Brake Disk Manufacturer Refines Quality Assessment with Vision System
Zebra Technologies, a leading digital solution provider, has announced that I.D.E.A. S.r.l. (Intelligent Development Engineered Applications), the Italian partner company of M.O.S.A.I.C. (Motion System and Information Control), has selected Zebra to drive efficiency and productivity in its quality assurance inspection process. An industrial automation provider for the automotive industry, I.D.E.A. can inspect 200 different brake discs with a single machine vision system built on Zebra’s Aurora Design Assistant software.
The new system enables I.D.E.A. to inspect every angle of a brake disc surface without the need to stop the production line or invert and rotate each disc on the line. With Aurora Design Assistant, I.D.E.A.’s engineers can create algorithms for analysis, implement loops and reconfigure steps dynamically to obtain the best performance results. Operators can also create different sets of parameters for each camera and customise the image colour-map to best match their needs.
“The Aurora Design Assistant is the right tool for our multiple camera system because it can spread the load over the available central processing unit (CPU) cores and synchronise all elements of the system with ease,” said Marco Pistilli, Project Manager, Machine Vision System and Software Developer, I.D.E.A. “Zebra’s software allows our developers to focus more on achieving our desired accuracy and performance goals instead of worrying about coding.”
A key inspection system in its automotive manufacturing operations is surface quality inspections of car and truck brake discs. The highest quality assurance is integral to the production of brake discs, which are made through a sand-casting process using sand as the mould material. After the sand-casting process, discs are cleaned of sand inside a big cleaning drum. Sometimes, the cleaning process is not optimal, and sandy residue remains.
The production process for brake discs now encompasses machine-vision inspection with images captured while the discs pass through the inspection tunnel on the production line. I.D.E.A.’s new system checks for sand and sintering traces on the disc surface so bad or non-optimal units are removed from the production line. To ensure the highest quality standards, the custom vision system is developed and tuned to highlight subtle defects that would be invisible to the naked eye.
“With human inspectors, it is natural that product samples, especially those close to the limit between good and bad, will be assessed differently by different operators,” said Pistilli. “The likelihood of these differences increases after operators have worked a few hours and are tired. But, with our machine vision system, in addition to the enhanced quality control, inspections are repeatable 24 hours a day and the machines perform faster than any operator could, and the user interface is so easy to use, it’s like using a tablet.”
Aurora Design Assistant software runs on a workstation equipped with a processor and three Zebra Concord power over ethernet (PoE) frame grabbers, which acquire and process the images captured from nine cameras with a custom-built lighting system using filters on camera lenses and spotlights.
Highest Levels of Quality and Compliance
“The automotive industry requires the highest levels of quality and compliance from its suppliers and partners, which is why machine vision solutions are needed across the automotive supply chain,” said Luca Gallo, Senior Manager, Machine Vision, Southern Europe, Zebra Technologies. “Zebra and our partners provide user-friendly solutions and the tools engineers and developers need to increase efficiency, power innovation, and optimise the frontline with new ways of working.”
Automotive manufacturers and their suppliers are increasingly turning to machine vision solutions in their operations. Zebra’s AI Machine Vision in the Automotive Industry Benchmark Report found that 56% of automotive business leaders surveyed in the UK and 43% in Germany are currently using some form of AI such as deep learning in their machine vision projects.