Home Enhancing the structural performance of engineering components using the geometric mean optimizer
Article
Licensed
Unlicensed Requires Authentication

Enhancing the structural performance of engineering components using the geometric mean optimizer

  • Pranav Mehta

    Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests are metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

         , Ali Riza Yildiz

    Dr. Ali Riza Yildiz is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

     EMAIL logo     , Sadiq M. Sait

    Dr. Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia.

         and Betül Sultan Yildiz

    Dr. Betül Sultan Yildiz is an Associate professor in the Department of Mechanical Engineering at Bursa Uludağ University, Bursa, Turkey. Her research interests are mechanical design, structural optimization methods, and meta-heuristic optimization algorithms.
Published/Copyright: May 1, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Materials Testing
From the journal Materials Testing Volume 66 Issue 7

Abstract

In this article, a newly developed optimization approach based on a mathematics technique named the geometric mean optimization algorithm is employed to address the optimization challenge of the robot gripper, airplane bracket, and suspension arm of automobiles, followed by an additional three engineering problems. Accordingly, other challenges are the ten-bar truss, three-bar truss, tubular column, and spring systems. As a result, the algorithm demonstrates promising statistical outcomes when compared to other well-established algorithms. Additionally, it requires less iteration to achieve the global optimum solution. Furthermore, the algorithm exhibits minimal deviations in results, even when other techniques produce better or similar outcomes. This suggests that the proposed approach in this paper can be effectively utilized for a wide range of critical industrial and real-world engineering challenges.

Keywords: airplane bracket; structural performance; robust design; robot gripper; suspension arm
Corresponding author: Ali Riza Yildiz, Department of Mechanical Engineering, Bursa Uludag University, Bursa, Türkiye, E-mail:

About the authors

Pranav Mehta

Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387001, Gujarat, India. He is currently a Ph.D. research scholar with the Dharmsinh Desai University, Nadiad, Gujarat, India. His major research interests are metaheuristics techniques, multi-objective optimization, solar–thermal technologies, and renewable energy.

Ali Riza Yildiz

Dr. Ali Riza Yildiz is a Professor in the Department of Mechanical Engineering, Bursa Uludağ University, Bursa, Turkey. His research interests are the finite element analysis of structural components, lightweight design, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

Sadiq M. Sait

Dr. Sadiq M. Sait received his Bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his Master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia.

Betül Sultan Yildiz

Dr. Betül Sultan Yildiz is an Associate professor in the Department of Mechanical Engineering at Bursa Uludağ University, Bursa, Turkey. Her research interests are mechanical design, structural optimization methods, and meta-heuristic optimization algorithms.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

References

[1] N. Sabangban, et al.., “Simultaneous aerodynamic and structural optimisation of a low-speed horizontal-axis wind turbine blade using metaheuristic algorithms,” Mater. Test., vol. 65, no. 5, pp. 699–714, 2023, https://doi.org/10.1515/mt-2022-0308.Search in Google Scholar

[2] Z. Meng, Q. Qian, M. Xu, B. Yu, A. R. Yıldız, and S. Mirjalili, “PINN-FORM: a new physics-informed neural network for reliability analysis with partial differential equation,” Comput. Methods Appl. Mech. Eng., vol. 414, p. 116172, 2023, https://doi.org/10.1016/j.cma.2023.116172.Search in Google Scholar

[3] M. Abdel-Basset, R. Mohamed, M. Jameel, and M. Abouhawwash, “Nutcracker optimizer: a novel nature-inspired metaheuristic algorithm for global optimization and engineering design problems,” Knowl-Based Syst., vol. 262, p. 110248, 2023, https://doi.org/10.1016/j.knosys.2022.110248.Search in Google Scholar

[4] S. Kumar, G. G. Tejani, N. Pholdee, S. Bureerat, and P. Mehta, “Hybrid heat transfer search and passing vehicle search optimizer for multi-objective structural optimization,” Knowl.-Based Syst., vol. 212, p. 106556, 2021, https://doi.org/10.1016/j.knosys.2020.106556.Search in Google Scholar

[5] P. Mehta, B. S. Yildiz, S. M. Sait, and A. R. Yildiz, “Hunger games search algorithm for global optimization of engineering design problems,” Mater. Test., vol. 64, no. 4, pp. 524–532, 2022, https://doi.org/10.1515/mt-2022-0013.Search in Google Scholar

