Fast Generation of High-Performance Driveshafts: A Digital Approach to Automated Linked Topology and Design Optimization
Authors: Willi Zschiebsch, Alrik Dargel, Sebastian Spitzer, Philipp Johst, Robert Böhm, Niels Modler
Abstract:
In this article, we investigate an approach that digitally links individual development process steps by using the drive shaft of an aircraft engine as representative example of a fiber polymer composite. Such high-performance lightweight composite structures have many adjustable parameters that influence the mechanical properties. Only a combination of optimal parameter values can lead to energy efficient lightweight structures. The development tools required for the Engineering Design Process (EDP) are often isolated solutions and their compatibility with each other is limited. A digital framework is presented in this study, which allows individual specialised tools to be linked via the generated data in such a way that automated optimization across programs becomes possible. This is demonstrated using the example of linking geometry generation with numerical structural analysis. The proposed digital framework for automated design optimization demonstrates the feasibility of developing a complete digital approach to design optimization. The methodology shows promising potential for achieving optimal solutions in terms of mass, material utilization, eigenfrequency and deformation under lateral load with less development effort. The development of such a framework is an important step towards promoting a more efficient design approach that can lead to stable and balanced results.
Keywords: Digital Linked Process, composite, CFRP, multi-objective, EDP, NSGA-2, NSGA-3, TPE.
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[1] J. A. H. Hult and F. G. Rammerstorfer, Eds., Engineering Mechanics of Fibre Reinforced Polymers and Composite Structures, ser. CISM Courses and Lectures. Wien ; New York: Springer-Verlag, 1994, no. no. 348.
[2] W. Zschiebsch, A. Filippatos, and R. Bohm, “A digital-based design methodology for the optimization of high-performance multi-material structures,” IOP Conference Series: Materials Science and Engineering, vol. 1226, no. 1, p. 012078, Feb. 2022.
[3] R. K. Rompicharla and K. Rambabu, “Design and Optimization of Drive Shaft with composite materials,” International Journal of Modern Engineering Research, vol. 2, no. 5, pp. 3422–3428, 2012.
[4] H. Bankar, V. Shinde, and P. Baskar, “Material optimization and weight reduction of drive shaft using composite material,” IOSR Journal of Mechanical and Civil Engineering, vol. 10, no. 1, pp. 39–46, 2013.
[5] A. Cherniaev and V. Komarov, “Multistep Optimization of Composite Drive Shaft Subject to Strength, Buckling, Vibration and Manufacturing Constraints,” Applied Composite Materials, vol. 22, no. 5, pp. 475–487, Oct. 2015.
[6] M. Norouzian, A. Khalkhali, and E. Nikghalb, “Multi-objective optimization of hybrid carbon/glass fiber reinforced epoxy composite automotive drive shaft,” International Journal of Engineering, vol. 28, no. 4, pp. 583–592, 2015.
[7] P. S. K. Reddy and Ch. Nagaraju, “Weight optimization and Finite Element Analysis of Composite automotive drive shaft for Maximum Stiffness,” Materials Today: Proceedings, vol. 4, no. 2, pp. 2390–2396, 2017.
[8] E. Bilalis, M. Keramidis, and N. Tsouvalis, “Structural design optimization of composite materials drive shafts,” Marine Structures, vol. 84, p. 103194, Jul. 2022.
[9] C. Pike-Burke, “Multi-Objective Optimization,” 2019.
[10] B. Zhang, K. Shafi, and H. Abbass, “On Benchmark Problems and Metrics for Decision Space Performance Analysis in Multi-Objective Optimization,” International Journal of Computational Intelligence and Applications, vol. 16, no. 01, p. 1750006, Mar. 2017.
[11] C.-T. Yeh, “An improved NSGA2 to solve a bi-objective optimization problem of multi-state electronic transaction network,” Reliability Engineering & System Safety, vol. 191, p. 106578, Nov. 2019.
[12] K. Deb and H. Jain, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577–601, Aug. 2014.
[13] M. Ehrgott and X. Gandibleux, “Multiobjective Combinatorial Optimization — Theory, Methodology, and Applications,” in Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys, F. S. Hillier, M. Ehrgott, and X. Gandibleux, Eds. Boston, MA: Springer US, 2003, vol. 52, pp. 369–444.
[14] R. Marler and J. Arora, “Survey of multi-objective optimization methods for engineering,” Structural and Multidisciplinary Optimization, vol. 26, no. 6, pp. 369–395, Apr. 2004.
[15] H. Shinichi, “NSGA-III: New Sampler for Many Objective Optimization,” Jul. 2023.
[16] R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, “A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization,” IEEE Transactions on Evolutionary Computation, vol. 20, no. 5, pp. 773–791, Oct. 2016.
[17] H. Jain and K. Deb, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 602–622, Aug. 2014.
[18] O. Marko, D. Pavlovi´c, V. Crnojevi´c, and K. Deb, “Optimisation of crop configuration using NSGA-III with categorical genetic operators,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion. Prague Czech Republic: ACM, Jul. 2019, pp. 223–224.
[19] J. E. Fieldsend, “University staff teaching allocation: Formulating and optimising a many-objective problem,” in Proceedings of the Genetic and Evolutionary Computation Conference. Berlin Germany: ACM, Jul. 2017, pp. 1097–1104.
[20] S. G. Fitzgerald, G. W. Delaney, D. Howard, and F. Maire, “Evolving polydisperse soft robotic jamming grippers,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion. Boston Massachusetts: ACM, Jul. 2022, pp. 707–710.
[21] Y. Ozaki, Y. Tanigaki, S. Watanabe, and M. Onishi, “Multiobjective tree-structured parzen estimator for computationally expensive optimization problems,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference. Canc´un Mexico: ACM, Jun. 2020, pp. 533–541.
[22] A. Kaszynski, J. Derrick, German, Natter1, A. Kaszynski, FredAns, Jleonatti, Simonmarwitz, 1081, D. Correia, D. Addy, JackGuyver, Jazztekk, Jkbgbr, and Spectereye, “Pyansys/pymapdl: V0.60.3,” Zenodo, Nov. 2021.
[23] T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019.
[24] W. Zschiebsch, “Digitaldriveshaft,” Aug. 2023. (Online). Available: https://doi.org/10.5281/zenodo.8234632