Author: Jeneefa T
AI is one of the significant parts of the Industry 4.0. It has brought a change in the pattern of operation and driven an interaction between the machine and the man. Now, the industrial revolution has been raised to the industry 4.0 which is the fourth industrial revolution which includes development of AI, cyber-physical systems, cloud computing and Internet of things(IoT) operations. Each industrial revolution is driven by the technological innovations. As AI emerges into our real life, there is an urgent need for systematic development and implementation to see its impact in the next generation of industrial revolution.
The term Industry 4.0 is given to the current trend of development and data exchange in manufacturing technologies. The introduction of Artificial Intelligence and Machine Learning in industry 4.0 produces a big change in manufacturing industries that can introduce new business opportunities and result in the efficient improvement. It represents a new stage in the organization and control of industrialized value. Industry 4.0 is generally referred as a transformation in analyzing data in a faster and more flexible manner to produce higher-quality goods at reduced costs.
Figure-1
Industrial Revolutions:
The industrial revolutions have been triggered by the technological innovations during historical timeline. It produces a path for future development. Here is the breakdown of industrial revolutions and remarkable changes in respective categories.
Figure-2
INDUSTRY 1.0:The first industrial revolution is due to the rapid development of steam machines and all sorts of other machines which led to the industrial transformation with mechanization of manufacturing. This also includes the innovation of power mills, railway lines etc.
INDUSTRY 2.0:The second industrial revolution is seen as a period of revolutionary in Ford Company and some extend to automation. This includes the introduction of electricity as energy and the mass production of cars.
INDUSTRY 3.0:The third industrial revolution is leads to the rise of computer networks and the birth of the Internet was the biggest innovation in this period. This also includes the discovery of semiconductors, large scale automation in manufacturing and rise of robotics, oil refineries in industries.
INDUSTRY 4.0:The industry 4.0 is the fourth generation of industrial revolution. This is the current trend of automation in manufacturing industries. This provides the path for digitalization in manufacturing sector. This includes cyber-physical systems, cloud computing, big data analysis, exploring human machine interaction which brings the industry revolution to the next level.
Applications of Industry 4.0:
Figure-3: Application of Industry 4.0
In industry 4.0, few technologies that play a vital role are Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analysis. This brought a change in the pattern of revolution in industry and also driven a new form of interaction between AI and Ml.
AI in Industry 4.0:
Artificial Intelligence is one most disruptive technology to create revolution in the management and production organizations. Management is now driven by effective data management and Artificial Intelligence that will predict the future. For the benefit of management organization more data sets are fed to the computer to observe the needs and decisions to be taken. AI techniques such as Machine Learning (ML), Deep Learning (DL) are implemented in a significant way.
Need of AI:
The Artificial Intelligence is a combination of several technologies, which makes the machines to learn and to empower computer to mimic human intelligence such as human intelligence such as decision making, text processing and visual perception.
Through AI Automatic learning will improves product quality by providing prediction method, and by replacing visual inspections with robots that produce efficient result with more accuracy.
The industrial production due to AI in industry 4.0 can achieve higher efficiency compared to labour force.
Intelligent management, the optimization can be done in manufacturing process by utilizing real-tile data analysis, AI and ML.
Benefits of AI in Industry 4.0:
The AI techniques raised in industry 4.0 for the following reasons:
Increased Productivity.
Reduction in human error.
Optimization of product costs.
Increased product development.
The other benefits such as:
The Predictive mechanism leads to cost reduction, which means labor costs, materials wastage etc.
The real time production has been reached a new level of optimization.
The AI is mostly used in agriculture, transportation, and in many other fields.
The production of new products has been increased.
The improvement in the human-machine collaboration by employing machines in risky areas.
Limitations:
Software programs need regular upgrading to adapt to the changing business environment which is time consuming and costly.
Integration challenges and lack of understand and interoperability with other systems.
The automation technology may leads to ethical issues like rise in job losses etc.
AI IN BIRDS SPECIES CLASSIFICATION:
STEP 1: Import Dataset: Here we will be importing Kaggle dataset:
STEP 2:Mount drive:
from google.colab import drive
drive.mount('/content/gdrive')STEP 3: Import Libraries:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.utils import plot_model
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.utils import to_categoricalHere we will be using keras library for creating and training our model and for plotting and better visualising we will use matplot.
STEP 4 : Image processing of train and test set:
train_data = ImageDataGenerator( preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input,validation_split=0.2)
Test_data = ImageDataGenerator(
preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input)The MobileNet v2 models of keras allows different width models to reduce the number of multiply-adds and thereby reduce inference cost on mobile devices.
After further processing output is like:
STEP 5: Determine figure shape for analysis:
Figure-4
STEP 6: Plotting test categories:
Figure-5
Here we are creating separate labels and plotting the random images.
STEP 7:Plotting accuracy:
Figure-6:Accuracy: 93%
Applications:
Figure-7: Application of AI
Conclusion:
AI is one of the most important components in this revolution. Industry is rebooting to a new digital world by adopting smart factory. The industry revolution will make the manufacturing more intense and competitive. This manufacturing revolution results in increased productivity, improved revenue growth and by change the organizations in a better way.
GitHub Link:
References:
https://zif.ai/pivotal-role-of-ai-and-machine-learning-in-industry-4-0-and-manufacturing/
https://towardsdatascience.com/a-very-brief-introduction-to-ai-in-the-industry-4-0-14e6f4b46cd1
https://dzone.com/articles/role-of-artificial-intelligence-and-machine-learni
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