[6] Q. Zhang, H. Gao, Z.-H. Zhan, J. Li, and H. Zhang, “Growth Optimizer: a powerful metaheuristic algorithm for solving continuous and discrete global optimization problems,” Knowl.-Based Syst., vol. 261,p. 110206, 2023, https://doi.org/10.1016/j.knosys.2022.110206.Search in Google Scholar

[7] L. Abualigah, “Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications,” Neural Comput. Appl., vol. 33, no. 7, pp. 2949–2972, 2021, https://doi.org/10.1007/s00521-020-05107-y.Search in Google Scholar

[8] P. Mehta, B. S. Yıldız, S. M. Sait, and A. R. Yıldız, “Gradient-based optimizer for economic optimization of engineering problems,” Mater. Test., vol. 64, no. 5, pp. 690–696, 2022, https://doi.org/10.1515/mt-2022-0055.Search in Google Scholar

[9] F. Rezaei, H. R. Safavi, M. Abd Elaziz, and S. Mirjalili, “GMO: geometric mean optimizer for solving engineering problems,” Soft Comput., vol. 27, no. 15, pp. 10571–10606, 2023, https://doi.org/10.1007/s00500-023-08202-z.Search in Google Scholar

[10] M. Azizi, S. Talatahari, and A. H. Gandomi, “Fire Hawk Optimizer: a novel metaheuristic algorithm,” Artif. Intell. Rev., vol. 56, no. 1, pp. 287–363, 2023, https://doi.org/10.1007/s10462-022-10173-w.Search in Google Scholar

[11] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. Baghalzadeh Shishehgarkhaneh, “Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization,” Sci. Rep., vol. 13, no. 1, p. 226, 2023, https://doi.org/10.1038/s41598-022-27344-y.Search in Google Scholar PubMed PubMed Central

[12] H. Jia, H. Rao, C. Wen, and S. Mirjalili, “Crayfish optimization algorithm,” Artif. Intell. Rev., vol. 56, pp. 1919–1979, 2023. https://doi.org/10.1007/s10462-023-10567-4.Search in Google Scholar

[13] M. Kopar and A. R. Yildiz, “Composite disc optimization using hunger games search optimization algorithm,” Mater. Test., vol. 65, no. 8, pp. 1222–1229, 2023, https://doi.org/10.1515/mt-2023-0067.Search in Google Scholar

[14] S. Kumar, et al.., “Chaotic marine predators algorithm for global optimization of real-world engineering problems,” Knowl.-Based Syst., vol. 261, 110192, 2023, https://doi.org/10.1016/j.knosys.2022.110192.Search in Google Scholar

[15] L. Wang, Q. Cao, Z. Zhang, S. Mirjalili, and W. Zhao, “Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 114, p. 105082, 2022, https://doi.org/10.1016/j.engappai.2022.105082.Search in Google Scholar

[16] B. S. Yildiz, et al.., “A novel hybrid flow direction optimizer-dynamic oppositional based learning algorithm for solving complex constrained mechanical design problems,” Mater. Test., vol. 65, no. 1, pp. 134–143, 2023, https://doi.org/10.1515/mt-2022-0183.Search in Google Scholar

[17] B. S. Yıldız, et al.., “A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems,” Knowl.-Based Syst., vol. 271, p. 110554, 2023, https://doi.org/10.1016/j.knosys.2023.110554.Search in Google Scholar

[18] P. Mehta, et al.., “A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems,” Mater. Test., vol. 65, no. 2, pp. 210–223, 2023, https://doi.org/10.1515/mt-2022-0259.Search in Google Scholar

[19] B. S. Yıldız, P. Mehta, N. Panagant, S. Mirjalili, and A. R. Yildiz, “A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems,” J. Comput. Des. Eng., vol. 9, no. 6, pp. 2452–2465, 2022, https://doi.org/10.1093/jcde/qwac113.Search in Google Scholar

[20] P. Savsani and V. Savsani, “Passing vehicle search (PVS): a novel metaheuristic algorithm,” Appl. Math. Model., vol. 40, nos. 5–6, pp. 3951–3978, 2016. https://doi.org/10.1016/j.apm.2015.10.040.Search in Google Scholar

[21] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems,” Comput. Aided Des., vol. 43, no. 3, pp. 303–315, 2011. https://doi.org/10.1016/j.cad.2010.12.015.Search in Google Scholar

[22] S. Yin, Q. Luo, and Y. Zhou, “EOSMA: an equilibrium optimizer slime mould algorithm for engineering design problems,” Arabian J. Sci. Eng., vol. 47, no. 8, pp. 10115–10146, 2022, https://doi.org/10.1007/s13369-021-06513-7.Search in Google Scholar

