Question 19
Scenario 2: Smart Manufacturing Quality Control System
An automotive manufacturer implements an IoT-based quality control system that monitors production line metrics, detects defects in real-time, and optimizes manufacturing processes across multiple assembly plants.
System Architecture:
● IoT sensors at production stations (coordinates provided for each plant)
● Quality control hub: (42.3601^(o) N, 71.0589^(o) W)
● Real-time data streams: temperature, pressure, vibration, visual inspection
● ML models for defect prediction and process optimization
Based on the above data, answer the given subquestions.
When detecting defects in real-time during high-speed production, which algorithmic approach best balances accuracy with processing speed requirements?
Data based statistical analysis
Optimized computer vision pipeline with cascaded classifiers and early rejection mechanisms
Manual visual inspection only
Random sampling for quality checks