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No description available.

The purpose of this research was to determine how team design and project management (planning and tracking) affected planning and design performance and the people involved in the process. A laboratory study was conducted to evaluate three factors: team design (individuals versus groups of three), project support (no project support versus manual project support versus automated project support), and the engineering design life-cycle, which includes conceptual design, preliminary design, and detailed design. There were six observations per treatment, involving a total of 72 undergraduate engineering students. The impact of these factors were evaluated for planning time, design cycle time, cost effectiveness, cost variance, schedule variance, mental workload, and job satisfaction. For treatments that called for groups, group process was evaluated in addition to group workload. The results showed groups took 61% more time to plan their projects compared to individuals (p<0.01). Planning time was 31% longer for participants with manual support compared to those with automated project support (p<0.01). Schedule variance (p<0.01) and cost variance (p<0.001) decreased 24% and 23%, respectively, over time during the design process. The design cycle time was 17% longer for participants without project support compared to those with automated project support (p<0.05). During design, groups and individuals allocated their time differently (p<0.05). Mental workload, measured with the NASA Task Load Index (TLX), showed workload increased 16% over time (p<0.001). In addition, the combination of design phase and project tracking support affected the TLX (p<0.01). Job satisfaction was 5% lower at the end of the design project compared to the beginning of design (p<0.05). From the analysis on group process, the type of project support affected the group process during planning. Groups with manual support interacted 83% more than those with automated support (effective behaviors: p<0.01; ineffective behaviors: p<0.05). During design, the roles individuals played within the group affected how much they contributed to the group's process (effective behaviors: p<0.0001; ineffective behaviors: p<0.01). There were several practical implications that can be drawn from this study. In the decision to use teams versus groups, there was evidence that groups were able to attend to more of the design requirements than individuals, which resulted in the design of systems with higher reliability. However the tradeoff of using groups were in the labor cost and in longer planning and status report meetings. Therefore the organization's goals need to be carefully considered before selecting the team design. For project support, there were clear benefits to automating the planning process. Automation resulted in better Gantt chart and planning sessions that were completed more quickly compared to those with manual support. Furthermore, systems designed with automated support resulted in lower design costs compared to systems designed without project support. / Ph. D.

This thesis describes the process of designing exercise machines that raise the weight of the user as the primary source of resistance. Most strength training machines use weight stacks or springs as the source of resistance. While such machines are highly evolved and provide an excellent workout, they typically have a number of disadvantages including high cost, and large size and weight. A user weight-based exercise design will reduce the cost, size, and weight of the machine. The design process considers some important issues. Parallelogram linkages are implemented to provide non-rotary motion without the disadvantage of linear bearings. The user input is located with respect to the user providing correct relative motion for the exercise. The design also considers proper resistance curves during the design process. Specific examples are given for each step of the design process. These examples include the evolution of ideas and the creation and use of kinematic and automatic tools. / Master of Science

A system which allows the computer to capture sketches made by a mechanical designer is described. The system not only recognizes basic features as they are sketched, but it also reasons the spatial relationships between features and builds a high level abstract representation (feature model) of the artifact. The temporal nature of the capture, one feature at a time, serves to form a feature graph that allows for parametric design. The system is composed of three subsystems: a two-dimensional freehand sketching subsystem¹ , a three-dimensional features recognition subsystem, and a spatial reasoning subsystem. The freehand sketching subsystem takes the user's input sketching actions and interprets them as simple, two-dimensional geometric elements, such as line segments, circles, and ellipses, etc. The features recognition subsystem interprets the collection of two-dimensional geometric elements to extract three-dimensional information from them and creates high level abstract representations, features. The spatial reasoning subsystem finds relationships between a new feature and existing features and integrates features to form a single part. The work of the Design Capture System is aimed at capturing sketches of a specific application domain: injected molding plastic parts. Twenty injected molding plastic parts were collected and analyzed to understand the distribution of features. Isometric sketching is selected as the basic constructing method for the system. The processes of freehand sketching and computer-aided drafting were studied to find a better scheme for computer-aided sketching. Conclusions are also presented. ¹The Freehand Sketching Subsystem was accomplished by Roger Fang as a Master project in 1987 at the Department of Mechanical Engineering, Oregon State University, Corvallis, Oregon. / Graduation date: 1991

