In decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.
Engineering design is increasingly recognized as a decision making process. Providing decision support is crucial to augment designers' decision-making capability in this process. In this paper, we present a template-based ontological method that integrates the decision-making mechanism with problem-specific information; thus, it can provide design decision support from both the “construct” and the “information” perspectives. The “construct,” namely, decision-making mechanism, is the utility-based Decision Support Problem (u-sDSP), which is a rigorous mathematical model that facilitates designers making multi-attribute selection decisions under uncertainty, while the information for decision making is archived as u-sDSP templates and represented using frame-based ontology to facilitate reuse, execution, and consistency-maintaining. This paper is an extension of our earlier work on the ontological modeling of the compromise decisions. The unique advantage of this ontology is that it captures both the declarative and procedural knowledge of selection decisions and represents them separately, thus facilitating designers reusing, executing previous documented decision knowledge to effect new decisions. The efficacy of ontology is demonstrated using a rapid prototyping (RP) resource selection example.
The design of complex engineering systems requires that the problem is decomposed into subproblems of manageable size. From the perspective of decision-based design (DBD), typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision-making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here, we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled decision support problem (DSP) construct, and two key classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.
In this paper we address the following question: How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve courses for future students? A learning statement is a structured text-based construct for students to record what they learned by reflecting on authentic immersive experiences in a semester-long engineering design course. The immersive experiences include lectures, assignments, reviews, building, testing, and a post-analysis of an electro-mechanical device to address a given customer need. Over the past three years, in the School of Aerospace and Mechanical Engineering at the University of Oklahoma, Norman, we have collected almost 30,000 learning statements from almost 400 students. In the past few years, we have analyzed this data to improve our understanding of what students have learned by reflecting on doing and thence how we might improve the delivery of the course. In an earlier paper, we described a text mining framework to facilitate the analysis of a vast number of learning statements. Our focus, in the earlier paper, was on describing the functionalities (i.e., data cleaning, data management, text analysis, and visualization results) of the framework and demonstrating one of the text quantification methods — term frequency — using the learning statements. In this paper, we focus on demonstrating another text quantification method, namely, text similarity, to facilitate instructors’ gaining new insights from students’ learning statements. In the method of text similarity, we measure the cosine distance between two text vectors and is typically used to compare the semantic similarity between documents. In this paper, we compare the similarity between what students learned (embodied in learning statements) and what instructors expected the students to learn (embodied in the course booklet), thus providing evidence-based guidance to instructors on how to improve the delivery of AME4163 – Principles of Engineering Design.
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