Startseite Enhanced Greylag Goose optimizer for solving constrained engineering design problems
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Enhanced Greylag Goose optimizer for solving constrained engineering design problems

  • Dildar Gürses

    Dr. Dildar Gürses is a lecturer at Bursa Uludağ University, Bursa, Turkey. She completed her BSc, MSc, and Ph.D. degrees at Uludağ University, Bursa, Turkey. Her research interests are thermodynamics, electric vehicles, meta-heuristic optimization algorithms, and applications to industrial problems.

     EMAIL logo     , Pranav Mehta

    Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387,001, 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.

         , 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.

         und 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.
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Abstract

This paper introduces an improved optimization algorithm based on migration patterns of greylag geese, known for their efficient flying formations. The Modified Greylag Goose Optimization Algorithm (MGGOA) is modified by augmenting the levy flight mechanism and artificial neural network (ANN) strategies. The algorithm is detailed, presenting mathematical formulations for both phases. Subsequently, the paper applies the MGGOA to various engineering optimization problems, including heat exchanger design, car side impact design, spring design optimization, disc clutch brake optimization, and structural optimization of an automobile component. Statistical comparisons with benchmark algorithms demonstrate the efficacy of MGGOA in finding optimal solutions for these design engineering problems.

Keywords: Greylag Goose optimizer; artificial neural network; constrained engineering design; heat exchanger design; car side impact design

1 Introduction

Optimization algorithms play a pivotal role in various engineering disciplines, facilitating the design and improvement of complex systems and structures [1], [2], [3], [4]. Swarm-based algorithms have gained prominence in recent years. Accordingly, several benchmark algorithms are utilized for performance evaluations and validation of new proposed algorithms. Moreover, Figure 1 shows the benchmark algorithms vis-a-vis the latest optimizers proposed in the domain of MHs.

Figure 1: Enlisting benchmark and newly established optimizers.
Figure 1:

Enlisting benchmark and newly established optimizers.

Metaheuristics is able to handle several critical constraints for achieving global optimization of the various applications and challenges. However, augmenting several performance improvement techniques may realize better convergence and a significant balance between the exploration and exploitation phase of the algorithm. Accordingly, artificial neural networks (ANN), chaotic maps, levy flight mechanisms, oppositional learning techniques, and hybridization of algorithms are some of the best techniques for improving algorithm performance [5], [6], [7], [8], [9], [10], [11]. ANN works on the imaginary brain neurons that enable all inputs from each search domain and create a domain of all possible outcomes considering best to worst. This can provide excellent search techniques vis-a-vis global optimum solutions prior to trapping in local optima by any metaheuristic algorithm.

Despite the effectiveness of existing optimization algorithms, there remains room for improvement, particularly in addressing the challenges posed by complex engineering design problems [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57]. Inspired by the collective behavior and migration patterns of greylag geese, we propose a modified optimization algorithm, namely the Modified Greylag Goose Optimization Algorithm (MGGOA), which combines elements of exploration and exploitation to efficiently search for optimal solutions in design engineering problems. Through an enhanced comprehension of the MGGOA and its application to various engineering optimization tasks, the aim of this paper is to contribute robust optimization algorithms for solving complex design problems in engineering and related fields.

In this paper, we introduce a modified optimization algorithm inspired by the migration patterns of greylag geese, known for their efficient flying formations. The Modified Greylag Goose Optimization Algorithm (MGGOA) combines elements of exploration and exploitation, leveraging insights from the behavior of these birds to tackle design engineering problems. The following sections provide an enhanced comprehension of the MGGOA and its application to various optimization tasks in engineering.

2 Enhanced comprehension of MGGOA- modified Greylag Goose Optimization Algorithm

Greylag geese are well-known birds, especially for their migrating capabilities over thousands of kilometers over a single day. They migrate across different countries according to the season. They form a special V-shaped pattern while flying in the sky, which enables them to improve their efficiency by 70 % compared to individual traveling due to optimizing air resistance by maintaining such a pattern. The Greylag Goose Optimization (GGO) algorithm was developed based on inspiration from these goose migrations and the special techniques observed, putting this algorithm into the swarm-based algorithm category. The sub-section further gives information regarding the mathematical background of the studied algorithm.

