Additive manufacturing-oriented concurrent robust topology optimization considering size control

Li, Zeshang; Wang, Lei; Lv, Tangqi

doi:10.1016/j.ijmecsci.2023.108269

Cited by 32 publications

(2 citation statements)

References 70 publications

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“…A disadvantage is that this technique needs expert opinions to select the level cut or membership level into RBTO. From the literature review, RBTO needed more choices for alleviating the complexity in analysis in both TO and UQ, especially for a new edge of additive manufacturing (AM) [43,44]. At present, TO design can reach the production phase with AM technology, but uncertainties from manufacturing factors, such as random defects, voids [44], local damage [41], etc., must be included in the design.…”

Section: Introductionmentioning

confidence: 99%

“…From the literature review, RBTO needed more choices for alleviating the complexity in analysis in both TO and UQ, especially for a new edge of additive manufacturing (AM) [43,44]. At present, TO design can reach the production phase with AM technology, but uncertainties from manufacturing factors, such as random defects, voids [44], local damage [41], etc., must be included in the design. Local damage failure and a robust worst-case approach bring fail-safe topology optimization (FSTO) [45].…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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Reinforcing dental restoration material PMMA with SS 316L nanoparticles for mechanical performance analysis

Punia,

Garg

2024

Prog Addit Manuf

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Additive manufacturing-oriented concurrent robust topology optimization considering size control

Cited by 32 publications

References 70 publications

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Reinforcing dental restoration material PMMA with SS 316L nanoparticles for mechanical performance analysis

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