Guoxin Wang

TL;DR: A data management method for digital twin of product based on blockchain technology is proposed and the results show that the proposed method can solve the abovementioned data management problems simultaneously.

TL;DR: The proposed AutoSM, unlike previous EA-based automatic surrogate model selection methods, is not a black box and is interpretable, and can find the promising surrogate model and associated hyper-parameter in 9 times less than other automatic selection approaches while maintaining the same accuracy and robustness in surrogatemodel selection.

TL;DR: This paper proposes a method to build an EoS that is both accurate and less computationally expensive and demonstrates that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions.

TL;DR: A dynamic complexity-based RMS reconfiguration point decision method based on information entropy theory and cusp catastrophe theory that can effectively identify the RMS state catastrophe moment so that system reconfigurations is implemented promptly to improve RMS’s responsiveness to the market.

TL;DR: In this paper, a method for the formation of part family that considers bypassing moves and idle machines is presented for a reconfigurable manufacturing system to simultaneously consider efficiency and flexibility, and a similarity coefficient algorithm is designed, which is used as the basis for part clustering and family formation.