TOWARDS AN EXPERT SYSTEM FOR THE MANUFACTURING

SYSTEM PLANNING OF PRODUCTS WITH GRADED

PROPERTIES

Mariana Reyes-Perez, Jan Broekelmann and Juergen Gausemeier

Heinz Nixdorf Institute, University of Paderborn, Fuerstenallee 11, Paderborn, Germany

Keywords: Expert System, Ontology, Knowledge Base, Graded properties, Manufacturing Process Planning.

Abstract: Ontologies open new ways for representing, sharing and reusing knowledge. This paper is based on the in-

vestigations within the Collaborative Research Centre (CRC) Transregio 30. In the CRC, thermo-

mechanically coupled processes are developed and analyzed. They provide the possibility to produce func-

tional graded components. Functional gradation is the targeted and reproducible adaptation of a material mi-

crostructure with the intention of establishing the macroscopic properties of the component. The objective is

the steady progress of the microstructure´s variation through at least one spatial dimension. To support the

manufacturing system’s planning of products with graded properties, we develop an expert system. An ex-

pert system is a software which emulates the reasoning of an expert. One of the components of our intended

expert system is an ontology. It assists researchers with the use and reuse of the acquired knowledge of the

CRC, as well as with communication between the different projects within the CRC. In this paper we ex-

plain the architecture of the intended expert system as well as its elements, placing special emphasis on the

ontology. Then we explain the ontology´s development based on knowledge extraction and representation.

1 INTRODUCTION

Ontologies open new ways for representing, sharing

and reusing knowledge. The Collaborative Research

Centre (CRC) Transregio 30 investigates structures

with a functional gradation and their manufacturing

processes. “Functional gradation is the targeted and

reproducible adaptation of a material microstruc-

ture with the intention to establish the macroscopic

properties of the component. The objective is the

steady progress of the microstructure´s variation

through at least one spatial dimension” (Reyes-

Perez et. al., 2009).

Graded components require a sophisticated

manufacturing process design which considers the

interaction between the manufacturing processes, the

sequences of the manufacturing steps and the attri-

butes of the part, the appliances to be used, the suit-

able tools, the simulations and all the peripheral ac-

tivities that are involved within the process. Due to

the complexity of these types of components, col-

laboration between experts of different domains is

required.

The CRC consists of research groups from engi-

neering, material sciences, applied mathematics,

production and manufacturing technologies as well

as information technology. It is comprised of diverse

faculties of three Universities: Technische Universi-

taet Dortmund, Universitaet Kassel and Universitaet

Paderborn, in Germany.

The research is divided into the project groups

A,B,C and D. Each project group is divided in sev-

eral projects and each project deals with issues of

functional gradation. Nevertheless, each field has a

different topic and approach. Consequently the rela-

tions between each field are implicit, rather than ex-

plicit.

Typical difficulties within such collaborative

works are the communication, the integration of the

generated information and dilemmas caused by se-

mantic and terminology obstacles. The challenge of

these types of projects increases when not only the

research fields are different, but also the collabo-

ration is geographically distributed.

We decided to create an ontology with different

goals in mind. In the short-term:

 To surmount the communication problems;

226

Reyes-Perez M., Broekelmann J. and Gausemeier J. (2009).

TOWARDS AN EXPERT SYSTEM FOR THE MANUFACTURING SYSTEM PLANNING OF PRODUCTS WITH GRADED PROPERTIES.

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 226-232

DOI: 10.5220/0002301502260232

 SciTePress

 For the integration of the information acquired

in the CRC;

 For modelling the domain knowledge of the

project acquired in the CRC;

In the long-term:

 As part of an intended expert system which will

help the developers with the manufacturing

planning of products with graded properties.

An expert system is a software which emu-

lates the reasoning of an expert.

The structure of the paper is as follows: First, we

explain some definitions and basic concepts used for

the development of the work. Then we discuss our

ontology and the structure of the intended expert

system. After that, we give details about the ontol-

ogy development. Finally we describe our future

work.

2 DEFINITIONS

Here we review some definitions and basic concepts

used in the development of the work presented here.

