Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6549
REDESIGNING THE KNOWLEDGE OF ROBOTIC SYSTEM
ENGINEERS THROUGH THE MODEL-PARAMETRIC SPACE
OF INFORMATION TECHNOLOGY
OLEKSIY RYKHALSKYY
Teache , Depa men o Compu e Science and So wa e Enginee ing, Facul y o In o ma ion Sys ems and
Technologies, P i a e Highe Educa ion Ins i u ion “Eu opean Uni e si y”, Kyi , Uk aine.
E-mail: alexey. [email protected]
ABSTRACT
The ele ance o he opic is d i en by he apid de elopmen o digi al echnologies and he g owing
complexi y o designing obo ic sys ems, which equi es new app oaches o he ep esen a ion, s uc u ing,
and e i ica ion o knowledge. The need o in eg a e he e ogeneous models and pa ame e s in o a single
in o ma ion en i onmen de e mines he impo ance o he s udy o indus ial and scien i ic applica ions.
The pu pose o he s udy is o in es iga e me hods o ep esen ing he knowledge o obo ic sys em designe s
based on he model-pa ame e space and o desc ibe he possibili ies o hei p ac ical use. The objec o
esea ch is he p ocess o o malizing knowledge and in eg a ing models in he design o complex echnical
sys ems. The me hodological basis o he s udy is a sys ema ic app oach, seman ic modeling me hods,
in e al analysis, and heo e ical se ope a ions on knowledge neighbo hoods. As a esul o he wo k, he
concep o building a model-pa ame e space (<M,P>) was o med, a classi ica ion o basic concep s and
ca ego ies in he ield o design was de eloped, and o mal measu es o compa ibili y, in eg i y, and
comple eness o knowledge we e p oposed. Algo i hms o cons uc ing knowledge neighbo hoods and
in eg a ing models in o holis ic me hodologies wi h au oma ed consis ency checking ha e been de eloped.
The s uc u e o he ool and so wa e complex o suppo he p ocesses o model analysis and syn hesis in
obo ic sys ems is p esen ed. The p ac ical signi icance o he esul s lies in he possibili y o using he
p oposed app oaches in he de elopmen o digi al enginee ing pla o ms, educing he isk o design e o s,
inc easing he adap abili y o design solu ions, and educing he de elopmen ime o complex echnical
sys ems.
Keywo ds: Model, Pa ame e , Sys em, Analysis, Design, Resea ch, Complex Objec , Knowledge Base,
Consis ency, He e ogeneous Models, In o ma ion Technology
1. INTRODUCTION
Mode n indus ial p oduc ion and au oma ed
echnological p ocesses canno be imagined wi hou
he in oduc ion o obo ic sys ems. The
de elopmen o Indus y 4.0, digi al wins, and cloud
pla o ms signi ican ly complica es he s uc u e o
knowledge used by designe s in de eloping
echnical solu ions. The g owing numbe o
pa ame e s, ela ionships, and models equi es a
sys ema ic o ganiza ion and a o malized app oach
o p esen ing in o ma ion. The p oblem is no only
he accumula ion o knowledge, bu also i s
s uc u ing, e i ica ion, and in eg a ion in o a single
in o ma ion en i onmen . Gi en he cons an
dynamics o echnological de elopmen , he sea ch
o ools ha would main ain da a consis ency,
lexibly upda e in o ma ion models, and ensu e
adap abili y o new p oduc ion equi emen s is
pa icula ly ele an . The global scien i ic
communi y pays g ea a en ion o he o ganiza ion
o knowledge in he ield o obo ic design. The
wo ks by Choi and Kim [1], Yao e al. [2], and Xu e
al. [3] analyze he ole o digi al wins and machine
lea ning in imp o ing model accu acy and
op imizing p oduc ion p ocesses. The s udies by Guo
e al. [4] and Shen e al. [5] demons a e he bene i s
o seman ic ne wo ks and on ological app oaches o
isualizing complex ela ionships be ween
pa ame e s. Liu and Xu [6], Yu e al. [7] emphasize
he p ospec s o cloud p oduc ion and decen alized
knowledge s o age sys ems, and Zhou [8] desc ibes
heo e ical app oaches o assessing he eliabili y o
in o ma ion unde unce ain y. Howe e , despi e he
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6550
di e si y o scien i ic app oaches, he issue o
building a holis ic model-pa ame e space wi h
o mal measu es o compa ibili y and consis ency
emains unde de eloped.
Much o he esea ch ocuses on speci ic aspec s:
indi idual elemen s o in elligen design, simula ion,
o op imiza ion. Ins ead, he e a e no gene alized
me hods ha allow in eg a ing dispa a e models and
pa ame e s in o a single in o ma ion en i onmen
wi h he abili y o au oma ically check hei
consis ency. The issues o c ea ing ool and so wa e
sys ems o isualizing and con olling he in eg i y
o knowledge, con enien o use in indus ial
en i onmen s, emain poo ly de eloped. Especially
ele an is he lack o app oaches ha would
combine heo e ical igo wi h p ac ical adap abili y
in mul i-agen and cloud en i onmen s.
