Generative Design for Social Manufacturing

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.

[1]  S. Turner,et al.  The influence of experience on contest assessment strategies , 2017, Scientific Reports.

[2]  Jean-Charles Pomerol,et al.  Artificial intelligence and human decision making , 1997 .

[3]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[4]  Christopher K. I. Williams,et al.  The shape variational autoencoder: A deep generative model of part‐segmented 3D objects , 2017, Comput. Graph. Forum.

[5]  Xi Zhang,et al.  A Framework for Implementing Social Manufacturing System Based on Customized Community Space Configuration and Organization , 2013 .

[6]  P. Dayan,et al.  Choice values , 2006, Nature Neuroscience.

[7]  D. Wolpert,et al.  Changing your mind: a computational mechanism of vacillation , 2009, Nature.

[8]  Rebecca Duray.  Mass customization origins: mass or custom manufacturing? , 2002 .

[9]  Christopher Burgess,et al.  beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.

[10]  Lawrence Carin,et al.  Syntax-Infused Variational Autoencoder for Text Generation , 2019, ACL.

[11]  Driss Aboutajdine,et al.  Kernel-Based Laplacian Smoothing Method for 3D Mesh Denoising , 2012, ICISP.

[12]  Barry Berman,et al.  3D printing: the new industrial revolution , 2012, IEEE Engineering Management Review.

[13]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[14]  Leonardo Rosado,et al.  Circular economy – From review of theories and practices to development of implementation tools , 2017, Resources, Conservation and Recycling.

[15]  Mohammad Taher Pilehvar,et al.  On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation , 2019, NGT@EMNLP-IJCNLP.

[16]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[17]  Leonidas J. Guibas,et al.  ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.

[18]  Pingyu Jiang,et al.  Towards a cyber-physical-social-connected and service-oriented manufacturing paradigm: Social Manufacturing , 2016 .

[19]  Guoyin Wang,et al.  Topic-Guided Variational Auto-Encoder for Text Generation , 2019, NAACL.

[20]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[21]  Giovani J.C. da Silveira,et al.  Mass customization: Literature review and research directions , 2001 .

[22]  Sanja Fidler,et al.  Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research , 2019, ArXiv.

[23]  Babak Mohajeri.  Paradigm Shift from Current Manufacturing to Social Manufacturing , 2015 .

[24]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[25]  Diederik P. Kingma,et al.  An Introduction to Variational Autoencoders , 2019, Found. Trends Mach. Learn..

[26]  Guillaume Desjardins,et al.  Understanding disentangling in β-VAE , 2018, ArXiv.

[27]  Leyland Pitt,et al.  Disruptions, decisions, and destinations: enter the age of 3-D printing and additive manufacturing , 2015, IEEE Engineering Management Review.