Digital Twins of Manufacturing Systems as a Base for Machine Learning

In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.

[1]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

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

[3]  Alexander Verl,et al.  Estimation of stability lobe diagrams in milling with continuous learning algorithms , 2017 .

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

[5]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[6]  Jan Eilers,et al.  On solving the inverse kinematics problem using neural networks , 2017, 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP).

[7]  Amit Agarwal,et al.  CNTK: Microsoft's Open-Source Deep-Learning Toolkit , 2016, KDD.

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

[9]  Sergey Levine,et al.  Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.

[10]  Alexander Verl,et al.  Hardware-in-the-Loop Simulation for Machines based on a Multi-Rate Approach , 2018, Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016.

[11]  Pieter Abbeel,et al.  Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.

[12]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[13]  Oliver Niggemann,et al.  System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[14]  Yajie Miao,et al.  EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[15]  Günter Pritschow,et al.  “Hardware in the Loop” Simulation of Machine Tools , 2004 .