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. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

The Interaction between Internet, Sustainable Development, and Emergence of Society 5.0

“…where the communication and information network provides the connection between all the connected systems [142]. Artificial intelligence is increasingly concerned with data analysis, planning and the management of smart, sustainable and human-centered product development processes [143]. The smart factory of the future will be based on the sustainable mutual interaction between machine and human [144].…”

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

“…In recent years, the fourth industrial revolution with intelligent manufacturing as its main feature is influencing the world economy with unprecedented depth, breadth and speed, which is likely to further deepen the development gap existing in different countries, regions or strata. At the same time, intelligent manufacturing also causes people to worry about the social impact of "replacing workers with machine", such as unemployment, shortage of senior talents, and the coming "singularity" of environmental impact [9][10][11][12].…”

“…The sustainability of intelligent manufacturing is a new concept brought about by the implementation of sustainable development in the field of intelligent manufacturing [11,12]. Faced with more and more severe environmental constraints and pressures, in order to avoid the adverse effects of intelligent manufacturing in the future and promote the development towards sustainable intelligent manufacturing, enterprises must consider the feasibility from the perspective of sustainability, and the sustainability assessment of intelligent manufacturing (SAoIM) plays an indispensable role.…”

Optimal Policy-Making for Municipal Waste Management Based on Predictive Model Optimization

“…Some of the improvements in the current literature are to devise policies which are not generic for all the area but customized for different residential grids because different people have different behaviour for waste disposal. Another improvement could be to offload the expert system and involve machines and sensors, generating a massive volume of data to drive more intelligent solutions [24] [25]. Similarly, the policies devised should not only meet the hygiene requirement of the area but also should be cost-effective.…”