The Applications of the Combinations of Deep Learning and
Blockchain Technology in Manufacturing Production Process: A
Comprehensive Investigation
Chuanhui Zhou
School of Information Science and Engineering, Zhejiang Sci-Tech University, 310018, Hangzhou, Zhejiang, China
Keywords: Deep Learning, Blockchain, Manufacturing
Abstract: In recent times, both deep learning and blockchain technology have garnered significant interest, attributed to
their respective strengths in predictive accuracy and data security enhancement. As the push towards
intelligent manufacturing gains momentum, the integration of these two technologies holds the potential to
fundamentally transform the manufacturing production process. This paper explores the applications and
significance of the combination of deep learning and blockchain technology in manufacturing production
process, discussing the benefits and areas for further improvement. Specifically, this work first briefly
introduces the related concepts of deep learning and blockchain technology. Furthermore, the possible
applications and benefits of the combination of deep learning and blockchain technology in manufacturing
are discussed. The three potential proposed applications include predictive maintenance, quality control, and
supply chain management. Finally, after discussion, this work points out that challenges such as large
computational resource requirements, large training data labeling effort and training data privacy and security
issues still need to be improved.
1 INTRODUCTION
In recent years, the convergence of deep learning and
blockchain technology has recently emerged as a
promising approach to revolutionize the
manufacturing production process. As a branch of
machine learning, deep learning has demonstrated
remarkable success in extracting meaningful
information from complex datasets, enabling accurate
predictions and decision-making. Meanwhile,
blockchain technology, originally designed for secure
and transparent transactions in cryptocurrencies, has
found diverse applications due to its decentralized
and immutable nature. By combining the strengths of
deep learning and blockchain, manufacturers can
potentially overcome significant challenges, boost
productivity, and established more robust and
efficient supply chains.
Deep learning algorithms, specifically deep
neural networks, have shown exceptional capabilities
in processing and analysing large volumes of data
generated in the manufacturing sector. By leveraging
techniques such as Convolutional Neural Networks
(CNN) and Recurrent Neural Networks (RNN),
manufacturers can learn high-level patterns, make
accurate predictions, and optimize production
processes (LeCun et al. 2015). These algorithms can
analyse sensor data from machinery, predict
equipment failures, detect anomalies in real-time, and
optimize maintenance schedules (Zhao et al. 2016).
Additionally, deep learning algorithms can be
employed to improve product quality control by
analysing image data, ensuring consistent and reliable
manufacturing standards (Soori et al. 2023).
On the other hand, blockchain technology
provides a decentralized and secure platform for
recording, sharing, and verifying data throughout the
manufacturing supply chain. The irreversibility and
transparency of blockchain records ensure
information’s integrity, making it an ideal solution for
traceability and provenance verification (Nakamoto
2008). By immutably recording data related to
production processes, quality control, and logistics on
the blockchain, manufacturers can enhance the
visibility of supply chain, reduce the risks of
counterfeit, and strengthen trust among stakeholders
(Jackson et al. 2023).
Zhou, C.
The Applications of the Combinations of Deep Learning and Blockchain Technology in Manufacturing Production Process: A Comprehensive Investigation.
DOI: 10.5220/0012821200004547
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Data Science and Engineering (ICDSE 2024), pages 281-285
ISBN: 978-989-758-690-3
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
281
The combination of deep learning and blockchain
technology offers a few potential applications in
manufacturing production process. For example,
predictive maintenance, a critical aspect of
manufacturing operations, stands to benefit greatly
from this combination. Deep learning algorithms can
predict equipment failures by analysing historical
sensor data, while the blockchain can secure and
distribute these predictions to relevant stakeholders,
enabling proactive maintenance and minimizing
downtime (Lee et al. 2020). Furthermore, supply
chain management can be improved through the
integration of deep learning and blockchain. Deep
learning models can analyse historical data to identify
patterns, optimize inventory management, forecast
demand, and identify potential bottlenecks in the
supply chain (Tadayonrad & Ndiaye 2023).
Figure 1: The architecture of the neural network (Photo/Picture credit : Original).
Given the novelty of the combination of deep
learning and blockchain in manufacturing, it is
critical to understand their potential applications in
manufacturing and the implications for production
and supply chain management. In order to gain a
deeper understanding of this field, this review paper
aims to explore the applications and significance of
the combination of deep learning and blockchain
technology in manufacturing production process.
