Hierarchical combinatorial design and optimization of non-periodic metamaterial structures

Wang, Jun; Callanan, Jesse; Ogunbodede, Oladapo; Rai, Rahul

doi:10.1016/j.addma.2020.101710

Cited by 17 publications

(8 citation statements)

References 56 publications

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“…Going forward, as Industry 4.0 transforms the manufacturing environments into cyber-physical environments, AI will be indispensable for data analysis and subsequent decision-making (Rai and Sahu 2020). Product design (Huang and Rai 2018), inspection (Zhang et al 2019a), geometry processing (Zhang, Jaiswal, and Rai 2018), material informatics (Wang et al 2021), control of dynamic systems ) are few more applications of ML in manufacturing. The utility of ML in manufacturing applications reflects that ML can be integrated into every phase of product lifecycle, starting from design to disposal of the product in a manufacturing environment.…”

Section: Discussionmentioning

confidence: 99%

Machine learning in manufacturing and industry 4.0 applications

Rai

Tiwari

Ivanov

et al. 2021

International Journal of Production Research

Self Cite

288

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The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and production systems.

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Section: Discussionmentioning

confidence: 99%

Machine learning in manufacturing and industry 4.0 applications

Rai

Tiwari

Ivanov

et al. 2021

International Journal of Production Research

Self Cite

288

View full text Add to dashboard Cite

show abstract

“…Instead, this high-level classification of all aperiodic cellular materials is arrived at empirically, by observation of structures in nature, as well as a review of the engineering literature. In addition to these observations, engineering applications leveraging aperiodic cellular materials have traditionally involved metal foams, but there is a growing body of research that explores ideas such as the programmable insertion of defects into periodic cellular materials [9,10], gradation in cell size or thickness [11], and the use of multiple-unit cell shapes and managing transitions between them [12,13]. Several approaches have also been proposed for the design of these aperiodic cellular materials [14], and a selection of these methods, most commonly the Voronoi tessellation, have also been implemented in commercial design software [15].…”

Section: Types Of Aperiodic Cellular Materialsmentioning

confidence: 99%

A Classification of Aperiodic Architected Cellular Materials

Ramirez-Chavez

Anderson

Sharma

et al. 2022

Designs

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Architected cellular materials encompass a wide range of design and performance possibilities. While there has been significant interest in periodic cellular materials, recent emphasis has included consideration of aperiodicity, most commonly in studies of stochastic and graded cellular materials. This study proposes a classification scheme for aperiodic cellular materials, by first dividing the design domain into three main types: gradation, perturbation, and hybridization. For each of these types, two design decisions are identified: (i) the feature that is to be modified and (ii) the method of its modification. Considerations such as combining different types of aperiodic design methods, and modulating the degree of aperiodicity are also discussed, along with a review of the literature that places each aperiodic design within the classification developed here, as well as summarizing the performance benefits attributed to aperiodic cellular materials over their periodic counterparts.

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“…A similar interpolation strategy was commonly used to design variable-density cellular structures to generate a smooth connection between unit cells with different volumes [16]. Wang et al [63] tackled the geometric compatibility by proposing non-periodic implicit functions that can generate two compatible unit cells with good overlap at the intersection face. In this paper, our approach takes advantage of the integrated TO and IH-GAN framework to generate functionally graded cellular structures with multiple types of unit cells and address the connectivity bottleneck simultaneously without a need for compatibility optimization.…”

Section: Structures Assembled By Multiple Types Of Cellular Unit Cellsmentioning

confidence: 99%

“…The major risk that arises when merging different types of cellular unit cells is the lack of sufficient interface connection area [63]. If the geometries at the intersection between two adjacent unit cells are significantly different, the common face's potential overlap can be low, leading to poor connectivity.…”

Section: Connectivitymentioning

confidence: 99%

IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures

Wang,

Chen,

et al. 2021

Preprint

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Variable-density cellular structures can overcome connectivity and manufacturability issues of topologicallyoptimized, functionally graded structures, particularly when those structures are represented as discrete density maps. One naïve approach to creating variable-density cellular structures is simply replacing the discrete density map with an unselective type of unit cells having corresponding densities. However, doing so breaks the desired mechanical behavior, as equivalent density alone does not guarantee equivalent mechanical properties. Another approach uses homogenization methods to estimate each pre-defined unit cell's effective properties and remaps the unit cells following a scaling law. However, a scaling law merely mitigates the problem by performing an indirect and inaccurate mapping from the material property space to singletype unit cells. In contrast, we propose a deep generative model that resolves this problem by automatically learning an accurate mapping and generating diverse cellular unit cells conditioned on desired properties (i.e., Young's modulus and Poisson's ratio). We demonstrate our method via the use of implicit function-based unit cells and conditional generative adversarial networks. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy (relative error < 5%), 2) create functionally graded cellular structures with high-quality interface connectivity (98.7% average overlap area at interfaces), and 3) improve the structural performance over the conventional topology-optimized variabledensity structure (84.4% reduction in concentrated stress and extra 7% reduction in displacement).

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Hierarchical combinatorial design and optimization of non-periodic metamaterial structures

Cited by 17 publications

References 56 publications

Machine learning in manufacturing and industry 4.0 applications

Machine learning in manufacturing and industry 4.0 applications

A Classification of Aperiodic Architected Cellular Materials

IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures

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