REDESIGNING THE KNOWLEDGE OF ROBOTIC SYSTEM ENGINEERS THROUGH THE MODEL-PARAMETRIC SPACE OF INFORMATION TECHNOLOGY

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