Everything you need to know about Generative AI in Manufacturing

Defect Detection in Manufacturing

Glossary post
Within a manufacturing plant, manufacturing defect detection refers to the systems and processes in place to ensure that defective products don’t reach customers.
Glossary post

What is defect detection in manufacturing?

Within a manufacturing plant, manufacturing defect detection refers to the systems and processes in place to ensure that defective products don’t reach customers.

In most settings, defect detection is performed with a visual defect inspection—in-line or end-of-line. Quality-assurance personnel are essentially asking: Does the product coming off the production line meet all specifications?

Quality control is essential for any manufacturing business. If you are in manufacturing, you already know that the closer you can get your defect rate to zero, the better. Additionally, spotting problems earlier in the process lets you save more time and money.

What you need to know about manufacturing defect detection

A commitment to producing quality products is at the core of most manufacturing businesses. It’s easy to see why. You have a reputation to protect, but beyond that, in some industries, failing quality standards can lead to harm or even cost lives. 

However, ensuring quality comes with a price tag—according to Aberdeen research, the cost of quality assurance is 10% to 15% of sales revenue, on average, but could be as high as 40%.

Anything you can do to bring down that price will help improve your margins. And one of today’s best options is improving the efficiency of defect detection in manufacturing.

Defect detection can save costs, for example, by preventing waste of raw materials, safeguarding your reputation, and helping you avoid fines or recalls. Manufacturers are looking for a competitive edge anywhere they can find it, and smarter manufacturing defect detection is easier than you might think.

How can you implement manufacturing defect detection?

Visual defect inspection is the traditional method of handling manufacturing defect detection. An employee with expertise inspects each item as it is manufactured, or a number of representative samples, to ensure that they meet product specifications.
Old-fashioned visual inspection for production defect detection creates a few additional challenges, as it:

  • Is hard to scale up if your production output increases
  • Relies on the expertise of just a few specially trained employees
  • Can be fallible if an employee is having an “off day” (or quits!)
  • Is a subjective process with results that could vary from day to day
  • Lacks insight into trends and trouble spots

Most importantly, with this method, by the time a problem is spotted, it’s often too late to do anything about it. 

Today, manufacturers can take advantage of newer processes combining AI and automation with optical defect detection for far-simpler defect detection and management. These apply predetermined quality standards for greater objectivity that doesn’t rely on any particular team member.

Optical defect detection technology never gets tired or has an off day. It gives you an instant heads-up about potential problems, letting you resolve them fast. However, typically, harnessing AI for manufacturing is not for the faint of heart. These processes frequently come with their own challenges:

  • R&D effort to build and train the model
  • Dedicated and specially trained team members for data engineering (data scientists)
  • Model drift, meaning you have to keep revising or retraining the model

How modern manufacturing defect detection will help you

Modern purpose-built manufacturing defect detection, found in automated optical inspection platforms, offers you the best of both worlds. You get production defect analysis that combines the superior speed and power of automation and AI, coupled with the adaptability and resilience of human learning models.

This gives you…

  • Fast deployment with no specialized staff required
  • Easy scaling as your demands/needs change
  • Less ongoing maintenance

You’ll save on costs of raw materials and personnel while gaining analytics and real-time insights to spot trends, identify trouble spots, and further optimize your production line. One recent McKinsey Global Survey of manufacturers found that adopting AI can lead to cost reductions of more than 10% while also boosting revenue by over 10%. 

Let Vanti help you get started with AI-based manufacturing defect detection in just hours—for faster, higher-quality production line throughput without sacrificing performance.