The benefits of Vision AI for quality inspection

Virtually every production line is equipped with some sort of process for identifying defective parts, faulty products or incorrect packaging. Defects can have major consequences for manufacturers, resulting in costly recalls, legal repercussions, rejected items and lost customers. In addition, defects can damage a company's image, reputation and brand.

Visual inspections

An essential way to reduce risk, reduce waste and increase productivity is to correctly identify product defects early in the manufacturing process. Some manufacturers rely on manual inspection by workers, while others use traditional machine vision. And some go a step further by integrating artificial intelligence (AI) into their inspection process. Artificial intelligence is changing the way visual inspections can be performed. It allows manufacturers to implement automation faster, cheaper and more accurate than ever by harnessing the power of deep learning. Applying AI to visual inspections, rather than just machine vision, also gives manufacturers more flexibility for changes in the production line, allowing factories to respond to varying customer demand and supply chain disruptions. AI models can be created and deployed to adapt to new product types, new components or new packaging types, eliminating the cost of reprogramming traditional vision systems and making vision AI for quality inspection applicable to a wide range of industries.

Limitations of machine vision

Machine Vision has been used for decades. Machine vision systems can assess details in images too small to be seen by the human eye, and inspect them with greater reliability and accuracy than any human inspector. To be effective, machine vision systems are best deployed when simple logical rules can be applied to assess compliance, in a process that is well defined and rarely changes. Examples of these applications include part presence/absence, part orientation and measurements. But machine vision has its limitations. These systems face significant challenges with variability - identifying whether the right product is in the right place at the right time. Each variation requires time to reprogram the system for each variable, which can be very expensive. Machine vision systems can also struggle to tell the difference between visually similar images. Vision AI, on the other hand, is ideal for inspections with variability in product type, color, location of the object being inspected, surface damage or dents in metals and plastics, or the quality of a weld. In addition to product defects, vision AI can determine, for example, whether a box is correctly sealed and labeled, whether the pallet is damaged, etc. And if the environment of the objects to be inspected contains variations in lighting or reflections, vision AI is better equipped to automate these inspections.

When is Vision-AI better suited for quality inspection?

Surface inspection

Finding defects on surfaces (such as baked goods, wood, sheet metal and painted surfaces) is an application where AI excels. These defects are easy for operators to see and identify, but because of their highly variable nature in size, location and even type of defect, they can be difficult for traditional machine vision to find. An anomaly recognizer can be quickly trained on only good parts to find the defective items efficiently.

Inspection of packaging

Within all types of packaging, things can go wrong. Because packaging can change often, and because of its variability, it is not always the most cost-effective to automate inspection. With AI, inspecting a box for all parts, a pallet for completeness, confirming that a product is packaged correctly, or even checking labeling is quick and cost-effective. Early problem identification can prevent machine breakdowns, reduce downtime and even lower costs.

Complex assembly

Complex assembly, especially with variation between production runs, can be costly to automate. PCBA manufacturing and general electronics assembly, for example, can have so many components that traditional rule-based machine vision requires too much time or cost to become operational. With Classifiers and Anomaly Recognizers, visual inspection can be set up quickly with a small data set and minimal labeling.

At EKB, we are happy to think with you about how you can apply machine vision in a way that suits your business. More information: www.ekb.nl