2.1 Manufacturing System Planning

There are different approaches and methods used for

manufacturing system planning. Some of them focus

on the creation and evaluation of technology chains

(Brecher et al., 2005) or provide product-process or

even product-process-resource models in combina-

tion with methods to use them (Feldmann et al.,

2008). Nevertheless, existing approaches do not

provide a solution for the manufacturing system

planning of products with graded properties.

At the Heinz Nixdorf Institute we are currently

developing a specification technique for the concep-

tual design of the product (Gausemeier et al., 2008)

and the production system. In this work, we made an

adaptation of these specifications in order to support

the conceptual design of a manufacturing system for

products with graded properties.

A product can be considered using two ap-

proaches, the product design approach (functional-

ity, what is made for) and the manufacturing system

design (embodiment, what is made from). The

manufacturing system development starts with a

conceptual design phase (Gausemeier et al., 2006).

The result of this phase is the principle solution of

the manufacturing system. The description of the

principle solution of the manufacturing system is di-

vided into aspects. They form a coherent system and

are mapped on the computer by partial models (Fig-

ure 1).

Figure 1: Partial models for the domain-spanning descrip-

tion of the principle solution of manufacturing systems.

Thus, the principle solution consists of a coher-

ent system of partial models:

 Manufacturing Requirements: These are the

requirements posed to the production system

(e.g. the dimensions of the working spaces,

speed of operation). These requirements are

derived from the requirements stated on the

principle solution of the product (e.g. toleran-

ces, production volume);

 Process Sequence: The description of the

manufacturing operations as a chain of pro-

cesses. Each process is characterised by a ma-

nufacturing function and additional attributes;

 Resources: These are all the equipment, tools

and personnel that are required for the execu-

tion of processes (DIN69902, 1987). At least

one resource is allocated to each process of

the partial model “process sequence”. Ne-

vertheless, it is possible that one resource real-

ises more than one process;

 Shape: We refer to the shape as workspace, the

required floor space of machines or the ac-tive

areas of handling appliances;

These aspects are made of elements like:

 Product Properties: For example weight,

structure, surfaces of the product. They are

used to derive requirements on the applied

manufacturing equipment (e.g. tolerances, sur-

face finish, production volume);

TOWARDS AN EXPERT SYSTEM FOR THE MANUFACTURING SYSTEM PLANNING OF PRODUCTS WITH

GRADED PROPERTIES

227

 Process: These are all the activities that are

made within a production system. For exam-

ple: Manufacturing processes, logistic pro-

cesses and assembly processes;

 Manufacturing Processes: These are the pro-

cesses required to obtain a first shape or state

from the formless original state to change the

material properties. They are taken from the

standard DIN 8580 (DIN8580, 1974);

 Logistic Processes: These processes deal with

the procurement, distribution, maintenance,

and replacement of material and personnel.

For example “transport” or “storage”;

 Assembly Processes: These are the processes

that are used to add parts to a product in a se-

quential manner. For example “mounting”;

 Parameters: Parameters are used to describe

processes and resources. Process parameters

are specific values required by a process. Re-

sources parameters state the range of values

that the resources are capable to perform;

 Material Elements: Include all raw materials,

auxiliary materials, parts from suppliers and

trade goods, as well as raw, unfinished and

finished goods (Gienke & Kämpf, 2006);

 Material: The material from which the pro-

duct is made, e.g. Steel;

 Products: This class contains the analysed

products within the CRC, e.g. crashbox, a

flanged steel shaft and a door interior trim;

 Project Groups: There are four main groups

within the CRC:

o Project A: Process Design

o Project B: Material modelling

/ identification of parameters /

experimental validation

o Project C: Numerical Treat-

ment

o Project D: Product Optimiza-

tion Process

 Projects: Each project produces, measures or

simulates different attributes of the demon-

strators. The activities and the attributes that

they handle are classified. The relationships

and interdependencies between the projects

were also considered;

 Shape of the Product: The geometry of the

product;

2.2 Expert System

An expert system is a computer program that has en-

coded knowledge and information about a particular

field. This knowledge is represented in a machine

readable format. An expert system emulates the de-

cision making and problem solving capabilities of a

human expert (Nikolopoulos, 1997).

