A new interactive design approach for concept selection based on expert opinion

Lanzotti, Antonio; Carbone, Francesco; Grazioso, Stanislao; Renno, Fabrizio; Staiano, Michele

doi:10.1007/s12008-018-0482-8

Cited by 26 publications

(10 citation statements)

References 23 publications

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“…Although the overall process does not lead to the matching of requirements in the objects modeling/engineering, we see similar dynamics at play regarding the system design as exposed by Clegg [14], and we feel we are in vicinity to experts opinion based interactive engineering approaches (e.g. [30]).…”

Section: Interactive Modelingsupporting

confidence: 53%

Knowledge management for modeled Heritage objects, requirement specifications towards a tool for heterogeneity embracing

Jeanson

Laroche

Kerouanton

et al. 2020

Int J Interact Des Manuf

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Section: Interactive Modelingsupporting

confidence: 53%

Knowledge management for modeled Heritage objects, requirement specifications towards a tool for heterogeneity embracing

Jeanson

Laroche

Kerouanton

et al. 2020

Int J Interact Des Manuf

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“…Other authors (Elkaseer et al 2018;Lanzotti et al 2018) have implemented Mamdani fuzzy sets in designing for AM strategies for considering the uncertainty of AM processes outcomes. In their work, Elkaseer et al (2018), for instance, developed a fuzzy logic-based algorithm that mimicked knowledge-based expert systems to automatically take corrective actions to improve printing quality.…”

Section: Fuzzy Logic Implemented In the Product Development Processmentioning

confidence: 99%

“…In their work, Elkaseer et al (2018), for instance, developed a fuzzy logic-based algorithm that mimicked knowledge-based expert systems to automatically take corrective actions to improve printing quality. Lanzotti et al (2018) implemented a fuzzy approach for supporting the evaluation and selection of optimal design concepts where AM alternatives were evaluated. In these articles, however, the insights obtained from the fuzzy logic methods are not fed back to early design stages to improve a design.…”

Section: Fuzzy Logic Implemented In the Product Development Processmentioning

confidence: 99%

Fuzzy model-based design for testing and qualification of additive manufacturing components

2022

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The uncertainties and variation of additive manufacturing (AM) material properties and their impact on product quality trouble designers. The lack of experience in AM technologies renders the experts’ assessment of AM components and the establishment of safety margins difficult. Consequently, unexpected qualification difficulties resulting in expensive and lengthy redesign processes might arise. To reduce the risk of qualification failure, engineers might perform copious time-consuming and expensive specimen testing in early phases, or establish overconservative design margins, overriding the weight reduction benefits of AM technologies. In this article, a model-based design method is proposed for the conceptual design of AM space components with affordable test phases. The method utilizes fuzzy logics to systematically account for experts’ assessment of AM properties variation, and to provide an early estimation of a product qualification likelihood related to design parameters of interest, without the need for copious testing. The estimation of qualification likelihood can also point out which are the unique AM material uncertainties that require further specific testing, to enable the design of a product with a better performance and more affordable test phases. The method is demonstrated with the design for AM gridded of ion thrusters for satellite applications.

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“…As part of the study on the impact of wastewater on the Ganges river basin in India, industry ratings from experts were analyzed and aggregated using a fuzzy approach. Lanzotti et al [33] explored the assessment of different design candidates by means of product experts acting as assessors or customers enrolled as testers. They discussed the candidate identification using virtual and physical prototypes and a practical fuzzy approach toward the evaluation of the optimal design.…”

Section: Literature Reviewmentioning

confidence: 99%

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Ziemba

Becker

2020

Symmetry

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In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

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A new interactive design approach for concept selection based on expert opinion

Cited by 26 publications

References 23 publications

Knowledge management for modeled Heritage objects, requirement specifications towards a tool for heterogeneity embracing

Knowledge management for modeled Heritage objects, requirement specifications towards a tool for heterogeneity embracing

Fuzzy model-based design for testing and qualification of additive manufacturing components

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

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