Krevera utilizes an AI-powered vision system to automate quality control and optimize plastic injection molding processes, significantly reducing labor, scrap, and maintenance costs. By providing real-time defect detection and machine setting adjustments, Krevera enhances production efficiency and minimizes downtime for plastic manufacturers.
Funding
Funding not disclosed
Founders
Product
Problem
Plastic injection molding processes are plagued by quality control issues, leading to high scrap rates, increased labor costs, and frequent machine downtime. Inefficient process optimization and a lack of preventative maintenance further exacerbate these problems, resulting in significant financial losses for manufacturers.
Solution
Krevera offers an AI-powered vision system designed to automate quality control and optimize plastic injection molding. The system provides real-time defect detection, identifying the type, size, location, and severity of flaws with high accuracy. By analyzing process data and correlating machine settings with outcomes, Krevera enables automatic process drift detection and root cause analysis. The system then uses machine learning to optimize machine settings, reducing defect rates, increasing production, and minimizing downtime through virtual twin technology, ultimately enabling lights-out manufacturing.
Target Audience
Krevera's primary customers are plastic injection molding manufacturers seeking to reduce labor costs, minimize scrap rates, and optimize their production processes.
Features
- Defect detection identifies defect type, size, location, and severity.
- Live-adjustable defect tolerance.
- Robust to changes in lighting, camera placement, product orientation, and dust/dirt on lens.
- Process drift detection for preventative maintenance.
- Automatic root cause analysis.
- Auto-generates Process Setup Sheets.
- Correlates machine settings and sensor data with outcomes.
- Machine learning optimizes machine settings and automatically updates them.