According to Negnevitsky (Negnevitsky, 2005) a

rule-based expert system has 5 components:

 Input/output interface: Is the communication in-

terface between user and system (input/out-put

of the information);

 Inference engine: Is a software that tries to de-

rive answers from a knowledge base. It ma-

nipulates the knowledge in order to solve

problems;

 Knowledge base: Stores the domain knowledge.

The knowledge is represented as a set of rules;

 Data base: Stores the facts used to match

against the rules;

 Explanation facilities: Enables the user to ask

the software how a particular conclusion is

reached.

2.3 Ontology

In the field of informatics, Sure defined: “An ontol-

ogy is an explicit, formal specification of a shared

conceptualization of a domain of interest” (Sure,

2003). ‘Formal’ refers to the fact that the ontology

should be machine readable. ‘Shared’ means that it

is a common characterization of an application area.

‘Domain of interest’ indicates that a specific area is

modelled. In other words, ontologies are used as a

form of knowledge representation about a domain. If

the knowledge is categorized and formalized it is

possible to store it within an ontology.

An ontology has 2 types of relationships: hierar-

chical relationships and semantic relationships. With

the first ones, the elements within the ontology form

tree structures, or hierarchies. With the latter ones, it

is possible to link elements from different hierar-

chies. Semantic relationships could be any expres-

sion. Because of this, an ontology represents any re-

lationship between concepts (Tudorache, 2004). For

example “Element A measures Element B” or “Ele-

ment A defines Element B”. In this way an ontology

produces a clear domain model of the applied termi-

nology. If correctly specified, no ambiguities will

occur (Lewis et al., 2001).

Ontologies offer many applications. They solve

the problems caused by semantic obstacles, such as

those related to the definitions and approaches of the

experts of different fields. They also deal with the

management of the knowledge. Ontologies are a

way to represent the knowledge in a machine-

readable format. This representation allows us to use

the knowledge with different reasoning mechanisms

KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development

228

and problem-solving methods to generate more

knowledge. In our case, we use the knowledge that

we have of the manufacture of products with graded

properties in combination with the procedural

knowledge of a developer to generate a process

chain to manufacture a product with graded proper-

ties.

2.3.1 Ontologies for Manufacturing Systems

Currently, there are different approaches and meth-

ods for using ontologies in the manufacturing do-

main. Lemaignan (Lemaignan et al., 2006) presents

a proposal for a manufacturing upper ontology that

aims to draft a common semantic net in manufactur-

ing domain. Catalano presents the Product Design

Ontology (Catalano et al., 2008), an ontology for

product design. Other ontologies focus on develop-

ment of reconfigurable modular production systems,

for example, the work of (Lohse et al., 2004), a web-

enabled design of modular assembly systems.

Nevertheless, existing approaches do not provide

a solution for the manufacturing system planning of

products with graded properties. Because of this, we

developed OntoMaDa, which is described in section

2.4 Protégé

Protégé is a free, open source ontology editor and

knowledgebase framework. It lets the user create

any ontology with a graphical user interface.

Protégé represents knowledge in the form of

classes, slots, forms, instances, queries and graphical

tools. The classes are the concepts of our system.

The slots are the properties and characteristics of the

concepts. The slots have a value type (e.g. string, in-

teger), a cardinality (e.g. multiple) and a domain. Fi-

nally the instances are the values of the attributes.

JessTab (Eriksson, 2006) is a plug-in for Protégé

that allows the use of Jess and Protégé together. Jess

(Java Expert System Shell) is a rule engine. With

Jess (Freidman-Hill, 1995) it is possible to build

Java software that has “reasoning” capacity. Jess

uses the supplied knowledge in the form of declara-

tive rules. JessTab enables development of programs

that manage Protégé knowledge bases directly. Pro-

tégé, JessTab and Jess are the core of our expert sys-

tem structure.

3 ONTOLOGY OF

MANUFACTURING DATA

In our sub-project we developed an ontology which

aims to help with the design of the manufacturing

system planning of products with graded properties.

Its name is “OntoMaDa” (Ontology of Manufac-

turing Data).

