Numerical and experimental investigations of novel nature inspired open lattice cellular structures for enhanced stiffness and specific energy absorption

Ashok, Dara; Bahubalendruni, M. V. A. Raju; Mertens, A. Johnney; Murali, Gunji Bala

doi:10.1016/j.mtcomm.2022.103286

Cited by 36 publications

(14 citation statements)

References 49 publications

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“…Te averaged tensile test results with standard deviation have been represented in Figure 5(f ) and those are used for the characterization of structures. Te standard ceramic grey photopolymer resin mechanical properties are enlisted in Table 1 [33].…”

Section: Materials Propertiesmentioning

confidence: 99%

Design and Characterization of 2.5D Nature-Inspired Infill Structures under Out-Plane Quasi-Static Loading Condition

Ashok¹,

Bahubalendruni²

2023

Advances in Materials Science and Engineering

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The 2.5D (2.5-dimensional) structures are acquired to enhance safety and lightweight designs with better energy absorption in the aerospace and automobile sectors. Additive manufacturing (AM), which is practical to build suitable components in industrial and transportation applications, may produce these structures more efficiently. The current study aims to improve the 2.5D infilled structures mean crush force (MCF) and energy absorption capabilities. Under compression loading, the proposed novel nature-inspired 2.5D infilled structure is compared to six existing 2.5D geometries that are inspired by nature. These structures are made of cylindrical shells that are filled with various infill configurations and maintained at a consistent volume. Photopolymer resin is used as the material for the structures, which are created using a digital light processing (DLP) method under AM technology. The characterization of the constructed models was done under compressive out-plane quasi-static stress conditions. ANSYS numerical simulations have been carried out to confirm the dependability of experimental data. The impact of supporting ribs and infill designs on crushing behaviour is thoroughly discussed. Mean crush force (MCF) and specific energy absorption (SEA) under quasi-static compression loading are provided to the proposed unique nature-inspired 2.5D infilled structure to significantly boost crushing qualities, axial collapse, and energy absorption behaviours.

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Section: Materials Propertiesmentioning

confidence: 99%

Design and Characterization of 2.5D Nature-Inspired Infill Structures under Out-Plane Quasi-Static Loading Condition

Ashok¹,

Bahubalendruni²

2023

Advances in Materials Science and Engineering

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“…Open lattice structures are commonly used for lightweight applications as well as specific energy absorption applications (Dara et al , 2022). In this work, the sensitivity of c is is explored with a few open lattice structures, namely, Schwarz, gyroid, diamond, SplitP and lidinoid, and their internal structure complexity is determined using the proposed approach.…”

Section: Validation Of the Proposed Metricmentioning

confidence: 99%

A view similarity-based shape complexity metric to guide part selection for additive manufacturing

Jayapal

Kumaraguru

Varadarajan

2022

RPJ

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Purpose This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors. Design/methodology/approach The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection. Findings The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM. Research limitations/implications The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results. Originality/value This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

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“…A comparison analysis has been made between nature-inspired 2.5-Dimensional nature-inspired cellular structure and nature-inspired novel flower lattice. It is attained superior specific energy absorption capacity 24 , 25 .…”

Section: Introductionmentioning

confidence: 99%

Prediction and experimental validation approach to improve performance of novel hybrid bio-inspired 3D printed lattice structures using artificial neural networks

Doodi

Murali

2023

Sci Rep

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Novel Cellular lattice structures with lightweight designs are gaining more interest in the automobile and aerospace sectors. Additive manufacturing technologies have focused on designing and manufacturing cellular structures in recent years, increasing the versatility of these structures because of the significant benefits like high strength-to-weight ratio. In this research, a novel hybrid type of cellular lattice structure is designed, bio-inspired from the circular patterns seen in the bamboo tree structure and the overlapping patterns found on the dermal layers of fish-like species. The unit lattice cell with varied overlapping areas with a unit cell wall thickness of 0.4 to 0.6 mm. Fusion 360 software models the lattice structures with a constant volume of 40 × 40 × 40 mm. Utilizing the stereolithography (SLA) process and a vat polymerization type three-dimensional printing equipment is used to fabricate the 3D printed specimens. A quasi-static compression test was carried out on all 3D printed specimens, and the energy absorption capacity of each structure was calculated. Machine learning technique like the Artificial neural network (ANN) with Levenberg–Marquardt Algorithm (ANN-LM) was applied to the present research to predict the energy absorption of the lattice structure with parameters such as overlapping area, wall thickness, and size of the unit cell. The k-fold cross-validation technique was applied in the training phase to get the best training results. Overall, the results obtained using the ANN tool are validated and can be a favourable tool for lattice energy prediction with available data.

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Numerical and experimental investigations of novel nature inspired open lattice cellular structures for enhanced stiffness and specific energy absorption

Cited by 36 publications

References 49 publications

Design and Characterization of 2.5D Nature-Inspired Infill Structures under Out-Plane Quasi-Static Loading Condition

Design and Characterization of 2.5D Nature-Inspired Infill Structures under Out-Plane Quasi-Static Loading Condition

A view similarity-based shape complexity metric to guide part selection for additive manufacturing

Prediction and experimental validation approach to improve performance of novel hybrid bio-inspired 3D printed lattice structures using artificial neural networks

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