Sparse Data Enrichment by Context Oriented Model Reduction Techniques in Manufacturing Industry with an Example Laser Drilling Process

Authors

  • You Wang Nonlinear Dynamics of Laser Manufacturing Processes Instruction and Research Department (NLD) of RWTH Aachen University, Steinbachstraße 15, 52074, Aachen, Germany
  • Hasan Tercan Institute of Information Management in Mechanical Engineering (IMA) of RWTH Aachen University, Dennwartstraße 27, 52068, Aachen, Germany
  • Torsten Hermanns Fraunhofer Institute for Laser Technology, Steinbachstraße, 52074, Aachen, Germany
  • Thomas Thiele Institute of Information Management in Mechanical Engineering (IMA) of RWTH Aachen University, Dennwartstraße 27, 52068, Aachen, Germany
  • Tobias Meisen Institute of Information Management in Mechanical Engineering (IMA) of RWTH Aachen University, Dennwartstraße 27, 52068, Aachen, Germany
  • Sabina Jeschke Institute of Information Management in Mechanical Engineering (IMA) of RWTH Aachen University, Dennwartstraße 27, 52068, Aachen, Germany
  • Wolfgang Schulz Nonlinear Dynamics of Laser Manufacturing Processes Instruction and Research Department (NLD) of RWTH Aachen University, Steinbachstraße 15, 52074, Aachen, Germany; Fraunhofer Institute for Laser Technology, Steinbachstraße, 52074, Aachen, Germany

DOI:

https://doi.org/10.13052/jicts2245-800X.632

Keywords:

sparse data, industry data, model reduction, machine learning, virtual production intelligence

Abstract

Nowadays, the internet of things and industry 4.0 from Germany are all focused on the application of data analytics and Artificial Intelligence to build the succeeding generation of manufacturing industry. In manufacturing planning and iterative designing process, the data-driven issues exist in the context of the purpose for approaching the optimal design and generating an explicit knowledge. The multi-physical phenomena, the time consuming comprehensive numerical simulation, and a limited number of experiments lead to the so-called sparse data problems or “curse of dimensionality”. In this work, an advanced technique using reduced models to enrich sparse data is proposed and discussed. The validated reduced models, which are created by several model reduction techniques, are able to generate dense data within an acceptable time. Afterwards, machine learning and data analytics techniques are applied to extract unknown but useful knowledge from the dense data in the Virtual Production Intelligence (VPI) platform. The demonstrated example is a typical case from laser drilling process.

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Author Biographies

You Wang, Nonlinear Dynamics of Laser Manufacturing Processes Instruction and Research Department (NLD) of RWTH Aachen University, Steinbachstraße 15, 52074, Aachen, Germany

You Wang studied material science at RWTH Aachen University and got his master degree in 2015. He wrote his master thesis with the title “Modelling and Simulation of Glass Heating Process” at Fraunhofer IPT, Aachen. Now he is working as a research associate at the Department of Nonlinear Dynamics of Laser Manufacturing Processes (NLD). His main research interest is the field of model reduction and meta-modelling of laser manufacturing processes. Meta modelling techniques are aiming to set up fast responding and accurate data-driven models by analyzing numeric models or experimental data with multi-dimensional parameters. These performant meta-models lead to fast and frugal customer simulation tools which will strongly support industrial decision making processes.

Hasan Tercan, Institute of Information Management in Mechanical Engineering (IMA) of RWTH Aachen University, Dennwartstraße 27, 52068, Aachen, Germany

Hasan Tercan has been a scientific researcher at the Cybernetics Lab since July 2015. Mr. Tercan studied computer science at the Technical University of Darmstadt until January 2015. His major fields of study were databases, data warehousing and data analytics. In his master thesis, he investigated the use of various machine learning methods in the financial sector. At the IMA, Mr. Tercan investigates the use of methods of machine learning and artificial intelligence in the production context. The focus here is on AI-supported systems for decision support in production planning as well as automation in production processes.

Torsten Hermanns, Fraunhofer Institute for Laser Technology, Steinbachstraße, 52074, Aachen, Germany

Torsten Hermanns studied physics at RWTH Aachen and received his degree in 2012. His diploma thesis was written at the department “Nonlinear Dynamics of Laser Processing” (NLD) of RWTH Aachen University. In his thesis with the title “Mathematical Modelling and linear Stability Analysis of Laser Fusion Cutting” he derived a stability criterion for the melt film in laser fusion cutting. This stability criterion considered, for the first time, the intensity distribution of the laser beam. After completing his studies, he started working as a research associate at NLD on his dissertation.

Torsten Hermanns is focusing on modelling and simulation of the laser cutting of metallic materials as well as laser processing with short or ultra-short pulsed laser radiation. This is accomplished by developing reduced models that are based on integral, spectral or asymptotic methods as well as numerical methods. Furthermore, he is responsible for the development of software solutions especially designed for on-site use at the customer.

Wolfgang Schulz, Nonlinear Dynamics of Laser Manufacturing Processes Instruction and Research Department (NLD) of RWTH Aachen University, Steinbachstraße 15, 52074, Aachen, Germany; Fraunhofer Institute for Laser Technology, Steinbachstraße, 52074, Aachen, Germany

Wolfgang Schulz studied physics at Braunschweig University of Technology. He graduated from the Institute for Theoretical Physics and received a postgraduate scholarship in 1986 on the topic of “Hot electrons in metals”. In 1987, he accepted an invitation to the department Laser Technology at RWTH Aachen University. He received the “Borchers Medal” award in 1992 in recognition of his PhD thesis. In 1997, he joined the Fraunhofer Institute for Laser Technology in Aachen and, in 1999, received the “Venia Legendi” in the field “Principles of Continuum Physics applied to Laser Technology”. His postdoctoral lecture qualification (habilitation) was awarded with the prize of the Friedrich-Wilhelm Foundation at RWTH Aachen University. Since March 2005, he has represented the newly founded department “Nonlinear Dynamics of Laser Processing” at RWTH Aachen University and is the head of the newly founded department of “Modelling and Simulation” at the Fraunhofer Institute for Laser Technology in Aachen. Since 2007, he is the coordinator of the Excellence Cluster Domain “Virtual Production” at RWTH Aachen University.

His current work is focused on developing and improving laser systems and their industrial applications by combination of mathematical, physical and experimental methods. In particular, he applies the principles of optics, continuum physics and thermodynamics to analyse the phenomena involved in laser processing. The mathematical objectives are modelling, analysis and dynamical simulation of Free Boundary Problems, which are systems of nonlinear partial differential equations. Analytical and numerical methods for model reduction are developed and applied. The mathematical analysis yield approximate dynamical systems with small dimension in the phase space and is based on asymptotic properties like the existence of inertial manifolds.

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Published

2018-09-20

How to Cite

Wang, Y. ., Tercan, H. ., Hermanns, T. ., Thiele, T. ., Meisen, T. ., Jeschke, S. ., & Schulz, W. . (2018). Sparse Data Enrichment by Context Oriented Model Reduction Techniques in Manufacturing Industry with an Example Laser Drilling Process. Journal of ICT Standardization, 6(3), 203–216. https://doi.org/10.13052/jicts2245-800X.632

Issue

2018: Vol 6 Iss 3

Section

Articles

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