This paper will examine the current prevalent
methods and techniques, discuss their benefits and
limitations, and identify areas for further
improvement.
2 METHOD
2.1 Introduction of Deep Learning
As a subset of machine learning, deep learning
models and understands complex patterns in datasets
with artificial neural networks which have multiple
layers, which has been widely applied in many tasks
(Chen et al. 2023, Kayalibay et al. 2017, Qiu et al.
2022). The structure of a neural network shown in
Fig. 1 consists of an input layer, one or more hidden
layers, and an output layer. And all layers contain
nodes that connect to nodes in the subsequent layer,
forming a "web" of interconnected nodes or
"neurons" (LeCun et al. 2015). The functionality of
deep learning is based on the concept of 'learning
representations from data', which means, given a set
of features, the model learns to recognize patterns and
make predictions or decisions without being
explicitly programmed to perform the task. The
learning process involves adjusting the weights and
biases of the network through a process called
backpropagation and optimization algorithms like
stochastic gradient descent (LeCun et al. 2015).
2.2 Introduction of Blockchain
Technology
Blockchain technology, as a decentralized distributed
database, enables point-to-point transactions with no
intermediaries. It utilizes encryption, consensus
algorithms, and smart contracts to enhance security,
traceability, authenticity, and collaboration (Ye et al.
2017). The key technologies of blockchain basically
include distributed ledgers, asymmetric encryption,
consensus mechanisms, and smart contracts. 1)
Distributed ledger, as decentralized data storage
technology (Ølnes et al. 2017), enables decentralized
data storage and synchronization, reducing
maintenance costs and improving efficiency (Ostern
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2017). It uses a network composed of multiple nodes
to realize data sharing and synchronization. 2)
Asymmetric encryption utilizes public and private
keys for secure encryption and decryption (Mofer et
al. 2017). The public key is public and used for
encryption, and the private key corresponding to the
public key is private and used for decryption. At
present, common asymmetric encryption algorithms
include hash function, Rivest-Shamir-Adleman
(RSA), Elgamal, DiffieHellman key exchange (D-
H) and elliptic curve encryption algorithm (Shi et al.
2021). 3) Consensus mechanisms enable the
verification and confirmation of data in the network
of each node, thus ensuring data consistency and trust
among participants (Lashkari & Musilek 2021).
Common consensus mechanisms include Proof of
Work (PoW) and Proof of Stake (PoS) (Khamar &
Patel 2020). 4) Smart contracts, as a computer
protocol, executed automatically based on predefined
conditions, can facilitate trusted transactions without
intermediaries, reducing costs and improving
efficiency (Mourouzis 2019).
2.3 Predictive Maintenance
Predictive maintenance is a crucial aspect of modern
manufacturing. Deep learning algorithms can achieve
efficient predictive maintenance by analysing sensor
data from machinery, predicting equipment failures,
and optimizing maintenance schedules. For instance,
a study by Butte et al. (2018) demonstrated the use of
machine learning including deep learning like CNN
and Deep Belief Networks (DBN) for predictive
maintenance in a semiconductor manufacturing
industry. In their study, generalized distributed linear
model, distributed random forest, Gradient Boosting
Machine, DBN and CNN were applied to analyse
sensor data and predict device failures. It combines
predictions from multiple different models based on
cross-validation. It not only reduces downtime and
increases productivity, but also reduces the risk of
high variability and low accuracy because of relying
on a single method and makes Product Data
Management (PdM) systems robust. Blockchain
technology complements this by providing a secure
and transparent platform for sharing these predictions
with relevant stakeholders, ensuring proactive
maintenance (Rachad et al. 2023).
2.4 Quality Control
Quality control is another critical application area.
Deep learning algorithms can analyse image data to
ensure consistent and reliable manufacturing
standards. The study of Villalba-Diez et al. (2019)
shows an application of deep learning on increasing
the accuracy and decrease the computational
resources requirement of industrial visual inspection
process in the process of printing. And a study by
Jianqiang et al. (2023), proposed a blockchain-based
quality control methodology. In their research,
blockchain technology and machine learning are used
to secure the safety and privacy of information
operations and managing data sets, providing a secure
platform for delivering relevant predictions and
preserving relevant data sets. Its quality control was
established based on comprehensive techniques that
accurately reflected the intricate world and
determined the actual positivity rate of the platform’s
standard control methodology.
