Autonomous resource allocation of smart workshop for cloud machining orders

Abstract

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Prediction of the onset of shear localization based on machine learning

“…Several researchers have been devoted to the application of machine learning methods in the field of machining. Examples could be the low-carbon machining process planning (Chen et al, 2022), prediction of surface roughness in high-pressure jet-assisted turning (Kramar et al, 2016), modeling of charge geometry and parameters on the depth of penetration in explosive cutting (Nariman–Zadeh et al, 2003), optimization of machining process parameters (Famili, 1994; Pourmostaghimi et al, 2020), development of support systems for the proper selection of machine tools and machining process parameters (Rojek, 2017), selection of the proper cutting fluids based on the machining process such as milling, grinding, honing, and lapping (Mogush et al, 1988), prediction of the micro-end mill and micro-drills failure (Sevil and Ozdemir, 2011), and development of processing resource allocations for smart workshops in cloud manufacturing and its optimization (Hui et al, 2021). However, based on the authors’ knowledge, the use of machine-learning to predict the onset of shear localization has not been reported in the literature.…”

Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments

“…Please seeHui et al (2021) for an example application exploiting cloud compute for smart scheduling.…”