The pu pose o his pape is o in es iga e
app oaches o o ganizing he knowledge o obo ic
sys em designe s based on he model-pa ame e
space, desc ibe exis ing ools and app oaches,
iden i y weaknesses in he heo e ical and p ac ical
basis o his opic, and cha ac e ize ou own
app oach o building a oolki o suppo he
p ocesses o model analysis and syn hesis. The main
objec i es o he s udy we e o (a) analyze he
s uc u e and main elemen s o he <M, P> space in
mode n scien i ic sou ces, (b) ind ou he
possibili ies o i s in eg a ion in decision suppo
sys ems and (c) p opose equi emen s o u he
de elopmen and applica ion in indus ial design
en i onmen .
2. LITERATURE REVIEW
The ecen esea ch wo k in he ield o in o ma ion
echnology o ep esen a ion o he knowledge o
he obo ic sys em designe is based on digi al wins,
on ological modeling and model pa ame e spaces.
In pa icula , Choi and Kim [1] show ha in eg a ion
o digi al wins in he op imiza ion and simula ion
sys ems enhance accu acy and speed in he
enginee ing decision making. Yao e al. [2] also
men ion how he concep o me a-uni e se o
indus ial p oduc ion ough o be in oduced and how
o build adap i e digi al models o p oduc ion
p ocesses. In complex sys ems, con ol o model
compa ibili y and consis ency is paid conside able
a en ion o, and in sys ems whe e cen alized
con ol coo dina es ac i i ies a mul iple si es.
Valkman’s e . al. [9] undamen al s udy p oposes a
me hodology o c ea ing a model-pa ame e space
(<M,P>-space) o he design knowledge space in he
design p ocess so as o educe he possibili y o
con adic ions be ween model and pa ame e . Guo e
al. [4] also conside a simila app oach, howe e , in
his case, hey concen a e on he implemen a ion o
seman ic ne wo ks and isual con ol o au oma ed
manu ac u ing. Rykhalskyy [10] discusses speci ic
implemen a ion de ails.
Cu en ly, he p oblem o in eg a ing
he e ogeneous da a and models is ac i ely
de eloped. Fo ins ance, Xu e al. [3] apply machine
lea ning o au oma ed model adjus men in
p oduc ion p ocesses in o de o make hem able o
espond o changes in hei condi ions. Sinha and
Lee [11] conside a simila p oblem o enginee ing
in indus ial indus ial sys ems, bu speci ically he
p oblems wi h he da a eliabili y and consis ency in
he in oduc ion o a i icial in elligence. I is s ill a
ho opic, he o maliza ion o knowledge and
building o uni ied da abases. Using a digi aliza ion
mechanism and small and medium en e p ises
enginee ing knowledge managemen as a esea ch
opic, Chen e al. [12] analyze he ex en o which
digi aliza ion a ec s enginee ing knowledge
managemen . Shen e al. [5] p opose he ole o
a i icial in elligence and op imiza ion o in elligen
p oduc ion planning which is he g owing ole o
a i icial in elligence and op imiza ion algo i hms.
Mo eo e , esea ch is ac i ely conside ing
de elopmen o ool and so wa e sys ems o
knowledge suppo . Speci ically, Lee e al. [13]
ocus on cybe physical sys ems in indus ial
p oduc ion, and hei a chi ec u e o in eg a e
modelling, simula ion and p oduc ion p ocess.
Mo eo e , Kau and Dhindsa [14] ake knowledge
indexing me hods in o conside a ion ha a e i al o
an au oma ed sea ch o he ele an in o ma ion in
la ge da abases. The syne gy de ined be ween digi al
wins, seman ic modeling and a i icial in elligence
in knowledge ep esen a ion o he obo ic sys ems
design is demons a ed by mode n esea ch. Such
e iciency and eliabili y enginee ing solu ions is
enabled by he use o model pa ame e spaces and
algo i hmic analysis o model compa ibili y [9, 10,
1, 3, 11].
