Design is a ubiquitous human activity. Design is valued by individuals, teams, organizations, and cultures. There are patterns and recurrent phenomena across the diverse set of approaches to design and also variances. Designers can benefit from leveraging conceptual tools like process models, methods, and design principles to amplify design phenomena. There are many variant process models, methods, and principles for design. Likewise, usage of these conceptual tools differentiates in industrial contexts. We present an integrated process model, with exemplar methods and design principles that is synthesized from a review of several case studies in client based industrial design projects for product, service, and system development, professional education courses, and literature review. Concepts from several branches of design practice: (1) design thinking, (2) business design, (3) systems engineering, and (4) design engineering are integrated. A design process model, method set, and set of abstracted design principles are porposed.
The purpose of this paper is to investigate if early stage function models of design can be used to predict the market-value of a commercial product. In previous research, several metrics of complexity of graph-based product models have been proposed and suitably chosen combinations of these metrics have been shown to predict the time required in assembling commercial products. By extension, this research investigates if this approach, using new sets of combinations of complexity metrics, can predict market-value. To this end, the complexity values of function structures for eighteen products from the Design Repository are determined from their function structure graphs, while their market values are procured from different vendor quotes in the open market. The complexity and value information for fourteen samples are used to train a neural net program to define a predictive mapping scheme. This program is then used to predict the value of the final four products. The results of this approach demonstrate that complexity metrics can be used as inputs to neural networks to establish an accurate mapping from function structure design representations to market values to within the distribution of values for products of similar type.
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System of interest (SoI) failures can sometimes be traced to an unexpected behavior occurring within another system that is a member of the system of systems (SoS) with the SoI. This article presents a method for use when designing an SoI that helps to analyze an SoS for unexpected behaviors from existing SoS members during the SoI's conceptual functional modeling phase of system architecture. The concept of irrationality initiators—unanticipated or unexpected failure flows emitted from one system that adversely impact an SoI, which appear to be impossible or irrational to engineers developing the new system—is introduced and implemented in a quantitative risk analysis method. The method is implemented in the failure flow identification and propagation framework to yield a probability distribution of failure paths through an SoI in the SoS. An example of a network of autonomous vehicles operating in a partially denied environment is presented to demonstrate the method. The method presented in this paper allows practitioners to more easily identify potential failure paths and prioritize fixing vulnerabilities in an SoI during functional modeling when significant changes can still be made with minimal impact to cost and schedule.
A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system's heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.
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