2.1 Exploration phase of GGO

In this phase, geese try to identify the best location within their nearby location (search domain) for a promising location and reputedly compare it with the present location to avoid worst location identification. The updated position of geese can be given by Equation (1) [16].

(1) X t + 1 = X * t − A C . X * t − X t

The best/leader solution and updated position of the goose can be denoted as X *(t) and X(t+1), respectively. Also, A and C are vectors that are updated during the algorithm’s iteration process. The position of the exploring geese can be updated by updating the position of randomly selected three geese (paddling) in a group that can be given by Equation (2) [16].

(2) X t + 1 = w 1 X paddle 1 + z w 2 X paddle 2 − X paddle 3         + 1 − z w 3 X − X paddle 1

2.2 Exploitation phase of GGOA

In this phase, geese employ two prime techniques: shifting in the direction of an effective solution and exploring the area within the domain of the best solution. Equations (3) and (4) effectively give a mathematical form of updating the position in the exploitation phase and exploring the space within the best solution, respectively [16].

X 1 = X sentry 1 − A 1 C 1 X sentry 1 − X

(3) X 2 = X sentry 2 − A 2 C 1 X sentry 2 − X

X 3 = X sentry 3 − A 3 C 1 X sentry 3 − X

Geese continuously identify an effective location from the current best position for the betterment of food. Accordingly, the same strategy can be written in mathematical form as per Equation (4) [16].

(4) X t + 1 = x t + D 1 + z × w × X − X flock

2.3 Modification of GGO algorithm

The studied GGO algorithm may provide locally converged solutions and cannot identify effectively globally optimized solutions for critical engineering problems. To overcome the stated difficulties, the GGO algorithm is modified by augmenting the levy flight mechanism and artificial neural network (ANN) strategies. Accordingly, the levy flight strategy helps the algorithm explore the search space effectively, whereas ANN can improve search and optimization options effectively.

3 Application of modified GGO algorithm for engineering and structural components

In this section, four different applications are considered for the testing purpose of the modified GGO algorithm. Apart from this, benchmark algorithms test the statistical results obtained with published results from the literature. Some of the studied components are heat exchanger design, car side impact testing and optimization, spring optimization, and an automobile component for structural optimization. The results are compared with recent optimizers such as Harris Hawks optimizer (HHO), water wave optimizer (WWO), etc.

3.1 Optimization of rolling element bearing for load-carrying capacity

Fluctuating loads, Von-mises stress, and pressure are the parameters that affect bearing fatigue life, vibration level, and failure the most. The present section optimizes rolling element bearing for load-carrying capacity by considering five design variables and six critical constraints. Figure 2 shows a computerized view of bearing with design variables. Table 1 compares well-known algorithms for optimized design variables and statistical data. This being said the MGGO algorithm realizes effective results for objective functions with optimum deviation in the data compared to other optimizers.

Figure 2: Image of the rolling element bearing.
Figure 2:

Image of the rolling element bearing.

Table 1:

Results comparison of proposed optimizer with two competitive optimizers.

Parameters Optimizers
MGGOA WWO [57] HHO [57]
Dm 125.5231211 125.6724106 125.7345378
D B 21.35611338 21.41283783 21.41591361
Z 11 11 11
f i 0.515005467 0.515006273 0.515000003
fo 0.527443880 0.573600036 0.582459683
KDmin 0.420188123 0.430756909 0.421843166
KDmax 0.642530662 0.653031544 0.689366889
ε 0.300080978 0.301328038 0.300000008
e 0.027026249 0.058712000 0.086919693
ς 0.604892466 0.617604185 0.600000017
f best −85,040.6356 −85,430.7979 −85,465.9375

3.2 Car side impact body optimization using MGGOA

Figure 3 shows a standard 1996 Dodge Neon car model for the reduction of the overall weight of the system.

