This document provides an overview of a presentation on machine learning and deep learning in smart manufacturing. It defines key concepts like smart manufacturing, machine learning, Bayesian networks, support vector machines, deep learning, convolutional neural networks, and autoencoders. It discusses their applications in predictive maintenance, quality control, and process optimization. The presentation notes advantages like time and cost optimization but also disadvantages like higher initial investment and reduced human involvement. It concludes that deep learning provides advanced analytics with great potential for smart manufacturing.

1. Presentation
Title :MACHINE LEARNING AND DEEP LEARNING IN SMART MANUFACTURING
Technical seminar
by
SYED ASADULLA
( 1HK19ME025)
VIII Semester : B.E (Mechanical) 2022-23

2. MACHINE LEARNING AND
DEEP LEARNING IN
SMART MANUFACTURING

3. OVERVIEW
INTRODBAYESIANUCTION
• SMART MANUFACTURING
• MACHINE LEARNING
• BAYESIAN NETWORKS
• SUPPORT VECTOR MACHINE
• DEEP LEARNING
• CONVENTIONAL NEURAL NETWORK
• AUTO ENCODER
• ADVANTAGES
• DISADVANTAGES
• CONCLUSION

4. What is smart manufacturing?
• Smart manufacturing is subset that
employs computer control and high levels
of adaptability while manufacturing is a
multiphase process of creating a product
out of raw materials.

5. INTRODUCTION TO MACHINE LEARNING
AND DEEP LEARNING

6. What is machine Learning ?
• . Machine learning is the science of getting
machines to take action without specific
programming.
• Machine learning has brought us self- driving
vehicles.
• Machine Learning is a branch of the broader
field of artificial intelligence that makes use of
statistical models to develop predictions.
• machine learning is used for automation,
portfolio optimization and to provide financial
advisory services to investors (robo-advisors)

7. Bayesian Networks
An application of SLT is BNs or NBN. BNs
describe the probability relationship between
several variables. Similar to BN are NBNs, a
simplest form of Bayesian Networks. Present
our proposal about the structure of NBNs.
From a theoretic approach, NBNs could be
described as follows. Given a class label C, the
naive Bayesian classifier learns from the data
(training data) the conditional probability Ai
of each attribute.

8. Support Vector Machine
SVM is an algorithm for two group classification, who could best apply the
theoretical background of SLT. SVM achieve high performance, high accuracy
and has the ability to handle high- dimensional multivariate datasets. In order
to have a good generalization property, SVM keeps the value of training error
equal to zero or equal to some acceptable level and it minimizes the confidence
interval.

9. What is deep learning?
• Deep learning is a member of family of
machine learning
• It is a method based on learning data
reproduction
• Interpretation of Information processing
and communication patterns works like
biological nervous system. (Neurons
interconnected by neural coding)

10. Conventional Neural Network (CNN)
• A multi layer feed forward artificial neural
network
• Proposed for 2D image processing , like
image reognition
• Convolutional Neural Networks (CNNs) have
an important role in smart manufacturing
• It consist of a visible (v) and a hidden layer(h)
• The visible layer is used to input data while
the hidden layer is used to extract features

11. Auto Encoder
• Extract features from input data without label
information needed.
• Consists of 2 parts:
• Encoder
• Decoder
◦ Encoder perform data compression
◦ Decoder reonstruct the approximation of input

13. Advantages
• Time optimization
• Cost optimization
• Higher productivity
• Higher quality
• Integrated environment

14. Disadvantages
• Initial investment is higher
• Training of traditional workers
• Reduce human involvement
• Active engineers will be required to avoid sudden failure

15. Application
1. Predictive maintenance: Machine learning algorithms can predict machine failures and
prevent unexpected downtime.
2. Quality control: Deep learning algorithms can detect product defects to ensure high-quality
products.
3. Process optimization: Machine learning can analyze process data to identify patterns and
optimize manufacturing efficiency.
4. It can be useful in the area of:
• Product Quality Inspection
• Fault Diagnosis
• Design and Performance
• Forecasting
• Material Handling

18. Conclusion
• Deep learning provides advanced analytics and offers great potential to smart manufacturing in
the age of big data
• Also having decision making capabilities as well as real time performance , takes the
manufacturing sector into a new era

19. Thank
you