In addi ion, Zhou [8] made a signi ican
con ibu ion o he de elopmen o he me hodology
o o malized ep esen a ion o knowledge by
looking in o wha unc ions can be pe o med by
belie s o e such sys ems o la ices o he
dis ibu ion ypes used o es ima e he eliabili y o
in o ma ion in a complex mul i ac o ial sys em. In
hei compa a i e analysis, Liu and Xu [6] a gue ha
bo h Indus y 4.0 and cloud manu ac u ing a e
cha ac e ized by many cha ac e is ics including
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6551
scalabili y and decen alized knowledge
managemen o digi al manu ac u ing pla o ms. In
hei p oposed blockchain a chi ec u e o cloud
based manu ac u ing [7] o secu ely dis ibu ed
model and pa ame e da a in he dis ibu ed
en i onmen . Based on he ac ha i is impo an o
build dynamic upda ed Model in <M,P>-space,
app oaches o au oma ic gene a ion o con olle s
o obo ic sys ems om dependencies o
mul isenso a iables ha e been conside ed by
Kobayashi e al. [15].
Mo eo e , i is key o men ion he s udy o
Sydo o e al. [16], abou de eloping an app oach on
how s yles may be applied in so wa e enginee ing.
The ele ance o his s udy is o he au oma ed
gene a ion o empla es and echniques when
building knowledge bases owa ds syn hesis o
in eg al sys ems o models and pa ame e s. Use o
he o malized s yles makes he s uc u e o
knowledge consis en and allows o inc ease he
quali y o he in eg a ion o he e ogeneous
in o ma ion componen s.
Sequei a and Ge asio [17] a e also wo hy, as
hey in es iga e he explainabili y o agen s in
ein o cemen lea ning ha can con ibu e o he
c ea ion o sel lea ning design models. Malus e al.
[18] p oposed a eal ime me hod o asks
dispa ching o a lee o au onomous mobile obo s
wi h p ac ical alue o mul i agen app oach.
Çapunaman and Gü soy [19] p esen compu a ional
app oaches in obo ic manu ac u ing using machine
ision ha aims o imp o e he modelling accu acy
o complex objec . The esea ch o Chu e al. [20] is
ela ed o consensus heo y in mul i agen sys ems
wi h delays and is ele an o o malizing
asynch onous in e ac ion in la ge p ojec s. In
addi ion, Roeh e al. [21] and S ojano ski e al. [22]
emphasize he impo ance o au oma ed esea ch on
obo beha iou and new app oaches o compu e -
aided a chi ec u al design, espec i ely, which
expands he applica ion o knowledge o maliza ion
me hods. Kusiak [23] emphasizes ha digi al
manu ac u ing should ully in eg a e big da a
analysis, which co ela es wi h he idea o using
dis ibu ed da abases and in elligen in e aces.
Thus, cu en esea ch ocuses on combining he
model-pa ame ic app oach wi h digi al wins,
a i icial in elligence, blockchain echnologies, and
mul i-agen sys ems [8, 6, 7, 15]. A he same ime,
he e a e s ill p oblems wi h scaling buil solu ions
o indus ial cloud pla o ms and insu icien
in eg a ion o ools o au oma ed adap a ion o
dynamic changes in p ojec asks.
3. METHODS
The s udy was conduc ed h ough a sys ema ic
analysis o mode n scien i ic sou ces and
publica ions on he design o obo ic sys ems and
in elligen in o ma ion echnologies. The
me hodological basis was seman ic modelling
me hods o o malizing he ela ionships be ween
models and pa ame e s, a se - heo e ic app oach o
de ining ope a ions on knowledge neighbo hoods,
and in e al analysis o es ima ing he anges o
pa ame e alues and con olling hei eliabili y.
The model-pa ame e space was s uc u ed using
g aph models and ma ix desc ip ions. The
algo i hms we e buil and es ed by de eloping
expe imen al examples based on ypical design asks
o indus ial obo ic sys ems and au onomous
mobile pla o ms. The p ac ical s udy was conduc ed
using publicly a ailable da abases o echnical
speci ica ions and simula ion esul s, as well as
ma e ials om scien i ic con e ences and
in e na ional esea ch in he ield o digi al
enginee ing.
4. RESULTS AND DISCUSSION
Knowledge ep esen a ion is highly jus i ied in he
mode n obo ic enginee ing, as i enables an
e ec i e design p ocess. The de elopmen and
inclina ion o he in ellec ualiza ion o de elopmen
and modelling o complex echnical objec s in he
con ex o apid de elopmen o digi al echnologies
p o es o be he p edomina ion o o maliza ion o
knowledge and da abase a eas. The e a e essen ially
h ee knowledge ep esen a ion me hods like
decla a i e, p ocedu al, and hyb id.
1. Me hods o decla a i e desc ip ion o he
pa ame e s and s uc u al cha ac e is ics o sys ems
include he use o ames, seman ic ne wo ks and
on ologies. Because i is a s uc u ed ep esen a ion
o in o ma ion, i is e ec i e o he expe ien ial
le el, he design case, o p o iding s uc u ed
ep esen a ions o in o ma ion and o au oma ed
model analysis [1].
