Supporting designers: moving from method menagerie to method ecosystem

Supporting designers is one of the main motivations for design research. However, there is an ongoing debate about the ability of design research to transfer its results, which are often provided in form of design methods, into practice. This article takes the position that the transfer of design methods alone is not an appropriate indicator for assessing the impact of design research by discussing alternative pathways for impacting design practice. Impact is created by different means – first of all through the students that are trained based on the research results including design methods and tools and by the systematic way of thinking they acquired that comes along with being involved with research in this area. Despite having a considerable impact on practice, this article takes the position that the transfer of methods can be improved by moving from cultivating method menageries to facilitating the evolution of method ecosystems. It explains what is understood by a method ecosystem and discusses implications for developing future design methods and for improving existing methods. This paper takes the position that efforts on improving and maturing existing design methods should be raised to satisfy the needs of designers and to truly support them

Extension of the target cascading formulation to the design of product families

The target cascading methodology for optimal product development is extended to product families with predefined platforms. The single-product formulation is modified to accommodate the presence of shared systems, subsystems, and/or components and locally introduced targets. Hierarchical optimization problems associated with each product variant are combined to formulate the product family multicriteria design problem, and common subproblems are identified based on the shared elements (i.e. the platform). The solution of the overall design problem is coordinated so that the shared elements are consistent with the performance and behaviour of the product variants. A simple automotive design example is used to demonstrate the proposed methodology.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/41910/1/20240293.pd

A COMPREHENSIVE METRIC FOR COMPARING TIME HISTORIES IN VALIDATION OF SIMULATION MODELS WITH EMPHASIS ON VEHICLE SAFETY APPLICATIONS

ABSTRACT Computer modeling and simulation are the cornerstones of product design and development in the automotive industry. Computer-aided engineering tools have improved to the extent that virtual testing may lead to significant reduction in prototype building and testing of vehicle designs. In order to make this a reality, we need to assess our confidence in the predictive capabilities of simulation models. As a first step in this direction, this paper deals with developing a metric to compare time histories that are outputs of simulation models to time histories from experimental tests with emphasis on vehicle safety applications. We focus on quantifying discrepancy between time histories as the latter constitute the predominant form of responses of interest in vehicle safety considerations. First we evaluate popular measures used to quantify discrepancy between time histories in fields such as statistics, computational mechanics, signal processing, and data mining. Then we propose a structured combination of some of these measures and define a comprehensive metric that encapsulates the important aspects of time history comparison. The new metric classifies error components associated with three physically meaningful characteristics (phase, magnitude and topology), and utilizes norms, cross-correlation measures and algorithms such as dynamic time warping to quantify discrepancies. Two case studies demonstrate that the proposed metric seems to be more consistent than existing metrics. It is also shown how the metric can be used in conjunction with ratings from subject matter experts to build regression-based val- * Corresponding author, Phone/Fax: (734) 615-8991/647-8403 idation models

Towards Confidence Extrapolation of Validated Simulations

Upprättat; 2011; 20111118 (andbra

Exploring the potential of digital twin-driven design of aero-engine structures

As the diversity of customer needs increases within the aerospace industry, so does the need for improved design practices to reduce quality issues downstream. When designing new products, design engineers struggle with applying tolerances to features, which often leads to expensive late design iterations. To mitigate this, one aerospace company is looking to reuse tolerance deviation data yielded during manufacturing in design. In the long term these data could provide the basis for a Digital Twin that can be used for improved product development. This article explores how data from production are used today, what issues prevents such data from being exploited in the design phase, and how they potentially could be used for design purposes in the future. To understand the current situation and identify the untapped potential of production data in design, an interview study was conducted in conjunction with a literature review. In this paper the current situation and primary barriers are presented and a possible path for further research and development is suggested