Validation of the Mechanical Behavior of an Aeronautical Fixing Turret Produced by a Design for Additive Manufacturing (DfAM)

Veiga, Fernando; Bhujangrao, Trunal; Suárez, Alejandro Rosete; Aldalur, Eider; Goenaga, Igor; Gil-Hernandez, Daniel

doi:10.3390/polym14112177

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…However, further experimental verification is still needed. The main purpos study is to explore the feasibility of a multi-objective optimization scheme for au stainless-steel performance and provide optimized composition to guide the nex experimental design [48,49]. After defining the search space and constraints, the parameters of the NSGA-III algorithm are determined.…”

Section: Genetic Algorithm Optimizationmentioning

confidence: 99%

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm

et al. 2023

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As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.

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Section: Genetic Algorithm Optimizationmentioning

confidence: 99%

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm

et al. 2023

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“…where the objective function f in Equation ( 2) is the structural compliance, K is the global stiffness matrix, F is the external load vector, and U is the displacement vector. The first constraint equation, Equation (3), is an equilibrium equation on solid structural topology optimization. The second constraint equation, Equation ( 4), refers to the limits of the entire designed structural volume, V * .…”

Section: Topology Optimization For the Further Weight Reductionmentioning

confidence: 99%

“…The additive manufacturing (AM) technique, also popularly called 3D printing, exhibits remarkable technical advantages for the rapid and free design and fabrication of geometrically complex parts, compared with conventional manufacturing processes [ 1 , 2 ]. Customized lightweight parts of various metals and/or non-metallic materials can be 3D printed via multiple AM methods, such as powder bed fusion (PBF), stereolithography apparatus (SLA), fused deposition modeling (FDM), etc., following the computer-aided design (CAD) of digital models [ 3 ]. AM has developed rapidly in many industrial fields [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Solid Stress-Distribution-Oriented Design and Topology Optimization of 3D-Printed Heterogeneous Lattice Structures with Light Weight and High Specific Rigidity

Shen

2022

Polymers

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Lightweight structural design is greatly valued in the aviation, aerospace, and automotive industries. Three-dimensional (3D) printing techniques provide viable and popular technical pathways for the rapid design and manufacturing of lightweight lattice structures. Unlike the conventional design idea of a geometrically homogenized lattice structure, this work provides a design method for structurally heterogeneous lattice according to the spatial stress state of 3D-printed parts. Following the quasi-static stress numerical simulations of solid components, finite element mesh units were inconsistently replaced by lattice units with different specific rigidities corresponding to the localized stress levels. Relying on the topology optimization further lightened the lattice structure under quasi-static stress after removing some parts with extremely low stress from the overall structure. As an embodiment of this design idea, face-centered cubic (FCC) lattice units with different strut diameters were employed to non-uniformly and adaptively fill a solid part under localized loading. The topological optimization was conducted on the solid part globally. Then, the topologically optimized solid and the heterogeneous lattice structure were subjected to the geometric Boolean operation. Stereolithographic 3D printing was utilized to fabricate the homogeneous and heterogeneous lattice structural parts for comparative tests of three-point bending. Three evaluation indicators were defined for the standardized assessment of the geometrically complex lattice structures for the performance evaluation. This demonstrated that the heterogeneous lattice part exhibited better comprehensive mechanical performance than the uniform lattice. This work proved the feasibility of this new perspective on 3D-printed lightweight structure design and topology optimization.

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“…On the other hand, with desktop 3D printers, a single print head is usually used to create a support structure from the same filament as the element to be manufactured, but usually with a lower fill rate [11]. However, such generated support can significantly extend the printing process [9,22]. Details printed in the FDM/FFF technique can be subjected to additional finishing treatments.…”

Section: Introductionsmentioning

confidence: 99%

Selected Aspects of 3D Printing for Emergency Replacement of Structural Elements

Jasiński¹,

Murawski²,

Kluczyk³

et al. 2023

Adv. Sci. Technol. Res. J.

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The paper presents a synthetic characterization of modern methods of manufacturing or regenerating machine elements. Considered methods are machining and additive methods, in particular 3D printing in the FDM/FFF technique. For the study, the authors made samples of the holder bracket using selected methods. Samples made by machining operations, 3D printing with various filling were tested. The paper contains a technical and economic analysis of the production of a holder bracket using the discussed methods. The dynamics of steel and FDM/FFF printed samples were also assessed by determining their resonance curves. The vibration magnification fac-tors were analyzed -the quotient of the vibration amplitudes in the resonance to the static deformations that occurred under the influence of the constant force and the location of the vibration resonances -the natural frequencies for individual vibration modes. The study's main objective is to assess the possibility of emergency changing the manufacturing technology of selected machine components. The authors were interested in partially replacing costly and not environmentally friendly milling with 3D printing. Machine elements can be manufactured by printing in classical machine building and emergency conditions to replace a damaged component temporarily (e.g., on a ship, for the time of arrival at a port or shipyard). The main assumption guiding the authors during the preparation of this publication was the analysis of the possibility of using the production of "ad hoc" prepared spare parts and their use in the event of a lack of access to parts made of the intended materials.

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Validation of the Mechanical Behavior of an Aeronautical Fixing Turret Produced by a Design for Additive Manufacturing (DfAM)

Cited by 14 publications

References 23 publications

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm

Solid Stress-Distribution-Oriented Design and Topology Optimization of 3D-Printed Heterogeneous Lattice Structures with Light Weight and High Specific Rigidity

Selected Aspects of 3D Printing for Emergency Replacement of Structural Elements

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