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Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

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Abstract

Nowadays, injection molds are manually designed by humans using computer-aided design (CAD) systems. The placement of ejector pins is a critical step in the injection mold design to enable demolding complex parts in the production. Since each injection mold is unique, designers are limited in using standard ejector layouts or previous mold designs, which results in high design time so that an automation of the design process is needed. For such a system, human knowledge is essential. Therefore, we propose a human-centric machine learning (HCML) approach for the automatic placement of ejector pins for injection molds. In this work, we extract mental models of injection mold designers to obtain machine-readable fundamental design rules and train a machine learning model using an ongoing human-machine learning approach.

The work of RWTH Aachen University within the AutoEnSys joint research project is funded by the German Federal Ministry of Education and Research (BMBF) under the grant number 01IS20081C.

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References

  1. Ehrlenspiel, K., Kiewert, A., Mörtl, M., Lindemann, U.: Kostengünstig Entwickeln und Konstruieren. Kostenmanagement bei der integrierten Produktentwicklung, 8th edn. Springer, Heidelberg (2020)

    Google Scholar 

  2. Kwak, S., Kim, T., Park, S., Lee, K.: Layout and sizing of ejector pins for injection mould desian using the wavelet transform. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 217(4), 463–473 (2003). https://doi.org/10.1243/095440503321628143

  3. Chin, K.-S., Wong, T.N.: An expert system for injection mold cost estimation. Adv. Polym. Technol. 14(4), 303–314 (1995). https://doi.org/10.1002/ADV.1995.060140404

    Article  Google Scholar 

  4. Sterman, J.D.: Business Dynamics. Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill, Boston (2000)

    Google Scholar 

  5. Wang, Z., et al.: Optimum ejector system design for plastic injection moulds. Int. J. Comput. Appl. Technol. 9(4), 211–218. (1996). ScholarBank@NUS Repository. https://doi.org/10.1504/IJMPT.1996.036339

  6. Ferreira, I., Cabral, J.A., Saraiva, P., Oliveira, M.C.: A multidisciplinary framework to support the design of injection mold tools. Struct. Multidisc. Optim. 49(3), 501–521 (2014). https://doi.org/10.1007/s00158-013-0990-x

    Article  Google Scholar 

  7. Alvarado-Iniesta, A., Cuate, O., Schütze, O.: Multi-objective and many objective design of plastic injection molding process. Int. J. Adv. Manuf. Technol. 102(9–12), 3165–3180 (2019). https://doi.org/10.1007/s00170-019-03432-8

    Article  Google Scholar 

  8. Li, K., Yan, S., Zhong, Y., Pan, W., Zhao, G.: Multi-objective optimization of the fiber-reinforced composite injection molding process using Taguchi method, RSM, and NSGA-II. Simul. Model. Pract. Theory 91, 69–82 (2019). https://doi.org/10.1016/j.simpat.2018.09.003

    Article  Google Scholar 

  9. Chin, K.-S., Wong, T.N.: An expert system for injection mold cost estimation. Adv. Polym. Technol. 9(4), 303–314 (1995). https://doi.org/10.1002/ADV.1995.060140404

    Article  Google Scholar 

  10. Kaluarachchi, T., Reis, A., Nanayakkara, S.: A review of recent deep learning approaches in human-centered machine learning. 21(7) (2021). https://doi.org/10.3390/s21072514

  11. Sperrle, F., et al.: A survey of human-centered evaluations in human-centered machine learning. Comput. Graph. Forum 40(3), 543–568 (2021). https://doi.org/10.1111/cgf.14329

    Article  Google Scholar 

  12. Gary, M.S., Wood, R.E.: Unpacking mental models through laboratory experiments. Syst. Dyn. Rev. 32(2), 101–129 (2016). https://doi.org/10.1002/sdr.1560

    Article  Google Scholar 

  13. Bubeck, S.: Convex Optimization. Algorithms and Complexity. Foundations and Trends® in Machine Learning Ser, 26th. Now Publishers, Norwell (2015)

    Google Scholar 

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Authors and Affiliations

  1. Information Management in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

    Robert Jungnickel, Johanna Lauwigi, Vladimir Samsonov & Daniel Lütticke

Authors
  1. Robert Jungnickel
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  2. Johanna Lauwigi
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  3. Vladimir Samsonov
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  4. Daniel Lütticke
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Corresponding author

Correspondence to Robert Jungnickel .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Jungnickel, R., Lauwigi, J., Samsonov, V., Lütticke, D. (2023). Human-Centric Machine Learning Approach for Injection Mold Design: Towards Automated Ejector Pin Placement. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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