emergent methodology for solving tool inventory sizing problems in a complex production system

Abstract Based on recently established correlations between emergent synthesis classes, a Class III synthesis problem concerning tool inventory management in a complex make-to-order manufacturing environment is addressed. Such environment is shown to be affected by significant non-random uncertainty involving tool delivery time fluctuations and unpredictable tool demand. The trade-off typical of the inventory sizing dilemma is introduced with reference to reusable tools, such as grinding wheels, and a satisfactory solution is achieved by means of a dynamic purpose assignment approach. This leads to a global behavior, expressed by a recurrently oscillating pattern, affecting the inventory level trend in the nearby of a peculiar attraction band: the oscillation amplitude mainly depends on the attractor's bandwidth as well as on the peaks attained by the tool demand rate during the tool management period

Developing sensor signal-based digital twins for intelligent machine tools

Abstract Digital twins can assist machine tools in performing their monitoring and troubleshooting tasks autonomously from the context of smart manufacturing. For this, a special type of twin denoted as sensor signal-based twin must be constructed and adapted into the cyber-physical systems. The twin must (1) machine-learn the required knowledge from the historical sensor signal datasets, (2) seamlessly interact with the real-time sensor signals, (3) handle the semantically annotated datasets stored in clouds, and (4) accommodate the data transmission delay. The development of such twins has not yet been studied in detail. This study fills this gap by addressing sensor signal-based digital twin development for intelligent machine tools. Two computerized systems denoted as Digital Twin Construction System (DTCS) and Digital Twin Adaptation System (DTAS) are proposed to construct and adapt the twin, respectively. The modular architectures of the proposed DTCS and DTAS are presented in detail. The real-time responses and delay-related computational arrangements are also elucidated for both systems. The systems are also developed using a Java™-based platform. Milling torque signals are used as an example to demonstrate the efficacy of DTCS and DTAS. This study thus contributes toward the advancement of intelligent machine tools from the context of smart manufacturing