Matching Items (7)
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- All Subjects: Product Design
- Creators: Herring, Donald
- Creators: Pan, Rong
- Creators: Boradkar, Prasad
Description
In the three phases of the engineering design process (conceptual design, embodiment design and detailed design), traditional reliability information is scarce. However, there are different sources of information that provide reliability inputs while designing a new product. This research considered these sources to be further analyzed: reliability information from similar existing products denominated as parents, elicited experts' opinions, initial testing and the customer voice for creating design requirements. These sources were integrated with three novels approaches to produce reliability insights in the engineering design process, all under the Design for Reliability (DFR) philosophy. Firstly, an enhanced parenting process to assess reliability was presented. Using reliability information from parents it was possible to create a failure structure (parent matrix) to be compared against the new product. Then, expert opinions were elicited to provide the effects of the new design changes (parent factor). Combining those two elements resulted in a reliability assessment in early design process. Extending this approach into the conceptual design phase, a methodology was created to obtain a graphical reliability insight of a new product's concept. The approach can be summarized by three sequential steps: functional analysis, cognitive maps and Bayesian networks. These tools integrated the available information, created a graphical representation of the concept and provided quantitative reliability assessments. Lastly, to optimize resources when product testing is viable (e.g., detailed design) a type of accelerated life testing was recommended: the accelerated degradation tests. The potential for robust design engineering for this type of test was exploited. Then, robust design was achieved by setting the design factors at some levels such that the impact of stress factor variation on the degradation rate can be minimized. Finally, to validate the proposed approaches and methods, different case studies were presented.
ContributorsMejia Sanchez, Luis (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Jesus R (Committee member) / See, Tung-King (Committee member) / Arizona State University (Publisher)
Created2014
Description
Some disabled users of assistive technologies (AT) have expressed concerns that their use of those AT devices brings particular attention to their disability and, in doing so, stigmatizes them in the eyes of their peers. This research studies how a wide range of design factors, influence how positively or negatively users of wearable technologies are perceived, by others. These factors are studied by asking survey respondents to estimate the degree to which they perceive disabilities in users of various products. The survey was given to 34 undergraduate Product Design students, and employed 40 pictures, each of which showed one person using a product. Some of these products were assistive technology devices, and some were not. Respondents used a five-bubble Likert scale to indicate the level of disability that they perceived in this person. Data analysis was done using SPSS software. The results showed that the gender of the respondent was not a significant factor in the respondent's estimation of the level of disability. However, the cultural background of the respondent was found to be significant in the respondent's estimates of disability for seven of the 40 pictures. The results also indicated that the size of AT, its familiarity to the mainstream population, its wearable location on the user's body, the perceived power of the user, the degree to which the AT device seemed to empower the user, the degree to which the AT device was seen as a vehicle for assertion of the user's individuality, and the successfulness of attempts to disguise the AT as some mainstream product reduced the perceived disability of the user. In contrast, symbols or stereotypes of disability, obstructing visibility of the face, an awkward complex design, a mismatch between the product's design and its context of use, and covering of the head were factors that focused attention on, and increased the perception of, the user's disability. These factors are summarized in a set of guidelines to help AT designers develop products that minimize the perceived disability and the resulting stigmatization of the user.
ContributorsValamanesh, Ronak (Author) / Velasquez, Joseph (Thesis advisor) / Black, John (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation is to address product design optimization including reliability-based design optimization (RBDO) and robust design with epistemic uncertainty. It is divided into four major components as outlined below. Firstly, a comprehensive study of uncertainties is performed, in which sources of uncertainty are listed, categorized and the impacts are discussed. Epistemic uncertainty is of interest, which is due to lack of knowledge and can be reduced by taking more observations. In particular, the strategies to address epistemic uncertainties due to implicit constraint function are discussed. Secondly, a sequential sampling strategy to improve RBDO under implicit constraint function is developed. In modern engineering design, an RBDO task is often performed by a computer simulation program, which can be treated as a black box, as its analytical function is implicit. An efficient sampling strategy on learning the probabilistic constraint function under the design optimization framework is presented. The method is a sequential experimentation around the approximate most probable point (MPP) at each step of optimization process. It is compared with the methods of MPP-based sampling, lifted surrogate function, and non-sequential random sampling. Thirdly, a particle splitting-based reliability analysis approach is developed in design optimization. In reliability analysis, traditional simulation methods such as Monte Carlo simulation may provide accurate results, but are often accompanied with high computational cost. To increase the efficiency, particle splitting is integrated into RBDO. It is an improvement of subset simulation with multiple particles to enhance the diversity and stability of simulation samples. This method is further extended to address problems with multiple probabilistic constraints and compared with the MPP-based methods. Finally, a reliability-based robust design optimization (RBRDO) framework is provided to integrate the consideration of design reliability and design robustness simultaneously. The quality loss objective in robust design, considered together with the production cost in RBDO, are used formulate a multi-objective optimization problem. With the epistemic uncertainty from implicit performance function, the sequential sampling strategy is extended to RBRDO, and a combined metamodel is proposed to tackle both controllable variables and uncontrollable variables. The solution is a Pareto frontier, compared with a single optimal solution in RBDO.
