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• W4384298663 abstract "Additive manufacturing (AM) is a leading technology in many fields, such as medicine and aerospace, to make prototypes and functional part fabrication. The energy requirements of the AM process are considerable and have severe consequences for environmental health and long-term viability. Research in both the private and public sectors has shifted its attention to the problem of predicting and optimizing the amount of energy that AMs use. Material state, process operation, part and process design, working environment, and other factors play a role in this problem. Existing research shows that design-relevant aspects have a significant role in AM energy consumption (EC) modeling in reality, although this topic has not received enough attention. As a result, this research starts by analyzing the design of relevant features (DRFs) from the perspective of energy modeling. Before production, these features are usually decided by a part designer (PD) and process operator (PO). An ANN-driven cluster-aware enhanced spider monkey optimization algorithm (CAESMOA) is suggested to improve the energy utility using the novel modeling methodology. Deep learning is used to strengthen the global best of CAESMOA and solve several concerns, including speeding up search times. To verify the accuracy of the suggested modeling technique, DRFs are obtained from a functioning AM system in the production line. In our research, we use a normalization strategy to filter out extraneous information. At the same time, optimization has been performed to direct PD and PO towards design and decision modifications that lessen the energy requirements of the specified AM system under investigation. The effectiveness of the suggested approach is examined, and the efficiency is also contrasted with that of other current methods. These statistics showed that our approach to energy optimization in AM delivered the most trustworthy outcomes." @default.
• W4384298663 created "2023-07-15" @default.
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• W4384298663 date "2023-07-14" @default.
• W4384298663 modified "2023-09-26" @default.
• W4384298663 title "Energy optimization in additive manufacturing based on cluster-aware enhanced spider monkey optimization" @default.
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• W4384298663 doi "https://doi.org/10.1007/s00170-023-11846-8" @default.
• W4384298663 hasPublicationYear "2023" @default.
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