[23] J. Huang, L. Gao, and X. Li, “An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes,” Appl. Soft Comput., vol. 36, pp. 349–356, 2015, https://doi.org/10.1016/j.asoc.2015.07.031.Search in Google Scholar

[24] P. Champasak, et al.., “Grid-based many-objective optimiser for aircraft conceptual design with multiple aircraft configurations,” Eng. Appl. Artif. Intell., vol. 126, no. Part B, p. 106951, 2023. https://doi.org/10.1016/j.engappai.2023.106951.Search in Google Scholar

[25] P. Mehta, et al.., “A novel hybrid Fick’s law algorithm-quasi oppositional-based learning algorithm for solving constrained mechanical design problems,” Mater. Test., vol. 65, no. 12, pp. 1817–1825, 2023. https://doi.org/10.1515/mt-2023-0235.Search in Google Scholar

[26] S. C. Chu, T. T. Wang, A. R. Yildiz, and J. S. Pang, “Ship rescue optimization: a new metaheuristic algorithm for solving engineering problems,” J. Internet Technol., vol. 25, no. 1, pp. 61–78, 2024. https://doi.org/10.53106/160792642024012501006.Search in Google Scholar

[27] H. Abderazek, F. Hamza, A. R. Yildiz, and S. M. Sait, “Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design,” Mater. Test., vol. 63, no. 3, pp. 266–271, 2021, https://doi.org/10.1515/mt-2020-0039.Search in Google Scholar

[28] D. Gures, S. Bureerat, S. M. Sait, and A. R. Yildiz, “Comparison of the arithmetic optimization algorithm, the slime mold optimization algorithm, the marine predators algorithm, the salp swarm algorithm for real-world engineering applications,” Mater. Test., vol. 63, no. 5, pp. 448–452, 2021, https://doi.org/10.1515/mt-2020-0076.Search in Google Scholar

[29] B. S. Yildiz, N. Pholdee, S. Bureerat, M. U. Erdas, A. R. Yildiz, and S. M. Sait, “Comparision of the political optimization algorithm, the Archimedes optimization algorithm and the Levy flight algorithm for design optimization in industry,” Mater. Test., vol. 63, no. 4, pp. 356–359, 2021, https://doi.org/10.1515/mt-2020-0053.Search in Google Scholar

[30] P. Mehta, S. M. Sait, B. S. Yildiz, M. U. Erdaş, M. Kopar, and A. R. Yildiz, “A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems,” Mater. Test., vol. 66, no. 4, pp. 544–552, 2024. https://doi.org/10.1515/mt-2023-0332.Search in Google Scholar

[31] B. S. Yildiz, N. Pholdee, S. Bureerat, A. R. Yildiz, and S. M. Sait, “Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm,” Exp. Syst., vol. 38, no. 3, p. e12666, 2021. https://doi.org/10.1111/exsy.12666.Search in Google Scholar

[32] B. S. Yildiz, S. Kumar, N. Pholdee, S. Bureerat, S. M. Sait, and A. R. Yildiz, “A new chaotic Levy flight distribution optimization algorithm for solving constrained engineering problems,” Exp. Syst., vol. 39, no. 8, p. 12992, 2022. https://doi.org/10.1111/exsy.12992.Search in Google Scholar

[33] N. Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz, and S. Mirjalili, “A comparative study of recent multi-objective metaheuristics for solving constrained truss optimisation problems,” Arch. Comput. Methods Eng., vol. 28, no. 5, pp. 4031–4047, 2021. https://doi.org/10.1007/s11831-021-09531-8.Search in Google Scholar

[34] C. M. Aye, N. Pholdee, A. R. Yildiz, S. Bureerat, and S. M. Sait, “Multi-surrogate-assisted metaheuristics for crashworthiness optimisation,” Int. J. Veh. Des., vol. 80, nos. 2/3/4, p. 223, 2019, https://doi.org/10.1504/IJVD.2019.109866.Search in Google Scholar

[35] B. S. Yildiz, N. Pholdee, N. Panagant, S. Bureerat, A. R. Yildiz, and S. M. Sait, “A novel chaotic Henry gas solubility optimization algorithm for solving real-world engineering problems,” Eng. Comput., vol. 38(Suppl 2), pp. 871–883, 2022. https://doi.org/10.1007/s00366-020-01268-5.Search in Google Scholar

[36] A. R. Yildiz and F. Öztürk, Hybrid Taguchi-Harmony Search Approach for Shape Optimization, Berlin, Germany, Springer, 2010, pp. 89–98.10.1007/978-3-642-04317-8_8Search in Google Scholar

