Quality Prediction Models and Process Optimization Algorithms
Automatically derive quality predictions and optimal process conditions based on XGBoost and genetic algorithms
Quality prediction/process optimization
Vision x Validation
Industry domain-specific language model
V² collects and analyzes images and process data from manufacturing sites in real time, and automatically detects micro-defects such as scratches and contamination based on AI deep learning.
By merging multimodal sensor data, quality can be predicted quantitatively, and high-speed inspection and derivation of optimal conditions are possible without interrupting the process using a lightweight model based on edge devices.
Automatically derive quality predictions and optimal process conditions based on XGBoost and genetic algorithms
Highly efficient vision anomaly detection with AI models based on normal images that can detect even subtle anomalies without learning
CNN-based deep learning algorithm automatically extracts quality and defect information from unstructured images and analyzes them precisely
Realize virtual sensors by analyzing data from various sensors and improve real-time judgment throughout the process
Real-time product
quality inspection
Quality prediction
and process
optimization analytics
Automate image
alignment and
quality inspection
Analyzing and visualizing
process characteristic
importance
Provide dashboard about
information summarized
on quality results
Suggest optimal
process conditions
Starting with the construction of a smart factory in 2019,
IMPIX has created best practices optimized for SMEs
through various AX(AI Transformation) projects.