It was constructed with basic elements which al-

low themselves to be used as construction blocks for

the generation of other solutions if the corre-

sponding rules are followed. It integrates experi-

mental data, conceptual knowledge, empirical mo-

dels and concepts in such a way that the developers

can generate new results. Please refer to (Reyes-

Perez et al., 2009) for more information about this

ontology.

Based on this ontology, two enhancements were

made. First, information about the peripheral activi-

ties of the manufacturing process and information

about the sub-projects research and interaction was

included. The ontology facilitates the data exchange

between the different domain researchers. The items

within the ontology have links between them. Each

link is a relationship (hierarchical or semantic).

These links allow researchers to follow a path in

which the relationships explain how the items are

connected. The researcher can start in any item and

then follow the different paths and “navigate”

through the Transregio project. The projects and

sub-projects within Transregio as well as their work,

are explicitly defined within the ontology.

The second enhancement was in the structure of

the ontology. It was changed to facilitate the manu-

facturing system planning conception. In the follow-

ing paragraphs we explain the concept.

We can see the manufacturing system design as

a “chain of decisions” which starts with the re-

quirements of the product. Each link between the

different items of the ontology, offers different pos-

sibilities. Because of this, the selection of the next

link must be carefully taken. For example, one prod-

uct requirement could be the material (e.g. polypro-

pylene, steel). Each possible material implies a dif-

ferent manufacturing process. A specific example: a

thermo-mechanical process can achieve a tailored

graded hardness in the case of a monomaterial,

whereas a chemical process could be used for a

composite material.

This chain of decisions, which we can see as a

“linear and sequential design”, is what we want to

depict in the ontology. But an ontology allows us to

create relations between the items and as a conse-

quence we create a net with these relations. These

TOWARDS AN EXPERT SYSTEM FOR THE MANUFACTURING SYSTEM PLANNING OF PRODUCTS WITH

GRADED PROPERTIES

229

relations could be dependencies, sequences, restric-

tions or any type of relation. Commonly, the manu-

facturing process design has a sequence and starts

with the definition of the product requirements.

Nevertheless, the ontology doesn´t follow a particu-

lar sequence. It is a net of intertwined hierarchies

and items. Each item was carefully classified in or-

der to allow an intuitive use of the ontology. This al-

lows the developers to start a new design based on

previous information and, what is more important:

the process design is not “linear” any more. It could

start not only with the requirements. It could start

with restrictions, for example: use only the available

machines and processes. In other words, the inten-

tion is to enable the user to start his or her search

from any item of a manufacturing system. Then he

or she can navigate through this net, either forwards

or backwards. In this way, the user can see other

possibilities for manufacturing a product.

4 STRUCTURE OF THE SYSTEM

The structure of the expert system is shown in

Figure 2. Its components are:

 Data bank (Information): Here is where the in-

formation about the manufacturing pro-cesses,

resources (e.g. machines) and the pro-ject is

saved. Among other things are pictures,

mathematical models, etc.;

 Rules module: It is used to define the rules used

to represent the procedural knowledge. The

rules have the format:

If (condition), Then (conclusion)

 Ontology: Here are all the elements and rela-

tionships used to model the domain knowl-

edge;

 User and developer interfaces: We are building

a user interface and a developer interface

within Protégé. The first one is where the user

states his or her queries to the system. The lat-

ter one is where the developers control and

modify the contents of the system;

 Inference engine: We will use the Jess infer-

ence engine as the inference engine of the ex-

pert system;

 Explanation facilities: Enables the user to ask

the software how a particular conclusion is

reached. It is made in Protégé.

Figure 2: Structure of our system.

The knowledge base is the core of the system. It

stores the knowledge of the CRC (factual and heu-

ristic). Its three components are: Data bank, rules

module and the ontology.

The user interface is the input interface of the

system. First, the user writes his or her query. Then,

the interface will invoke the inference engine to ac-

tivate the decision rules, and the procedural knowl-

edge will be activated. If the system finds a possible

solution to the query, the solution will be shown in

the user interface.

The expert system is developed in Java. For the

communication between Jess and Protégé we use the

JessTab plug-in of Protégé.