Figure 3: Car side impact animation image.
Figure 3:

Car side impact animation image.

It involves determining the optimal thicknesses of various sections of the car wall, represented by 11 decision variables (x 1–x 11). The aim is to minimize weight while adhering to constraints related to crashworthiness and vibration. Table 2 lists all the decision variables along with their optimal values obtained through different optimization techniques, including MGGOA. Table 3 provides the statistical outcomes of these optimization algorithms applied to the design problem for the side impact of the car. All algorithms successfully completed the tests and found their respective best solutions. Notably, the MGGOA algorithm outperformed others, achieving the lowest objective function value of 22.8431 with a minimal standard deviation of 0.25023.

Table 2:

Optimized 11 design parameters for car side design at different points.

Parameters Optimizers
MGGOA WWO [57] HGSO [57] WOA [57]
x 1 0.5 0.58231 0.66497 0.5
x 2 1.11641 1.14379 1.16209 1.32317
x 3 0.5 0.52386 0.5569 0.5
x 4 1.30217 1.28768 1.20816 1.07918
x 5 0.5 0.56545 0.88163 0.52856
x 6 1.5 1.16623 1.04022 0.64714
x 7 0.5 0.52124 0.52033 0.52124
x 8 0.345 0.345 0.34500 0.345
x 9 0.192 0.192 0.192 0.192
x 10 −19.552 −7.7083 0.4649 17.4293
x 11 0.27941 −0.3227 0.73053 18.8118
f best 22.8431 23.7121 24.7113 23.4369
Table 3:

Comparison of studied algorithm results with three optimizers.

Optimizers Best Mean Worst SD FEs
MGGOA 22.8431 23.0823 23.5369 0.25023 20,000
WWO [57] 23.7121 23.7571 24.57680 0.34622 40,000
HGSO [57] 24.7113 25.4618 26.3350 0.34610 40,000
WOA [57] 23.4369 25.3717 29.5335 1.3717 40,000

3.3 Weight optimization of Belleville spring using MGGOA

The main aim of designing the Belleville spring is weight minimization. Table 4 presents the decision variables essential for this optimization, including thickness (t), height (h), and inner and outer diameters (Di, De), along with their respective optimal values derived from tested algorithms. Figure 4 shows the design system. Table 5 outlines the statistical outcomes of the used algorithms. Among them, the MGGO algorithm emerges as the most effective based on statistical metrics. It achieves the best function value of 1.98037 with a success rate of 6 %.

Table 4:

Optimization achieved by all the compared optimizers.

Parameters Optimizers
MGGOA WWO [57] HGSO [57] WOA [57]
D e 12.0072 11.5836 11.9456 11.8815
D i 10.0265 9.43404 9.92867 9.85235
t 0.20415 0.20586 0.20598 0.20585
h 0.20005 0.20527 0.20000 0.20049
f best 1.98037 2.06727 2.01997 2.01784
Figure 4: Image of Belleville spring.
Figure 4:

Image of Belleville spring.

Table 5:

Statistical data for each algorithm.

Optimizers Best Mean Worst SD FEs
MGGOA 1.9803 2.00136 2.1325 0.031236 12,000
WWO [57] 2.06727 2.08628 2.1893 0.041232 15,000
HGSO [57] 2.01997 2.22390 2.5016 0.10471 15,000
WOA [57] 2.01784 2.21995 2.4770 0.096826 15,000

3.4 Weight optimization of multiple disc clutch brake (MDCB)

Figure 5 shows the image of a multiple-disc clutch brake optimized in this section to reduce the system’s overall weight by MGGOA. Table 6 displays the discrete design variables alongside their optimal values obtained through various tested algorithms. The MGGO algorithms yield the most optimal solutions, achieving success rates of 78 %. Additionally, the WWO algorithm demonstrates the lowest standard deviation (0.00526911) (Tables 7 and 8).

