Combined learning processes for injection moulding based on simulation and experimental data. Hopmann, C., Jeschke, S., Meisen, T., Thiele, T. D., Tercan, H., Liebenberg, M., Heinisch, J., & Theunissen, M. In 33rd International Conference of the Polymer Processing Society : PPS-33 : Cancun, Mexico, December 10-14, 2017, Lawrence, KS, Dec, 2017. 33rd International Conference of the Polymer Processing Society, Cancún (Mexico), 10 Dec 2017 - 14 Dec 2017, Polymer Processing Society.
Combined learning processes for injection moulding based on simulation and experimental data [link]Paper  abstract   bibtex   
Injection moulding enables the production of complex formed high-quality plastics parts in a single production step. To achieve a high and constant product quality, an appropriate process set-up with regards to product quality and process robustness is essential. A conventional process set-up requires expensive and time consuming experiments and know-how from the machine operator. One way to overcome this challenge is to make use of machine learning methods for process set-up. These methods can model the relationship between setting parameters and quality values and thus enable the identification of optimal working points. However, the training required for accurate modelling needs experimental data from extensive experiments for each process, too. Numerical simulation can predict quality values based on setting parameters without practical experiments. Whereas trends and the general dependencies between the parameters can be predicted with a satisfying accuracy, a certain discrepancy between the prediction of the simulation and the real process cannot be excluded. A combined approach using data from injection moulding simulations as well as experimental data appears promising to overcome the detriments of the solitary use of simulation for the training of machine learning algorithms. General dependencies could be gained from simulations without practical experiments and fine-tuning could be achieved by experimental trials with a minimal scope. In this paper, data obtained from a 2.5 D injection moulding simulation is compared with experimental data from a plate specimen and a complex formed injection moulded part. Therefore, central composed designs of experiments are used to identify differences in the effects and interdependencies of six setting parameters on quality values like part weight and dimensions. Furthermore, differences in the absolute values and the functionality of the effects are considered. On this basis, a combined machine learning concept using simulation and experimental data is presented.
@inproceedings {LiebenbergPPS17,
      author       = {Hopmann, Christian and Jeschke, Sabina and Meisen, Tobias
                      and Thiele, Thomas David and Tercan, Hasan and Liebenberg,
                      Martin and Heinisch, Julian and Theunissen, Matthias},
      title        = {{C}ombined learning processes for injection moulding based
                      on simulation and experimental data},
      address      = {Lawrence, KS},
      publisher    = {Polymer Processing Society},
      reportid     = {RWTH-2018-222255},
      year         = {2017},
      comment      = {33rd International Conference of the Polymer Processing
                      Society : PPS-33 : Cancun, Mexico, December 10-14, 2017},
      booktitle     = {33rd International Conference of the
                       Polymer Processing Society : PPS-33 :
                       Cancun, Mexico, December 10-14, 2017},
      month         = {Dec},
      date          = {2017-12-10},
      organization  = {33rd International Conference of the
                       Polymer Processing Society, Cancún
                       (Mexico), 10 Dec 2017 - 14 Dec 2017},
      cin          = {417810 / 416910 / 052100},
      cid          = {$I:(DE-82)417810_20140620$ / $I:(DE-82)416910_20140620$ /
                      $I:(DE-82)052100_20140620$},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://publications.rwth-aachen.de/record/719619},
      abstract     = {Injection moulding enables the production of complex
                      formed high-quality plastics parts in a single production
                      step. To achieve a high and constant product quality, an
                      appropriate process set-up with regards to product quality and
                      process robustness is essential. A conventional process
                      set-up requires expensive and time consuming experiments
                      and know-how from the machine operator. One way to overcome
                      this challenge is to make use of machine learning methods
                      for process set-up. These methods can model the relationship
                      between setting parameters and quality values and thus enable
                      the identification of optimal working points. However,
                      the training required for accurate modelling needs experimental
                      data from extensive experiments for each process, too.
                      Numerical simulation can predict quality values based on
                      setting parameters without practical experiments. Whereas
                      trends and the general dependencies between the parameters
                      can be predicted with a satisfying accuracy, a certain
                      discrepancy between the prediction of the simulation and
                      the real process cannot be excluded. A combined approach
                      using data from injection moulding simulations as well as
                      experimental data appears promising to overcome the detriments
                      of the solitary use of simulation for the training of machine
                      learning algorithms. General dependencies could be gained
                      from simulations without practical experiments and fine-tuning
                      could be achieved by experimental trials with a minimal scope.
                      In this paper, data obtained from a 2.5 D injection moulding
                      simulation is compared with experimental data from a plate
                      specimen and a complex formed injection moulded part.
                      Therefore, central composed designs of experiments are used
                      to identify differences in the effects and interdependencies
                      of six setting parameters on quality values like part weight
                      and dimensions. Furthermore, differences in the absolute
                      values and the functionality of the effects are considered.
                      On this basis, a combined machine learning concept using
                      simulation and experimental data is presented.},
}
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