Original Article
  • Stiffness Enhancement of Piecewise Integrated Composite Robot Arm using Machine Learning
  • Seungmin Ji*, Seokwoo Ham*, Seong S. Cheon*†

  • * Department of Mechanical Engineering, Graduated School, Kongju National University
    ** Lacomtech Co. Ltd

  • 머신 러닝을 이용한 PIC 로봇 암 강성 향상에 대한 연구
  • 지승민* · 함석우* · 전성식*†

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

References
  • 1. Liu, Y., Bi, Q., Yue, X., Wu, J., Yang, B., and Li, Y., “A Review on Tensegrity Structures-based Robots,” Mechanism and Machine Theory, Vol. 168, 2022, 104571.
  •  
  • 2. Mukherjee, D., Gupta, K., Chang, L.H., and Najjaran, H., “A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings,” Robotics and Computer-Integrated Manufacturing, Vol. 73, 2022, 102231.
  •  
  • 3. Oh, J.H., Lee, D.G., and Kim, H.S., “Composite Robot end Effector for Manipulating Large LCD Glass Panels,” Composite Structures, Vol. 47, No. 1-4, 1999, pp. 497-506.
  •  
  • 4. Lee, C.S., Lee, D.G., Oh, J.H., and Kim, H.S., “Composite Wrist Blocks for Double Arm Type Robots for Handling Large LCD Glass Panels,” Composite Structures, Vol. 57, No. 1-4, 2002, pp. 345-355.
  •  
  • 5. Lee, C.S., and Lee, D.G., “Manufacturing of Composite Sandwich Robot Structures Using the Co-cure Bonding Method,” Composite Structures, Vol. 65, No. 3-4, 2004, pp. 307-318.
  •  
  • 6. Oh, J.H., Kim, Y.G., and Lee, D.G., “Optimum Bolted Joints for Hybrid Composite Materials”, Composite Structures, Vol. 38, No. 1-4, 1997, pp. 329-341.
  •  
  • 7. Kuo, J.L., “Multi-objective Optimal Design of Motion Precision for Fork Robot Arm in LCD Panel Manufacturing Process System,” Microelectronics Reliability, Vol. 99, 2019, pp. 19-30.
  •  
  • 8. Vo, T.P., and Lee, J., “Flexural-torsional Behavior of Thin-walled Closed-section Composite Box Beams,” Engineering Structures, Vol. 29, No. 8, 2007, pp. 1774-1782.
  •  
  • 9. Cook R.D., and Young W.C., Advanced Mechanics of Materials, Pearson Prentice Hall Pub, US, 1985.
  •  
  • 10. Ghazavi, A., and Gordaninejad, F., “A Comparison of the Control of a Flexible Robot Arm Constructed from Graphite/epoxy Versus Aluminum,” Computers & Structures, Vol. 54, No. 4, 1995, pp. 621-632.
  •  
  • 11. Bicos, A.S., and Springer, G.S., “Design of Composite Boxbeam,” Journal of Composite Materials, Vol. 20, 1986, pp. 86-109.
  •  
  • 12. Wu, Y., Lai, Y., Zhang, X., and Zhu, Y., “A Finite Beam Element for Analyzing Shear Lag and Shear Deformation Effects in Composite-laminated Box Girders,” Computers & Structures, Vol. 82, No. 9-10, 2004, pp. 763-771.
  •  
  • 13. Wu, Y., Wang, X., Su, Q., and Lin, L., “A Solution for Laminated Box Beams under Bending Loads Using the Principle of Complementary Energy,” Composite Structures, Vol. 79, No. 3, 2007, pp. 376-380.
  •  
  • 14. Loughlan, J., and Ata, M., “The Analysis of Carbon Fibre Composite Box Beams Subjected Torsion with Variable Twist,” Computer Methods in Applied Mechanics and Engineering, Vol. 152, No. 3-4, 1998, pp. 373-391.
  •  
  • 15. Ghiasi, H., Fayazbakhsh, K., Pasini, D., and Lessard, L., “Optimum Stacking Sequence Design of Composite Materials Part II: Variable Stiffness Design,” Composite Structures, Vol. 93, No. 1, 2010, pp. 1-13.
  •  
  • 16. Jeong, C.H., Oh, H.S., Ham, S.W., Kim, G.S., Son, S.N., Cho, Y.S., and Cheon, S.S., “Crash Simulation of a Piecewisely-integrated Composite Bumper Beams,” Mechanical and Production Engineering, Vol. 6, 2018, pp. 37-40.
  •  
