IFAC MIM 2013

Preprints of the 2013 IFAC Conference on ManufacturingModelling, Management, and Control, Saint PetersburgState University and Saint Petersburg National ResearchUniversity of Information Technologies, Mechanics, andOptics, Saint Petersburg, Russia, June 19-21, 2013

ThB3.6

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Preprints of the 2013 IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg State University and SaintPetersburg National Research University of Information Technologies, Mechanics, and Optics, Saint Petersburg, Russia, June 19-21, 2013

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knowledge as well as the resources specifications used to

process the product at a node in consideration. The Query

Generator takes each node one by one and generates relevant

query. The query engine takes each query generated against

each node and infers potential environmental impact of the

node in consideration. In case the sought information is

found in the knowledgebase, the query engine delivers

information to Evaluation and Decision Making Module. If

no result is found, that would infer that the case is completely

new and therefore the node has to be sent to the LCA

simulation tool for environmental impact assessment. The

model synthesizer takes information concerning the selected

node and generates appropriate query. This query scans the

knowledgebase and generates the possible potential

environmental impact as well as the relevant indicators and

nodal input to environmental indictor output model. This

model is actually input-output simulation model which can be

discovered or inferred from the application specific

knowledgebase. The planner can visualize the model and can

reject or accept the model. Upon approval, the input-output

model information is sent to the simulation tool for

simulation of potential environment impact. The calculated

results are delivered to both the Evaluation and Decision

Making Module and the Knowledgebase. The system works

in connection with the existing legacy software tools for

planning through web-service over the web. This allows

planners to share as well as store information over the web

about simulated cases and make the inference capabilities of

the tool powerful in assisting the choice of optimal

production schemes based on environmental impact.

The implicit knowledge of the planners is collected through

formal means by providing a web-based Delphi survey

conducted among the experts of the particular domain. Delphi

method has been used which is a well establish method and

acknowledged in the field of information science (Okoli and

Pawlowski 2004, Cegielski 2001, Hayne and Pollard

2000,Holsapple and Joshi 2002, Lai and Chung 2002,

Mulligan 2002, Nambisan et al. 1999, Schmidt et al. 2001).

The advantages of Delphi surveys are that the experts can

describe and supplement their cases over several rounds and

can post comments to the cases of the other experts

anonymously. This ensures that each case is viewed and

reviewed several times. Lastly, there is an opportunity to

evaluate the cases and make statements regarding their

quality. It is also possible to conduct the Delphi survey via

web-based application as a location independent solution.

The survey is supported by a monitoring team. The team is

responsible for identifying experts and for organizing the

survey. During the survey, they are supporting the experts,

summarizing interim results and may obtain information for

the next round. In addition, they carry out preparatory and

follow-up steps of Delphi survey. A generic process of a

Delphi survey has been described in (Okoli and Pawlowski

2004, Weimer and Seuring 2008, Skulmoski et al. 2007); the

survey for this research activity will be structured in the same

manner. During the Delphi survey, the illustrated process will

be conducted in each round. Initially, the questionnaire is

developed. After each round, the monitoring team conducts

evaluation and prepares for next rounds. After four rounds,

the cases developed are stored in the case base. Subsequently,

the whole case base is evaluated. Based on the evaluation, the

individual cases are entered into the case base of the CBR

system. The task of the ontology-based case-based reasoning

(CBR) for automotive ramp-ups is intended for storing

practical knowledge of the employees. It is particularly useful

to be reused in case a knowledge holder leaves the company.

Hence, it is used pragmatically to solve problems that would

occur in future automotive ramp-ups. Employees can use

experience from previous ramp-ups to help solve current

problems and therefore make the automotive ramp-up process

more efficient (Puppe et al. 2003). Case-based reasoning, as a

technique of artificial intelligence, compares a new problem

(case) with problem cases from previous ramp-ups already

stored in the system. When an inquiry is made, a new case is

compared with the cases already stored in the case base

(Aamodt and Plaza 1994). The programmed mathematical

algorithm calculates the similarity of the cases stored in the

case base with the new case. The experience and problem-

solving knowledge contained in the most similar cases can be

adapted or used as an aid for the new case in current

automotive ramp-ups and projects. By conducting repetitions,

the user ascertains whether the solution was successful or

unsatisfactory for the new case. However, the new case is

saved in the case base regardless of the result of this revision.

