# Integration of Design Intent during the Product Lifecycle Management

Min-Jung Yoo, Jumyung Um, Ian Stroud, Soumaya El Kadiri, Dimitris Kiritsis

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Min-Jung Yoo, Jumyung Um, Ian Stroud, Soumaya El Kadiri, Dimitris Kiritsis. Integration of Design Intent during the Product Lifecycle Management. 11th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2014, Yokohama, Japan. pp.101-110, 10.1007/978-3-662-45937-9_11. hal-01386481

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# Integration of Design Intent during the Product Lifecycle Management

Min-Jung Yoo, Jumyung Um, Ian Stroud,
Soumaya El Kadiri, Dimitris Kiritsis

Laboratory of Computer-Aided Design and Production,
EPFL – Ecole polytechnique fédérale de Lausanne, Switzerland
{min-jung.yoo, jumyung.um, ian.stroud, soumaya.elkadiri, dimitris.kiritsis}
@epfl.ch

**Abstract.** There has been significant achievement in integrating product data during the whole lifecycle phases with shared common ontologies while taking advantage of intelligent retrieval mechanisms. In order to support integrated decision making on product redesign or maintenance operations, we should solve a challenging issue: 'how the product lifecycle management (PLM) stores and retrieves the know-how and the knowledge of an organization concerning manufactured products'. This paper describes the extension of a previously developed PLM Semantic Ontology Model toward integration with design intent. The proposed approach uses OWL2 to represent product lifecycle data and design knowledge. The approach was applied to the redesign of a car door part for laser welding. Our work demonstrates how to retrieve design intent as a specific type of knowledge data in the context of design decisions. Such an approach can ultimately contribute to reducing design time, making knowledge transfer clear and thus improving the quality of designed products.

**Keywords:** product lifecycle, semantic ontology, design intent, knowledge, know-how, OWL, Protégé, QLM

## 1 Introduction and motivation

The closed-loop product lifecycle management (PLM) system focuses on tracking and managing the information of the whole product lifecycle, with possible feedback on information to product lifecycle phases. It provides opportunities to reduce the inefficiency of lifecycle operations and improve competitiveness (Kiritsis 2013). Thanks to the advent of hardware and software related to product identification technologies, e.g., radio frequency identification (RFID) technology, closed-loop PLM has been recently highlighted as a tool for companies to enhance the performance of their business models. However, the information on PLM has primarily dealt with some predefined physical product data, i.e., material characteristics of designed products and their usage information. Knowledge data concerning the design of products has not been dealt with sufficiently in depth in currently available PLM approaches. Within the range of our knowledge, there are very few research results which handle the design knowledge coming from the product design phases or redesign phases. The impact of design changes on the
other manufacturing information has not been sufficiently studied, so there is a
lack of experience in this area.

The management of companies’ intangible assets and intellectual capital has long been a key issue in the domain of business and management science. Some parts of product data should be shared with the other intangible assets of a company. Product design requires intensive communication between designers and production engineers. In a general case, only the geometry and numerical data remain in the company's database once a product shape has been determined. This entails the loss of information about why this design was determined and what designers intended during redesign. The design is defined as this-is-the-way-we-do-things in the company. Lost knowledge, however, is critical information when introducing new manufacturing technology or new composite materials.

For the reasons mentioned above, domain professionals have already acknowl-
edged the importance of representing and sharing product data during the different
phases of the product lifecycle. There has been significant achievement in integrat-
ing product data from beginning of life (BOL), to the middle of life (MOL), until
its end of life (EOL), especially using shared common ontologies and intelligent
retrieval mechanisms (Matsokis and Kiritsis 2011). PLM has specific objectives in
each phase of the lifecycle: During the BOL, the improvement of product design
and production quality is the main concerns; During the MOL, improving reliabil-
ity, availability, and maintainability of products is the most interesting issue; In the
EOL, optimizing EOL products’ recovery operations is one of the most challeng-
ing issues.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Product lifecycle phase and data sharing</figcaption></figure>

