Role and Applications of Artificial Intelligence and Machine Learning in Manufacturing Engineering: A Review

Sara Bunian1,#

Meshari A. Al-Ebrahim2,#,Email

Amro A. Nour3,#

1Kuwait Petroleum Corporation (KPC), Oil Sector Complex, Shuwaikh Industrial 1, Block 1 St 74, Safat 13126, Kuwait.

2State Audit Bureau (SAB), Shamiya 71661, Kuwait.

3American University of Kuwait (AUK), Safat 13034, Kuwait.

#These authors contributed to this work equally.

Abstract

The use of artificial intelligence (AI), machine learning (ML), embedded systems, cloud computing, Big Data, and the Internet of Things (IoT) is influencing the paradigm shift toward advanced technologies and highly efficient manufacturing processes in Industry 4.0. The need for AI is increasing day by day due to the rapid progress contributed by the successful utilization of intelligent and learning machines. AI is implanted in smart manufacturing to solve crucial sustainability issues and to optimize the supply chain, use of energy and resources, and waste management. Industry 4.0 is striving for customer-driven manufacturing capabilities for enhanced agility, sustainability, and productivity. AI and ML are primarily used in the optimization and monitoring of modern industrial processes. Industrial AI system research is a multidisciplinary field with contributions from ML, robotics, and IoT. Industrial AI develops, validates, deploys, and maintains solutions for sustainable manufacturing. Because of the rise in cloud computing and a significant decrease in data storage costs, a massive amount of information and data can now be stored and transmitted to ML and AI algorithms to streamline and automate different processes of an organization. The framework of smart manufacturing and Industry 4.0 is based on smart process design, monitoring, control, scheduling, and industrial applications. Smart manufacturing encompasses a broad range of domains, originally referred to as IoT-based technologies.

Role and Applications of Artificial Intelligence and Machine Learning in Manufacturing Engineering: A Review