Thirty different views of a lens design solution space: a good example for teaching students how to design and not to design a lens

2005 ◽  
Author(s):  
Julie L. Bentley
Keyword(s):  
Solution Space ◽  
Design Solution ◽  
Lens Design

A Generalized Performance Sensitivity Synthesis Methodology for Four-Bar Mechanisms

1992 ◽  
Author(s):  
Ming-Yih Lee ◽  
Arthur G. Erdman ◽  
Salaheddine Faik
Keyword(s):  
Solution Space ◽  
Design Solution ◽  
Link Length ◽  
Performance Sensitivity ◽  
Closed Chain ◽  
Synthesis Methodology ◽  
Position Synthesis ◽  
The Relationship ◽  
Accuracy Performance ◽  
Sensitivity Maps

Abstract A generalized accuracy performance synthesis methodology for planar closed chain mechanisms is proposed. The relationship between the sensitivity to variations of link lengths and the location of the moving pivots of four-link mechanisms is investigated for the particular objective of three and four position synthesis. In the three design positions case, sensitivity maps with isosensitivity curves plotted in the design solution space allow the designer to synthesize a planar mechanism with desired sensitivity value or to optimize sensitivity from a set of acceptable design solutions. In the case of four design positions, segments of the Burmester design curves that exhibit specified sensitivity to link length tolerance are identified. A performance sensitivity criterion is used as a convenient and a useful way of discriminating between many possible solutions to a given synthesis problem.

scholarly journals A Smart Wing Rib Structure Suitable for Design for Additive Manufacturing (DfAM) Process

2021 ◽  
pp. 1-21
Author(s):  
Ramona Dogea ◽  
◽  
Xiu T Yan ◽  
Richard Millar ◽  
◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Performance Monitoring ◽  
Equivalent Stress ◽  
Solution Space ◽  
Design Solution ◽  
Smart Devices ◽  
Design For Additive Manufacturing ◽  
Wing Structures ◽  
Rib Structure ◽  
Stress And Deformation

Additive manufacturing has been adopted widely across various industries for producing parts mainly due to their ability to create complex geometries, eliminate material wastage and enable faster production rate, among others. Additive manufacturing has also increased design solution space by enabling exploration of mechatronic solutions for mechanical structures. This includes the integration of smart devices into wing structures to achieve a datadriven predictive maintenance-based system. For this, there is still the need to continuously explore various ways of integrating sensory capability into a mechanical structure during the manufacturing processes to ensure improvement and reliability of aircraft components. The scope of this paper was to analyse different wing rib geometries and the influence of embedding sensory capability via design for additive manufacturing process. In this work, three wing rib geometries with cut-outs and for sensory placement were designed and analysed to estimate their equivalent stress and deformation when such sensory locations are introduced. The results confirm the idea that it is feasible to introduce holding cavities for structural performance monitoring sensors without compromising the structural design requirements. The results also show that deformation and stress are highly dependent on the rib thickness and the insertion of sensory locations

scholarly journals Automated Exploration of Design Solution Space Applying the Generative Design Approach

2019 ◽  
Vol 1 (1) ◽  
pp. 1085-1094 ◽  
Author(s):  
Haibing Li ◽  
Roland Lachmayer
Keyword(s):  
Solution Space ◽  
Complex Problem ◽  
Design Approach ◽  
Design Solution ◽  
Generative Design ◽  
Design Elements ◽  
Design Requirements ◽  
Design Solutions ◽  
Feasible Solutions

AbstractDesign is a complex problem-solving activity that transforms design restrictions and requirements into a set of constraints and explores the feasible solutions to satisfy those constraints. However, design solutions generated by traditional modeling approaches are hardly to deal with such constraints, particularly for the exploration of the possible design solution space to enhance the quality of the design outputs and confront the evolving design requirements. In this regard, the Generative Design Approach (GDA) is considered as an efficient method to explore a large design solution space by transforming the design problem into a configuration problem. Fundamentally, GDA explores and stores all the necessary knowledge through a design skeleton and a set of design elements. Thus, design solution space is easily explored by configuring variable design elements via iterative design processes. Further, the output model is not only a design solution but also a design concept that designers could manipulate to explore unconsidered design configurations. Finally, a crank creation as a running example confirmed that GDA provides concrete aids to enhance the diversity of design solutions.