Turning to the research on representing know-how and knowledge, we can find
some literature about approaches using meta-data and history-based models in de-
sign activities of BOL. At the end of the 1970s and early 1980s Kjellberg proposed
having a 'description of origin' associated with geometric elements (Kjellberg
1983). After the publication of standard for product data exchange, Pratt men-
tioned the necessity to capture the intention of the designer which can get lost dur-
ing product changes (Pratt and Anderson 2001). Mun defined 'design intent' and
proposed macro files for saving the modelling commands with the parametric ap-
proach, which was standardized by ISO 10303 (Mun et al. 2003). Han also real-
ized a macro-parametric method in commercial CAD systems (Han 2010).
Through the efforts of these researchers, the fundamental of capturing design in-
tents is possible. In the context of aircraft manufacture, Price tried to maintain in-
tent by redesigning the parameters of frames related to joining processes in (Price
et al. 2013).
2. Definition of the “Competence” concept

3. Integration of the concept of “Service” which is on the hub of four other concepts, i.e., “Competence”, “Knowledge”, “Material Resource”, and “Actor”

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** PROMISE Product Data and Knowledge Management (PDKM) Semantic Object Model (SOM)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Extended model of PLM SOM – High-level concept</figcaption></figure>

The service concept represents general business activities offered by a human actor, a team or an organization. In that context, “Competence” is an important intangible asset for achieving the required service by a distinctive actor. Competence can be reinterpreted as being capable of doing some activities; capable of managing some situations; capable of using a tool or an application. Knowledge Data has two sub-concept definitions: i) Knowledge Resource, and ii) Know-How.

The Know-how class represents the company’s information on cumulative, lifelong experience and activities which are related with the tangible and intangible
product creation and improvement. Different to the technical knowledge on physi-
cal products, Know-How data may contain even some negative experience and re-
sulting outcomes. From the point of a company's competence development, such
kind of know-now management is important for the purpose of studying past cases
and use it for "learning from experience". Therefore the following two points have
driven this research direction: i) how to integrate an industry's intangible assets
which are related to the product PLM data; and, ii) how to reuse them.

For that purpose, the model extension has been achieved being based on the
high-level semantic model presented in Fig. 3. The resulting model was then fur-
ther specialized in order to be suitable for an industrial design case: the welding
assembly line of the car door frame (Fig. 4). Here we use the words ‘class’ and
‘concept’ as mutually interchangeable (similar) terms, which are the object class
definition in Protégé (Protégé).

1. The class of DesignжParameter is created for the purpose of modelling some important parameters which are considered through design decision making.

2. Design Process Activiy class is defined as a subclass of existing Activity concept.

3. Computer_Aided_Tool is further specialized containing several subclasses: CAD_tool, CAM_tool, Specific_Software.

4. In this extended model, another product information in design phase is added, i.e., Design_InProgress, for the purpose of associating data which can either be produced or referenced during the product design phase. Consequently, the classes which indicate product lifecycle phases are also distinguished while representing more specialized product lifecycle phases, i.e., Product_BOL_Design and Product_BOL_Supply.

5. The Design_Intent class is newly created as a subclass of Know_How. An-
other subclass of Know_How, Consumer_Intent plays a similar role from the
customer sides and internal service designe teams, which will not be detailed
in this paper.

6. The existing class PhysicalResource is specialized providing three sub-
classes: i) Environmental_Asset which represents information such as build-
ings, workshops; ii) Equipement; iii) Materials.

## 2.2 Model description

The above-presented semantic model was built and tested using the Protégé-OWL
ontology tool. Since its creation, OWL-DL(Description Logic) was used for the
purpose of describing classes and individuals included in PLM PDKM SOM.
OWL-DL was initially developed to provide the maximum expressiveness in tan-
dem with guaranteeing both computational completeness (all conclusions are guar-
anteed to be computable) and decidability (all computations will finish in finite
time). OWL-DL includes all OWL language constructs (such as transitive proper-
ties, which allow more of the semantics of sequences to be represented explicitly
than in RDF or OWL lite) and it allows modelling at multiple levels of abstraction
(and thus, sequences of classes can be characterized by their general or more spe-
cific properties).
In PROMISE SOM, there are already more than 70 object property definitions which relate different concepts and individuals. Some of them are given below as an example:

- `isDesigned`: in order to relate a physical product to its design details;

- `hasDefined`: as an inverse relation of `isDesigned`, that is to say, from a designed product to a physical product;

- `Life_Cycle_Phase2Activity`: which links the life cycle phase of a product to its activity details;

- `Life_Cycle_Phase2Physical_Product`: similarly to `Life_Cycle_Phase2Activity`, this property allows to retrieve the physical product of a given life cycle phase;

- `EquipmentResource2Property`: for the purpose of searching the property of a given equipment resource, and so on.

For the purpose of complementing the existing object properties, we have newly created some supplementary relationships. Some key elements are given below.

