As a new model of networked manufacturing services, cloud manufacturing (CMfg) aims to allocate enterprise manufacturing resources, realize rational utilization of manufacturing resources, and adapt to increasingly complex user needs. However, previous studies on service composition and optimal selection (SCOS) in CMfg environments do not incorporate carbon emissions into the quality of service (QoS) evaluation indicators. Therefore, a SCOS model for CMfg under a low-carbon environment is firstly proposed in this paper. Secondly, based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) algorithm, a hybrid multi-objective evolutionary algorithm, named the NSGA-II-SA algorithm, is proposed to solve the model and obtain the Pareto optimal solution set. Then, an algorithm result optimization strategy combining subjective and objective is proposed to filter the Pareto optimal solution set, so as to make the final decision. Finally, taking natural gas cylinder head production as an example, the proposed algorithm is compared with other algorithms, and the results show that the proposed algorithm can obtain more non-dominated solutions, and the quality of the solutions in the four dimensions is better than the other. Therefore, it is proved that the proposed algorithm has better comprehensive performance in SCOS under a low-carbon environment.
The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering the real-time status of job shop manufacturing resources. A dynamic configuration mechanism of manufacturing resources based on the configuration threshold is proposed. Then, a real-time state-driven multi-objective manufacturing resource optimization allocation model is established, taking machine tool energy consumption and tool wear as carbon emission sources and combined with the maximum completion time. An improved imperialist competitive algorithm (I-ICA) is proposed to solve the model. Finally, taking an actual production process of a discrete job shop as an example, the proposed algorithm is compared with other low-carbon multi-objective optimization algorithms, and the results show that the proposed method is superior to similar methods in terms of completion time and carbon emissions. In addition, the practicability and effectiveness of the proposed dynamic resource allocation method are verified in a machine failure situation.
Bottleneck identification is of great interest in discrete manufacturing fields, as they limit the system’s throughput. However, the bottlenecks are difficult to accurately identify due to the instability and complexity of discrete manufacturing systems. This paper proposes a dynamic bottleneck identification method (DBI-BS) that is based on effective buffers and fine-grained machine states to identify bottlenecks accurately. First, the complex manufacturing system (CMS) with strong coupling between elements is decoupled into several independent parts under the guidance of the effective buffer theory. Then, the machine activity duration method is improved through further fine-grained division, and the machine states are described by the timing flow model. The method to quantify the degree of bottleneck that restricts the system throughput (TH) is proposed on the basis of the turning point theory, and the one-to-one mapping relationship between the simulated and authentic complex manufacturing systems is also studied. Simulation results show that the DBI-BS can effectively identify dynamic bottlenecks in complex manufacturing processes, and the decoupling of complex systems can effectively improve the accuracy of dynamic bottleneck identification.
The optimal configuration of flexible workshop resources is critical to production efficiency, while disturbances pose significant challenges to the effectiveness of the configuration. Therefore, this paper proposes a hybrid-driven resource dynamic configuration model and an improved Imperialist Competitive Algorithm hybrid Neighborhood Search (IICA-NS) that incorporates domain knowledge to allocate resources in flexible workshops. First, a hybrid-driven configuration framework is proposed to optimize resource configuration strategies. Then, in the revolutionary step of the Imperialist Competitive Algorithm (ICA), the bottleneck heuristic neighborhood structure is adopted to retain the excellent genes in the imperial so that the updated imperial is closer to the optimal solution; And a population invasion strategy is proposed further to improve the searchability of the ICA algorithm. Finally, the simulation experiments are carried out through production examples on flexible workshop production cases, and the proposed algorithm is applied. Compared with traditional ICA, genetic algorithm (GA), particle swarm optimization algorithm (PSO), moth-flame optimization (MFO) and sparrow search algorithm (SSA), the proposed method and algorithm effectively solve flexible workshops’ resource dynamic configuration problems.
In this work, mechanical alloying of the alternating stacked pure Al and Zn thin foils was accomplished via high-pressure torsion (HPT). In the alloyed Al-Zn system, an exotic phase transformation from hexagonal close-packed (HCP) to facecentered cubic (FCC) was identified. The atomic-scale evolution process and underlying mechanism of phase transformation down to atomic scale are provided by molecular dynamics simulation and high-resolution transmission electron microscopy. The HCP → FCC phase transformation was attributed to the sliding of Shockley partial dislocations generated at the Al-Zn grain boundaries, which resulted in an [2110]∕[011] and (0001)/(111) orientation relationship between the two phases. This work provides a new approach for the in-depth study of the solid phase transformation of Al-Zn alloys and also shed lights on understanding the mechanical properties of the HPT processed Al-Zn alloys.
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