Due to their significant role in creative design ideation, databases of causal ontology-based models for biological and technical systems have been developed. However, creating structured database entries through system models using a causal ontology requires the time and effort of experts. Researchers have worked toward developing methods that can automatically generate representations of systems from documents using causal ontologies by leveraging machine learning (ML) techniques. However, these methods use limited, hand-annotated data for building the ML models and have manual touchpoints that are not documented. While opportunities exist to improve the accuracy of these ML models, more importantly, it is required to understand the complete process of generating structured representations using causal ontology. This research proposes a new method and a set of rules to extract information relevant to the constructs of the SAPPhIRE model of causality from descriptions of technical systems in natural language and report the performance of this process. This process aims to understand the information in the context of the entire description. The method starts by identifying the system interactions involving material, energy and information and then builds the causal description of each system interaction using the SAPPhIRE ontology. This method was developed iteratively, verifying the improvements through user trials in every cycle. The user trials of this new method and rules with specialists and novice users of the SAPPhIRE modeling showed that the method helps in accurately and consistently extracting the information relevant to the constructs of the SAPPhIRE model from a given natural language description.

Analogies provide better concept generation in engineering design. This ideation can be measured by metrics such as usefulness, novelty, variety, quality, completeness, and quantity. In bioinspired design, biological analogies are used to inspire design concepts. Biological analogies have been provided in earlier studies to measure ideation effectiveness. Tools like IDEA-INSPIRE, DANE, etc., allow designers to search analogies using functions, behaviors, and structures. However, we wanted to inquire about the effect of providing a very large number of biological analogies (26), fulfilling the same function to develop bioinspired solutions. In this paper, an empirical study has been performed to analyze the effect of biological analogies on ideation. The designers are exposed to provided multiple biological analogies and generate concepts for which four ideation metrics: novelty, variety, quality, and quantity metrics are evaluated. The results are compared to the unaided condition where other designers are given the same task. A new method to measure variety using a 2D matrix has been presented. The results suggest that designers can generate bioinspired solutions when multiple biological analogies performing similar functions are provided in a presentable format. Statistically, exposure to multiple biological analogies in idea generation can significantly increase the variety of design ideas. The novelty, quality, and quantity for the biological group and control group remain the same.

In product design engineering (PDE), ideation involves the generation of technical behaviours and physical structures to address specific functional requirements. This differs from generic creative ideation tasks, which emphasise functional and technical considerations less. To advance knowledge about the neural basis of PDE ideation, we present the first fMRI study on professional product design engineers practising in industry. We aimed to explore brain activation during ideation, and compare activation in open-ended and constrained tasks. Imagery manipulation tasks were contrasted with ideation tasks in a sample of 29 PDE professionals. The key findings were: (1) PDE ideation is associated with greater activity in left cingulate gyrus; (2) there were no significant differences between open-ended and constrained tasks; and (3) a preliminary association with activity in the right superior temporal gyrus was also observed. The results are consistent with existing fMRI work on generic creative ideation, suggesting that PDE ideation may share a number of similarities at the neural level. Future work includes: functional connectivity analysis of open-ended and constrained ideation to further investigate potential differences; investigating the effects of aspects of design expertise/training on processing; and the use of novelty measures directly linked to the designer’s internal processing in fMRI analysis.

In many creative design processes, cross-domain knowledge is required to inspire the new design result. Thus, in knowledge-based design, how we represent the cross-domain knowledge becomes a key issue. In this paper, we present a formalism for design knowledge representation. By analyzing function representation in different design domains, from graphic design and industrial design to architectural and engineering device designs, we find that although the focus of each kind of design is different, the function representation can be generalized into a small number of categories. This formalism can be used in an explorative model of design by analogy, where designs from different design domains are sources to help produce a new design.

Creative Stimulator (CreaStim) is an intelligent interface for pattern design that behaves as a semiactive partner to human designers rather than as a passive graphical or computational tool. By making adjustments to psychological differentials and/or design parameters, CreaStim is able to help designers to explore innovative pattern designs and to get inspiration, producing different types of novel designs. In this article, the mechanism, the technique, the implementation, and the testing of CreaStim are described. The basic principle of CreaStim is the catastrophe theory, which implies that sudden realization in the thinking process of design may lead to creativity. CreaStim tries to stimulate and/or impact designers' creativity in design process using the output of it, rather than to simulate the sudden realization. The core of the CreaStim is a neural network-based imagining engine, a data repository, and its learning strategies considering psychological factors. The psychological factors, which are thought one of the key influences to creative design, are based on the questionnaires completed by designers about the existing successful designs. The repository contains not only a traditional database storing functional attributes, economic attributes, graphic description, structural description, and psychological attributes, but also methods, rule-based knowledge, and pattern-type knowledge. And it is managed by an application program called Design Template Group (DTG) manager. Trained with 12 pieces of successful pattern designs and 528 pieces of pseudo-examples produced and evaluated by the authors, CreaStim is implemented for a PC and an evaluation poll from five designers shows that designers may most likely get some inspiration from the produced patterns and some of them can even be adopted as the design alternatives directly.