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

Abstract

The advent of cutting-edge technology and more efficient production processes in Industry 4.0 is being shaped by the use of
AI, ML, embedded systems, cloud computing, Big Data, and the IoT. As smart and learning machines continue to make great
strides, the need for AI is only going to grow. The integration of AI into smart manufacturing has the potential to address critical
sustainability concerns while simultaneously improving supply chain efficiency, resource use, and waste management. The
goal of customer-driven manufacturing capabilities, which are the foundation of Industry 4.0, is to increase productivity, sustainability,
and agility. The primary use of AI and ML in contemporary manufacturing is process improvement and monitoring.
Numerous disciplines, including machine learning, robotics, and the internet of things, contribute to the study of industrial AI
systems. Sustainable manufacturing solutions are created, validated, deployed, and maintained using industrial AI. The proliferation
of cloud computing and the subsequent precipitous decline in the price of data storage have made it possible to store and
transfer vast amounts of data to ML and AI algorithms, which in turn automate and expedite many business processes. Smart
process design, monitoring, control, scheduling, and industrial applications form the basis of smart manufacturing and Industry
4.0. Originally known as Internet of Things (IoT)-based technology, smart manufacturing now covers a wide variety of fields.