5 ONTOLOGY DEVELOPMENT

To develop the ontology it was necessary to collect

the necessary knowledge of the domain (knowledge

extraction) and then make the formalization for its

further use (knowledge representation). The formal-

ized knowledge was saved in Protégé.

5.1 Knowledge Extraction

Knowledge extraction is the process of collecting the

necessary knowledge for problem solving in the spe-

cific domain. Here is where the knowledge is gath-

ered and structured or classified in a conceptual

model.

Our intended expert system has 2 types of

knowledge: domain knowledge and procedural

knowledge. The first captures knowledge in a

specific area or domain, in this case, all the elements

that form part of the manufacturing system planning

and relationships between these elements. The latter

KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development

230

captures the manufacturing system planner’s proce-

dure for planning new manufacturing systems.

The domain knowledge was classified in terms

of a manufacturing system, according to the classifi-

cation described in the section 2.1. On the other

hand, procedural knowledge is extracted in form of

steps.

5.2 Knowledge Representation

Knowledge representation is the formalization of the

extracted and classified knowledge, to make it ma-

chine-readable (Nikolopoulos, 1997).

We used an ontology to model the domain

knowledge and it is planned to translate the steps of

the procedural knowledge into rules and save the

rules in Jess.

5.3 Structure of the Ontology

We use the ontology to model the knowledge that

we have of the manufacture of products with graded

properties. Figure 3 shows some of the hierarchies

contained within the ontology.

Figure 3: Part of the ontology.

We can see that different elements of the ontol-

ogy are matched with lines. Each line represents a

hierarchical relation. This hierarchical relation, in-

trinsically, provides the basis for a logical inference

within the knowledge base. Multiple inheritances is

one kind of inference explicitly represented in the

ontology (Lewis et al., 2001). A relation represents

the dependency between classes in a domain. We

used this characteristic in the development of the

query system of our ontology. One element can have

one or more relations. Some of the relations that we

made in our ontology are:

ProjectGroup_A4-“measures”-Specific_temperature

ProjectGroup_D3-“measures”-Internal_friction

There are different ways to structure the informa-

tion. But the challenge is to do it in a useful way.

The generation of the knowledge depends on the re-

lations. If the relations are excessively abstract there

could be loss of information. If the relations are ex-

cessively concrete the complexity will increase sig-

nificantly and confusion might be expected. Up to

now, the information retrieval is made in two ways:

 Query tab in Protégé;

 Visualization of the ontology with SHriMP

Simple Hierarchical Multi-Perspective (Jam-

balaya Widget, software within Protégé) and

OntoViz;

6 CONCLUSIONS AND FUTURE

WORK

The focus of this project is to help the developer

with manufacturing planning of products with

graded properties, regarding the interaction between

product and production system. To achieve this goal,

we develop an instrumentarium, which, among other

things, includes an expert system. A key element of

this expert system is the ontology explained here.

Future work includes finishing the user interface

and the developers interface. Another task is to

translate the steps of the procedural knowledge into

rules and save the rules in Jess. Once the rules are

saved we are going to join Jess and the ontology by

means of JessTab.

Up to this point, the expert system will be able to

suggest possible manufacturing processes for the re-

quired products, but to know the exact values of the

machine parameters, the user needs to open the em-

pirical models on Matlab. We are working on an in-

terface between Matlab and Protégé. The idea is that

the user receives the optimal machine parameters to

manufacture his or her product, without opening

Matlab.

At the moment a prototype of the software tool

for the description of functional graded components

is developed and programmed. The tool uses the ex-

ported CAD model of the component and transforms

this model into a voxel model (a voxel is the three-

dimensional equivalent of a pixel), where the user

can save the desired properties values to manufac-

ture. We are working on an interface between this

program and Protégé. This interface will be the final

user interface of our expert system.

TOWARDS AN EXPERT SYSTEM FOR THE MANUFACTURING SYSTEM PLANNING OF PRODUCTS WITH

GRADED PROPERTIES

231

ACKNOWLEDGEMENTS

This paper is based on investigations of the collabo-

rative research centre Transregio30, which is kindly

supported by the “Deutsche Forschungs-

gemeinschaft” (DFG).

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