2. P ocedu al me hods ollow he algo i hmic
me hods and a e used when he knowledge has o be
ep esen ed in he o m o some sequences o
ac ions. Fo a speci ic example, ha is, he p oduc
ules o he au oma ic selec ion o echnical
solu ions [11].
3. Examples o uses o hyb id me hods
include he use o uzzy logic o neu al ne wo ks
wi h classical knowledge bases in o de o enhance
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6552
he adap abili y and accu acy in p edic ing he
pa ame e s o obo ic sys ems [3].
As applica ion a eas, model pa ame e space [9]. The
design pa ame e s a e ep esen ed by an a chi ec u e
ha allows hem o be e ec i ely o ganized oge he
wi h ela ionships be ween di e en models used in he
design p ocess, and an au oma ed analysis o pa ame e
consis ency is p o ided. The in eg a ion o he a i icial
in elligence, digi al wins, and knowledge managemen
echnologies a e he basis o mode n oolki s o
compu e aided design o obo ic sys ems.
1. The digi al win o he physical sys em
allows c ea ing i ual copies, o e ing he
oppo uni y o es and op imize he pa ame e s a
he design s age [2]. The educ ion in de elopmen
ime and eliabili y o he s uc u e is his ac o
e y i al.
2. Knowledge managemen sys ems (KMS)
a e used in he design p ocess in o de o s o e,
analyse and econ igu e in o ma ion used in he
design p ocess. These kind o sys ems enable
enginee s o in e ac e ec i ely wi h da abases o
a i icial in elligence in o de o ob ain… op imal
solu ions [12].
3. In e ac ion be ween he modeling,
simula ion and p oduc ion s ages is p o ided by
In eg a ed CAD/CAE/CAM sys ems (Siemens NX,
CATIA, SolidWo ks, e c.). Such sys ems help o
au oma e he decision making p ocess and ele a es
he wo k e iciency o a g ea ex en [13].
Knowledge ep esen a ion in a mode n design
o a obo ic sys em is ac i ely de eloped by
in eg a ing on ological, digi al wins, and AI
echnologies. The p ocess o model analysis and
syn hesis can be au oma ed using
model(pa ame ic) space and ool and so wa e
sys ems and as a esul ensu es quali y o he
design and educes ime o i s ealiza ion.
To sys ema ize knowledge and enhance e ec i e
in o ma ion echnology de elopmen , in he ield o
obo ic sys ems design, he e a e a lo o concep s
and ca ego ies ha ha e o be o e ed in clea ways.
This enables o maliza ion o he subjec a ea, a
uni ica ion o knowledge base, and gua an ees o
in eg i y o design decisions [5]. The main
ca ego ies and concep s o he subjec a ea o
obo ic sys em design is u he expanded in he
ollowing able (Table 1).
Table 1. Key ca ego ies and basic concep s o he subjec a ea o obo ic sys em design
Ca ego y
Basic concep
Desc ip ion and ea u es
Design objec Robo ic sys em A se o ha dwa e and so wa e componen s designed o
au oma e p oduc ion o se ice p ocesses [4]
Pa ame e Cha ac e iza ion o he sys em A o malized desc ip ion o a p ope y o s a e o an
elemen o sys em as a whole, used in models [9]
Model Fo malized ela ionship A ep esen a ion o he ela ionships be ween sys em
pa ame e s o desc ibe, p edic , o op imize he sys em [1]
Me hodology Sys em o models An o de ed, consis en sys em o models ha enables he
calcula ion and es ima ion o a ge pa ame e s [3]
Con ex Condi ions o in e p e a ion A o malized desc ip ion o he ange o accep able
pa ame e alues and condi ions o applying models [13]
The neighbou hood o
knowledge
Sys em g oup o models Linked models and pa ame e s g ouped a ound a cen al
elemen o local analysis [2]
Compa ibili y In e ac ion o models The deg ee o ag eemen be ween models ha allows hem
o be used oge he in in eg a ed calcula ions [11]
In eg i y o knowledge Comple eness o model
co e age
Re lec ing he abili y o co e all he necessa y aspec s o
he s udy wi hou con adic ions [12]
Sou ce: c ea ed by he au ho based on [4, 9, 1, 3, 13, 2, 11, 12]
In conclusion, he o maliza ion o he impo an
concep s and ca ego ies pe mi s he c ea ion o
uni ied knowledge bases, and helps in he
de elopmen o in eg a ed in o ma ion echnologies
o acili a e he de elopmen o obo ic sys ems. I
was on his basis ha he u he cons uc ion o
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6553
model schema spaces aking e ec in eg a ion and
e i ica ion o models we e made.
In o de o design e ec i ely complex obo ic
sys ems, hese sys ems mus ake ad an age o
mode n knowledge bases and da abases ha will
enable he in eg a ion o dispa a e models and
pa ame e s as a single in o ma ion en i onmen .
