Improve Visual Inspections &
Achieve High-Precision
Automation
HACARUS Check AI Software
The AI developed by HACARUS undergoes iterative learning from good data samples, enabling automation of visual inspection with the same level of accuracy as experienced inspectors.
Examples of Targeted Work and Defect Detection
Use Cases
Surface Inspections
Cosmetic Defects Such as Scratches, Dents and Contaminations
Completeness Check
Foreign Object Contamination in Electronic Circuit Boards, Soldering Defects, and more
Examples of Defect Detection by Inspection Algorithms
Scratches on Metal Plates
Dents on Metal Workpieces
Foreign Object Contamination on Electronic Circuit Boards
Are you facing challenges
like these?
Wanting to automate visual inspection but haven't been able to do so
- Challanges in collecting and labeling defect in large quantities
- Unable to conduct extensive re-training every time the workpiece changes
Using rule-based systems to implement inspection equipment
- Already have inspection equipment
- You want to prepare the imaging equipment on your own
- Automated inspections take time to get up and running
AI has been introduced but not effectively utilized
- AI in use but there are accuracy issues
- AI (Deep Learning) has not been validated and cannot be applied to mass production.
- Defects missed due to unknown anomalies
Features
An AI visual inspection software producing highly accurate predictions, trained with small data & only with good samples - at high speed. It does not require individual development for each inspection target or the use of GPU, running on-premise for both training and predictions.
Until now
Each Inspection Target Requires Development of a New Inspection Model
Each inspection target needs its own AI model, which requires extensive development work.
After Introduction
No development required for each inspection target
AI Visual Inspection Models are created by iteratively learning from Small Data.
Comparison with other technologies
| Inspection Standardization |
Fast Inspections |
Model Training with Good Data |
Training Data Needed |
Learning Time |
GPU | Repeated Learning |
|
|---|---|---|---|---|---|---|---|
| Manual Visual Inspection |
× | △ | － | － | － | － | － |
| Rule Base Based Visual Inspections |
○ | ○ | × | － | － | △ | × |
| Deep Learning Based Visual Inspections |
○ | ○ | △ | Large | A Few Hours | Required | × |
| HACARUS | ○ | ○ | ○ | Small | A Few Minutes | Unnecessary | ○ |
Feel free to test out training and inspection using our HACARUS Check AI Software
Case Study
30% Reduction in Time Spent On Visual Inspections
Secondary inspection of automotive precision parts (gears), with the goal of reducing defects missed and inspection man-hours in the existing inspection system.
(Training Data: 20 Good Goods)
AI Visual Inspection System
HACARUS Check
AI Visual Inspection System for detecting defects in complex-shaped workpieces
Enabling complete automation of visual inspections
HACARUS Check for
FANUC CRX Series
Integration system with FANUC's collaborative robot CRX series