Table 4.

A sample of articles covering manufacturing and supply chain digital twin research.

Domain	Physical Asset	Sensors	Physical–Digital Data Flow	Form of Digital Asset	Research Objective	Ref.
CNC machining	Cutting torque in end milling	Force sensors	Process parameters and database of historical torque signals and analysis	Dashboard of simulated and real cutting torque and analysis	Machine tool condition monitoring	[35]
CNC machining	Servo system of a 5-axis laser drill	Onboard CNC sensing	In-process CNC data (e.g., body motion, actuator ripples, and vibration modes)	Real-time visualization of servo dynamic models	Develop nonlinear multi-variant dynamic models of multi-axis machine tools	[36]
CNC machining	Milling machine	Onboard CNC sensing (e.g., torque current and tachometer)	Fusion of tool, workpiece, and process monitoring data	Visual dashboard of part geometry, process data, and analysis	Development of digital process twin	[37]
Cyber-Physical System	CTTP 4.0 production cell	Optical sensors	Fusion of real-time operational parameters and sensor readings	Interactive visual replication of production cell	Practical implementation of digital twin complied to industry standards	[38]
Additive manufacturing	Temperature and strain profiles	Embedded distributed fiber sensors	Fusion of process parameters and sensor readings	FEA simulation of temperature and strain	Model temperature and strain with embedded distributed fiber sensors	[39]
Production planning	Body-in-white (BIW) production system	Onboard sensors and LiDAR	Fusion of CPS indicators, production data, and point cloud of the plant	3D production plant model with optimized production planning	Demonstrate the automated creation and updating of a BIW production digital twin	[40]