This ensures that unsuccessful solutions are not attempted

repeatedly. Thus the case base continues to grow through this

automatic learning, following a circular process (Cocea

2011). With the help of this methodology, the practical

knowledge of the employees is stored systematically and

used pragmatically in future automotive projects.

5. IMPLEMENTATION AND VALIDATION

Two individual software solutions were implemented based

on the unified concept and validated based on two different

pilot cases. The first case (Case I) represents potential

environmental impact of manufacturing of customized hood

assembly in decentralized manufacturing environment. The

other case (Case II) relates to ramp-up management in

screwing of automotive components. The Case I is shown in

Fig. 1. at the left side of the partition line XY. The web client

application of Evaluation and Decision Making Module

(EDMM) loads the production scheme file (xml file format)

from the web server. The Scheme Parser dissolves the

production scheme into several production nodes. The

information is sent nodewise to the Query Generator which

later on creates query relevant to the selected node in a

SPARQL format. A shark fish search based algorithms are

implemented to enable search in the knowledgebase based on

several ontologies. The results are generated by the Query

Engine and stored back to the knowledgebase as well as sent

to EDMM in an xml format over the web. The

knowledgebase is modelled using ontologies. A semantic

web reasoner is developed based on Jena Engine and

enriched with semantic web-rules concerning pragmatic cases

of the potential environmental impact of the possible

materials, resources and process technologies to consolidate

inference capabilities of knowledgebase. Fig.2. illustrates the

ontology modelled for this purpose with a specific case of

manufacturing customized hood assembly in a decentralized

manufacturing network.


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Fig. 2. Ontology for customized hood assembly

manufacturing process environment

For planners to search knowledge through the intelligent

assistance system, the client applications allows users

connected over web to customize their search more concrete

instead of searching it using natural language query where

precision of the searched information is a not ensured to a

great extent and often fetch high number of results.

Therefore, a graphical user interface has been developed that

guides a planner for customizing query for more concrete

search of information. This graphical user interface can

customize the specifications of product, process technology

and resource to support easy generation of SPARQL based

query which is easy to navigate information through the RDF

structure. Similarity algorithms are implemented along with

heuristic searching rules to navigate the information inside

the knowledgebase fast. The searched information is then

compiled in an xml file format and finally uploaded on

server. The software module connects the several modules

including databases, the ontologies and the graphical user

interface to the web server using freely available Apache

Tomcat Server.

For the second case (Case II), the software prototype has

been developed that provides planners and experts to conduct

several rounds of Delphi surveys in production ramp-up of

screwing processes. The automatic generation of

questionnaires for Delphi surveys is supported using

ontologies. The cases are generated after completion of

Delphi surveys. This ontology relates to ramp-up of screwing

processes in automotive sector and serves as a starting point

for gathering expert knowledge. On one hand, it is used to

represent the topics and terms from ramp-up management

area, for the experts in a hierarchical and connected manner.

On the other hand, the ontology structures the gathered expert

knowledge so that it can then be used by the CBR System.

The ramp-up management of screwing processes is validated

based on the concept with software programming of three

selected, refined, and linked methods (Ontology, Delphi

method, and CBR) in practice, This prototype is able to

model and represent the ontology as well as to conduct

Delphi surveys with its individual rounds efficiently. In

addition, the CBR system and the similarity algorithm are

also integrated. Since these three methods are to be linked to

each other, a common platform is created for their

intercommunications. The illustration (see Fig. 4. ) shows the

IT structure of the prototype.

Fig. 3. Graphical user interface for customizing user queries

The prototype offers an opportunity to implement ontologies

modeled with the standardized software like Protégé into the

system. In addition, the cases from the database can be linked

through the web server. The web server functions as an

interface for the direct exchange of information with the

database. A freely available Apache Tomcat server is used

for this purpose; the application for the Delphi survey is

installed on it.