C ea ion o such da abases allows o ha e a
consis en in e p e a ion o exis ing esea ch objec s,
o maliza ion o syn hesis and analysis me hods, and
allows o ha e a mul i-le el e i ica ion o
in o ma ion in eg i y [14, 9, 10]. Knowledge
managemen and he managemen o in o ma ion
base should se e no only o accumula ion o da a
bu also sea ching and in eg a ion o he knowledge
om mul iple sou ces conside ing hei
he e ogenei y and di e si y [2]. Table 2 shows he
main me hods used o build such sys ems.
Table 2: Me hods o c ea ing and using knowledge and da a bases o suppo he analysis and syn hesis o obo ic
sys em models
Me hod
Desc ip ion and ea u es o applica ion
On ological modeling C ea ing seman ic s uc u es ha desc ibe he ela ionships be ween objec s,
pa ame e s, and p ocesses [5]
Using seman ic ne wo ks Visualiza ion o complex ela ionships be ween models and pa ame e s o
acili a e analysis and sea ch o dependencies [4]
In e al analysis Es ima ing he eliabili y o pa ame e s and de e mining he anges o possible
alues o imp o e he eliabili y o calcula ions [3]
Model-pa ame ic app oach Building knowledge neighbo hoods and o ming uni e sal da abases and
knowledge using o mal appa a uses [9, 10]
In eg a ion o digi al wins Combining knowledge bases wi h simula ion models o p edic he beha io o
sys ems in di e en scena ios [2]
Me hods o knowledge indexing Au oma ed o ganiza ion and classi ica ion o in o ma ion o quick sea ch and
selec ion o ele an models [14]
Building a e i ica ion sys em Implemen a ion o mechanisms o checking he consis ency and consis ency o
knowledge s o ed in da abases [11]
Au oma ed o ma ion o me hods The use o empla es and algo i hms o gene a e comple e sys ems o models and
pa ame e s based on inpu da a [12].
Sou ce: c ea ed by he au ho based on [5, 4, 3, 9, 2, 14, 11, 12]
O e all, we can summa ize ha he
es ablishmen o such e ec i e knowledge bases
o he suppo o obo ic sys em's analysis and
syn hesis based on he combina ion o in e al
es ima es app oach, on ological app oach,
seman ic ne wo ks, and he model pa ame ic
s uc u e p o ides. This allows he au oma ion o
enginee ing decision making, he imp o emen o
modeling quali y and he de elopmen o a
lexible in o ma ion en i onmen o he
de elopmen o complex sys ems.
One o an impo an ask in design o he obo ic
sys ems, is o o malize he ep esen a ion o he
knowledge in he <M,P>-space (model pa ame e
space). I p o ides o s uc u ing models,
pa ame e s, and in e ela ionships, and o ganizing
in o ma ion o sys em analysis and syn hesis o new
models [9].
The model pa ame e space has he ollowing
main componen s:
 pa ame e s ha desc ibe he p ope ies o an
objec o p ocess;
 models ha e lec he ela ionship be ween
pa ame e s;
 me hodologies, which a e o de ed sys ems o
models;
 knowledge neighbou hoods ha would help
localizing agmen s o knowledge a ound
ce ain models o pa ame e s.
A diag am (Figu e 1) shall be p oposed o
isualize he s uc u e o he <M, P>-space.

Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6554
Figu e 1: S uc u e o he model-pa ame e space o designing obo ic sys ems
Sou ce: de eloped by he au ho
In his space, algo i hms cons uc knowledge
neighbou hood se s o models and pa ame e s
connec ed o he cen al elemen o na iga e his
space. The i s , second, and subsequen neighbou
o de s in a e de e mined [9]. Calcula ing dis ance
be ween models in <M,P>-space in ol es he
sea ching o he minimum numbe o s eps in
elemen o dependency g aph. The dis ance is used
as a measu e o seman ic p oximi y also o
compa ibili y es ing and in he con ex o
inco po a ing models in o one uni ied
me hodologies. This wo k p oposes a uni ied
app oach o o ganizing design o obo ic sys ems in
he space o model pa ame e using s uc u e o
model pa ame e space. The wo k on he
cons uc ion o knowledge neighbou hoods and
calcula ion o model dis ance leads in o e ec i e
model consis ency and able o wo k model
in eg a ion based on which high quali y design
solu ions a e accomplished.
Fo designing complex obo ic sys ems,
knowledge om di e en sou ces mus be
in eg a ed, pa icula ly in di e en o ma s, and in
di e en s uc u al app oaches. Fo his, hey equi e
o malized ope a ions o wo king wi h model
pa ame e neighbou hoods, ha a e local g oups o
in e connec ed model and pa ame e uples [9]. The
basis o in eg a ion is wo key se heo e ic
ope a ions o knowledge neighbou hood union and
in e sec ion. Thei applica ion allows o econcile
knowledge, iden i y duplica ions, con adic ions,
and o m a mo e comple e pic u e o he subjec a ea
[2]. Below is an ex ended desc ip ion o he
ope a ions wi h hei in e p e a ion o p ac ical use
(see Table 3).
