reasonably priced AI-based solutions for many different uses, such as industrial inspection.

The Special Advantages of AI in Quality Inspection

Due to its repetition and high level of predictability, industrial quality inspection is well-suited for AI. As a result, it is now possible to gather a dataset of photos to train a particular neural network for classification, object recognition, or anomaly detection.

Products that require a subjective assessment, sometimes from an experienced visual inspector, are highly suitable for deep learning-based inspection systems, even though 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.

For instance, medical device inspection greatly benefits from AI inspection to find flaws like zipper lines, debris, and gels across many medical balloons. This type of inspection would be complicated and time-consuming to handle with a standard rule-based system, particularly handling edge (or corner) cases associated with several fault categories and inspection objectives.

Video 01: Artificial Intelligence in Quality Inspection