A Coordination-Based Approach to Robust Product Design

1999 ◽  
Author(s):  
Li Chen
Keyword(s):  
Product Design ◽  
Robust Design ◽  
Adaptive Design ◽  
Product Performance ◽  
Computational Aspect ◽  
Design Coordination ◽  
The One ◽  
Fuzzy Control Algorithm ◽  
Compromise Design ◽  
Robust Product Design

Abstract Robust product design is a two-objective optimization problem in nature. On the one hand, it is wished to minimize variations in product performance (i.e. first goal); on the other hand, it is desired to maximize product functionality (i.e. second goal). As known, these design goals may not arrive at their own optima at the same time; therefore, coordination between the two perfonning goals is needed during the design iterations to help achieve high robustness in product performance in the sense that a best compromise design is reached. In this work, a coordination-based robust design approach is developed to control the computational aspect of robust design process in a coordinated fashion, allowing that high-quality engineered products are produced with designing-in of performance precision and accuracy through robust design. A fuzzy control algorithm in terms of coordination rules prescribed is generated for handling design coordination by tuning an adaptive design parameter β, defined over the domain from 0 to 1.0. Three coordinated design models are discussed along with the study of a design example.

Robust Product Design Using Manufacturing Adjustments

1994 ◽  
Author(s):  
Kevin Otto
Keyword(s):  
Product Design ◽  
Product Performance ◽  
Variable Model ◽  
Voltage Supply ◽  
Manufacturing Errors ◽  
Robustness To Noise ◽  
Product Variation ◽  
Better Than ◽  
Factory Floor ◽  
Robust Product Design

Abstract Tuning variables represent factory floor manufacturing adjustments commonly used to correct variational noise errors in a product. Some examples are voltage supply adjustments, adjustable links and screws, or shims. This paper presents methods to determine the increased performance and robustness when using different possible manufacturing adjustments. All possible potential tuning adjustments that exist in a design are identified. Given this, the tuning variable model can be used to calculate the reduced product variation when using any of the potential tuning adjustments, including none at all. This process can be used to help select which product variables should be adjusted, based on the increased robustness to noise and the increased difficulty in manufacture. Doing so allows for more robust product performance at reduced manufacturing expense, by allowing the potential adjustment on many different possible variables. This is better than always adjusting the source of the manufacturing errors, which can be expensive.

scholarly journals Creating products in the absence of markets: a robust design approach

2008 ◽  
Vol 19 (3) ◽  
pp. 407-420 ◽  
Author(s):  
Philippe Silberzahn ◽  
Christophe Midler
Keyword(s):  
Longitudinal Study ◽  
Product Design ◽  
Robust Design ◽  
Practical Implication ◽  
Collaborative Work ◽  
High Tech ◽  
Content Type ◽  
Exploration Process ◽  
Strategic Process ◽  
Alternative Approach

PurposeThe purpose of this study is to examine how firms deal with a situation of true uncertainty about their potential markets and technologies. Specifically, it asks how firms can create products when the corresponding market does not exist.Design/methodology/approachThis paper is based on a longitudinal study of a high‐tech firm, combined with analysis of existing theory in product design and entrepreneurship.FindingsMarkets and products are usually a defining choice made early on by firms in their strategic process. Such a choice guides their development by providing a “stable concept” to which decisions can be related. When markets do not exist yet, however, this approach is not effective. Early choice of products and markets limits firms' flexibility by constraining their ability and willingness to adapt, while fundamental new technical and market information is likely to emerge during the project that will prove the initial assumptions wrong. The paper shows an alternative approach where products and markets actually result from a generic process of products and markets exploration driven by the firm. It is suggested that this approach forms a robust design in that it allows the firm to deal with the uncertainty by simultaneously developing its products and exploring markets, while preserving the flexibility to adapt to the changing environment.Practical implicationsThe practical implication of this paper is to suggest an alternative approach to deliberate planning in high‐tech ventures. With this approach, rather than markets and products, strategy defines a market and technology exploration process.Originality/valueThe paper is original in three ways. It links the product design and market exploration processes in high‐tech firm development; it is based on an in‐depth longitudinal study; and it results from an academic‐practitioner collaborative work.