Table 3: Theo e ical se ope a ions on model-pa ame ic neighbou hoods and hei p ac ical applica ion
Ope a ion
Fo mal de ini ion
P ac ical signi icance
In e sec ion o
neighbou hoods
A se o elemen s ha simul aneously belong o
each o he neighbou hoods:
O
₁
∩ O
₂
I is used o iden i y common knowledge and
pa ame e s, use ul o checking he compa ibili y
o di e en app oaches.
Me ge
neighbou hoods
A se o elemen s ha belong o a leas one o
he neighbou hoods:
O
₁
∪
O
₂
I allows combining knowledge om di e en
sou ces and ge a mo e comple e pic u e o an
objec o p ocess.
Di e ence o
neighbou hoods
A se o elemen s ha belong o one
neighbou hood bu do no belong o ano he :
O₁ O₂
I is used o iden i y unique knowledge and o
iden i y specialized ea u es o models.
Model-pa ame e space (<M,P>)
Pa ame e s Models Me hods
Local bases Knowledge neighbo hoods A chi al models
Compa ibili y analysis
In eg a ion o knowledge
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6555
Symme ic
di e ence
A se o elemen s ha belong o one o he
neighbou hoods, bu no bo h a he same ime:
O
₁
△
O
₂
I is used o analyse he di e ences be ween
knowledge se s and assess con lic zones.
Sou ce: c ea ed by he au ho based on [9, 4, 3, 11, 2]
Theo e ical and se - heo e ic ope a ions o
combining, in e sec ing, and di e ing knowledge
neighbou hoods a e impo an ools o in eg a ing
he e ogeneous models and pa ame e s in o a single
in o ma ion en i onmen . Thei use helps o iden i y
con adic ions, o m consis en me hodologies, and
imp o e he quali y o design decisions in he
de elopmen o obo ic sys ems. Designing complex
obo ic sys ems in ol es he use o many models
c ea ed by di e en esea che s o depa men s. To
ensu e he co ec ope a ion o such sys ems, i is
necessa y o o mally assess he deg ee o
compa ibili y and consis ency o models, as well as
de e mine he le el o in eg i y, comple eness, and
consis ency o he model-pa ame e space [9].
Fo mal measu es o model compa ibili y. The
compa ibili y o wo models in <M,P>-space can be
es ima ed using he compa ibili y ec o :
𝐿𝑉 = (𝐿𝑉𝑇; 𝐿𝑉𝐾; 𝐿𝑉𝑇𝐾; 𝐿𝑉𝐾𝑇) (1)
whe e:
 LVT — he no malized le el o compa ibili y o
he ex ual pa s o he models (common
pa ame e s in he model ex s);
 LVK — no malized le el o con ex
compa ibili y (in e sec ions o applica ion
condi ions);
 LVTK — consis ency o ex ual and con ex ual
connec ions;
 LVKT — consis ency o con ex ual and ex ual
ela ions [9].
Fo a comp ehensi e assessmen o he <M,P>-
space, he ollowing indica o s o in eg i y,
comple eness, and consis ency a e used (see Table 4):
Table 4: Quali y indica o s o he model-pa ame e space
Indica o
Fo malized de ini ion
P ac ical signi icance
In eg i y (I) The a io o he numbe o in eg a ed
models o he o al numbe o models in
he space
I allows o assess how he models a e in e connec ed
and o m a single in o ma ion en i onmen [4]
Comple eness (C) Ra io o he numbe o co e ed
pa ame e s o he o al numbe o ele an
pa ame e s
Re lec s he deg ee o co e age o all aspec s o he
subjec a ea in he design [3]
Non-con adic ion
(NC)
Sha e o models o which no con lic s in
ex s and con ex s we e de ec ed
The indica o is impo an o he co ec in eg a ion
o knowledge and he o ma ion o sa e echnical
solu ions [11]
Sou ce: c ea ed by he au ho based on [9, 4, 3, 11]
The cons uc ed o mal measu es o model
compa ibili y and consis ency allow us o quan i y
he quali y o he model-pa ame e space. Indica o s
o in eg i y, comple eness, and consis ency se e as
a ool o diagnosing and op imizing he knowledge
sys em, ensu ing inc eased eliabili y o design
decisions in he ield o obo ic sys ems. Le us
conside he de elopmen o p inciples o he
implemen a ion o p oblem-based in o ma ion
echnology o suppo he design o obo ic sys ems
and he c ea ion o a so wa e and in o ma ion
oolki o managing da abases and knowledge.
