Estimation of aircraft component production cost using knowledge based engineering techniques

Zhao, Xiaojia; Verhagen, Wim J. C.; Curran, Richard

doi:10.1016/j.aei.2015.05.004

Cited by 20 publications

(11 citation statements)

References 55 publications

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“…Additional research exists in the field of cost estimation using KBE, as is seen in the work of Van der Laan [14], Zhao et al [15] or Quintana-Amate et al [5]. Nevertheless, these models compute cost without having a complete overview of the production processes, since cost is estimated using cost estimation relationships (CERs), which are mathematical expressions that relate cost to independent variables and are usually based on empirical data.…”

Section: B Knowledge-based Engineering Methodsologiesmentioning

confidence: 99%

Development of a Knowledge-Based Engineering Framework for Modeling Aircraft Production

Risueño

Nagel

2019

AIAA Aviation 2019 Forum

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Production modeling and cost estimation are among the main drivers in contemporary approaches to design aircraft and aircraft components. However, the existing methodologies presented in academia to evaluate these disciplines are not widely used in the industry because of (a) black-box behavior of the current production modeling systems, (b) lack of connection between the production modeling systems and the design process, (c) elevated cost and time needed to implement the methods, (d) confidentiality of production information, and (e) resistance to change the working methods. The research presented in this paper proposes a methodology that aims to overcome these limitations by combining knowledge-based engineering with semantic web technologies in the modeling of aircraft production processes. The methodology is realized by a generic, flexible, adaptable and reusable framework for modeling production, which can evaluate the influence of different production concepts, to quantify the interactions between production processes and aircraft design and to conduct studies of performance, and production costs.

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Section: B Knowledge-based Engineering Methodsologiesmentioning

confidence: 99%

Development of a Knowledge-Based Engineering Framework for Modeling Aircraft Production

Risueño

Nagel

2019

AIAA Aviation 2019 Forum

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“…Obviously, choosing one method instead of another is deeply influenced by the stage of the development process (Rush and Roy, 2000), as well as by the type and amount of data available (Niazi et al, 2006;Altavilla et al, 2018). In general, Qualitative Approaches are more appropriate at the conceptual design stage (Layer et al, 2002;Niazi et al, 2006), using historical data (Seo, 2006) or structure/unstructured experts knowledge (H'mida et al, 2006;Zhao et al, 2015). Quantitative methods instead employ more accurate product and process information, generally available at the detail design stage (Kaufmann et al, 2008;Johnson and Kirchain, 2009;Haroun, 2015).…”

Section: Review Of the Lifecycle Cost Methodsmentioning

confidence: 99%

When Lifecycle Cost Analysis Enables Strategic Design Considerations

Altavilla¹,

Montagna²

2018

Proceedings of the DESIGN 2018 15th International Design Conference

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The literature on cost-estimation is full of examples that methodologically explain the construction of models. In this paper, we showed how such methods when correctly implemented represent a source of structured information for in-depth design decisions. We investigated the impact of variety proliferation overall lifecycle costs, assessing differences in product architectures using a cluster analysis. The evidence collected serves as validation of a cost model approach and provides a decision-making support for choosing between platform solutions against a knowledge of costs consequences.

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“…Several product cost estimation methods were proposed in the past; however, they suffer because of various shortcoming such as rough estimates, labor and time intensive, low reusability, specific to design changes and low traceability (Zhao et al , 2015). These cost estimation methods were classified into different ways.…”

Section: Review Of Literaturementioning

confidence: 99%

Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

Kasie

Bright

2021

JM2

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Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

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Estimation of aircraft component production cost using knowledge based engineering techniques

Cited by 20 publications

References 55 publications

Development of a Knowledge-Based Engineering Framework for Modeling Aircraft Production

Development of a Knowledge-Based Engineering Framework for Modeling Aircraft Production

When Lifecycle Cost Analysis Enables Strategic Design Considerations

Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

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