As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Digital twin-based sustainable intelligent manufacturing: a review

The purpose of the current work is to solve the SIR nonlinear model based on dengue fever using a stochastic numerical computing scheme together with the artificial neural networks (ANNs) optimized by a well-known global genetic algorithm (GA) and local refinements of sequential quadratic programming (SQP), i.e., ANN-GA-SQM. The optimization of an error based merit function is performed by using the concepts of differential model along with the initial conditions to solve the SIR nonlinear model based dengue fever. The stochastic ANN-GA-SQM capability to solve the SIR nonlinear model based dengue fever is scrutinized to examine the correctness, precision, efficiency and constancy of the ANN-GA-SQM. The obtained numerical results of the SIR nonlinear model based dengue fever via ANN-GA-SQP are compared with the Adams results that authenticate the significance of the ANN-GA-SQM. Furthermore, statistical deliberations using the ‘semi interquartile range’, ‘mean absolute deviation’ and ‘Theil’s inequality coefficient’ have been implemented to authenticate the convergence and precision of the designed ANN-GA-SQM.

A stochastic numerical computing heuristic of SIR nonlinear model based on dengue fever

An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing

Since the Third Industrial Revolution, technology and the global economy have developed rapidly. Driven by market demand and the development of science and technology, the organisational model of t...

The evolution of production scheduling from Industry 3.0 through Industry 4.0

A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

At present, as the candidate services in the cloud service pool increase, the scale of the service composition increases rapidly. When the existing intelligent optimization algorithms are used to solve the large-scale cloud service composition and optimization (CSCO) problem, it is difficult to ensure the high precision and stability of the optimization results. To overcome such drawbacks, a new dynamic ant-colony genetic hybrid algorithm (DAAGA) is proposed in this paper. The best fusion evaluation strategy is used to determine the invoking timing of genetic and ant-colony algorithms, so the executive time of the two algorithms can be controlled dynamically based on the current solution quality, then the optimization ability is maximized and the overall convergence speed is accelerated. An iterative adjustment threshold is introduced to control the genetic operation and population size in later iterations, in which the effect of genetic algorithm is reduced when the population closes to optimal solution, only the mutation operation is implemented to reduce the calculation, and the population size is increased to find the optimal solution more quickly. A series of comparison experiments are carried out and the results show that the accuracy and stability of DAAGA are significantly improved for the large-scale CSCO problem, and the time consumption of the algorithm is also optimized.

Xiao Shu

Papers

A dynamic ant-colony genetic algorithm for cloud service composition optimization