EXPLOITING AI AND SOURCE BLOCKCHAIN FRAMEWORK TO MITIGATE RISKS IN CLOUD MANUFACTURING I N I N D U S T R Y 3 . 0

Abstract

Cloud manufacturing is an evolving networked
framework that enables multiple manufacturers to
collaborate in providing a range of services, including
design, development, production, and post-sales support.
The framework operates on an integrated platform
encompassing a range of Industry 4.0 technologies, such as
Industrial Internet of Things (IIoT) devices, cloud
computing, Internet communication, big data analytics,
artificial intelligence, and blockchains. The connectivity of
industrial equipment and robots to Internet opens the cloud
manufacturing to the massive attack surface of
cybersecurity and cybercrime threats caused by external
and internal attackers. The impacts can be severe because
physical infrastructure of industries is at stake. One
potential method to deter such attacks involves utilizing
blockchain and artificial intelligence to track the provenance
of IIoT devices. This research explores a practical approach
to achieve this goal by gathering provenance data associated
with operational constraints defined in smart contracts and
identifying deviations from these constraints through
predictive auditing using artificial intelligence. A software
architecture comprising IIoT communications to machine
learning for comparing the latest data with the predictive
auditing outcomes and logging appropriate risks was
designed, developed, and tested. The state changes in the
smart ledger of smart contracts were linked with the risks
such that the blockchain peers can timely detect high
deviations and take actions. The research defined the
constraints related to physical boundaries and weight lifting
limits allocated to three forklifts and showcased the
mechanisms of detecting risks of breaking these constraints
with the help of artificial intelligence. It also demonstrated
state change rejections by blockchains at medium and highrisk
levels. This research followed software development in
Java 8 using JDK 8, CORDA blockchain framework, and
Weka package for random forest machine learning. As a
result of this, the model, along with its design and
implementation, has the potential to enhance efficiency and
productivity, foster greater trust and transparency in the
manufacturing process, booster risk management,
strengthen cybersecurity, and advance sustainability efforts.