[37] S. Anosri, et al.., “A comparative study of state-of-the-art metaheuristics for solving many-objective optimization problems of fixed wing unmanned aerial vehicle conceptual design,” Arch. Comput. Methods Eng., vol. 30, no. 6, pp. 3657–3671, 2023. https://doi.org/s11831-023-09914-z.10.1007/s11831-023-09914-zSearch in Google Scholar

[38] A. R. Yildiz, N. Kaya, F. Öztürk, and O. Alankuş, “Optimal design of vehicle components using topology design and optimisation,” Int. J. Veh. Des., vol. 34, no. 4, pp. 387–398, 2004, https://doi.org/10.1504/IJVD.2004.004064.Search in Google Scholar

[39] B. S. Yıldız, “Marine predators algorithm and multi-verse optimisation algorithm for optimal battery case design of electric vehicles,” Int. J. Veh. Des., vol. 88, no. 1, p. 1, 2022, https://doi.org/10.1504/IJVD.2022.124866.Search in Google Scholar

[40] A. Karaduman, B. S. Yıldız, and A. R. Yıldız, “Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry,” Int. J. Veh. Des., vol. 80, nos. 2/3/4, p. 330, 2019, https://doi.org/10.1504/IJVD.2019.109875.Search in Google Scholar

[41] Y. Kanokmedhakul, S. Bureerat, N. Panagant, T. Radpukdee, N. Pholdee, and A. R. Yıldız, “Metaheuristic-assisted complex H-infinity flight control tuning for the Hawkeye unmanned aerial vehicle: a comparative study,” Expert Syst. Appl., vol. 248, p. 123428, 2024. https://doi.org/10.1016/j.eswa.2024.123428.Search in Google Scholar

[42] B. S. Yıldız, “Robust design of electric vehicle components using a new hybrid salp swarm algorithm and radial basis function-based approach,” Int. J. Veh. Des., vol. 83, no. 1, p. 38, 2020, https://doi.org/10.1504/IJVD.2020.114779.Search in Google Scholar

[43] S. M. Sait, P. Mehta, D. Gürses, and A. R. Yildiz, “Cheetah optimization algorithm for optimum design of heat exchangers,” Mater. Test., vol. 65, no. 8, pp. 1230–1236, 2023. https://doi.org/10.1515/mt-2023-0015.Search in Google Scholar

[44] B. S. Yildiz, S. Bureerat, N. Panagant, P. Mehta, and A. R. Yildiz, “Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints,” Mater. Test., vol. 64, no. 10, pp. 1504–1511, 2022, https://doi.org/10.1515/mt-2022-0048.Search in Google Scholar

[45] D. Gürses, P. Mehta, V. Patel, S. M. Sait, and A. R. Yildiz, “Artificial gorilla troops algorithm for the optimization of a fine plate heat exchanger,” Mater. Test., vol. 64, no. 9, pp. 1325–1331, 2022, https://doi.org/10.1515/mt-2022-0049.Search in Google Scholar

[46] B. S. Yildiz, V. Patel, N. Pholdee, S. M. Sait, S. Bureerat, and A. R. Yildiz, “Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design,” Mater. Test., vol. 63, no. 4, pp. 336–340, 2021, https://doi.org/10.1515/mt-2020-0049.Search in Google Scholar

[47] D. Gürses, P. Mehta, S. M. Sait, and A. R. Yildiz, “African vultures optimization algorithm for optimization of shell and tube heat exchangers,” Mater. Test., vol. 64, no. 8, pp. 1234–1241, 2022, https://doi.org/10.1515/mt-2022-0050.Search in Google Scholar

[48] J. Luo, H. Chen, A. A. Heidari, Y. Xu, Q. Zhang, and C. Li, “Multi-strategy boosted mutative whale-inspired optimization approaches,” Appl. Math. Model., vol. 73, pp. 109–123, 2019, https://doi.org/10.1016/j.apm.2019.03.046.Search in Google Scholar

[49] B. S. Yıldız, G. Li, C. T. Zhong, S. Mirjalili, and A. R. Yildiz, “Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study,” Struct. Multidiscip. Optim., vol. 66, no. 8, 2023, Art no. 191. https://doi.org/10.1007/s00158-023-03639-0.Search in Google Scholar

[50] A.R. Yildiz, U.A. Kılıçarpa, E. Demirci, and M. Doğan, “Topography and topology optimization of diesel engine components for light-weight design in the automotive industry,” Mater. Test., vol. 61, no. 1, pp. 27–34, 2019. https://doi.org/10.3139/120.111277.Search in Google Scholar

