A Welding Capability Assessment Method (WCAM) to support multidisciplinary design of aircraft structures

Madrid, Julia; Forslund, Anders; Söderberg, Rikard; Wärmefjord, Kristina; Hoffenson, Steven; Vallhagen, Johan; Andersson, Petter

doi:10.1007/s12008-017-0429-5

Cited by 10 publications

(26 citation statements)

References 37 publications

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“…As demonstrated in recent work (Madrid et al, 2017;So¨derberg et al, 2018), the producibility model represents an information framework with which to generate and reuse producibility knowledge. The identification and assessment of control parameters acting as constrains may support the systematic assessment of product-production alternatives as a basis for putting inferior alternatives aside.…”

Section: Modeling Of Production Operationsmentioning

confidence: 99%

Dynamic platform modeling for concurrent product-production reconfiguration

Landahl

Jiao

Madrid

et al. 2020

Concurrent Engineering

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To meet a wide range of customer needs, a variety of product concepts can be modeled employing a platform approach. Whereas frequent market changes can be accommodated by dynamically modifying product concepts in iterations, capabilities in production are seldom well incorporated as part of design iterations. In this paper, a dynamic platform modeling approach that supports concurrent product-production reconfiguration is presented. The approach builds on Set-Based Concurrent Engineering (SBCE) processes and a function modeling technique is used to represent product-production variety streams inherent in a production operation model. To demonstrate the approach, a comprehensive case from the aerospace industry is presented. Conceptual representations of a set of aero engine sub-systems and a variety of welding configurations, including their inherent constraints, are mutually modeled and assessed. The results show that a set of product-production alternatives can be dynamically controlled by integrating product-production constraints using a production operation model. Following SBCE processes, inferior alternatives can be put aside until new information becomes available and a new set of alternatives can be reconfigured. The dynamics and concurrency of the approach can potentially reduce the risk of late and costly modifications that propagate from design to production.

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Section: Modeling Of Production Operationsmentioning

confidence: 99%

Dynamic platform modeling for concurrent product-production reconfiguration

Landahl

Jiao

Madrid

et al. 2020

Concurrent Engineering

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“…Most KC identification methods are based on qualitative analysis for KC acquisition that uses the concept of a KC flowdown [16,17], such as in Boeing [10] and GM Motors [11]. A KC flow-down is a hierarchical approach to tracing/ propagating a key characteristic for an assembly or product down to key characteristics on its subassemblies, details, and processes believed to affect the variation of the top-level key requirement KCs [10].…”

Section: Qualitative Analysis For Kc Acquisitionmentioning

confidence: 99%

Identification of key design characteristics for complex product adaptive design

Han

Wang

et al. 2017

Int J Adv Manuf Technol

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Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product's functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product's functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)'s bogie.

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“…However, for welded and integrated products, in which multiple functions are satisfied by a single continuous structure, the automation and simulation capabilities to evaluate welding producibility and predict welding quality are limited as discussed in Madrid et al [8]. The consequence is that predictions of welding producibility are often neglected in early design analyses.…”

Section: Introductionmentioning

confidence: 99%

“…The research work published in this paper builds upon a seven-year academic-industrial collaboration aimed at developing methods and tools with which to predict product quality after welding, in addition to controlling welding -manufacturing variation and defects during early design phases. In this paper, the results from a number of previous research publications have been incorporated, see [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Automated and interactive evaluation of welding producibility in an multidisciplinary design optimization environment for aircraft components

Madrid

Andersson

Söderberg

et al. 2021

Int J Interact Des Manuf

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The automation capabilities and virtual tools within engineering disciplines, such as structural mechanics and aerodynamics, enable efficient Multidisciplinary Design Optimization (MDO) approaches to evaluate and optimize the performance of a large number of design variants during early design stages of aircraft components. However, for components that are designed to be welded, in which multiple functional requirements are satisfied by one single welded structure, the automation and simulation capabilities to evaluate welding-producibility and predict welding quality (geometrical deformation, weld bead geometrical quality, cracks, pores, etc) are limited. Besides the complexity of simulating all phenomena within the welding process, one of the main problems in welded integrated components is the existing coupling between welding quality metrics and product geometry. Welding quality can vary for every new product geometrical variant. Thus, there is a need of analyzing rapidly and virtually the interaction and sensitivity coefficients between design parameters and welding quality to predict welding producibility. This paper presents as a result an automated and interactive welding-producibility evaluation approach. This approach incorporates a data-based of welding-producibility criteria, as well as welding simulation and metamodel methods, which enable an interactive and automated evaluation of welding quality of a large number of product variants. The approach has been tested in an industrial use-case involving a multidisciplinary design process of aircraft components. The results from analyzing the welding-producibility of a set of design variants have been plotted together with the analysis results from other engineering disciplines resulting in an interactive tool built with parallel coordinate graphs. The approach proposed allows the generation and reuse of welding producibility information to perform analyses within a big spectrum of the design space in a rapid and interactive fashion, thus supporting designers on dealing with changes and taking fact-based decisions during the multidisciplinary design process.

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A Welding Capability Assessment Method (WCAM) to support multidisciplinary design of aircraft structures

Cited by 10 publications

References 37 publications

Dynamic platform modeling for concurrent product-production reconfiguration

Dynamic platform modeling for concurrent product-production reconfiguration

Identification of key design characteristics for complex product adaptive design

Automated and interactive evaluation of welding producibility in an multidisciplinary design optimization environment for aircraft components

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