Deep learning for big data applications in CAD and PLM - Research review, opportunities and case study

Abstract With the increasing amount of available data, computing power and network speed for a decreasing cost, the manufacturing industry is facing an unprecedented amount of data to process, understand and exploit. Phenomena such as Big Data, the Internet-of-Things, Closed-Loop Product Lifecycle Management, and the advances of Smart Factories tend to produce humanly unmanageable quantities of data. The paper approaches the aforesaid context by assuming that any data processing automation is not only desirable but rather necessary in order to prevent prohibitive data analytics costs. This study focuses on highlighting the major specificities of engineering data and the data-processing difficulties which are inherent to data coming from the manufacturing industry. The artificial intelligence field of research is able to provide methods and tools to address some of the identified issues. A special attention was paid to provide a literature review of the most recent (in 2017) applications, that could present a high potential for the manufacturing industry, in the fields of machine learning and deep learning. In order to illustrate the proposed work, a case study was conducted on the challenging research question of object recognition in heterogeneous formats (3D models, photos and videos) with deep learning techniques. The DICE project – DMU Imagery Comparison Engine – is presented and has been completely open-sourced in order to encourage reuse and improvements of the proposed case-study. This project also leads to the development of an open-source research dataset of 2000 CAD Models, called DMU-Net available at: https://www.dmu-net.org .

[1]  Xiaolong Wang,et al.  2D-3D face recognition via Restricted Boltzmann Machines , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[2]  Thomas F. Edgar,et al.  Smart manufacturing, manufacturing intelligence and demand-dynamic performance , 2012, Comput. Chem. Eng..

[3]  Michael J. Shaw,et al.  Applying machine learning to model management in decision support systems , 1988, Decis. Support Syst..

[4]  Yu Zhang,et al.  Proactive Decision Support using Automated Planning , 2016, ArXiv.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Lionel Fine,et al.  A priori evaluation of simulation models preparation processes using artificial intelligence techniques , 2017, Comput. Ind..

[7]  Pierre Alliez,et al.  Towards the Semantics of Digital Shapes: The AIM@SHAPE Approach , 2004, EWIMT.

[8]  Yi Lu Murphey,et al.  An intelligent real-time vision system for surface defect detection , 2004, ICPR 2004.

[9]  W. Gao,et al.  Multiresolutional similarity assessment and retrieval of solid models based on DBMS , 2006, Comput. Aided Des..

[10]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

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

[12]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[13]  Quoc V. Le,et al.  On optimization methods for deep learning , 2011, ICML.

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ali Farhadi,et al.  Target-driven visual navigation in indoor scenes using deep reinforcement learning , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[17]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[18]  Jun Zhang,et al.  Benchmark datasets for 3D computer vision , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[19]  Kunwoo Lee,et al.  Similarity comparison of mechanical parts to reuse existing designs , 2006, Comput. Aided Des..

[20]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[21]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[22]  Remco C. Veltkamp,et al.  A Survey of Content Based 3D Shape Retrieval Methods , 2004, SMI.

[23]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[24]  Ivan Bratko,et al.  Machine learning applied to quality management - A study in ship repair domain , 2007, Comput. Ind..

[25]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[26]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[27]  Jason Weston,et al.  Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing , 2012, AISTATS.

[28]  Francesco Ricci,et al.  Context-Aware Recommender Systems , 2011, AI Mag..

[29]  Bowen Zhou,et al.  Sequence-to-Sequence RNNs for Text Summarization , 2016, ArXiv.

[30]  Connie M. Borror,et al.  Robustness of the Markov-chain model for cyber-attack detection , 2004, IEEE Transactions on Reliability.

[31]  Alina A. von Davier,et al.  Cross-Validation , 2014 .

[32]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[33]  Ethan Fetaya,et al.  StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation , 2015, BMVC.

[34]  Karthik Ramani,et al.  Developing an engineering shape benchmark for CAD models , 2006, Comput. Aided Des..

[35]  Pieter Abbeel,et al.  Learning Generalized Reactive Policies using Deep Neural Networks , 2017, ICAPS.

[36]  R. Radharamanan,et al.  Sales forecasting using time series and neural networks , 1996 .

[37]  Trevor Darrell,et al.  Learning Features by Watching Objects Move , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Chao Shang,et al.  VIGAN: Missing view imputation with generative adversarial networks , 2017, 2017 IEEE International Conference on Big Data (Big Data).

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

[40]  Ramesh Sharda,et al.  Model-driven decision support systems: Concepts and research directions , 2007, Decis. Support Syst..

[41]  Paul A. Fishwick,et al.  Time series forecasting using neural networks vs. Box- Jenkins methodology , 1991, Simul..

[42]  Alberto Gómez,et al.  A review of machine learning in dynamic scheduling of flexible manufacturing systems , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[43]  Geoffrey E. Hinton,et al.  Robust Boltzmann Machines for recognition and denoising , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Sanjay Chawla,et al.  Robust, Deep and Inductive Anomaly Detection , 2017, ECML/PKDD.

[45]  Martin Pielot,et al.  Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions , 2017, EMDL '17.

[46]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[47]  Vincent Lepetit,et al.  Learning descriptors for object recognition and 3D pose estimation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Karthik Ramani,et al.  Three-dimensional shape searching: state-of-the-art review and future trends , 2005, Comput. Aided Des..

