A Framework to Improve Automobile Design Work
Efficiency Using Functional Decomposition Tree andOntologies
Kunika Iida1 , Risa Kubo1 and Munehiko Sasajima1
1

University of Hyogo, 8-2-1, Gakuennishimachi, Nishi-ku, Kobe-shi, Hyogo, 651-2103, Japan

Abstract
Automobile design work is not being performed efficiently. In particular, when changing the function ofa component in a design review, it is necessary to infer and enumerate possible faults of the function
or part to be changed from past examples, which is a time-consuming task. Therefore, a framework
to infer possible faults exhaustively in the part while viewing the automobile from the perspective of
its function is needed. We aim to solve this problem by improving the search system when collecting
information about known faults to perform design change work. First, the expected faults are derived
from the function of a component. For this purpose, the knowledge of a skilled designer is modeledusing functional decomposition tree and ontologies. Furthermore, information can be collected without
excesses or deficiencies by using the derived faults as search queries. This paper focuses on efforts torealize ”fault reasoning,” which enables the derivation of possible faults described above.

Keywords
ontology, functional decomposition tree, fault reasoning,

1. Introduction
The goal of this research is to improve the efficiency of automobile design work.
In recent years, the automotive industry has been undergoing a once-in-a-century transformation 1 , and there is a need to improve the efficiency of automobile design work. Currently,
however, it is difficult to say that automobile design work is being done efficiently.
Automobile design work can be roughly divided into three categories: information-collectingwork, report-writing work, and design change work. Before design change work, the designercollects information on the components to be redesigned in a design review. The designer
investigates what kinds of faults have occurred in the past and what kinds of faults are likely tooccur if the shape or material is changed. Furthermore, since automobile design work is done
by a team, a report is prepared to share the information with the team. Only after the team
decides on a policy based on the report can the design change work be done.
According to one automobile manufacturer designer when automobile design work is assumed
to be 100%, 40% of the time is spent on collecting information on design changes and 40% on
IJCKG 2023 Poster and Demo track
Envelope-Open ad23d005@guh.u-hyogo.ac.jp (K. Iida); ad23g017@guh.u-hyogo.ac.jp (R. Kubo); sasajima@sis.u-hyogo.ac.jp(M. Sasajima)© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

CEUR Workshop Proceedings (CEUR-WS.org)
https://www.toyota.co.jp/pages/contents/jpn/investors/library/annual/pdf/2018/ar2018_1.pdf
CEURWorkshop
Proceedings1

http://ceur-ws.org
ISSN 1613-0073

preparing reports on past information on design changes, leaving only about 20% of the time
available for automobile design work that should be done by the designer.
Currently, information is collected by searching a database that has accumulated internal
documents, patents, and recall information related to past fault examples. However, the searchsystem currently in use outputs not only information related to the part of interest, but also alarge amount of information unrelated to the part as search results. This is a cause of inefficiency
in automotive design work.
Against this background, we identified two issues. First, there is a lot of know-how required
to select appropriate queries for search, and only skilled designers can collect information.
Second, irrelevant information (noise) is included in the search results. We thought that theseproblems could be solved by using functional decomposition tree and ontologies, which areknowledge models.

2. Method
To accomplish two issues, we devised a framework consisting of a two-step process. In the firststage, we create a body of knowledge possessed by a skilled designer. This enables the possible
faults from each component to be systematically derived. This is called fault inference. The
second stage uses the fault knowledge derived in the first stage. Then, a search query is createdthat can extract information without excesses or deficiencies. The fault reasoning currently
underway with respect to these frameworks will be described in detail.

2.1. Functional decomposition tree and ontologies
Artifacts reflect human intention and have different effects on the external world than naturalobjects. This effect on the external world is called ”function”. In this way, understanding anartifact is to know how its internal structure interacts with the external world to produce its
function [1]. As can be seen from this, to understand artifacts such as an automobile, it isnecessary to know how the artifact works in its function. We believe that one of the approachesof skilled designers is to consider what function each component of an automobile has and to
do design change work. Therefore, the body of knowledge to be constructed should be close to
the knowledge possessed by skilled designers, so that information can be collected from theperspective of function. The body of knowledge to be constructed is represented by a functional
decomposition tree and an ontologies.
A functional decomposition tree is a knowledge model that develops a function to be realized
into a series of sub-functions that can achieve it [2]. In this research, a tool called FWTEditor isused based on the idea that each component of an automobile has its own function and that theautomobile can run safely when its function is working properly. A functional decomposition
tree is created with ”The drive is driven. (Transmits power and moves it.)” as the root(Figure 1left).
FWTEditor is a tool for building functional decomposition trees and performing fault inference.
The fault inference mechanism is explained in Section 2.2. FWTEditor represents a function asa square node. By developing the functions in detail to a granularity that is captured by skilled
designers, it is possible to describe what functions each component of an automobile has.

Figure 1: Functional Decomposition Tree and Ontologies(This research is being conducted in Japanese,and some of the figures in this paper have been translated into English for explanation purposes.)

We create ontologies using HOZO 2 to define detailed behaviors and subject to functional
effects concerning the components described in the functional decomposition tree. A fault is afunction that is not working properly and defines which component is at fault and for whatcause(Figure 1 right).
The following process is used to create the functional decomposition tree and ontologies. First,
we extract knowledge from technical books on automobiles. The extracted knowledge reflects
on the functional decomposition tree and the ontologies. The reflected functional decomposition
tree and ontologies will be discussed and refined by skilled designers. By repeating this process,we create the knowledge of skilled designers.
Currently, 389 basic conceptual ontologies have been defined.

2.2. Fault reasoning
Fault reasoning is performed using FWTEditor and HOZO. When a node in FWTEditor isselected, the sentence in the selected node is subjected to morphological analysis to extract
word groups. The word groups is then used to perform an OR search on the ontologies describedin HOZO. The possible faults are systematically derived from the word groups. When a fault
name is reached, the search is terminated and the results are displayed in a circle.
Figure 2 is used as an example. Morphological analysis of ”The damper spring dampens theimpact.” extracts three words: ”damper spring,” ”impact,” and ”dampens”. Using these threewords, we search for possible faults. As a result, the faults ”occurrence of strange noises” and”clutch judder” are derived.
Currently, we have not yet had any discussions with the designers. Therefore, we willcontinue to improve the knowledge model to bring it closer to the knowledge of experienced
designers. We also plan to evaluate the method and content of presenting the fault knowledge
resulting from fault reasoning.
2

http://www.hozo.jp/index_jp.html

Figure 2: Example of Fault Reasoning Execution(This research is being conducted in Japanese, andsome of the figures in this paper have been translated into English for explanation purposes.)

3. CONCLUSION
This research considers a framework for improving the efficiency of Automobile Design Work,
which consists of two steps: fault reasoning and search query generation, using functional
decomposition tree and ontologies. In this paper, we describe our work on fault reasoning. In
the future, we will consider how to use the components and fault knowledge retrieved by fault
reasoning in the search query. And we plan to continue our research through discussions withdesigners.

Acknowledgments
This work was supported by the New Energy and Industrial Technology Development Organization (NEDO) JPNP18002.

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