[51] T. Kunakote, et al.., “Comparative performance of twelve metaheuristics for wind farm layout optimisation,” Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 717–730, 2022. https://doi.org/10.1007/s11831-021-09586-7.Search in Google Scholar

[52] A. R. Yildiz, “Optimal structural design of vehicle components using topology design and optimization,” Mater. Test., vol. 50, no. 4, pp. 224–228, 2008, https://doi.org/10.3139/120.100880.Search in Google Scholar

[53] M. Erdaş, M. Kopar, B. S. Yildiz, and A. R. Yildiz, “Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm,” Mater. Test., vol. 65, no. 12, pp. 1767–1775, 2023. https://doi.org/10.1515/mt-2023-0201.Search in Google Scholar

[54] H. Abderazek, S. M. Sait, and A. R. Yildiz, “Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics,” Int. J. Veh. Des., vol. 80, nos. 2/3/4, p. 121, 2019, https://doi.org/10.1504/IJVD.2019.109862.Search in Google Scholar

[55] H. Abderazek, A. R. Yildiz, and S. M. Sait, “Mechanical engineering design optimisation using novel adaptive differential evolution algorithm,” Int. J. Veh. Des., vol. 80, nos. 2/3/4, p. 285, 2019, https://doi.org/10.1504/IJVD.2019.109873.Search in Google Scholar

[56] D. Gürses, P. Mehta, S. M. Sait, S. Kumar, and A. R. Yildiz, “A multi-strategy boosted prairie dog optimization algorithm for global optimization of heat exchangers,” Mater. Test., vol. 65, no. 9, pp. 1396–1404, 2023. https://doi.org/10.1515/mt-2023-0082.Search in Google Scholar
Published Online: 2024-05-01
Published in Print: 2024-07-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Development and validation of a test facility for bending corrosion fatigue of hybrid laminates
  3. A comparative analysis of corrosion assessment techniques for steel in reinforced concrete exposed to brine water environments
  4. Experimental analysis of S–N curves of welded joints with different fatigue life extension approaches
  5. New generation steels for light weight vehicle safety related applications
  6. Fatigue life evaluation of laser welded lap joints of dissimilar aluminum alloys
  7. Weld interface metallurgy of dissimilar alloys welded by TIG technique
  8. Wear characteristics of cold tool steels for recycling plastic preprocessing
  9. Influence of backing layers on the interlaminar fracture toughness energy – Mode I – of quasi-unidirectional GFRP
  10. Influence of Borides on microstructure and mechanical properties of a Ni alloy
  11. Characterization of material flow behavior in friction stir welded AA2014 aluminum alloy joints
  12. Enhancing the structural performance of engineering components using the geometric mean optimizer
  13. Stress relaxation experimental research and prediction of GFRP pipes under ring deflection condition
  14. Experimental testing and numerical simulations of 3D-printed PETG pins used for vehicle pedals
  15. Low-temperature creep performance of additive manufactured Ti–6Al–4V
https://doi.org/10.1515/mt-2024-0005
Keywords for this article
airplane bracket; structural performance; robust design; robot gripper; suspension arm

Articles in the same Issue

  1. Frontmatter
  2. Development and validation of a test facility for bending corrosion fatigue of hybrid laminates
  3. A comparative analysis of corrosion assessment techniques for steel in reinforced concrete exposed to brine water environments
  4. Experimental analysis of S–N curves of welded joints with different fatigue life extension approaches
  5. New generation steels for light weight vehicle safety related applications
  6. Fatigue life evaluation of laser welded lap joints of dissimilar aluminum alloys
  7. Weld interface metallurgy of dissimilar alloys welded by TIG technique
  8. Wear characteristics of cold tool steels for recycling plastic preprocessing
  9. Influence of backing layers on the interlaminar fracture toughness energy – Mode I – of quasi-unidirectional GFRP
  10. Influence of Borides on microstructure and mechanical properties of a Ni alloy
  11. Characterization of material flow behavior in friction stir welded AA2014 aluminum alloy joints
  12. Enhancing the structural performance of engineering components using the geometric mean optimizer
  13. Stress relaxation experimental research and prediction of GFRP pipes under ring deflection condition
  14. Experimental testing and numerical simulations of 3D-printed PETG pins used for vehicle pedals
  15. Low-temperature creep performance of additive manufactured Ti–6Al–4V
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 13.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/mt-2024-0005/html?lang=en
Scroll to top button