Integrating Capacity and Logistics of Large Additive Manufacturing Networks
| Autor: | Luigi Rarità, Nicola Mastrandrea, Massimo De Falco |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Cloud Manufacturing Industry 4.0 Computer science Variable size Distributed computing Additive Manufacturing Scheduling (production processes) Smart Manufacturing 02 engineering and technology Production efficiency Manufacturing services Industrial and Manufacturing Engineering Shared resource Scattered Manufacturing 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Artificial Intelligence Sustainability Cloud manufacturing |
| Popis: | Among future manufacturing systems, we are going to see networks of intelligent and autonomous entities sharing manufacturing resources, knowledge and information. Possible advantages are relevant, such as increasing overall production efficiency and product variety as well as reducing responsiveness and lead times. This paper focuses on the architectures and dynamics of productive-demanding nodes in a Scattered Manufacturing (SM) Network, with an application in Additive Manufacturing scenarios. SM allows launching production orders everywhere anytime inside the domain of the network. These autonomous nodes can rely on on-demand manufacturing services by sharing resources in a geographically distributed network. One possible approach is the introduction of a platform to coordinate the dynamics along the network according to principles of sustainability, equated shared resources and transparency by managing communication activities among nodes. To identify variables/factors that affect the system, a unique model is proposed by combining different perspectives, which focus on: a) decomposition and localization of demanding node’s order into subtasks of variable size; b) tasks allocation criteria among geographically distributed nodes; c) logistics issues related to the localization of productive nodes. In particular, the model, with the aim of optimizing the overall manufacturing and logistics costs, suggests either logistics paths along the sub-network or tasks assignment criteria and scheduling in geographically distributed nodes. |
| Databáze: | OpenAIRE |
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