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Improved Output in Modular Function Deployment Using Heuristics
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Elements.
2009 (English)In: Proceedings of the 17th International Conference on Engineering Design (ICED’09), Vol. 4, 2009, 1-12 p.Conference paper (Refereed)
Abstract [en]

In Modular Function Deployment, technical solutions are grouped into modules according tothe product properties and the strategic intentions of the company. Statistical methods such ashierarchical clustering are useful in the formation of potential modules, but a significantamount of manual adjustment and application of engineering common sense is generallynecessary. We propose a method for promoting better output from the clustering algorithmused in the conceptual module generation phase by adding Convergence Properties, acollective reference to data identified as option properties, geometrical information, flowheuristics, and module driver compatibility. The method was tested in a case study based on acordless handheld vacuum cleaner.

Place, publisher, year, edition, pages
2009. 1-12 p.
Keyword [en]
Conceptual product development, modular products, Modular Function Deployment, module drivers, clustering algorithm, hierarchical clustering, statistical approach, heuristic methods
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-96487ISI: 000302735800001ScopusID: 2-s2.0-84859254254ISBN: 9-781904-670087OAI: diva2:530886
17th International Conference on Engineering Design

QC 20120605

Available from: 2012-06-05 Created: 2012-06-05 Last updated: 2012-09-18Bibliographically approved
In thesis
1. Approaches to Modularity in Product Architecture
Open this publication in new window or tab >>Approaches to Modularity in Product Architecture
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Modular product architecture is characterized by the existence of standardized interfaces between the physical building blocks. A module is a collection of technical solutions that perform a function, with interfaces selected for company-specific strategic reasons. Approaches to modularity are the structured methods by which modular product architectures are derived. The approaches include Modular Function Deployment (MFD), Design Structure Matrix (DSM), Function Structure Heuristics and many other, including hybrids. The thesis includes a survey of relevant theory and a discussion of four challenges in product architecture research, detailed in the appended papers.

One common experience from project work is structured methods such as DSM or MFD often do not yield fully conclusive results. This is usually because the algorithms used to generate modules do not have enough relevant data. Thus, we ask whether it is possible to introduce new data to make the output more conclusive. A case study is used to answer this question. The analysis indicates that with additional properties to capture product geometry, and flow of matter, energy, or information, the output is more conclusive.

If product development projects even have an architecture definition phase, very little time is spent actually selecting the most suitable tool. Several academic models are available, but they use incompatible criteria, and do not capture experience-based or subjective criteria we may wish to include. The research question is whether we can define selection criteria objectively using academic models and experience-based criteria. The author gathers criteria from three academic models, adds experience criteria, performs a pairwise comparison of all available criteria and applies a hierarchical cluster analysis, with subsequent interpretation. The resulting evaluation model is tested on five approaches to modularity. Several conclusions are discussed. One is that of the five approaches studied, MFD and DSM have the most complementary sets of strengths and weaknesses, and that hybrids between these two fundamental approaches would be particularly interesting.

The majority of all product development tries to improve existing products. A common criticism against all structured approaches to modularity is they work best for existing products. Is this perhaps a misconception? We ask whether MFD and DSM can be used on novel product types at an early phase of product development. MFD and DSM are applied to the hybrid drive train of a Forwarder. The output of the selected approaches is compared and reconciled, indicating that conclusions about a suitable modular architecture can be derived, even when many technical solutions are unknown. Among several conclusions, one is the electronic inverter must support several operating modes that depend on high-level properties of the drive train itself (such as whether regeneration is used). A modular structure for the electronic inverter is proposed.

Module generation in MFD is usually done with Hierarchical Cluster Analysis (HCA), where the results are presented in the form of a Dendrogram. Statistical software can generate a Dendrogram in a matter of seconds. For DSM, the situation is different. Most available algorithms require a fair amount of processing time. One popular algorithm, the Idicula-Gutierrez-Thebeau Algorithm (IGTA), requires a total time of a few hours for a problem of medium complexity (about 60 components). The research question is whether IGTA can be improved to execute faster, while maintaining or improving quality of output. Two algorithmic changes together reduce execution time required by a factor of seven to eight in the trials, and improve quality of output by about 15 percent.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. 50 p.
Trita-MMK, ISSN 1400-1179 ; 2012:11
Clustering Algorithm, Design Structure Matrix, Modular Function Deployment, Product architecture, Product family, Product platform
National Category
Engineering and Technology
urn:nbn:se:kth:diva-96491 (URN)978-91-7501-390-9 (ISBN)
2012-06-11, Rum B242, Brinellvägen 83, KTH, Stockholm, 10:00 (English)
QC 20120605Available from: 2012-06-05 Created: 2012-06-05 Last updated: 2012-06-07Bibliographically approved

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