ContributorsZhuang, Xiaotian (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Zhang, Muhong (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Created2012
Description
All too often, industrial designers face seemingly intractable obstacles as they endeavor to, as Simon (1996, p. 111) describes, devise "courses of action aimed at changing existing situations into preferred ones." These problems, described by Rittel and Webber (1973) as "wicked," are insurmountable due to the contradictory and changing nature of their requirements. I argue that that industrial design (ID) is largely subject to Rittel's quandary because of its penchant for producing single solutions for large populations; such design solutions are bound, in some senses, to fail due to the contradictory and changing nature of large and, thus, inherently diverse populations. This one-size-fits-all approach is not a necessary attribute of ID, rather, it is a consequence of the time in which it came into being, specifically, the period of industrial mass production. Fortunately, new, agile manufacturing techniques, inexpensive sensors, and machine learning provide an alternative course for ID to take, but it requires a new way of thinking and it requires a new set of methods, which I will elaborate in this thesis. According to Duguay, Landry, and Pasin (1997), we are entering an age where it will be feasible to produce individualized, one-off products from large-scale industrial manufacturing facilities in a way that is not only cost effective, but in many ways as cost effective as the existing techniques of mass production. By availing ourselves of these opportunities, we can tame the problem, not by defeating Rittel's logic, rather by reducing the extent to which his theories are appropriate to the domain of ID. This thesis also describes a test study: an experiment whose design was guided by the proposed design methodologies. The goal of the experiment was to determine the feasibility of a noninvasive system for measuring the health of the forearm muscles. Such a tool would provide the basis for assessing the true impact and possible pathogeny of the manual use of products or modifications to products. Previously, it was considered impossible to use surface electromyography (as opposed to needle or wire based electromyography) to assess muscular activity and muscular health due to the complexity of the arrangement of muscles in the forearm. Attempts to overcome this problem have failed because they have tried to create a single solution for all people. My hypothesis is that, by designing for each individual, a solution may be found. Specifically, I show that, for any given individual, there is a high correlation between the EMG signal and the movements of the fingers that, ostensibly, those muscles control. In other words, by knowing, with great accuracy, the position and the motion of the hand then it would become possible to disambiguate the mixed signals coming from the complex web of muscles in the forearm and enable the assessment of the forearm's health by non-invasive means.
ContributorsBraiman, Stuart (Author) / Giard, Jacques (Thesis advisor) / Black Jr., John A (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2012
Description
A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results.
The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages.
In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules.
In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression.
In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.
The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages.
In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules.
In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression.
In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.
ContributorsLee, Dongjin (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Wu, Teresa (Committee member) / Du, Xiaoping (Committee member) / Arizona State University (Publisher)
Created2018
Description
The purpose of this research project is to outline the design process and to uncover some of the considerations that designers must keep in mind in an effort to design more empathetically. These will include a focus on appropriate aesthetics, user experience design, and designing for innovation. These findings are then applied to a three-part design project to illustrate the importance of these guidelines.
ContributorsTerminel Iberri, Carlos Martin (Author) / Boradkar, Prasad (Thesis director) / McDermott, Lauren (Committee member) / McVey, Patrick (Committee member) / Barrett, The Honors College (Contributor) / The Design School (Contributor)
Created2014-05
Description
Game design and product design are natural partners. They use similar tools. They reach the same users. They even share the same goal: to provide great user experiences.
This thesis asks, "Can game design build better product learning experiences, and if so, how?" It examines the learning situations created by and necessary for product design. It examines the principles of game learning. Then it looks for opportunities to apply game learning principles to product learning situations. The goal is to create engaging and successful product learning experiences, without turning products into games.
This study uses an auto-ethnographic evaluation of a gameplay session as well as participant observation and interviews with gamers to gather qualitative data. That data is sorted with an A(x4) framework and used to create user experience profiles.
The final outcome is a toolkit that identifies areas where game design could improve the design of product user experiences, especially for product learning.
This thesis asks, "Can game design build better product learning experiences, and if so, how?" It examines the learning situations created by and necessary for product design. It examines the principles of game learning. Then it looks for opportunities to apply game learning principles to product learning situations. The goal is to create engaging and successful product learning experiences, without turning products into games.
This study uses an auto-ethnographic evaluation of a gameplay session as well as participant observation and interviews with gamers to gather qualitative data. That data is sorted with an A(x4) framework and used to create user experience profiles.
The final outcome is a toolkit that identifies areas where game design could improve the design of product user experiences, especially for product learning.
ContributorsReeves, James Scott (Author) / Boradkar, Prasad (Thesis advisor) / Gee, Elisabeth (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2016