Applications of machine learning in manufacturing: Benefits, issues, and strategies

Abstract

The manufacturing sector now has access to data at a level never before possible. This information could comprise sensor readings from the assembly line, information about the surrounding area, machine tool settings, etc. These data may also take many different forms and have a variety of interpretations. The manufacturing sector and its grip on the expanding manufacturing data repositories have a lot of potential to change in the future thanks to recent developments in some fields. But there are many different machine learning algorithms, concepts, and strategies. This provides a hurdle to the use of these potent technologies for many industrial specialists and may hinder them from taking advantage of the enormous volumes of data that are becoming accessible. After a detailed study, we can say that machine learning (ML) is now a potent tool for many applications in (intelligent) industrial systems and smart manufacturing, and that its importance will only grow in the future. There is a need for cooperation between a number of academic fields, including computer science, industrial engineering, mathematics, and electrical engineering. Both enormous opportunity and substantial risk are generated by this relationship.