Engineering Methods for Robust Product Design

Technometrics ◽  
1996 ◽  
Vol 38 (3) ◽  
pp. 286-287
Author(s):  
Henry W. Altland
Keyword(s):  
Product Design ◽  
Robust Product Design

A Framework for Choice Modeling in Usage Context-Based Design

2010 ◽  
Author(s):  
Lin He ◽  
Christopher Hoyle ◽  
Wei Chen ◽  
Jiliang Wang ◽  
Bernard Yannou
Keyword(s):  
Data Collection ◽  
Product Design ◽  
Consumer Choice ◽  
Choice Model ◽  
Consumer Preferences ◽  
Choice Modeling ◽  
Product Performance ◽  
Choice Data ◽  
Modeling Framework ◽  
Usage Context

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. A framework and a step-by-step procedure for implementing consumer choice modeling in UCBD are presented in this work. To implement the proposed approach, methods for common usage identification, data collection, linking performance with usage context, and choice model estimation are developed. For data collection, a method of try-it-out choice experiments is presented. This method is necessary to account for the different choices respondents make conditional on the given usage context, which allows us to examine the influence of product design, customer profile, usage context attributes, and their interactions, on the choice process. Methods of data analysis are used to understand the collected choice data, as well as to understand clusters of similar customers and similar usage contexts. The choice modeling framework, which considers the influence of usage context on both the product performance, choice set and the consumer preferences, is presented as the key element of a quantitative usage context-based design process. In this framework, product performance is modeled as a function of both the product design and the usage context. Additionally, usage context enters into an individual customer’s utility function directly to capture its influence on product preferences. The entire process is illustrated with a case study of the design of a jigsaw.

Contribution of Wall Thickness and Corner Design for a Robust Design of Thermoplastic Products

2016 ◽  
Vol 834 ◽  
pp. 15-21
Author(s):  
Ștefan Cotescu
Keyword(s):  
Product Design ◽  
Robust Design ◽  
Wall Thickness ◽  
Design Features

This paper presents relevant data for a robust design of thermoplastic products, as a support for optimization of product design. The study emphasis rules and recommendations related to wall thickness and corner design through which the developer could anticipate quality problems that might appear in the product’s lifetime, avoiding design features that might be difficult to be produced or produced with a cost that may not be feasible.

Development of CAD Model Annotation System Based on Design Intent

2017 ◽  
Vol 863 ◽  
pp. 368-372
Author(s):  
Qin Yi Ma ◽  
Li Hua Song ◽  
Da Peng Xie ◽  
Mao Jun Zhou
Keyword(s):  
Product Design ◽  
3D Model ◽  
Adaptive Design ◽  
Design Knowledge ◽  
Cad Model ◽  
Design Intent ◽  
Design Information ◽  
Annotation System ◽  
Modeling Analysis ◽  
Model Annotation

Most of the product design on the market is variant design or adaptive design, which need to reuse existing product design knowledge. A key aspect of reusing existing CAD model is correctly define and understand the design intents behind of existing CAD model, and this paper introduces a CAD model annotation system based on design intent. Design intents contained all design information of entire life cycle from modeling, analysis to manufacturing are marked onto the CAD model using PMI module in UG to improve the readability of the CAD model. Second, given the problems such as management difficulties, no filter and retrieval functions, this paper proposes an annotation manager system based on UG redevelopment by filtration, retrieval, grouping and other functions to reduce clutter on the 3D annotations and be convenient for users to view needed all kinds of annotations. Finally, design information is represented both internally within the 3D model and externally on a XML file.

A Proposal of Robust Design Method for Deriving Adjustable Range

2011 ◽  
Vol 311-313 ◽  
pp. 2332-2335 ◽  
Author(s):  
Suguru Kimura ◽  
Takeo Kato ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Robust Design ◽  
Design Method ◽  
Previous Method ◽  
Product Performance ◽  
Design Solution ◽  
Control Factor ◽  
Design Problems ◽  
Solution Design ◽  
Adjustable Range ◽  
Robust Design Method

Due to increased individuation of user needs and market globalization, the demands for product performance have diversified. However, a diversified performance is difficult to be obtained a unique design solution (design value). In a previous study, the robust design method (RDM), which ensures products with a robust performance under diverse conditions, was improved. This method was used to evaluate performance robustness with respect to an adjustable control factor (ACF), which is a factor that can be adjusted by the user. Unfortunately, the RDM is not applicable to design problems that have several ACFs due to the increased calculation amount. To resolve this issue, this study improves the previous method by applying a genetic algorithm and a method to extract the values of ACFs employed in the robustness calculation. The improved RDM was applied to two numerical examples to confirm its effectiveness.

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