Figure 5: Image of multiple disc clutch brake.
Figure 5:

Image of multiple disc clutch brake.

Table 6:

Optimum values for compared optimizer.

Parameters Optimizers
MGGOA WWO HGSO HHO ALO WOA SCA DA
r i 70 70 70 70 70 70 70 70
ro 90 90 90 90 90 90 90 90
t 1 1 1 1 1 1 1 1
F 930 790 840 920 930 1,000 1,000 930
Z 3 3 3 3 3 3 3 3
f best 0.313656 0.3136 0.313656 0.313656 0.313656 0.313656 0.313656 0.313656
Table 7:

Statistical trends and comparison for each optimizer.

Optimizers Effective values Average values Worst values SD FEs
MGGOA 0.31365661 0.314236 0.32963 4.3589E-03 3,000
WWO [57] 0.31365661 0.318753 0.33718 5.2690E-03 3,000
HGSO [57] 0.31365661 0.317084 0.340709 7.5778E-03 3,000
HHO [57] 0.31365661 0.327771 0.392070 2.8735E-02 3,000
ALO [57] 0.31365661 0.330658 0.401872 2.5813E-02 3,000
WOA [57] 0.31365661 0.352656 0.441079 4.2526E-02 3,000
SCA [57] 0.31365661 0.3277589 0.441079 2.6248E-02 3,000
DA [57] 0.31365661 0.3402953 0.476369 4.0793E-02 3,000
Table 8:

Results of modified optimizer.

Optimizer Best Mean Worst Std FEs
MGGOA 798 806 810 1.8 2,500

3.5 Structural optimization of automobile components using MGGOA

The automobile structural component is optimized using the MGGO algorithm. The following steps are effectively employed for the volume and structural optimization of vehicle brackets.

  • Step 1: Initialize population parameters

  • Step 2: Generate set-design sets using the latin hypercube sampling (LHS) method

  • Step 3: Finite element analysis is employed to generate the objective function and constraints at the initial stage of sampling.

  • Step 4: Construct response surface models (RSM) using simulation results to establish relationships between objective/constraint functions and design variables

  • Step 5: Implement optimization using metaheuristic optimization algorithms based on obtained metamodels, such as the MGGOA

  • Step 6: Obtain optimal solution and terminate optimization when termination condition is met

Volume reduction by structural optimization of vehicle brackets is pursued using MGGOA optimizer in this section. The optimization problem aims to minimize structural mass while satisfying stress constraints by searching for optimal structural shapes represented by design variables (x). Mathematically, it can be modeled as given by Equations (5)–(7) [56].

(5) F x = mass x

Constraints:

(6) g x ≤ 0

(7) x i l ≤ x i ≤ x i u , i = 1 , NDV,

Initial design configurations and limiting conditions can be depicted in Figures 6 and 7, respectively. Initially, topology optimization was conducted by keeping volume constraints in mind. Accordingly, Figure 8 shows the redesigned bracket by considering manufacturing constraints. Shape optimization of the automobile bracket is achieved using the MGGOA algorithm with design parameters x 1, x 2, x 3, and x 4, as depicted in Figure 9. The upper and lower boundaries for these variables are set as follows: 50 < x 1 < 78, 60 < x 2 < 100, 180 < x 3 < 265, and 98 < x 4 < 180, with units in millimeters. The optimized bracket design using MGGOA is shown in Figure 10.

Figure 6: Initial computer-aided design model of bracket.
Figure 6:

Initial computer-aided design model of bracket.

Figure 7: Limiting condistions on bracket.
Figure 7:

Limiting condistions on bracket.

Figure 8: Volume reduction of component.
Figure 8:

Volume reduction of component.

Figure 9: Optimized design variables.
Figure 9:

Optimized design variables.

