CASE STUDY — MANUFACTURING
Quality Control AI
Control material quality with computer vision
Sector:
Manufacturing, Consumer Goods, Energy
Focus:
AI Quality Control (Computer Vision)
BUSINESS CHALLENGE
Manual inspections causing defects and inefficiencies
Manual quality inspections in the manufacturing process were slow and error-prone, leading to defects and inefficiencies. Is needed an automated system to accurately detect product defects in real-time and integrate seamlessly into their production line.
SOLUTION IMPLEMENTED
An AI system detecting defects in real time with computer vision
Implemented an AI-powered quality control system that integrates computer vision to detect defects in real time. IoT Hub collects data from sensors and cameras on the production line, while Event Hub streams this data to Databricks, where machine learning models analyze it for potential defects. This data is then stored in a Cosmos DB.
TECHNOLOGY
Built to capture, stream, and analyze production data in real time
DATA PLATFORM
IoT Hub, Event Hub, Databricks & Cosmos DB
An IoT Hub controls the extraction of data, passed through an Event Hub to Databricks, storing results in Cosmos DB.
SERVING
Power BI
Real-time insights integrated into interactive dashboards, visualizing production metrics and possible piece errors.
GOVERNANCE
PURVIEW
Ensures data quality, compliance, and secure governance across all production data.
Results & ROI
What changed once the system went live
SATISFACTION
Improved manufacturing process times, leading to higher customer satisfaction and loyalty.
QUALITY
Errors are identified earlier in the manufacturing process, leading to fewer errors in the final product.
SAVINGS
Detecting errors in this early phase leads to savings in manufacturing costs and other processes.
