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 D5the 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.