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Generative design for Social Manufacturing

Authors: Gonzalez-Val, Carlos; Muinos-Landin, Santiago;

Generative design for Social Manufacturing

Abstract

{"references": ["K. Ding, P. Jiang, and X. Zhang, \"A framework for implementing social manufacturing system based on customized community space configuration and organization,\" Advanced Materials Research, vol. 2450, no. 712, pp. 3191-3194, 2013.", "R. Duray, \"Mass customization origins: mass or custom manufacturing?,\" International Journal of Operations Production Management, Vol., pp. 314-328, 2002.", "B. D. . F. F. S. Da Silveira, G., \"Mass customization: Literature review and research directions,\" International Journal of Production Economics, 72(1), 1-13, 2001.", "B. Mohajeri, \"Paradigm shift from current manufacturing to social manufacturing,\" Aalto University, 2015.", "F. Wang, \"From social computing to social manufacturing: The coming industrial revolution and new frontier in cyber-physical-social space,\" Bulletin of Chinese Academy of Sciences, vol. 6, no. 1,pp.658-669, 2012.", "P. Jiang, K. Ding, and J. Leng, \"Towards a cyber-physical-socialconnected and service-oriented manufacturing paradigm: Social manufacturing,\" Manufacturing Letters, vol. 7 pp. 15-21, 2016.", "J. Parrish and W. Hamner, \"Animal groups in three dimensions: How species aggregate,\" Cambridge University Press, 1997."]}

Social Manufacturing is a novel approach where different members of a community can interact within a cyber-physical social space in order to achieve specific and personalized solution for manufacturing processes. In such digital scenario, interactions, driven by information flows, can be divided in different branches depending on the actors involved in the whole process. One particularly critic branch for such collective production, is the one that captures the information related with product design, due to its direct link with creativity. Here we show how active tools based on Artificial Intelligence triggers artificial creativity that can be used for the user capabilities augmentation. We show how the use of deep generative models, based on Variational Autoenconders, offers solutions for a particular social manufacturing platform for furniture design combined with additive manufacturing to drive the transition from a digital framework to a real context.

Keywords

information flow, Social Manufacturing, Artificial Intelligence, Artificial Creativity, user capabilities augmentation, Variational Autoenconders

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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