See what your paint process really does.

Continuously validate how your application behaves over time. Compare every pattern against a golden standard, benchmark applicator performance and reduce variation across production before it turns into a defect.

• Dynamic deposition behavior
• Applicator repeatability
• Pattern consistency
• Robot-to-robot variation
• Transfer efficiency behavior
• Process drift

Analyze patterns sprayed with the applicator standing still. Size, shape and volume are measured objectively, so you see exactly how the applicator itself performs.

• Size, shape and volume
• Comparison against a reference pattern

Analyze patterns sprayed in motion, the way your robot paints. Compare them side by side with their thickness profile, width and area.

• Side-by-side pattern comparison
• Thickness profile across the pattern

Measure exactly what transfer efficiency your application reaches today. Then find settings that raise it while keeping the coating quality you need.

Find the source of a defect before it reaches the body. The software highlights contamination, dirt and droplets directly in the scan and quantifies droplet distribution.

• Contamination detection
• Droplet size and density
• Statistics for a zoomed area

Apply test foil to the body, paint it on the line and scan it. You get a full surface thickness map of the real part instead of a few spot measurements.

thickness range mapped on the body

5–15 µm

Track applicator status, pattern metrics and trends across production. Teams see which applicators are stable, where variation comes from and what needs to be corrected first.

• Which applicators are stable
• Where variation is coming from
• When a process is drifting
• Where material is being wasted

We collect a data set on your line, digitize the patterns and train a neural network on them. The result is an atomizer behavior model that drives 3D simulation and brush optimization.

thickness accuracy

± 1 µm

• Collect data set
• Pattern digitalization
• Neural network
• Atomizer behavior model

Predictable simulations require predictable application behavior. The repeatability analysis module validates whether the process is stable enough to generate trustworthy digital twins and predictive models.

Change shaping air, flow rate or bell speed and see the effect on film thickness across the whole body right away.

Simulate overall paint consumption and maximum film thickness per layer, and find the setup that reaches target film build with less material.

Thickness measured on a real hood compared with the SprayBrush simulation at 16 measurement points.

average thickness match between simulation and reality

98 %

Simulated thickness maps that follow the robot's actual path. Play the build-up of paint step by step along a timeline and spot overlaps and undercoated areas at a glance.

• Follows the exact robot motion
• Clear paint overlaps and gaps
• Timeline playback of the build-up

Split the simulation by layer and by robot. Switch individual layers on and off to see how each one contributes to the final film build.

Move a trajectory point or change its speed, and SprayBrush recalculates film thickness on the part in real time.

Color the trajectory by velocity to see where the robot slows down or speeds up, and how that shows in the thickness distribution.

Moving to a new atomizer usually means re-tuning every parameter and reworking trajectories color by color. SprayBrush uses digital atomizer models to translate your existing brush table to the new atomizer, with no trajectory reprogramming.

to migrate 65 colors and 2,700 parameters at Magna Liberec

3 days