P oblem-based in o ma ion echnology in he ield
o obo ic sys em design should suppo all s ages o
he li e cycle o c ea ing complex echnical objec s:
om o mula ing equi emen s and building models
o in eg a ion, compa ibili y es ing, and knowledge
e i ica ion [9, 10].
Implemen ing P oblem Based In o ma ion
echnology can be desc ibed by he basic p inciples
as ollows:
1. Mul imodel – simul aneous suppo o se e al
o ms o knowledge ep esen a ion
(ma hema ical models, logical connec ions,
g aphical isualiza ions) [5].
2. In eg a i e – is he abili y o combine dispa a e
da a sou ces and synch onize hem in o a single
en i onmen [4].
3. Hie a chicali y – building a knowledge s uc u e
wi h di e en le els o de ail: om global
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6556
sys emic connec ions o local pa ame ic
desc ip ions [3].
4. Con ex ual adap abili y is he dynamic
adjus men o algo i hms and da a p ocessing
mechanisms o he condi ions and objec i es o a
pa icula p ojec [11].
5. Au oma ed e i ica ion is he a ailabili y o
mechanisms o con ol he consis ency and
compa ibili y o models and pa ame e s in he
p ocess o hei in eg a ion [9, 10].
The s uc u e o he ins umen al so wa e and
in o ma ion complex is shown in Figu e 2. Be o e
c ea ing he diag am, i should be no ed ha he
abo e s uc u e shows he main componen s o a
da abase and knowledge managemen sys em o
designing obo ic sys ems.
The p oposed p inciples o implemen ing
p oblem-based in o ma ion echnology allow
c ea ing a uni e sal en i onmen o he
accumula ion, s uc u ing, and in eg a ion o
knowledge in he design o obo ic sys ems. The
ins umen al so wa e and in o ma ion complex
p o ides suppo o design decision-making,
au oma ed compa ibili y con ol, and he o ma ion
o an in eg al model-pa ame e space, which
signi ican ly inc eases he e iciency and quali y o
enginee ing de elopmen s.
Figu e 2: S uc u e o a so wa e and in o ma ion complex o suppo he design o obo ic sys ems
Sou ce: c ea ed by he au ho on he basis o [9, 5, 4, 3, 11]
The pu pose o his s udy was o subs an ia e and
build an in o ma ion echnology o ep esen ing he
knowledge o obo ic sys em designe s based on he
model-pa ame e space, as well as o c ea e
app op ia e ools o suppo design decisions. The
esul s con i m he hypo hesis ha he use o a
model-pa ame ic app oach can inc ease he
in eg i y and consis ency o he knowledge equi ed
o design complex echnical sys ems. This is in line
wi h he indings o Valkman e al. [9], who
subs an ia ed he e ec i eness o using <M,P>-
space in building complex in o ma ion models.
A he same ime, he e a e di e en iews in he
li e a u e on he ex en o which in eg a ion o
he e ogeneous models is easible and o wha ex en
au oma ed sys ems can con ol he consis ency o
such knowledge. Fo example, Choi and Kim [1]
poin ou he high isk o e o s when combining
models buil using di e en desc ip ion languages
and a di e en s ages o he li e cycle. Howe e , ou
esea ch posi ion is ha hese p oblems can be
educed by using clea o mal in e ope abili y
measu es and specialized e i ica ion modules in
so wa e and in o ma ion sys ems, which was
implemen ed in his s udy. This app oach is also
suppo ed by Xu e al. [3], who emphasize he
impo ance o c ea ing algo i hms o con olling
inconsis encies in indus ial design en i onmen s.
So wa e and in o ma ion complex
In e aces
Use ’s In e aces o exchange wi h
CAD sys ems
Da abase o pa ame e s and models (<M,P>)
Sys em analysis and e i ica ion modules
Ca alog Knowledge neighbo hoods A chi e o models
Compa ibili y module Consis ency module Coa ing analysis module
Gene a o o me hods and au oma ed epo s
Jou nal o Theo e ical and Applied In o ma ion Technology
15 h Sep embe 2025. Vol.103. No.17
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
6557
The s udy by Yao e al. [2] emphasizes ha
excessi e o maliza ion o models can limi he
lexibili y o enginee ing hinking and adap a ion o
new equi emen s. We can pa ially ag ee wi h his
opinion, bu i is wo h emphasizing ha he use o
he concep o knowledge neighbo hoods allows
localizing knowledge agmen s o lexible adap i e
analysis wi hou comp omising he in eg i y o he
o e all sys em. Thus, he p oposed app oach
combines a o malized s uc u e wi h he possibili y
o local expansion and e inemen o in o ma ion.
