1.
Roles of AI in Smart Manufacturing
AI plays a crucial role in smart manufacturing by optimizing production, improving efficiency,
and enabling automation. Key roles include:
• Predictive Maintenance: AI analyzes sensor data to predict equipment failures and
schedule proactive maintenance.
• Quality Control: Computer vision ensures product quality by identifying defects in real-
time.
• Automation: AI automates repetitive assembly line tasks to improve efficiency.
• Demand Forecasting: AI models analyze sales data to optimize production schedules.
• Supply Chain Optimization: AI tracks and improves supply chain processes to reduce
bottlenecks.
• Digital Twin Technology: AI-driven virtual models simulate manufacturing processes for
better decision-making.
2. Development and Key Features of AI/ML in Industry
AI and ML technologies are transforming industries by introducing automation, predictive
analytics, and enhanced decision-making. Key features include:
• Simulation: AI-based simulations improve production accuracy, efficiency, and risk
management.
• Virtual Reality (VR): Digital twins enhance product design and monitoring in
manufacturing.
• Speech Recognition: AI chatbots and voice assistants streamline customer support and
operations.
• Process Optimization: AI analyzes real-time data to improve production efficiency.
3. Key Applications of IoT in Industry 4.0 and How IoT Enables Industry 4.0
IoT is integral to Industry 4.0 by connecting industrial devices and enabling real-time data
collection. Applications include:
• Predictive Maintenance: IoT sensors monitor equipment health and predict failures.
• Quality Control: IoT-enabled sensors detect defects in production lines.
• Production Optimization: Real-time analytics adjust manufacturing processes
dynamically.
• Supply Chain Visibility: IoT tags track materials and optimize inventory.
• Worker Safety: Wearable IoT devices monitor worker health and hazardous conditions.
4. Cyber-Physical Systems (CPS) and Their Features
A Cyber-Physical System (CPS) integrates computational and physical processes. Key features
include:
• Reactive Computation: CPS continuously interacts with its environment.
• Network Connectivity: CPS enables real-time communication between devices.
• Robustness & Reliability: CPS ensures safe and reliable operations.
• Concurrency: CPS performs multiple tasks simultaneously.
• Real-Time Computation: CPS processes real-world data dynamically.
• Safety-Critical Applications: CPS prioritizes safety in operations like self-driving cars and
industrial automation.
5. Supervised, Unsupervised, and Reinforcement Learning in Industry 4.0
• Supervised Learning: AI predicts machine failures using labeled sensor data.
• Unsupervised Learning: AI clusters sensor data to detect anomalies.
• Reinforcement Learning: AI optimizes manufacturing processes by rewarding efficient
actions.
6. Image Processing Techniques
• Image Resizing: Adjusting image dimensions while maintaining aspect ratio.
• Eroding an Image: Removing small details in binary images.
• Blurring an Image: Applying Gaussian blur to reduce noise.
• Scaling, Rotating, Shifting: Transforming images for different perspectives.
• Edge Detection: Identifying object boundaries using techniques like Canny Edge
Detection.
7. Histogram Equalization & Thresholding Techniques
• Histogram Equalization: Enhancing contrast by distributing pixel intensity.
• Simple Thresholding: Converting grayscale images to binary using a fixed threshold.
• Adaptive Thresholding: Adjusting threshold values dynamically.
• Otsu Thresholding: Automatically determining the optimal threshold.
• Segmentation using Thresholding: Separating objects in images using intensity
differences.
8. Object Detection and Pattern Recognition
Example: AI-powered defect detection in manufacturing detects faulty products using deep
learning models like YOLO (You Only Look Once) or Faster R-CNN.
9. OpenCV Object Detection Example
Using OpenCV’s cv2 library:
import cv2
import numpy as np
def detect_objects(image_path):
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
cv2.imshow('Detected Edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()
detect_objects('sample.jpg')
This code detects object edges in an image using the Canny edge detection algorithm.