The module of the Delphi survey implemented in the

software prototype is constructed flexibly. This means that

the layout or the user interface is made adjustable to different

requirements (company divisions). It is also important that

the different ontologies from the respectively relevant areas

of ramp-up management can be integrated. The structure of

the individual survey rounds is adjustable to the

corresponding ontologies. Through this way, the usability of

the software across divisions has been guaranteed. The

programmed CBR application is also implemented. The

database system used is PostgrSQL, which is also freely

available. It can be used to create user profiles and

passwords, for example, which allow the assignment of the

cases to the respective experts and govern the individual

rights of use. The expert knowledge gathered by the Delphi

surveys and the CBR System is further saved in the database.

The database recognizes whether a case entered within the

Delphi survey is completely concluded or must still be

improved by the experts in further survey rounds. Also, the


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detailed information from the CBR survey for determining

similar cases from the past is saved in the database. This data

is used again finally as an input for further development of

the case base at the later stage. After having been shown the

results of similar cases from past ramp-ups via the web

server, the user can ascertain from them new solution

approaches for confronted ramp-up problem. The newly

gained knowledge during the solution should be input back to

the CBR case base so that it is available to other users for

global access. For this reason, the database makes available

the data that was previously entered into the system while

searching, in order to be able to save new cases in the system

built upon it.

Fig. 4. Structure of the prototype for screw process ramp-up

management

With the IT structure shown, it is possible to access the

programmed applications from any workstation globally

through the internet. For example, experts residing on

different continents may participate in Delphi surveys. It is

also possible to use the CBR system from anywhere in the

world. A global knowledge management system is thus

created making it possible for the globally available know-

how in the area of ramp-up management to be identified

(Delphi survey), stored in an intelligent structure (ontology),

and utilized pragmatically by all users (CBR system). This

global usage of the system is of particular importance. By this

means a comprehensive knowledge base for the ontology-

supported CBR System can be built and used to master the

challenges for a knowledge management system for vehicle

ramp-ups described at the beginning.

6. CONCLUSIONS AND OUTLOOK

A unified concept for integrating the two software solutions,

for extracting and reusing the implicit knowledge from the

planners as well as the planning related softwares have been

developed and validated through pilot cases. The first pilot

case relates to the environmental impact assessment of

customized hood manufacturing in the decentralized

manufacturing network. Web-based software solution

comprised of customized user query interface, ontology

based knowledge base and Scheme parser is implemented

and validated for the pilot case. The software takes input

through two means i.e. direct from users through customized

user queries for searching the information from the

knowledgebase and deliver results relevant to the query. This

query is generated after the user has customized his input

information. In case of data file as input to the intelligent

assistance system, the information contained in the data file is

first parsed through Scheme Parser and Query is generated in

a sequential manner against the parsed information. The

query engine is used to search information from ontologies.

The information is delivered to the decision module for

processing further in decision making process. The second

pilot cases relates to the extraction, storage and automatic

learning from personal implicit knowledge through formal

means. The knowledge is discovered through web-based

multi-round Delphi surveys and later stored in ontology part

of Case Base Reasoning module. The software is tested for

ramp-up of screwing process.

In future, the software will be extended to implement and use

ontologies for inferring generic input-output environment

simulation model of material, process and resource based on

the nature of manufacturing process. This model will be

directly used inside the LCA simulation tool. The generated

results will be used to enrich the knowledgebase for better

inference capabilities. The validation scenario will be

expanded to assist decision making for manufacturing of

other products such as customized orthotics in a decentralized

manufacturing network. For enhancing the software module

dedicated for screwing process ramp-up, the same approach

will be used for rolling out further topic areas in the ramp-up

management. For this purpose ontologies that structure the

knowledge domains of the respective areas must first be

created with the relevant experts. It will be followed by

Delphi surveys, so that the case base can be filled with the

valuable empirical knowledge of ramp-up management

experts.

For both modules of the software representing the

corresponding pilot cases, the unified software will be tested

by employees and further refinement will be made based on

the feedback from the end users.

7. ACKNOWLEDGEMENTS

The work reported is this paper is partially supported by the

European Commission Project e-Custom FP7-2010-NMP-

ICT-FoF 260067.

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IFAC MIM 2013