Compa ed o Guo e al. [4], who conside knowledge
in eg a ion in he ield o au oma ed manu ac u ing
wi h an emphasis on isual in e p e a ion, ou s udy
goes beyond isualiza ion and ocuses on o mal
logical ela ionships and an analy ical amewo k
ha allows quan i ying he compa ibili y and
comple eness o he model.
The esul s a e consis en wi h he hypo hesis, bu
he e a e ce ain limi a ions o he s udy. Fi s , he
buil me ics and in eg a ion p inciples we e es ed
on a limi ed numbe o es asks and cases, which
does no exclude he need o u he esea ch o
scale hem up. Secondly, he issue o adap ing his
echnology o eal indus ial CAD sys ems emains
open and equi es addi ional expe imen al wo k in
p oduc ion condi ions. The p ac ical applica ion o
he esea ch esul s is seen in he implemen a ion o
knowledge suppo ool modules in indus ial
so wa e and in o ma ion sys ems o he design o
au oma ed and obo ic sys ems. This will no only
educe design ime bu also educe he numbe o
design e o s and inc ease he eliabili y o echnical
solu ions.
In gene al, he esul s ob ained a e consis en wi h
some o he exis ing scien i ic app oaches, while
demons a ing he need o de elop he concep o
model-pa ame e space in he di ec ion o
adap abili y and in eg a ion in o indus ial digi al
en i onmen s. Fu he esea ch should be di ec ed o
he de elopmen o me hods o scaling
compu a ional p ocedu es and es ing he echnology
in he condi ions o eal manu ac u ing en e p ises.
5. CONCLUSION
The esul s ob ained show ha he use o he model-
pa ame e space in combina ion wi h o mal
measu es o model compa ibili y and consis ency
makes i possible o imp o e he quali y o design
solu ions o complex obo ic sys ems. The p oposed
concep o knowledge neighbo hoods has
demons a ed he abili y o localize and adap i ely
in eg a e agmen s o in o ma ion om di e en
sou ces wi hou comp omising he o e all in eg i y
o knowledge. Unlike classical app oaches, whe e
isualiza ion ools and in o mal in eg a ion me hods
domina e, he de eloped app oach is based on
igo ous ma hema ical models and quan i ies
comple eness and consis ency, which is inno a i e
o p ac ical p ojec asks. A he same ime, he
s udy showed ha scaling such solu ions o la ge
indus ial sys ems equi es signi ican op imiza ion
o compu a ional algo i hms and lexibili y o
in e aces o use s wi h di e en le els o
compe ence. I is impo an o no e ha he p oposed
app oaches wo k mos e ec i ely in sys ems wi h a
clea ly s uc u ed subjec a ea, while in dynamic
en i onmen s wi h equen changes in pa ame e s
and asks, u he expansion o au oma ic sel -
adap a ion mechanisms is equi ed. I is
ecommended o ocus u he esea ch on he
de elopmen o cloud e sions o such so wa e and
in o ma ion sys ems, which will allow p ocessing
la ge amoun s o da a in eal ime and in eg a ing
wi h indus ial digi al pla o ms. Ano he p omising
a ea is he combina ion o he model-pa ame ic
app oach wi h a i icial in elligence me hods o
au oma ically gene a e new models and design
scena ios based on he expe ience and da a om
p e ious p ojec s.
REFERENCES:
[1] S. H. Choi and B. S. Kim, “In elligen ac o y
layou design amewo k h ough collabo a ion
be ween op imiza ion, simula ion, and digi al
win”, Jou nal o In elligen Manu ac u ing,
Vol. 36, No. 3, 2025, pp. 1547–1561.
h ps://doi.o g/10.1007/s10845-024-02340-3
[2] X. Yao, N. Ma, J. Zhang, K. Wang, E. Yang and
M. Faccio, “Enhancing wisdom manu ac u ing as
indus ial me a e se o indus y and socie y 5.0”,
Jou nal o In elligen Manu ac u ing, Vol. 35,
No. 1, 2024, pp. 235–255.
h ps://doi.o g/10.1007/s10845-022-02027-7
[3] Z. Xu, Z. Xu, F. Chen, J. Lu, Z. Shen, S. Wang
and L. Cao, “Machine lea ning in elligen assis ed
co ec ion o ool ca hode o blisk
elec ochemical machining”, In e na ional
Jou nal o P ecision Enginee ing and
Manu ac u ing, Vol. 36, No. 1, 2025, pp. 1–17.
h ps://doi.o g/10.1007/s12541-025-01231-8
[4] Y. Guo, M. Sun, F. P. W. Lo and B. Lo, “Visual
guidance and au oma ic con ol o obo ic
pe sonalized s en g a manu ac u ing”, In 2019
In e na ional Con e ence on Robo ics and
Au oma ion (ICRA) (pp. 8740–8746). IEEE,
2019.
h ps://doi.o g/10.1109/ICRA.2019.8794123