figure 2. We analyze a given geometric model of a mechanical assembly to infer how the individual parts move and interact with each
other and encode this information as a time-varying interaction graph. once the user indicates a driver part, we use the interaction graph
to compute the motion of the assembly and generate an annotated illustration to depict how the assembly works. We also produce a
corresponding causal chain sequence to help the viewer mentally animate the motion.
analyzed
assembly
parts
( 1)
( 2)
( 3)
interaction
graph
( 4)
( 5)
( 6)
motion analysis
driver
annotated illustration
causal chain
causal chain of mechanical interactions, as well as simple
animations of the assembly in motion.
2. DesiGNiNG Ho W-tHiNGs-WoRk VisuaLiZatioNs
Illustrators and engineers have produced a variety of
books2, 3, 15, 18 and websites (e.g., howstuffworks.com) that
are designed to show how complex mechanical assemblies
work. These illustrations use a number of diagrammatic
conventions to highlight the motions and mechanical interactions of parts in the assembly. Cognitive psychologists
have also studied how static and multimedia visualizations
help people mentally represent and understand the function
of mechanical assemblies. 19 For example, Narayanan and
Hegarty24, 25 propose a cognitive model for comprehension
of mechanical assemblies from diagrammatic visualizations, which involves (i) constructing a spatial representation of the assembly and then (ii) producing the causal chain
of motions and interactions between the parts. To facilitate
these steps, they propose a set of high-level design guidelines to create how-things-work visualizations.
Researchers in computer graphics have concentrated on refining and implementing design guidelines
that assist the first step of the comprehension process.
Algorithms for creating exploded views, 13, 16, 20 cutaways, 4, 17, 27 and
ghosted views6, 29 of complex objects apply illustrative conventions to emphasize the spatial locations of the parts
with respect to one another. In contrast, the problem of
generating visualizations that facilitate the second step of
the comprehension process remains largely unexplored
within the graphics community. While some researchers
have proposed methods for computing motion cues from
animations26 and videos, 8 these efforts do not consider
how to depict the causal chain of motions and interactions between parts in mechanical assemblies.
2. 1. Helping viewers construct the causal chain
In an influential treatise examining how people pre-
dict the behavior of mechanical assemblies from static
visualizations, Hegarty9 found that people reason in a step-
by-step manner, starting from an initial driver part and trac-
ing forward through each subsequent part along a causal
chain of interactions. At each step, people infer how the rel-
evant parts move with respect to one another and then deter-
mine the subsequent action(s) in the causal chain. Although
all parts may be moving at once in real-world operation of
the assembly, people mentally animate the motions of parts
one at a time in causal order.
