Figure 9. Knowledge-type-aware analysis of a creative design process.
It is clear from characteristics of concept maps C1–C7 that a creative design process entails definitional knowledge, analytic-a-priori-based creative knowledge, and informal-induction-based knowledge. Formal computation-based knowledge (e.g., secondary relations of ideas, complex induction-based knowledge, and synthetic a posteriori-based knowledge) hardly dominates a creative design process. This is perhaps a characteristic of creative design processes. Similar investigations can be performed to determine characteristics of other design processes, such as embodiment design, parametric design, and detailed design. Thus, a more comprehensive and systematic structure of each design process can be established. This, in turn, would help digitize knowledge-intensive activities more effectively from both human- and machine-learning viewpoints. The same augment is true for knowledge-intensive activities relevant to manufacturing. As a result, knowledge-type-aware concept mapping activities can benefit engineering design and manufacturing as well as engineering informatics.
Findings discussed thus far in this article refer to the scenario schematically illustrated in Figure 10. As can be seen in Figure 10, five integrated domains denoted by D1–D5 must work collectively to realize the objectives of engineering informatics relevant to engineering design and manufacturing. The first domain (D1) is where records of prior operational, analytical, and creative activities are available. These activities refer to pieces of knowledge claim, provenance, and inference that help create domain (D2). D2 must be integrated with D3, which possesses the capability to organize elements of knowledge into knowledge-type-aware concept maps, to facilitate human and machine learnings. D4 is populated with outcomes of D3; that is, it represents a set of knowledge-type-aware concept maps. Contents of D4 are fed into D5the domain of smart manufacturing, wherein distributed embedded systems (cyber-physical systems; IoT-embedded manufacturing enablers, such as machine tools, robots, and material handling devices) function. Research endeavors explicitly aimed at addressing the construction of and interplay between these domains would offer benefits to smart manufacturing techniques, in particular, as well as engineering design and manufacturing, in general. In doing so, types and categories of knowledge presented in this article will play a pivotal role.