Using autoencoded voxel patterns to predict part mass, required support material, and build time

[1]  Yiannis Kompatsiaris,et al.  Deep Learning Advances in Computer Vision with 3D Data , 2017, ACM Comput. Surv..

[2]  Sang-In Park,et al.  A Multilevel Upscaling Method for Material Characterization of Additively Manufactured Part Under Uncertainties , 2015 .

[3]  Jiajun Wu,et al.  Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling , 2016, NIPS.

[4]  F. Martina,et al.  Design for Additive Manufacturing , 2019 .

[5]  Sebastian Scherer,et al.  VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Charlie C. L. Wang,et al.  Support slimming for single material based additive manufacturing , 2015, Comput. Aided Des..

[7]  Carolyn Conner Seepersad,et al.  Challenges and Opportunities in Design for Additive Manufacturing , 2014 .

[8]  Christopher B. Williams,et al.  Preparing industry for additive manufacturing and its applications: Summary & recommendations from a National Science Foundation workshop , 2017 .

[9]  Christopher B. Williams,et al.  Multiple-Material Topology Optimization of Compliant Mechanisms Created Via PolyJet Three-Dimensional Printing , 2014 .

[10]  Kristin L. Wood,et al.  CROWDSOURCED DESIGN PRINCIPLES FOR LEVERAGING THE CAPABILITIES OF ADDITIVE MANUFACTURING , 2015 .

[11]  Anja Maier,et al.  Data-driven engineering design research: Opportunities using open data , 2017 .

[12]  Feng Shi,et al.  A data-driven text mining and semantic network analysis for design information retrieval , 2017 .

[13]  Christopher McComb,et al.  Toward the Rapid Design of Engineered Systems Through Deep Neural Networks , 2018, Design Computing and Cognition '18.

[14]  Nabil Anwer,et al.  Assembly Based Methods to Support Product Innovation in Design for Additive Manufacturing: An Exploratory Case Study , 2015 .

[15]  Krishnan Suresh,et al.  Support structure constrained topology optimization for additive manufacturing , 2016, Comput. Aided Des..

[16]  Yoshua Bengio,et al.  A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.

[17]  C. K. Kwong,et al.  Predicting Future Importance of Product Features Based on Online Customer Reviews , 2017 .

[18]  David W. Rosen,et al.  Research supporting principles for design for additive manufacturing , 2014 .

[19]  José García Rodríguez,et al.  PointNet: A 3D Convolutional Neural Network for real-time object class recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[20]  Timothy W. Simpson,et al.  3D Printing Disrupts Manufacturing , 2013 .

[21]  Jianxi Luo,et al.  Mining Patent Precedents for Data-driven Design: The Case of Spherical Rolling Robots , 2017 .

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[24]  Conrad S. Tucker,et al.  Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers , 2017 .

[25]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[26]  Yonghua Chen,et al.  Manufacturable mechanical part design with constrained topology optimization , 2012 .

[27]  James K. Guest,et al.  Topology optimization considering overhang constraints: Eliminating sacrificial support material in additive manufacturing through design , 2016 .

[28]  Tahira Reid,et al.  The Design for Additive Manufacturing Worksheet , 2017 .

[29]  Guido A.O. Adam,et al.  Design for Additive Manufacturing—Element transitions and aggregated structures , 2014 .

[30]  Dirk Herzog,et al.  Design guidelines for laser additive manufacturing of lightweight structures in TiAl6V4 , 2015 .

[31]  Glaucio H. Paulino,et al.  Bridging topology optimization and additive manufacturing , 2015, Structural and Multidisciplinary Optimization.

[32]  Matthew L. Dering,et al.  A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form , 2017 .

[33]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[34]  Todd Palmer,et al.  Anisotropic tensile behavior of Ti-6Al-4V components fabricated with directed energy deposition additive manufacturing , 2015 .

[35]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[36]  David W. Rosen,et al.  A comparison of synthesis methods for cellular structures with application to additive manufacturing , 2010 .

[37]  L. D. Angelo,et al.  A neural network-based build time estimator for layer manufactured objects , 2011 .

[38]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[39]  Paul Witherell,et al.  A method for modularity in design rules for additive manufacturing , 2017 .

[40]  Jonathan Cagan,et al.  Discovering Structure in Design Databases Through Functional and Surface Based Mapping , 2013 .

[41]  Wei Li,et al.  Automated Extraction of Function Knowledge From Text , 2017 .

[42]  David C. Wilson,et al.  Personalised Specific Curiosity for Computational Design Systems , 2017 .

[43]  Jonathan Cagan,et al.  Expert representation of design repository space: A comparison to and validation of algorithmic output , 2013 .

[44]  Lin Wu,et al.  Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis , 2012, Journal of Intelligent Manufacturing.

[45]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[46]  P. Wright,et al.  Anisotropic material properties of fused deposition modeling ABS , 2002 .