Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

Ibhadode, Osezua; Zhang, Zhidong; Sixt, Jeffrey; Nsiempba, Ken M.; Orakwe, Joseph; Martinez-Marchese, A.; Ero, Osazee; Shahabad, Shahriar Imani; Bonakdar, Ali; Toyserkani, Ehsan

doi:10.1080/17452759.2023.2181192

Cited by 58 publications

(15 citation statements)

References 434 publications

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“…Metal additive manufacturing (MAM) is a revolutionary and technologically cutting-edge manufacturing process that fabricates complex or intricate shapes with the most design freedom [1,2]. Laser powder bed fusion (LPBF) is a MAM process that uses a laser source to selectively fuse and melt metal powders according to the process parameters and scan pattern to generate three-dimensional components layer by layer [3,4].…”

Section: Introductionmentioning

confidence: 99%

The impact of successive laser shock peening on surface integrity and residual stress distribution of laser powder-bed fused stainless steel 316L

Haribaskar,

Kumar

2024

Phys. Scr.

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The utilization of laser shock peening (LSP) in laser powder bed fused (LPBF) stainless steel (SS) 316L components enhances the mechanical characteristics and operational lifespan of the product quality through a significant reduction of residual stress and a noticeable increase in roughness parameters. The key objective of the study is to analyze the influence of consecutive laser shock peening (LSP) without ablative coating and low pulse energy on the surface properties, residual stress distribution, and microhardness of samples produced by LPBF with SS316L material. The surface quality of the sample subjected to consecutive laser shock peening shows a slight deterioration in its condition. This can be attributed to the combined impact of ablative surface and surface damage resulting from the production of high-energy plasma. However, the implementation of successive LSP results in a distinctive enhancement of compressive residual stresses (CRS) that are evenly distributed throughout the central axis and sharp edges. In contrast, the as-built condition exhibits non-uniform stress magnitudes. CRS observed in each LSP iteration exhibits a notable increase, reaching a maximum magnitude of -389 MPa compared to the initial stress level of 165 MPa in the as-built sample. This enhancement can be attributed to the repetitive impact of shock waves on the surface, leading to the formation of plastic deformation. The refinement of surface grains and the presence of favorable residual stresses were proven by the utilization of X-ray diffraction (XRD) studies and the Cos α plot. The XRD investigation also indicated the absence of any newly formed phases or secondary phases. A significant enhancement in microhardness was observed, with an increase of 58.3% achieved after the third consecutive peening process. The successive LSP samples displayed a gradual improvement in electrochemical behavior. Though the amplitude parameters increased after LSP, the increase in wear rate was observed.

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Section: Introductionmentioning

confidence: 99%

The impact of successive laser shock peening on surface integrity and residual stress distribution of laser powder-bed fused stainless steel 316L

Haribaskar,

Kumar

2024

Phys. Scr.

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“…Topology optimization (TO) enables this goal by determining the optimal material distribution within a design space that maximizes performance subject to design requirements (Bendsøe and Kikuchi, 1988;Bendsøe and Sigmund, 2003). There has been increased attention in the integration of TO methods into MAM design workflow, especially within the aerospace, automotive and medical industries (Ibhadode et al, 2023). For the aerospace industry, brackets (Seabra et al, 2016;L opez-Castro et al, 2017;Shi et al, 2020;Willner et al, 2020;Prathyusha and Babu, 2022), a cargo door component (Wiberg et al, 2021), a door lever (Berrocal et al, 2019), a housing component (Berrocal et al, 2019) and a connector support (Berrocal et al, 2019) were topologically optimized and produced via MAM machines.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a metal additive manufactured aircraft seat leg using topology optimization and part decomposition

Kim,

Crispo,

Patel

et al. 2024

RPJ

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Purpose The lightweight design of aircraft seats can significantly improve fuel efficiency and reduce greenhouse gas emissions. Metal additive manufacturing (MAM) can produce lightweight topology-optimized designs with improved performance, but limited build volume restricts the printing of large components. The purpose of this paper is to design a lightweight aircraft seat leg structure using topology optimization (TO) and MAM with build volume restrictions, while satisfying structural airworthiness certification requirements. Design/methodology/approach TO was used to determine a lightweight conceptual design for the seat leg structure. The conceptual design was decomposed to meet the machine build volume, a detailed CAD assembly was designed and print orientation was selected for each component. Static and dynamic verification was performed, the design was updated to meet the structural requirements and a prototype was manufactured. Findings The final topology-optimized seat leg structure was decomposed into three parts, yielding a 57% reduction in the number of parts compared to a reference design. In addition, the design achieved an 8.5% mass reduction while satisfying structural requirements for airworthiness certification. Originality/value To the best of the authors’ knowledge, this study is the first paper to design an aircraft seat leg structure manufactured with MAM using a rigorous TO approach. The resultant design reduces mass and part count compared to a reference design and is verified with respect to real-world aircraft certification requirements.

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“…Yang [38] proposed a massively efficient filter for topol-ogy optimization utilizing the splitting of tensor product structure. Filtering methods are also commonly employed in topology optimization for metal additive manufacturing [39]. Additionally, we have utilized an interpolation-based approach to reconstruct the optimized results.…”

Section: Introductionmentioning

confidence: 99%

A Reconstruction Approach for Concurrent Multiscale Topology Optimization Based on Direct FE2 Method

Zhao

Tan

et al. 2023

Mathematics

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The rapid development of material science is increasing the demand for the multiscale design of materials. The concurrent multiscale topology optimization based on the Direct FE2 method can greatly improve computational efficiency, but it may lead to the checkerboard problem. In order to solve the checkerboard problem and reconstruct the results of the Direct FE2 model, this paper proposes a filtering-based reconstruction method. This solution is of great significance for the practical application of multiscale topology optimization, as it not only solves the checkerboard problem but also provides the optimized full model based on interpolation. The filtering method effectively eliminates the checkerboard pattern in the results by smoothing the element densities. The reconstruction method restores the smoothness of the optimized structure by interpolating between the filtered densities. This method is highly effective in solving the checkerboard problem, as demonstrated in our numerical examples. The results show that the proposed algorithm produces feasible and stable results.

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Topology optimization for metal additive manufacturing: current trends, challenges, and future outlook

Cited by 58 publications

References 434 publications

The impact of successive laser shock peening on surface integrity and residual stress distribution of laser powder-bed fused stainless steel 316L

The impact of successive laser shock peening on surface integrity and residual stress distribution of laser powder-bed fused stainless steel 316L

Design of a metal additive manufactured aircraft seat leg using topology optimization and part decomposition

A Reconstruction Approach for Concurrent Multiscale Topology Optimization Based on Direct FE2 Method

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