[49]  John P. A. Ioannidis,et al.  A manifesto for reproducible science , 2017, Nature Human Behaviour.

[50]  Miin-Shen Yang,et al.  A control chart pattern recognition system using a statistical correlation coefficient method , 2005, Comput. Ind. Eng..

[51]  Yang Song,et al.  Learning Fine-Grained Image Similarity with Deep Ranking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[52]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[53]  Dunja Mladenic,et al.  Machine learning approach to machinability analysis , 1998 .

[54]  Apostol Natsev,et al.  YouTube-8M: A Large-Scale Video Classification Benchmark , 2016, ArXiv.

[55]  Akira Maeda,et al.  Unsupervised Outlier Detection in Time Series Data , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).

[56]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[57]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[58]  Volker Tresp,et al.  Predictive Clinical Decision Support System with RNN Encoding and Tensor Decoding , 2016, ArXiv.

[59]  Lovekesh Vig,et al.  LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection , 2016, ArXiv.

[60]  Ali Farhadi,et al.  Visual Semantic Planning Using Deep Successor Representations , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[61]  Jake K. Aggarwal,et al.  Model-based object recognition in dense-range images—a review , 1993, CSUR.

[62]  Martin White,et al.  Enterprise information portals , 2000, Electron. Libr..

[63]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[64]  Cemil Kuzey,et al.  A comparative analysis of machine learning systems for measuring the impact of knowledge management practices , 2013, Decis. Support Syst..

[65]  Tae Jo Ko,et al.  Tool Wear Monitoring in Diamond Turning by Fuzzy Pattern Recognition , 1994 .

[66]  Gian Antonio Susto,et al.  Machine Learning for Predictive Maintenance: A Multiple Classifier Approach , 2015, IEEE Transactions on Industrial Informatics.

[67]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[68]  Fernando A. Mujica,et al.  An Empirical Evaluation of Deep Learning on Highway Driving , 2015, ArXiv.

[69]  Silvio Savarese,et al.  Beyond PASCAL: A benchmark for 3D object detection in the wild , 2014, IEEE Winter Conference on Applications of Computer Vision.

[70]  Brendt Wohlberg,et al.  Detecting anomalous structures by convolutional sparse models , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[71]  S. M. Wu,et al.  Monitoring Drilling Wear States by a Fuzzy Pattern Recognition Technique , 1988 .

[72]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[73]  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).

[74]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[75]  Robert Jenssen,et al.  Recurrent Neural Networks for Short-Term Load Forecasting , 2017, SpringerBriefs in Computer Science.

[76]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[77]  Christian Szegedy,et al.  DeepPose: Human Pose Estimation via Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[78]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[79]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[80]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[81]  Hans-Peter Kriegel,et al.  A survey on unsupervised outlier detection in high‐dimensional numerical data , 2012, Stat. Anal. Data Min..

[82]  Andrew Sherlock,et al.  Benchmarking shape signatures against human perceptions of geometric similarity , 2006, Comput. Aided Des..

[83]  Yida Wang,et al.  Generative Model With Coordinate Metric Learning for Object Recognition Based on 3D Models , 2018, IEEE Transactions on Image Processing.

[84]  Xun Xu.  Integration Based on STEP Standards , 2009 .

[85]  Daniel Hsu,et al.  Anomaly Detection on Graph Time Series , 2017, ArXiv.

[86]  Baghdad Atmani,et al.  A hybrid decision support system : application on healthcare , 2013, ArXiv.

[87]  Dan Suciu,et al.  Adding Structure to Unstructured Data , 1997, ICDT.

[88]  Luming Zhang,et al.  Action2Activity: Recognizing Complex Activities from Sensor Data , 2015, IJCAI.

[89]  Jami J. Shah,et al.  A Discourse on Geometric Feature Recognition From CAD Models , 2001, J. Comput. Inf. Sci. Eng..

[90]  Enhong Chen,et al.  Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.

[91]  Radu-Emil Precup,et al.  An overview on fault diagnosis and nature-inspired optimal control of industrial process applications , 2015, Comput. Ind..

[92]  Eric Brill,et al.  Beyond PageRank: machine learning for static ranking , 2006, WWW '06.

[93]  Joseph M. Hellerstein,et al.  Quantitative Data Cleaning for Large Databases , 2008 .

[94]  Patrick Charpentier,et al.  Reconfiguration process for neuronal classification models: Application to a quality monitoring problem , 2016, Comput. Ind..

[95]  C. Walter.  Kryder's law. , 2005, Scientific American.

[96]  Andrew Lavin,et al.  Fast Algorithms for Convolutional Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[97]  Sze Zheng Yong,et al.  Weak adaptive submodularity and group-based active diagnosis with applications to state estimation with persistent sensor faults , 2017, 2017 American Control Conference (ACC).

[98]  Ali Shokoufandeh,et al.  Local feature extraction and matching partial objects , 2006, Comput. Aided Des..

[99]  Bogdan Filipič,et al.  Using inductive machine learning to support decision making in machining processes , 2000 .

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

[101]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[102]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[103]  Rustam M. Vahidov,et al.  Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..

[104]  Lawrence D. Jackel,et al.  Limits on Learning Machine Accuracy Imposed by Data Quality , 1995, KDD.