- `Contains_DesignedPart`: this object property helps, based on a given types of physical product, retrieve the information of a designed product part.

- `Defines`: this object property is added in order to get the evaluation criteria of a given resource characteristic. The material characteristics are retrievable by giving a certain design intent category with the help of 'DesignResource_Concerns' (see below).

- `DesignResource_Concerns`: this property links a particular design intent type to all related resources. Such an information is important for the purpose of retrieving a part of physical product information which were designed according to a certain design intent type.

- `Design_Concerns`: this property links a particular design intent type to a physical product for the purpose of retrieving information on past experience. With the help of this object property, we can answer to the questions as 'which products are designed as a result of design intent type xxx?'

- `Refers2_Parame ters`: in order to search the KPI parameters which are related with a give design intent and to find further information such as equipment and material resources.

## 3 Case study

An example of the importance of explicit design intent can be illustrated using the example of a car door and its use in the Remote Laser Welding project RLW Navigator. The RLW Navigator project studies laser welding, in particular in the car industry and uses the welding of a car door as an example.

Laser welding is an alternative to spot welding and offers several benefits over the traditional technology. However, it is not easy to realise all the benefits because current practice is built into the design at an early stage. In addition, the car door is a complex product for which commercial time pressures preclude rationalisation of the design process. However, without such rationalisation it is difficult to change
process elements and hence take advantage of new technological advances. The
car door example and its handling in the RLW project can be illustrated in Fig. 4.

<figure><img src="image_5.png" /><figcaption>Fig. 4. Operation procedure of guideline</figcaption></figure>

The input for the modification task is a CAD file which lacks design intent in-
formation. As for many other applications the design intent has to be supplied
through human interaction or semi-automatic feature recognition. The relevant
features are those which can be modified for laser welding, in this case the flanges.
The flange redesign tool is illustrated in Fig. 5. The flanges are classified, resized
and reintroduced into the design to make the final CAD model.

<figure><img src="image_6.png" /><figcaption>Fig. 5. Flange classification</figcaption></figure>

In terms of design intent, this work is hindered both by the lack of special CAD
tools and the integration of manufacturing information into the design at an early
phase. Logically there should be a shaping phase to determine the part to be made
and then a manufacturing phase to realise the shape, making appropriate modifica-
tions to the original shape to accommodate the manufacturing decisions. The real-
isation decision has various implications for manufacturing. For example, if the
door is to be made as a solid frame with overlaid panels then this would be record-
ed as a decision, the door frame shape extracted from the overall shape and the
door panels designed. A different decision about the manufacturing method would
start from this point.

Current practice involves making the door out of pressed sheet metal. This in-
volves creating a sheet metal frame which is done by splitting the door shape into
parts, pressing them and then welding them together. Another consequence of this
decision is that the hinge part needs to be reinforced because the rest of the door
has to be made from thin material for weight reasons which is too weak for normal
use. Other elements, such as a strengthening bar, are also added for different rea-
sons. All these decisions need to be recorded and structured in order to be able to
cope with future developments and avoid becoming locked into an inefficient pro-
duction cycle. The flanges are added to the design as a consequence of the deci-
sion to split the door shape. The flange shapes depend on the join line and it
would be logical to have an automatic tool to create the flange shape. This would
also mean that the shape can be classified automatically as a flange rather than
identified manually later. The use of advanced application tools to provide high-
level information about complex shapes is an important step in recording design
intent.

4 Instantiation of the semantic model for the purpose of data
popularization

The following individuals (i.e., instances of classes) are created in order to associ-
ate required field data with the PLM Semantic Ontology Model. Fig. 6 shows a
part of individuals created and visualized using Protégé:

1. The five parts of flanges are modeled as five distinctive individuals of the Complex_Physical_Product, e.g., Flange1, Flange2, Flange3, Flange4, and Flange5.

2. Individuals of Designirec tion : Efficiency, En-
ergy_Use, WeldingReduction, Processing_Time, Quality, Environmen-
tal_factor.

3. The individual elements of Design_Process_Activity have been created ac-
cording to the general concept described by Nigel Cross, (Engineering de-
sign methods, Cross 2008): Clarifying_objective, Establishing_function, Set-
ting_Requirement, Determining_characteristics, Generating_alternative, and
Evaluating_alternative, Improving_details.

