Cognitive dynamic digital twin: enhancements for digital twin platforms based on human cognition

Abstract

Manufacturing has entered the fourth industrial revolution. Modern manufacturing is reliant on assets such as robotics and computer numerical control (CNC) machine tools. To optimize the performance and value of these assets it would be wise to implement digital twin (DT) technology. DT technology has the ability to provide valuable services to owners of machine tools and other manufactruing assets. The current issue facing DTs is that they currently exist at a lower level of sophistication, meaning they are incapable of implementing more complex services. Cognitive dynamic systems (CDS) are a type of smart system based on human cognition which can augment the performance of many engineering systems. This paper proposes a framework of implementing aspects of CDSs to enable DTs to exist at a higher level of sophistication called the cognitive dynamic digital twin (CDDT). Examples exist in the literature of implementing cognitive based methods to improve DT services, they primarily implement artificial intelligence and estimation based methods. Most of these methods implement only one aspect of cognition at a time. In this work the CDDT framework was implemented to build a DT machine tool wear prediction service. The service was shown to be accurate at predicting the levels of wear in cutting tools. This service utilizing the CDDT framework used each of the aspects of human cognition to augment its performance. This framework can be used by many different sorts of DTs to improve their level of sophistication.