This document discusses the application of deep learning techniques for smart manufacturing. It begins with an overview that outlines key areas like introduction, evolution, neural network architecture, artificial neural networks, applications in smart manufacturing, advantages, and disadvantages. It then defines smart manufacturing as employing computer control and high adaptability during the manufacturing process. Deep learning is described as a machine learning method based on learning data representations through neural network-inspired architectures. Various deep learning architectures are discussed for smart manufacturing applications, including convolutional neural networks, recurrent neural networks, autoencoders, and restricted Boltzmann machines. Specific applications of deep learning discussed include product quality inspection using computer vision, fault diagnosis, design and performance optimization, material handling, and forecasting. The document

1. DEEP LEARNING FOR
SMART MANUFACTURING
METHODS AND APPLICATIONS
UNDER THE GUIDANCE OF:
Mr. H. S Dash
(ASSISTANT PROF. PRODUCTION
ENGG. DEPT)
PREPARED BY :
Sunil Kumar Pradhan
E-mail- pradhansunilkumar163@gmail.com

2. OVERVIEW
• Introduction
• Evolution
• Architecture of neural network
• Artificial neural network
• Application in smart manufacturing
• Advantages
• Disadvantages
• Conclusion

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

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

5. Evolution

6. Answer please!!!
Brian is Rhino.
Julius is a Rhino.
Lily is a Frog.
Brian is Green.
Greg is a Lion.
Bernhard is a Frog.
Greg is Yellow.
Lily is Green.
Question: What colour is Bernhard?
Answer : Green

7. Architecture of neural network for smart
manufacturing
Achieved by constructing Artificial Neural Networks(ANN)

8. Artificial Neural Networks(ANN)
• An ANN is based on a collection of connected
units called artificial neurons.
• Each connection (synapse) between neurons can
transmit a signal to another neuron
• Neurons are organized in layers. Different layers
may perform different kinds of transformations
on their inputs. Signals travel from the first
(input), to the last (output) layer, possibly after
traversing the layers multiple times.

9. Convolutional Neural Network(CNN)
• A multi layer feed-forward artificial neural network.
• Proposed for 2D image processing, like image
recognition.

10. Restricted Boltzmann Machine(RBM)
• Two layer neural network.
• Consisting visible and hidden layer.
• A symmetric connection between visible and hidden
units.
• But no connection between a same layer neurons.
• Visible layers are the input data and hidden layers are
extract features.

11. Recurrent Neural Network(RNN)
• Tropology connection between neurons.
• Suitable for feature learning from sequence
data.
• Exhibit dynamic temporal behaviour.
• Used for handwriting and speech recognition.

12. Auto Encoder
• Extracting features from input data without label
information needed.
• Consists of 2 parts:
1. Encoder
2. Decoder
• Encoder perform data compression.
• Decoder reconstruct the approximation of input.

13. Application
• It can be very useful in the area of:
1. Product Quality Inspection
2. Fault Diagnosis
3. Design & Performance
4. Material Handling
5. Forecasting
and so on…

14. Analytics in Product Quality Inspection
• It can be processed by 4 types of analytics:

15. Descriptive Analytics
• Useful for complex product inspection.
• Employment of High Definition Camera.
• Analysis descriptively by measuring point to
point distance and counting number of axis.
using Convolutional Neural Network(CNN).

16. Diagnostic analysis for fault
assessment
• Common causes of failure: Operating
condition, excessive load, over heating,
fracture, corrosion and wear.
• Can be solved by Convolutional neural
network(CNN) and Auto Encoder(AE).

17. Predictive analysis for defect prognosis
• Predict the machine condition.
• Historical data is works as reference data.
• Employment of Recurrent Neural
Network(RNN).

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

20. Disadvantages
• Initial investment is higher.
• Training of traditional workers.
• Reduce human involvement (form Society
Point of view)
• Active engineers will required for avoid
sudden failure of networks.

21. 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.

22. References
[1] Deep learning for smart manufacturing: Methods and
applications Jinjiang Wanga,Yulin Maa, Laibin Zhanga, Robert X.
Gao b, Dazhong Wu.
[2] Hu T, Li P, Zhang C, Liu R. Design and application of a real-time
industrial Ethernet protocol under Linux using RTAI. Int J Comput
Integr Manuf 2013;26(5):429–39.
[3] Ye Y, Hu T, Zhang C, Luo W. Design and development of a CNC
machining process knowledge base using cloud technology.
Int J Adv Manuf Technol 2016:1–13.

23. To Listening My Presentation

24. Question Please !!!