4. While using the existing class "Parameter" in PLM SOM, De-
signeirec tion is associated.

Afterward, we verified the model using “Pellet” reasoner (Protégé). As a proof
of concept of the case study, we used the DL Query and SPARQL Query tabs sup-
ported by Protégé, while looking for answers to the following queries:

- Which types of design intent were used in the past?

- Which design intent variables (primitives) are primarily considered for each
type of design decision?

- Which design intent types are used for the purpose of "Flange design", for
example?

- Which physical products are related with a given type of design intent? And in what PLM cycle (BOL, MOL, EOL) is the product currently?

- Considering a given material resource, which design intent is associated with it? This question is particularly important for the purpose of knowing the impact on the material resources and equipment in the case of choosing a certain design intent among several options.

## 5 Concluding remarks

The purpose of the work presented in this paper is to demonstrate how to extend and improve previously developed ontology for product lifecycle management with the design intent know-how data within a semantically understandable concepts and relationships. The benefits from such integration are clear:

1. The relationship between a) Design Decision data (i.e., design intent) and b) Designed Product data (manufacturing details determined through some design intent and design decision.) during the whole cycle of PLM is visible and traceable.

2. We can predict which parameters or manufacturing processes are important whenever new design is proposed, and which tools were used for evaluating previous design. It means how to use the know-how and how to collaborate with related persons.

3. Supposing that individuals of human resources shall be provided according to the defined ontology, we shall be able to find as well who are the persons related to the decision of each part design or manufacturing process, which means who has the know-how.

Our work demonstrates suitable ontology model as well as the way of retrieving a particular type of know-how (i.e., design intent) in the context of current design decisions. Such an approach can ultimately contribute to reducing design time, making knowledge transfer clear and thus improving the quality of designed products.

**Acknowledgement.** The work on the PLM Semantic Ontology Model and its extension was partly funded by the LinkedDesign (FP7-2011-NMP-ICT-FoF-284613). The work on the flange tool described in the case study was funded by the Resilient Automotive Factories (FP7-2011-NMP-ICT-FoF-285051).

## References

(Cross 2008) Cross, N., 2008. "Engineering design methods: strategies for product design", 4th edition, WILEY, Chichester.

(Han 2010) Han, S., 2010, "Macro-parametric: an approach for the history-based parametrics", International Journal of Product Lifecycle Management, 4(4): 321-325.
(Kiritsis 2013) Kiritsis, D., 2013. "Semantic technologies for engineering asset life cycle management", International Journal of Production Research, 51(23-24):7345-7371.

(Kjellberg 1983) Kjellberg, T.A., 1983. Stroud, I.A., Wingard, L.O. "GPM Volume module in CAM-I perspective", CAM-I solid modelling, Stockholm, Sweden.

(Matsokis and Kiritsis 2011) Matsokis, A. and Kiritsis, D., 2011. "An ontology-based implementation on a robotic assembly line for supporting lifecycle data management, Paper presented at the Sixth World Congress on Engineering Asset Management, Cincinnati, OH, October 3-5.

(Mun et al. 2003) Mun, D., Han, S., Kim, J., and Oh, Y., 2003. "A set of standard modeling commands for the history-based parametric approach," Computer Aided-Design, 35(13):1171–1179.

(OpenPlatform3) Open Group Open Platform3 white paper, downloadable from: https://www2.opengroup.org/ogsys/catalog/w147 (last access on 9 June 2014)

(Pratt and Anderson 2001) M. J. Pratt and B. D. Anderson, 2001. "A shape modelling applications programming interface for the STEP standard," Computer Aided-Design 33(7):531–543.

(Price et al. 2013) Price, M. A., Robinson, T. T., Soban, D., Murphy, Murphy, A., Armstrong, C. G., McConnell, R., Roy, R., 2013. "Maintaining design intent for aircraft manufacture," CIRP Annual - Manufacturing Technology, 62(1): 99–102.

(PROMISE 2009) PROMISE, 2009. "Research Deliverable 9.2", information available on http://www promiseso.no/ downloadfile.php?i=69adce107f7f7d035d7baf04342e1ca.

(Protégé) http://protegewiki.stanford.edu/wiki/Main_Page

(QLM) Open Group Quantum Lifecycle Management work group, information available on: http://www.opengroup.org/getinvolved/workgroups/qlm (last access on 9 June 2014)

untitled-ontology-35 (http://www semanticon web.org/ yoo/ ontologies/ 2014/0/untitled-ontology-35) : C:\Users\lyc

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** Extended PLM Semantic Object Model – Classes in Protégé (Protégé)</figcaption></figure>