Figure 10: Final structurally optimized view.
Figure 10:

Final structurally optimized view.

Results from MGGOA, ship rescue optimizer, and cheetah optimizer are listed in Table 9. The MGGOA realized a minimum mass of 798 g with a stress of 281 MPa, superior to the other algorithms. The bracket weight reduction was achieved using MGGOA.

Table 9:

Mass and stress values for the component in the structural optimization study.

Optimizer Mass (gram) Stress (MPa)
Formal design values 1,615 108
Developed design 916 281
Ship rescue algorithm 868 280
Cheetah optimizer 865 279
MGGOA 798 281

3.6 Thermal optimization of fin and tube heat exchanger

The economic optimization of fin and tube heat exchangers (FTHE) is investigated in this section. Heat exchangers (HEs) are heat recovery devices preferred in various industries due to their extensive heat duty capabilities. A representation of FTHE is shown in Figure 11. The specific use to reduce the temperature of the water-processed air is considered when optimizing the FTHE. The specific parametric conditions are the expected air temperature (51 °C) at the output and the inlet water and hot air temperatures (40 °C and 104 °C). The volumetric fluxes of water and air are maintained at 39 and 58 kg/s, respectively.

Figure 11: Fin and tube heat exchangers, (a) heat exchanger design; (b) parameters for the study.
Figure 11:

Fin and tube heat exchangers, (a) heat exchanger design; (b) parameters for the study.

The overall cost of the heat exchanger is optimized and selected as the aim function. Therefore, while determining the final economic parameters, the initial cost of the FTHE as well as any maintenance or handling charges are considered. The tier ranges and values of the seven choice variables that affect the design and several complex constraints that are taken into account for the optimization of the FTHE are given in [58]. The mathematical model for cost optimization that is being considered is explained as follows [58].

In this research, a special optimizer called MGGOA was used to analyze optimization of the FTHE. To ensure the MGGOA’s performance, the outcomes were also compared to a few benchmark algorithms that have been published in the literature. Table 10 demonstrates that MGGOA produces the best results (at the lowest cost). Furthermore, it can be confirmed that the results of the FTHE problem show a substantially smaller target standard deviation for the MGGOA. Additionally, the goal function’s best values equal 3,466.91.

Table 10:

Results obtained by the proposed algorithm.

Algorithm Best Worst Mean Deviations
MGGOA 3,465.97 3,470.93 3,467.73 1.108
Passing vehicle search [58] 3,466.97 3,470.93 3,468.63 1.17
Salp swarm optimizer [58] 3,466.97 3,470.45 3,468.81 1.09
Grey wolf optimizer [58] 3,466.93 3,470.77 3,468.59 1.73
Grasshopper optimizer [58] 3,466.91 3,468.55 3,467.01 0.365
Symbiotic organism Search [58] 3,466.91 3,467.32 3,466.93 0.0926
Ant lion optimizer [58] 3,466.91 3,470.45 3,467.73 1.30

4 Conclusions

In conclusion, the Modified Greylag Goose Optimization Algorithm (MGGOA) is a robust and effective tool for solving design engineering optimization problems. GGO is improved by augmenting the levy flight mechanism and artificial neural network (ANN) strategies. By drawing inspiration from the efficient flying patterns of greylag geese, the algorithm exhibits a balance between exploration and exploitation, enabling it to search for optimal solutions in complex design spaces efficiently. Through application to various engineering problems, including bearing optimization, automotive component design, and spring optimization, the MGGOA consistently outperforms benchmark algorithms in terms of objective function values, success rates, and convergence speeds. These results highlight the potential of MGGOA as a valuable optimization tool for diverse engineering applications.

