Temperature / Pressure Based
Quality Control
How a leading white goods supplier used Wisdom Test to detect manufacturing defects in real-time, and to improve production yield.
The Client
Tier-1 plastic parts supplier for global OEMs, producing millions of parts each year
The Challenge
Quality Inspection is manual intensive, error-prone, no quantitative explanation, the inspection queue is long
The Solution
Integrate Wisdom Test to monitor the quality inspection, oversee the process and provide predictions
The Outcome
The quality process is automatic, explainable, adaptive, continuously learning and has no human dependency
Technical Solution
Wisdom Test makes use of production / test data for training learning models and deploys them in real-time for extremely accurate quality predictions.
Overview of the production machine, identification of sensor types and locations
Analysis of product, mold and cooling channel setup, simulation support if needed
Feature importance analysis for optimizing the number of features for model training
Clustering analysis and prediction supported by labeling campaigns, calibration and adaptation
Multi-Sensor Data Support
Wisdom Test supports combinations of different types of sensor data, such as pressure, temperature, noise, vibration, vision and electricity
Semi-Supervised Training
Wisdom Test uses clustering algorithms to inspect the process and makes use of minimal number of human labeled instances
Adaptive and Continuous Learning
Wisdom Test provides the framework for adaptive learning against data drift and integrates AI tools for easy of use
Benefits
Measurable Benefits in Quality Control and Cost Reduction