Automating Defect Detection in Laminate Manufacturing Process
Robovision & Unilin Case Study
Real-time detection of rare and hard-to-see defects is not only a challenge, but it also creates an opportunity for manufacturers to raise the bar on their quality standards, increase efficiency for operators and improve production yields. The Unilin Group partnered with Robovision to build an AI-powered in-line quality control system that automates visual inspection for their product category – laminate flooring.
For one of the world’s largest flooring manufacturers, the Unilin Group, mostly known for its successful brand Quick-Step, uses Artificial Intelligence (AI) to ensure that they deliver the highest quality laminate flooring. They were looking for a solution partner, who can help them build an AI-powered in-line quality control system to aid their operators in detecting defects.
Their challenge was threefold. First, the high assembly line speed (100 metres / minute) makes it difficult for even experienced operators to accurately spot all possible flaws. Second, the shortage of trained personnels or domain experts is a huge constraint for manual quality inspection. Third, as their laminate flooring is almost the same as real wood, it is not easy to tell if it is a defect or just a natural element of the print.
As a quality-driven company, automating the quality control process with accurate defect detection can help them achieve a competitive edge. The Unilin Group approached Robovision – an award-winning leader in AI and computer-vision technology – to develop a scalable solution with their easy-to-use AI platform to automatically detect defects, across different laminate types and colours. Operators are alerted in real time when a defect occurs, so they can immediately remove the defective product out of production. They can also adjust production factors if necessary, in order to avoid producing more products with the same defect.
Thanks to the AI quality control system, the Unilin Group was able to achieve a higher production output and streamline the visual inspection process.
Figure 1: the Unilin defect detector
Figure 2: Collaborative Intelligence – human operative using AI
Figure 3: Defect in a laminate board
Figure 4: AI-powered Robovision Platform
Reaching Desirable Outcomes
AUTOMATE COMPLEX VISUAL TASKS
Detect subtle defects in a high-speed production environment.
Spot specific defects on every single laminate plate.
Help operators focus on other production tasks.
ACCURATE DEFECT DETECTION
Deep-learning models become smarter and autonomously learn all defects on different laminate types.
EASY TO ROLL OUT AI MODELS
Access to an easy-to-use platform with no AI knowledge required.
A HIGHLY SCALABLE SOLUTION
Scaling the solution across product types and production lines.