Corresponding author: Dildar Gürses, Hybrid and Electric Vehicle Technology, Vocational School of Gemlik Asım Kocabıyık, Bursa Uludağ University, Bursa, 16600, Türkiye, E-mail:

About the authors

Dildar Gürses

Dr. Dildar Gürses is a lecturer at Bursa Uludağ University, Bursa, Turkey. She completed her BSc, MSc, and Ph.D. degrees at Uludağ University, Bursa, Turkey. Her research interests are thermodynamics, electric vehicles, meta-heuristic optimization algorithms, and applications to industrial problems.

Pranav Mehta

Mr. Pranav Mehta is an Assistant Professor at the Department of Mechanical Engineering, Dharmsinh Desai University, Nadiad-387,001, 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.

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.

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.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

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

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Published Online: 2025-04-15
Published in Print: 2025-05-26

© 2025 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Review on three-point bending test for evaluating the mechanical properties, fracture behavior, and adhesion strength of coating/substrate systems
  3. Gas metal arc weldability of a Strenx 700MC-AISI304 dissimilar joint
  4. Effect of process parameters on mechanical properties of 5554 aluminum alloy fabricated by wire arc additive manufacturing
  5. Coating of TIG-welded micro-alloyed 38MnVS6 steel with flux-cored wire and FeB addition: microstructure, hardness, and wear properties
  6. Manufacturing parameters’ effects on the flexural properties of 3D-printed PLA
  7. Modified hot-spot stress method for fatigue life estimation of welded components
  8. Water as blowing agent in polyurethane resins creating porous cancellous bone surrogates for biomechanical osteosynthesis applications
  9. Enhancing cervical spine health: a vibration-focused multibody dynamics model for neck support system design
  10. Characterization of novel fibers extracted from Rumex obtusifolius L. plant for potential composite applications
  11. Interface metallurgical characteristics of dissimilar friction welded steels
  12. Effect of atmospheric pressure plasma treatment on the wettability and aging behavior of metal surfaces
  13. Microstructure and mechanical properties of dissimilar ferritic (S355)–austenitic (AISI 304) steel joints welded by robotic GMAW
  14. Enhanced Greylag Goose optimizer for solving constrained engineering design problems
  15. Effect of sustainable cooling and lubrication method on the hole quality and machinability performance in drilling of AA7075 alloy with cryogenically treated carbide drills
  16. Design optimization of a connecting rod for energy savings
https://doi.org/10.1515/mt-2024-0516
Schlagwörter für diesen Artikel
Greylag Goose optimizer; artificial neural network; constrained engineering design; heat exchanger design; car side impact design
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Frontmatter
  2. Review on three-point bending test for evaluating the mechanical properties, fracture behavior, and adhesion strength of coating/substrate systems
  3. Gas metal arc weldability of a Strenx 700MC-AISI304 dissimilar joint
  4. Effect of process parameters on mechanical properties of 5554 aluminum alloy fabricated by wire arc additive manufacturing
  5. Coating of TIG-welded micro-alloyed 38MnVS6 steel with flux-cored wire and FeB addition: microstructure, hardness, and wear properties
  6. Manufacturing parameters’ effects on the flexural properties of 3D-printed PLA
  7. Modified hot-spot stress method for fatigue life estimation of welded components
  8. Water as blowing agent in polyurethane resins creating porous cancellous bone surrogates for biomechanical osteosynthesis applications
  9. Enhancing cervical spine health: a vibration-focused multibody dynamics model for neck support system design
  10. Characterization of novel fibers extracted from Rumex obtusifolius L. plant for potential composite applications
  11. Interface metallurgical characteristics of dissimilar friction welded steels
  12. Effect of atmospheric pressure plasma treatment on the wettability and aging behavior of metal surfaces
  13. Microstructure and mechanical properties of dissimilar ferritic (S355)–austenitic (AISI 304) steel joints welded by robotic GMAW
  14. Enhanced Greylag Goose optimizer for solving constrained engineering design problems
  15. Effect of sustainable cooling and lubrication method on the hole quality and machinability performance in drilling of AA7075 alloy with cryogenically treated carbide drills
  16. Design optimization of a connecting rod for energy savings
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