  • 17. Ham, S.W., Cheon, S.S., and Jeong, K.Y., “Strength Optimization of Piecewise Integrated Composite Beam Through Machine Learning,” Transactions of the Korean Society of Mechanical Engineers A, Vol. 43, No. 8, 2019, pp. 521-528.
  •  
  • 18. Lantz, B., Machine Learning with R, 2nd ed., Packt Pub., UK, 2015.
  •  
  • 19. Qi, Z., Zhang, N., Liu, Y., and Chen, W., “Prediction of Mechanical Properties of Carbon Fiber Based on Cross-scale FEM and Machine Learning,” Composite Structures, Vol. 212, 2019, pp. 199-206.
  •  
  • 20. Gajowniczek, K., and Ząbkowski, T., “ImbTreeAUC: An R Package for Building Classification Trees Using the Area under the ROC Curve (AUC) on Imbalanced Datasets,” SoftwareX, Vol. 15, 2021, 100755.
  •  
  • 21. Muller, M.P., Tomlinson, G., Marrie, T.J., Tang, P., McGeer, A., Low, D.E., Detsky, A.S., and Gold, W.L., “Can Routine Laboratory Tests Discriminate between Severe Acute Respiratory Syndrome and other Causes of Community-acquired Pneumonia?” Journal of Clinical Infectious Diseases, Vol. 40, No. 8, 2005, pp. 1079-1086.
  •  
  • 22. Toray Composite Materials America, Inc., “2510 Prepreg System,” https://www.toraycma.com/wp-content/uploads/2510-Prepreg-System.pdf, 2017.
  •  
  • 23. Bai, Y., Teng, X., and Wierzbicki, T., “On the Application of Stress Triaxiality Formula for Plane Strain Fracture Testing,” Journal of Engineering Materials and Technology, Transactions of the ASME, Vol. 131, No. 2, 2009, 0210021.
  •  
  • 24. Bao, Y., and Wierzbicki, T., “On Fracture Locus in the Equivalent Strain and Stress Triaxiality Space,” International Journal of Mechanical Sciences, Vol. 46, No. 1, 2004, pp. 81-98.
  •  
  • 25. Gareth, J., Daniela, W., Trevor, H., and Robert, T., An Introduction to Statistical Learning: with Applications in R, Springer Pub. Co., Berlin, Germany, 2013.
  •  
  • 26. Vapnik, V., The Nature of Statistical Learning Theory, Springer Pub. Co., Berlin, Germany, 1999.
  •  
  • 27. Konjevoda, P., and Štambuk, N., Open-source Tools for Data Mining in Social Science, IntechOpen Limited Pub. Co., London, UK, 2012.
  •  
  • 28. Kim, Y.J., Kim, T.W., Yoon, J.S., and Kim, I.H., “Study on Prediction of Similar Typhoons through Neural Network Optimization,” Journal of Ocean Engineering and Technology, Vol. 33, No. 5, 2019, pp. 427-434.
  •  
  • 29. Kim, S.H., and Seo, D.H., “A Study on Occupancy Estimation Method of a Private Room Using IoT Sensor Data Based Decision Tree Algorithm,” Journal of the Korean Solar Energy Society, Vol. 37, No. 2, 2017, pp. 23-33.
  •  
  • 30. Bang, M., Kang, H., Lee, K., Oh, C., Choi, W., Park, G., and Kim, D., “Analysis of Gas Turbine Compressor Degradation Using Random Forest-based Machine Learning Model,” Journal of Transactions of the Korean Society of Mechanical Engineers, B, Vol. 46, No. 3, 2022, pp. 605-612.
  •  
  • 31. Feraboli, P., Wade, B., Deleo, F., Rassaian, M., Higgins, M., and Byar, A., “LS-DYNA MAT54 Modeling of the Axial Crushing of a Composite Tape Sinusoidal Specimen,” Composites Part A: Applied Science and Manufacturing, Vol. 42, 2011, pp. 1809-1825.
  •  
  • 32. Denk, L., Hatta, H., Misawa, A., and Somiya, S., “Shear Fracture of C/C Composites with Variable Stacking Sequence,” Carbon, Vol. 39, No. 10, 2001, pp. 1505-1513.
  •  
  • 33. Jeong, C.H., Ham, S.W., Kim, G.S., and Cheon, S.S., “Development of the Piecewisely-integrated Composite Bumper Beam Based on the IIHS Crash Analysis,” Composites Research, Vol. 31, No. 1, 2018, pp. 37-41.
  •  

This Article

Correspondence to

  • Seong S. Cheon
  • Department of Mechanical Engineering, Graduated School, Kongju National University

  • E-mail: sscheon@kongju.ac.kr