Redefining Quality Control with AI-powered
Visual Inspection for Manufacturing

The evolving industrial world

Emerging technology — from the introduction of assembly lines to the Internet of Things — has always defined manufacturing.

With the creation of computers and early automation came traditional machine vision, in which machines analyze photos of parts and components for defects based on a set of human-defined rules. While it reduces human error, traditional machine vision lacks the capacity to solve for pain points like complex defects and changing environments.

Today, more sophisticated artificial intelligence (AI), including machine learning (ML) and deep learning (DL), allows manufacturers to use AI-powered visual inspection to enhance quality and reduce costs. But even now, only 5% of manufacturing companies have a clearly defined strategy for implementing AI.

Companies need strategies to overcome challenges in visual inspection, which still relies heavily on human inspectors or inflexible rules-based machine vision. The cost of sending defective pieces to customers — both in reputation and in recalls — isn’t sustainable in a competitive global environment.

The right AI platforms offer tools that can enhance quality control and cut costs — after users tackle key obstacles.

From proof of concept (PoC) to production

Manufacturing companies can successfully create a proof of concept (PoC) of a visual inspection system in a few weeks or even a few days. But getting to a deployable solution ready for production and then scaling it threatens to bring manufacturers to a standstill.

Arriving at a PoC — which generally takes the form of offline tests run under highly controlled conditions — is a major milestone, but developers are still a long way from successful deployment. At this point, manufacturers only have less than 10% of the software needed for the first deployment, and the first deployment is a fraction of the software needed to scale to multiple production lines. Teams need to carefully plan, prepare and execute each step of AI deployment.

Too often, companies fail to scale solutions beyond an initial project or two. This is particularly pervasive in manufacturing due to the complex and unique nature of each project. In conventional detection built on rigid rules, you’ll need to invest massive amounts of time and money to adapt thousands of lines of code to account for small details and variables.

Manufacturers must overcome a uniquely complex…

Read more by downloading the Redefining Quality Control with AI-powered Visual Inspection for Manufacturing whitepaper.