Biological organisms, phenomena and strategies, herein referred to as biological systems, provide a rich set of analogies that can be used to inspire engineering innovation. Biologically-inspired, or biomimetic, designs are publicly viewed as creative and novel solutions to human problems. Moreover, some biomimetic designs have become so commonplace that it is hard to image life without them (e.g. velcro, airplanes). Although the biologically- inspired solutions are innovative and useful, the majority of inspiration taken from nature has happened by chance observation, dedicated study of a specific biological entity (e.g., gecko), or asking a biologist to explain the biology in simple terms. This reveals a fundamental problem of working across the engineering and biological domains. The effort and time required to become a competent engineering designer creates significant obstacles to becoming sufficiently knowledgeable about biological systems (the converse can also be said). This research aims to remove the element of chance, reduce the amount of time and effort required to developing biologically-inspired solutions, and bridge the seemingly immense disconnect between the engineering and biological domains. To facilitate systematic biologically-inspired design, a design methodology that relies on a framework of tools and techniques that bridge the two domains is established. The design tools and techniques that comprise the framework achieve: Identification of relevant biological solutions based on function; translation of identified biological systems of interest; functional representation of biological information such that it can be used for engineering design activities; and conceptualization of biomimetic engineering designs. Using functional representation and abstraction to describe biological systems presents the natural designs in an engineering context and allows designers to make connections between biological and engineered systems. Thus, the biological information is accessible to engineering designers with varying biological knowledge, but a common understanding of engineering design methodologies. This work has demonstrated the feasibility of using systematic design for the discovery of innovative engineering designs without requiring expert-level knowledge, but rather broad knowledge of many fields. / Graduation date: 2011

<p>The primary research question of this dissertation is, \textit{How do contestants make sequential design decisions under the influence of competition?} To address this question, I study the influence of three factors, that can be controlled by the contest organizers, on the contestants' sequential information acquisition and decision-making behaviors. These factors are (i) a contestant's domain knowledge, (ii) framing of a design problem, and (iii) information about historical contests. The \textit{central hypothesis} is that by conducting controlled behavioral experiments we can acquire data of contestant behaviors that can be used to calibrate computational models of contestants' sequential decision-making behaviors, thereby, enabling predictions about the design outcomes. The behavioral results suggest that (i) contestants better understand problem constraints and generate more feasible design solutions when a design problem is framed in a domain-specific context as compared to a domain-independent context, (ii) contestants' efforts to acquire information about a design artifact to make design improvements are significantly affected by the information provided to them about their opponent who is competing to achieve the same objectives, and (iii) contestants make information acquisition decisions such as when to stop acquiring information, based on various criteria such as the number of resources, the target objective value, and the observed amount of improvement in their design quality. Moreover, the threshold values of such criteria are influenced by the information the contestants have about their opponent. The results imply that (i) by understanding the influence of an individual's domain knowledge and framing of a problem we can provide decision-support tools to the contestants in engineering design contexts to better acquire problem-specific information (ii) we can enable contest designers to decide what information to share to improve the quality of the design outcomes of design contest, and (iii) from an educational standpoint, we can enable instructors to provide students with accurate assessments of their domain knowledge by understanding students' information acquisition and decision making behaviors in their design projects. The \textit{primary contribution} of this dissertation is the computational models of an individual's sequential decision-making process that incorporate the behavioral results discussed above in competitive design scenarios. Moreover, a framework to conduct factorial investigations of human decision making through a combination of theory and behavioral experimentation is illustrated. <br></p>

To address the need to improve student achievement in STEM disciplines, effort has been made to develop a new of tools for STEM education (Bybee, 2013). The Robotics and Engineering Design Curriculum (REDC) provides students an opportunity to develop systems thinking abilities while integrating science and mathematics concepts. Using an exploratory case study approach within a situated cognition framework, this study examines the effects of using REDC on 54 eighth grade students’ performance and engagement during 5-week integrative STEM instruction in the mathematics and science class. Situational factors that contribute to students’ success in learning STEM concepts are also examined. This study employed mixed-methods techniques. The quantitative data collected included pre/post achievement tests and pre/post motivation and engagement scale (MES) surveys. Quantitative data analysis included reliability analyses and paired sample t-tests. The results of the reliability analyses for the achievement test and MES survey report acceptable Cronbach’s alpha (.843 and .787, respectively). Qualitative data collected included semi-structured interviews, field notes and student artifacts (engineering notebook and printed prototypes). Qualitative data analysis used coding procedures suggested by Saldana (2012) where patterns were identified and grouped to allow the emergence of themes. Collectively, the data was triangulated to support six emerging themes. The emerging themes regarding the effect of using the curriculum are as follows: (1) the developing anthropomorphic relationship with the robot enhances engagement, (2) engagement is impacted by purposeful and intentional physical action, and (3) purposeful collaboration promotes engagement through the construction of meaning and interaction. Three themes emerged identifying factors that contribute to success: (1) learning environment must have transformative learning potential, (2) learning experiences underpinned by design thinking contribute to success and (3) contextual relevance is enhanced when students have the freedom to their design learning journey. This study addresses the need for research into the implementation of 3-D design and manufacturing in the middle school classroom. Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. National Science Teachers Association. Saldaña, J. (2012). The coding manual for qualitative researchers (No. 14). Los Angeles, Ca: Sage.

published_or_final_version / Applied Geosciences / Master / Master of Science

published_or_final_version / Applied Geosciences / Master / Master of Science