Minimize escapes with pixel-level accuracy

Defects recognized at the pixel-level

Normalized for orientation variability

9x reduced escape rate vs human operators

High speed decisions on the edge

AI models run on the edge without Internet or reliance on cloud

Supports high inference speeds up to 100 MP / sec for rapid detection

Protocol

Support

EtherNet/IP

EtherCAT

ADS

OPCUA

TCP/IP

Modbus

Profinet

IO

MES

Automate inspection in-line via integration

Seamlessly integrate CorteX Edge with all major PLC, MES, and FTP systems

Compatible with external imaging systems

Deploy UnitX’s AI inspection faster where external imaging systems are already installed

Not just defect detection. All-in-one inspection that provides:

The UnitX Advantage

Rule-Based Vision

Other AI / Deep Learning

UnitX CorteX

Effort

Requires engineers to develop complex, bespoke rules for each defect and part type

Requires 100s of images to train

Intuitive training interface requires as few as 5 images to train; no AI experience needed. Quick to adapt to new product and defect types

Accuracy

Only accurate for well-defined defects and parts with consistent environment, leading to escapes and overkill

Can fail on part position and orientation variability, sometimes requiring additional positioning tools

Accurate for defects that are complex and variable in shape, size, location, and presentation, automatically normalizing for position variability

Yield Optimization

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill

Easily tune quality criteria specific to each defect across a number of attributes. Visualize impact on yield before pushing changes to production

See how CorteX works as part of the greater UnitX solution

1

Effort

Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Requires engineers to develop complex, bespoke rules for each defect and part type

Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Requires 100s of images to train

UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Intuitive training interface requires as few as 5 images to train; no AI experience needed. Quick to adapt to new product and defect types

2

Accuracy

Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Only accurate for well-defined defects and parts with consistent environment, leading to escapes and overkill

Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Can fail on part position and orientation variability, sometimes requiring additional positioning tools

UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Accurate for defects that are complex and variable in shape, size, location, and presentation, automatically normalizing for position variability

3

Yield Optimization

Traditional Lighting

Limited lighting configuration self-service makes it difficult to adapt.

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill

Other AI / Deep Learning

Limited lighting configuration self-service makes it difficult to adapt.

Manual effort required to tune quality criteria. Unclear how criteria impact yield, leading to overkill

UnitX CorteX

Limited lighting configuration self-service makes it difficult to adapt.

Easily tune quality criteria specific to each defect across a number of attributes. Visualize impact on yield before pushing changes to production

What people say about us

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Jessica Clark

Operations Director

CorteX Datasheet

Discover our solution