AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes

Abstract--Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. Computing, in manufacturing, provides access to valuable data at different levels, i.e., manufacturing enterprise, manufacturing equipment, and manufacturing processes. VER recent decades, the manufacturing industry witnessed tremendous advances in the form of four major manufacturing insights. Manufacturing, then, can be controlled paradigm shifts. In the latest industrial revolution, Industry 4.0, by leading-edge CI and Artificial Intelligence (AI), and tasks manufacturing has embraced the Industrial Internet of Things are modelled based on experimental observations, to enhance (IIoT) [1]-[3] and Machine Learning (ML) to enable machinery productivity while reducing costs. In doing so, it is of so can make industry processes smart. Broadly speaking, Smart great importance to identify which factors play a pivotal role in Manufacturing (SM) can be defined as a data-driven approach process outcomes.