2.5 Supply Chain Management
Supply chain management also benefits greatly from
the integration of deep learning and blockchain
technology. Deep learning models can analyse
historical data to optimize inventory management,
forecast demand, and identify potential bottlenecks in
the supply chain. A study by Henkelmann (2018)
proposed an approach based on deep learning for
supply chain management in the automobile spare
part industry. The approach used deep learning
algorithms to analyse historical sales data and
forecast demand, thereby optimizing inventory
management. Blockchain technology can improve
this process by providing a secure and transparent
platform for sharing these forecasts with relevant
stakeholders, thereby enhancing supply chain
visibility (Saberi et al. 2018).
3 DISCUSSION
The combination of deep learning and blockchain
technology holds immense potential for
revolutionizing the manufacturing production
process. One of the significant advantages of deep
learning algorithms in manufacturing is their ability
for processing and analysing large volumes of data.
Deep neural networks, such as CNN and RNN, can
extract high-level patterns from sensor data and make
accurate predictions. This capability is particularly
useful in predictive maintenance, where the
algorithms can analyse historical sensor data to detect
anomalies and predict equipment failures (Butte et al.
2018). By proactively identifying potential issues,
manufacturers can optimize maintenance schedules,
reduce downtime, and increase productivity.
The Applications of the Combinations of Deep Learning and Blockchain Technology in Manufacturing Production Process: A
Comprehensive Investigation
283
Additionally, deep learning algorithms can
improve quality control in manufacturing by
analysing image data. This application is evident in
the printing industry, where deep learning models
have been employed to increase the accuracy and
reduce the computational resources requirement in
industrial visual inspection processes (Villalba-Diez
et al. 2019). By automatically detecting defects and
inconsistencies, manufacturers can ensure consistent
and reliable manufacturing standards, leading to
higher product quality.
Blockchain technology complements deep
learning in several ways. Its decentralized and
immutable nature makes it an ideal solution for
traceability and provenance verification. By
recording data related to production processes,
quality control, and logistics on the blockchain,
manufacturers can enhance supply chain visibility,
reduce counterfeit risks, and strengthen trust among
stakeholders (Jackson et al. 2023). For instance, a
blockchain-based quality control methodology can
secure information operations and manage datasets,
providing a secure platform for delivering relevant
predictions and preserving data integrity (Gu et al.
2023).
Another significant application of the
combination of deep learning and blockchain is in
supply chain management. Deep learning models can
analyse historical data to optimize inventory
management, forecast demand, and identify potential
bottlenecks in the supply chain (Henkelmann 2018).
By leveraging the power of deep learning,
manufacturers can make decisions driven by data
which can save cost and improve efficiency.
Blockchain technology can further enhance this
process by providing a secure and transparent
platform for sharing forecasting data with relevant
stakeholders, ensuring trust and collaboration (Saberi
et al. 2018).
Despite the numerous benefits, there are some
limitations and challenges to consider. Deep learning
algorithms require substantial computational
resources and large amounts of labeled training data.
Acquiring and labeling such datasets can be time-
consuming and costly, especially in complicated
manufacturing environments recognizing changes in
scenarios and situations is a must when collecting and
using datasets required for training (Gu et al. 2023).
Additionally, ensuring the privacy and security of
sensitive data used in deep learning models is a
significant concern. Manufacturers need to apply
robust data protection techniques to prevent
unauthorized access and data breaches. At present,
advancements in federated learning might address
privacy concerns associated with deep learning.
Federated learning allows training models on
distributed data without sharing the raw data, thereby
preserving data privacy (Kamp et al. 2021).
Implementing federated learning in the
manufacturing sector will help enable collaborative
model training while helping ensure data security and
privacy.
4 CONCLUSION
In this work, the applications and significance of the
combination of deep learning and blockchain
technology in manufacturing production process have
been explored. The author has discussed the current
prevalent methods and techniques, their benefits,
limitations, and identified areas for further
improvement in the field of deep learning, blockchain
and their integration in the manufacturing process.
This research shows that the integration of deep
learning and blockchain has several applications in
manufacturing, including predictive maintenance,
quality control, and supply chain management.
After discussing the current related applications,
there are still some challenges to overcome in the
future, including the need for substantial
computational resources, large training data labeling
effort and training data privacy and security issues.
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Comprehensive Investigation
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