SIG Library

Query returned 1553 results.

VISUAL ANALYSIS METHODS FOR NON-IDEAL ASSEMBLIES

Penzkofer, F.; Wittmann, S.; Winter, M. // 2008
We follow the “generate and test” approach to analyse tolerance specifications: Based on toleranced CAD data, non-ideal parts are generated. For these non-ideal parts we propose several visualization ...

WHAT IS DESIGN KNOWLEDGE FROM THE VIEWPOINT OF CPM/PDD?

Conrad, J.; Köhler, C.; Wanke, S.; Weber, C. // 2008
Knowledge is a very common term in engineering design and also extensively discussed. Even if it is not an explicit matter, it plays implicitly a role in the background. The ...

WHAT IS SUCCESS AND FAILURE IN PRODUCT AND SERVICE DESIGN?

Hollins, W. J. // 2008
Research has shown that most new products fail. When one attempts to define what actually constitutes a success or failure in the design of products and in the design process, and who is affected, ...

Drawing Standards for Early Design: Where Do We Stand?

Farrugia, Philip J.; Borg, Jonathan C.; Yan, Xiu-Tian; Camilleri, Kenneth P.; Bartolo, Alexandra // 2007

Exploring Design Space Using Multi-Instance Modelling

Singh, Baljinder; Matthews, Jason; Mullineux, Glen; Medland, Anthony J. // 2007

Presentation of Engineering Design Related Knowledge

Feldhusen, Jörg; Lemburg, Johannes // 2007

Research on Design Evaluation Indicators Drawn From the Good Design Award Jury Members' Comments

Sogabe, Haruka; Morita, Yoshitsugu; Ishibashi, Shinsuke // 2007

Use of Shape Preference Information in Product Design

Kelly,Jarod; Papalambros, Panos Y. // 2007

Visual Reasoning and Design Processes

Park, Jung Ae; Kim, Yong Se // 2007

Volume Visualization of Geometric Deviations

Wittmann, Stefan; Stoll, Tobias; Paetzold, Kristin // 2007

A FRAMEWORK FOR CAPTURING DESIGN ANALYSIS KNOWLEDGE FOR REUSE USING PROCESS MODELS

Goh, Y.M.; McMahon, C.A.; Booker, J.D. // 2006
Engineering companies are increasingly relying on information-intensive methods and tools to support decision-making in product design and development. Design analysis is a critical stage where ...

A SELF-TRAINING SYSTEM THAT LEARNS THROUGH EXPERIMENTATION

Braun, S.C.; Gero, J.S. // 2006
The paper introduces an adaptive system that, inspired by the diversity of human cognitive development processes, uses different kinds of machine learning to develop its expertise. The system ...

An Evaluation of a Language for Paper-based Form Sketching

Farrugia, P.J.; Borg, J.C.; Camilleri, K.P.; Bartolo, A.; Restrepo-Giraldo, J.D. // 2006

Boolean Searches

The following examples demonstrate some search strings that use boolean operators:

  • design community
    Find rows that contain at least one of the two words.
  • +design +community
    Find rows that contain both words.
  • +design community
    Find rows that contain the word “design”, but rank rows higher if they also contain “community”.
  • +design -community
    Find rows that contain the word “design” but not “community”.
  • +design ~community
    Find rows that contain the word “design”, but if the row also contains the word “community”, rate it lower than if row does not.
  • +design +(>community <decisions)
    Find rows that contain the words “design” and “community”, or “design” and “decisions” (in any order), but rank “design community” higher than “design decisions”
  • design*
    Find rows that contain words such as “design”, “designs”, “designing”, or “designer”.
  • "some words"
    Find rows that contain the exact phrase “some words” (for example, rows that contain “some words of wisdom” but not “some noise words”). Note that the " characters that enclose the phrase are operator characters that delimit the phrase. They are not the quotation marks that enclose the search string itself.

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