AUTOMOTIVE

INDUSTRY
PAIN POINTS

• Tight production line takt time

  01

  Assembly takt times for EV batteries and electric drive systems have been compressed to near-second levels.

  Why us

  Millisecond trigger and feedback without stopping the line.
  Based on Mega Phase hardware computational imaging technology (HWCI) and pixel-level parallel computing.  
  Computational offloading
  The built-in FPGA architecture independently computes and outputs crisp images, so the production lines are operated at top speed.

• Indistinguishable, Latent Defects

  02

  There are a lot of challenging parts to inspect, such as reflective paint, shiny metal parts, transparent light strips, and more.

  Why us

  3D Surface Reconstruction:  
  Our self-developed optical designs and algorithms fundamentally isolate reflection properties to resist interference, restoring true 3D topography and texture features.
  Micron-Level Precision:  
  Up to 20MP high-resolution CMOS accurately captures even the slightest scratches on lithium battery cases, subtle deformed sealing rings, or micron gaps in connectors.
  Robust HDR Adaptation:  
  Single-frame high dynamic range fusion technology ensures precise defect capture even under the complex, fluctuating lighting conditions of the factory floor.

• Constrained space requires smarter integration

  03

  Production lines in the automotive industry feature high-density layouts where every inch of floor space is a premium asset.

  Why us

  All-in-one design:  
  As the camera unifies lighting control and computes by itself based on HWCI, external cabling can be minimized. This slashes additional hardware procurement costs.  
  Rapid scalability:  
  User-friendly deployment logic allows for seamless replication across multiple lines. This significantly accelerates the SOP (Start of Production) ramp-up phase for new vehicle models.