scholarly journals COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK

2021 ◽  
Vol 1 ◽  
pp. 691-700
Author(s):  
Marija Majda Perisic ◽  
Mario Štorga ◽  
John S. Gero
Keyword(s):  
Cognitive Processes ◽  
Design Space ◽  
Computational Study ◽  
Linear Trend ◽  
Solution Space ◽  
Cognitive Effort ◽  
Design Solution ◽  
Agent Based ◽  
Emerging Patterns ◽  
Space Expansion

AbstractWhen observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.

scholarly journals Axiomatic Design for Creativity, Sustainability, and Industry 4.0

2019 ◽  
Vol 301 ◽  
pp. 00016
Author(s):  
Christopher A. Brown ◽  
Erwin Rauch
Keyword(s):  
Industry 4.0 ◽  
Design Thinking ◽  
Selection Process ◽  
Solution Space ◽  
Axiomatic Design ◽  
Design Solution ◽  
Design Parameters ◽  
Functional Requirements ◽  
New Ideas ◽  
Creativity And Innovation

This paper discusses how to foster creativity and sustainability during Axiomatic Design processes, including Industry 4.0 as an example application. Creativity is generating valuable, new ideas. Innovation is making new ideas viable. This paper explains how AD theory and methods can improve the selection process in evolution-inspired creativity for formulating functional requirements and generating and selecting design parameters. FR formulation is a key to creating value in design solutions. No design solution can be better than its FRs. The FRs must capture the true, underlying essence of customer needs. In addition, an FR must define the solution space appropriately, so that all the best DP candidates are included. Suh’s axioms are used to select the single best DPs from the candidates. In AD, viabilityis established systematically during the axiomatic decomposition and the physical integration processes. Methods for detecting poor design thinking are presented. Metrics and tests for evaluating FRs’ facility for creativity and innovation are proposed. Techniques for improving FRs are proposed, decomposed, and reviewed for their compliance with the axioms.

Ideation Variety in Mechanical Design: Examining the Effects of Cognitive Style and Design Heuristics

2015 ◽  
Author(s):  
Kathryn Jablokow ◽  
Wesley Teerlink ◽  
Seda Yilmaz ◽  
Shanna Daly ◽  
Eli Silk ◽  
...  
Keyword(s):  
Cognitive Style ◽  
Student Perceptions ◽  
Engineering Students ◽  
Mechanical Design ◽  
Solution Space ◽  
Cognitive Intervention ◽  
Design Solution ◽  
Design Heuristics ◽  
Self Assessments ◽  
Variety Performance

This paper examines ideation variety as a measure of the extent to which a design solution space has been explored. We investigated one cognitive factor (cognitive style) and one cognitive intervention (Design Heuristics cards) and their relationships with students’ ideation variety, both actual and perceived. Cognitive style was measured using the Kirton Adaption-Innovation inventory (KAI), while variety scores were computed using the metrics of Nelson et al. [18] and Shah et al. [20]; an adapted form of these metrics was also explored. A group of 132 sophomore mechanical engineering students generated ideas for two design problems (one with and one without Design Heuristics cards). They sketched and described their conceptual solutions in words and assessed the variety of their solutions after ideation. Linear statistical techniques were applied to explore the relationships among the variety scores, students’ self-assessments of variety, cognitive style, quantity of ideas, and the presence of the Design Heuristics intervention. Our results show statistically significant correlations between students’ perceived variety and their variety performance, and between cognitive style and both variety performance and student perceptions.

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