IBM > Case Studies > Make the Plant Smarter - Visual Quality Inspection Replacing Traditional Ways

Make the Plant Smarter - Visual Quality Inspection Replacing Traditional Ways

IBM Logo
 Make the Plant Smarter - Visual Quality Inspection Replacing Traditional Ways - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Computer Vision Software
  • Analytics & Modeling - Machine Learning
Applicable Industries
  • Automotive
Applicable Functions
  • Discrete Manufacturing
  • Quality Assurance
Use Cases
  • Computer Vision
  • System Integration
The Challenge

In the aspect of streamlined production and product quality inspection, it is sometimes necessary for human staff to observe, identify, and discover errors and omissions in the production process. No matter how strong a person's sense of responsibility is or how concentrated he/she is, he/she may be fatigued, neglected, distracted, not to mention, he may turn a blind eye to the mistakes of acquaintances, causing defective products to flow to the market. At the same time, if the all-manual inspection method is adopted on the assembly line, it is not only heavy work but also quite time-consuming. In a modern production line, such an inspection method cannot be accepted, and more importantly, it does not add any value to the production itself.

The Solution

IBM's cognitive vision inspection technology can magnify the surface details of products through high-precision cameras, take pictures and analyze at the subtle level, and can detect quality problems that cannot be found by the human eye, such as PCB board defects, mobile phone component defects, liquid crystal defects, wafer defects, optical lens defects, etc.

IBM's cognitive vision inspection technology has been applied in the automotive industry, helping leading auto factories to achieve high-precision inspection of auto parts, welding, painting and other processes. The IBM cognitive vision inspection system can formulate the action path of the robot arm according to the 3D data of the vehicle model, and cooperate with the matrix industrial camera and auxiliary light source to complete the 360-degree high-definition photo of the whole vehicle.

Operational Impact
  • [Process Optimization - Real Time Monitoring]

    A real-time data analysis system is deployed on the production line. The defect recognition system of the company analyzes the photos taken in real-time and identifies the defect location and defect type in the photos.

Case Study missing?

Start adding your own!

Register with your work email and create a new case study profile for your business.

Add New Record

Related Case Studies.

Contact us

Let's talk!
* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.

Thank you for your message!
We will contact you soon.