ABSTRACT
The introduction of machines transformed the manufacturing industry, which achieved further gains in productivity through the addition of electrification and automation [1]. It is now well accepted that further increases in manufacturing efficiency can be generated by incorporating artificial intelligence (AI) into machines [2] so that they ‘think for themselves,’ i.e., learn from historical data to perform a required task in the optimum possible way. Digital twins (DTs) (Figure 27.1) are ideally suited to be the means by which this goal may be achieved [3]. These cyber-physical systems (CPSs) can operate independently and make autonomous decisions, thanks to AI capabilities. DTs are expected to be a part of the Industry 4.0 landscape where high-end products, machines, assembly lines, etc. are embedded with digital sensors and are connected through the Industrial Internet of Things (IIoT). The AI algorithms and digital models help DTs analyse data from sensors and provide control commands to trigger corrective or proactive actions. DTs can also optimize the various ancillary operations within factories for improved profitability, sustainability, and safety.
