&lt;title&gt;Application of combinatorial approach in packaging material selection&lt;/title&gt;

Jarupan, Lerpong; Kamarthi, Sagar; Gupta, Surendra M.

doi:10.1117/12.599992

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…The error of the GONNS is found to be less than 1% when the neural networks-represented analytical functions and GAs are used to select the optimal conditions. Jarupan et al 139 presented a combinatorial approach that consists of a GA in selecting material packaging. They integrated the fuzzy set theory for transforming linguistic performance attributes into numerical values and later utilised GA to globally search for near-optimal or optimal design solutions.…”

Section: Optimisation and Mathematical Methodsmentioning

confidence: 99%

“…70 Combinational, heuristic and intelligent approaches also serve material selection well with subjective descriptions of product performance and design attributes. Jarupan et al 139 developed a two-phase selection method with fuzzy for the linguistic transformation of performance attributes and a GA for a global search for near-optimal or optimal design solutions. Similarly, Sakundarini et al 146 also proposed a fuzzy inference system and GA in the selection of recyclable materials.…”

Section: Fuzzy and Uncertaintymentioning

confidence: 99%

“…Jarupan et al. 139 developed a two-phase selection method with fuzzy for the linguistic transformation of performance attributes and a GA for a global search for near-optimal or optimal design solutions. Similarly, Sakundarini et al.…”

Section: Background and Overviewmentioning

confidence: 99%

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A systematic review on material selection methods

Rahim

Musa

Ramesh

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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Technological advancements and the growing material set in the world have created a large variety of options for industrial designers, but little attention has been given to the tools and methods that support material selection processes. In this paper, we report on a comprehensive systematic literature review (SLR) guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement on methods or approaches reported for supporting material selection processes. This review covers various published literature, spanning from 2000 to 2018. The purpose is to examine in detail the evolution of the extensive body of research, its research streams and to position possible areas for further research. A classification framework consisting of six categories of selection approaches was derived from the extensive literature. Additionally, a detailed analysis of predominant approaches was presented along with their advantages and limitations with respect to the material selection domain. Taken together, the insights gained from this study may be of assistance to new researchers and practitioners who are looking for potential selection methods for their specific applications. The review also found that there is an increasing trend of research in recent years in the area of OR-based method application specifically on the multi-criteria decision-making supporting material selection processes.

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Section: Optimisation and Mathematical Methodsmentioning

confidence: 99%

Section: Fuzzy and Uncertaintymentioning

confidence: 99%

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A systematic review on material selection methods

Rahim

Musa

Ramesh

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part L:

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“…The underlined complexity of the problem suggest also the use of evolutionary optimization tools, as already proposed by other authors to solve more conventionally formulated problem of multi-objective materials choice. 28,[30][31][32][33] In particular, it was considered that the use of a genetic algorithm could allow to overcome the criticality of other tools, operating directly on an objective function which interprets the different aspects of the whole problem, and investigating both the domain of discretized variables, which identify the choice of materials, and the complete ranges of continuous values of the geometric parameters to be sized. With these premises, a genetic algorithm has been specifically developed in the MATLAB environment.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of multi-component materials selection and sizing problem

Giudice

Fargione

Caponetto

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part L:

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Most of the research on materials selection has been characterized by a basic approach to the problem, isolating material choice from the other design variables, and focusing on single components. Instead, a complete approach aimed at optimizing material selection cannot be set aside from component sizing, and the analysis of constraints between components, that characterize the sub-assemblies or the whole system, and can influence the material choice itself. In this paper, a structured method to approach the simultaneous materials selection and sizing optimization of multi-component assemblies is proposed. It is based on a modeling of the problem, fully generalized in its formalization, so as to be applicable to the various cases, and formulated in order to take into account the systemic vision that must have the optimal choice in a multi-component perspective, and its close connection with the sizing of geometric variables, to effectively meet the required performances. In this regard, an efficiency criterion, which provides for the choice of material and variables sizing gauged on real needs, is also introduced. After presenting the framework and formalization of the method, the problem of searching for the best solution has been discussed. With regard to this, a genetic algorithm has been specifically developed according to the peculiarities of the generalized formulation, with the aim of enhancing the heuristic potential of the concurrent material choice and sizing approach in multi-component environment, in the search for the optimal solution. The application to a sub-system of a widely used plant device is reported in detail, so that use and capacity of the structured method are detailed and discussed.

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“…Packaging material selection problems have always been important to packaging designers and engineers (Japuran et al. ), where the decision determines not only the costs and EIs of packaging, but also the physical characteristics, associated manufacturing processes, and product recovery stages (Gungor and Gupta ; Japuran et al. ), with a first approach combining ANNs and GAs for optimizing material selection for sustainable products appearing in the work of Zhou and colleagues ().…”

Section: Introductionmentioning

confidence: 99%

A Methodological Study for Optimizing Material Selection in Sustainable Product Design

Tambouratzis¹,

Karalekas²,

Moustakas³

2013

J of Industrial Ecology

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A computational intelligence-based identification of the properties of maximally sustainable materials for a given application, as derived from key properties of existing candidate materials, is put forward. The correlation surface between material properties (input) and environmental impact (EI) values (output) of the candidate materials is initially created using general regression (GR) artificial neural networks (ANNs). Genetic algorithms (GAs) are subsequently employed for swiftly identifying the minimum point of the correlation surface, thus exposing the properties of the maximally sustainable material. The ANN is compared to and found to be more accurate than classic polynomial regression (PR) interpolation/prediction, with sensitivity and multicriteria analyses further confirming the stability of the proposed methodology under variations in the properties of the materials as well as the relative importance values assigned to the input properties. A nominal demonstration concerning material selection for manufacturing maximally sustainable liquid containers is presented, showing that by appropriately picking the pertinent input properties and the desired material selection criteria, the proposed methodology can be applied to a wide range of material selection tasks.

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<title>Application of combinatorial approach in packaging material selection</title>

Cited by 5 publications

References 26 publications

A systematic review on material selection methods

A systematic review on material selection methods

Modeling and optimization of multi-component materials selection and sizing problem

A Methodological Study for Optimizing Material Selection in Sustainable Product Design

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