In this paper, a published ontology of engineering design activities is modeled and analyzed using the design structure matrix (DSM). Specifically, the ontology analyzed in this research provides a basis for describing engineering design activities and subsequently design processes in an unambiguous manner. However, the proposed ontology lacks a computational representation and the information flow between activities is not adequately described. Thus, complex design processes cannot be represented using the ontology. The design activity ontology is modeled and analyzed using the DSM. First, the information flows between design activities are identified and their inter-relationships are described. Four different cases for representing the flow of information between design activities are modeled. In Case 1 and 2 feedback between information output and information input within an activity is captured. Whereas, in Case 3 and 4 it is assumed that no feedback between output and input exists within an activity. DSM analyses, including partitioning and tearing, are performed on the model. Observations and conclusions drawn from these analyses include the further decomposition of design activities, grouping of design activities, and lack of information flow between seemingly related activities. Based on these observations, recommendations are made to refine the ontology. Finally, additional research is required for developing a computational ontology of design activities.
This paper presents a method for evaluating the lightweightedness of a vehicle, specifically addressing those components whose primary purpose is to aid in manufacturing and assembly rather than to provide end-user function. Seven laziness indicators are described. These indicators are used to evaluate individual vehicle components to aid in identifying mass reduction potential and focus the attention of designers on components or assemblies with high potential for mass reduction. This method is applied to a complete automotive vehicle, demonstrating a mass savings potential of the overall vehicle of approximately 5% of the total mass of the vehicle.
Models of fatigue are based on physiological parameters such as Critical Power (CP) and Anaerobic Work Capacity (AWC). CP is a theoretical threshold value that a human can generate for an indefinite amount of time and AWC represents a finite expendable amount of anaerobic energy at intensities above CP. There is an increasing interest in developing mathematical models of energy expenditure and recovery for athletic training and human performance. The objective of this research is to propose and validate a model for recovery of AWC during a post exertion recovery interval of cycling. A cycling ergometer study is proposed which involves a VO2max ramp test to determine gas exchange threshold, a 3-min all-out intensity test to determine CP and AWC, and exertion-recovery interval tests to understand recovery of AWC. The results will be used to build a human in the loop control system to optimize cycling performance.
Function modeling is often used in the conceptual design phase as an approach to capture a form-independent purpose of a product. Previous research uses a repository of reverse-engineered function models to support conceptual-based design tools, such as similarity and design-by-analogy. These models, however, are created at a different level of abstraction than models created in conceptual design for new products. In this paper, a set of pruning rules is developed to generate an abstract, conceptual-level model from a reverse-engineered function model. The conceptual-level models are compared to two additional levels of abstraction that are available in a design repository. The abstract models developed through the pruning rules are tested using a similarity metric to understand their usefulness in conceptual design. The similarity of 128 products is computed using the Functional Basis controlled vocabulary and a matrix-based similarity metric at each level of abstraction. A matrix-based clustering algorithm is then applied to the similarity results to identify groups of similar products. A subset of these products is studied to further compare the three levels of abstraction and to validate the pruning rules. It is shown that the pruning rules are able to convert reverse-engineered function models to conceptual-level models, better supporting design-by-analogy, a conceptual-stage design activity.
Abstract Decision making in engineering design occurs throughout the design process. In early stages, design space exploration reveals potential solutions to designers, while tradespace exploration helps designers choose between solutions based on tradeoffs in utility or performance. The outcomes of these decisions can be influenced by the selective framing of information in the decision scenario. To understand how much influence framing has on engineering decision-making in these scenarios, a study was conducted using a simulated vehicle design tradespace. A team of designers was tasked with exploring possible designs and understanding the tradeoffs in utility between their design options in three differently-framed design scenarios. Explicitly framing the design problem as a group activity was found to reduce the time required to reach a decision without compromising on the utility achievement of the found solution. It also removed the need for initial sensemaking discussions before decisions could be made.
To evaluate the opportunities and extent to which open standards can be used in or enhanced for product lifecycle management frameworks, we have developed three metrics, namely, compatibility, completeness, and coverage, for assessing the degree-of-openness of engineering information. A simple test case shows that each of the three metrics provides a certain type of assessment of the degree-of-openness.
Improving a cyclist performance during a time-trial effort has been a challenge for sport scientists for several decades. There has been a lot of work on understanding the physiological concepts behind it. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, theoretically, can be continued for infinite amount of time. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP. In this paper we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of a cyclist. Then, the models are used to formulate an optimal control problem for a time trial effort on a 10.3 km course located in Greenville SC. The course is simulated in a laboratory environment using a CompuTrainer. At the end, the optimal simulation results are compared to the performance of one subject on CompuTrainer.
In this paper, we first report the use of experimental data from six human subjects to validate and calibrate our proposed dynamic models for fatigue and recovery of cyclists in [1]. These models are employed to formulate pacing strategy of a cyclist during a time trial as an optimal control problem. Via necessary conditions of Pontryagin Minimum Principle (PMP), we show that the cyclist's optimal power is limited to four modes in a time-trial. To determine when to switch between them, we resort to numerical solution via dynamic programming. One of the subjects is then simulated on four courses including the 2019 Duathlon National Championship in Greenville, SC. The DP simulation results show reduced time over experimental results of the self-paced subject who is a competitive amateur cyclist.
