KAUNAS UNIVERSITY OF TECHNOLOGY

ŽYDRŪNAS RUTKAUSKAS

DEVELOPMENT OF INTELLIGENTMODEL FOR PLASTICS INJECTION

MOLD DESIGN

Summary of Doctoral DissertationTechnology Sciences, Mechanical Engineering (09T)

2009, Kaunas

2

The research was accomplished during the period of 2004 – 2009 at KaunasUniversity of Technology, Manufacturing technology department and supportedby the Lithuanian State Science and Studies Foundation and during the period of2005 – 2007 at Ilmenau University of Technology, Faculty of MechanicalEngineering, Department Quality Management supported German AcademicExchange Service (DAAD).

Scientific supervisor:Prof. Dr. Habil. Algirdas BARGELIS (Kaunas University of Technology,Technology Sciences, Mechanical Engineering – 09T).

Council of Mechanical Engineering Science trend:Prof. Dr. Habil. Antanas ŽILIUKAS (Kaunas University of Technology,Technology Sciences, Mechanical Engineering– 09T) – chairman;Prof. Dr. Habil. Vytautas GINIOTIS (Vilnius Gediminas Technical University,Technology Sciences, Mechanical Engineering – 09T);Assoc. Prof. Dr. Eglė JOTAUTIENĖ (Lithuanian Agriculture University,Technology Sciences, Mechanical Engineering– 09T);Assoc. Prof. Dr. Kazimieras JUZĖNAS (Kaunas University of Technology,Technology Sciences, Mechanical Engineering– 09T);Prof. Dr. Habil. Donatas LEVIŠAUSKAS (Kaunas University of Technology,Technology Sciences, Informatics Engineering – 07T).

Official Opponents:Assoc. Prof. Dr. Valdas EIDUKYNAS (Kaunas University of Technology,Technology Sciences, Mechanical Engineering – 09T);Prof. Dr. Habil. Leonidas SAKALAUSKAS (Institute of Mathematics andInformatics, Physical Sciences, Informatics – 09P).

The official defense of the dissertation will be held at 10 a.m. on 22th of June,2009 at the Council of Mechanical Engineering Sciences trend public session inthe Dissertation Defense Hall at the Central Building of Kaunas University ofTechnology.Address: K. Donelaičio g. 73-403, 44029 Kaunas, Lithuania.Tel. (370) 37 300042, fax (370) 37 324144; e-mail mok.skyrius@ktu.lt

The summary of dissertation was sent on 22th of May, 2009.

The dissertation is available at the library of Kaunas University of Technology(K. Donelaičio g. 20, Kaunas).

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KAUNO TECHNOLOGIJOS UNIVERSITETAS

ŽYDRŪNAS RUTKAUSKAS

INTELEKTUALAUS MODELIOSUKŪRIMAS PLASTIKŲ LIEJIMO

FORMOMS PROJEKTUOTI

Daktaro disertacijaTechnologijos mokslai, mechanikos inžinerija (09T)

2009, Kaunas

4

Disertacija rengta 2004 – 2009 metais Kauno technologijos universitete,Gamybos technologijų katedroje, remiant Lietuvos valstybiniam mokslo irstudijų fondui ir 2005 – 2007 metais Ilmenau technikos universitete, Kokybėsužtikrinimo katedroje (Vokietija), remiant Vokietijos akademinių mainų tarnybai(DAAD).

Mokslinis vadovas:Prof. habil. dr. Algirdas BARGELIS (Kauno technologijos universitetas,technologijos mokslai, mechanikos inžinerija – 09T).

Mechanikos inžinerijos mokslo krypties taryba:Prof. habil. dr. Antanas ŽILIUKAS (Kauno technologijos universitetas,technologijos mokslai, mechanikos inžinerija – 09T) – pirmininkas;Prof. habil. dr. Vytautas GINIOTIS (Vilniaus Gedimino technikos universitetas,technologijos mokslai, mechanikos inžinerija – 09T);Doc. dr. Kazimieras. JUZĖNAS (Kauno technologijos universitetas,technologijos mokslai, mechanikos inžinerija – 09T);Doc. dr. Eglė JOTAUTIENĖ (Lietuvos žemės ūkio universitetas, technologijosmokslai, mechanikos inžinerija – 09T);Prof. habil. dr. Donatas LEVIŠAUSKAS (Kauno technologijos universitetas,technologijos mokslai, informatikos inžinerija – 07T).

Oficialieji oponentai:Doc. dr. Valdas EIDUKYNAS (Kauno technologijos universitetas, technologijosmokslai, mechanikos inžinerija – 09T);Prof. habil. dr. Leonidas SAKALAUSKAS (Matematikos ir informatikosinstitutas, fiziniai mokslai, informatika – 09P).

Disertacija bus ginama 2009 m. birželio 22 d. 10 val. viešame Mechanikosinžinerijos mokslo krypties tarybos posėdyje, kuris įvyks Kauno technologijosuniversitete, Centrinių rūmų disertacijų gynimo salėje.

Adresas: K. Donelaičio g. 73-403, 44029 Kaunas, Lietuva.Tel. 8 37 300042, faksas 8 37 324144; el. paštas mok.skyrius@ktu.lt

Disertacijos santrauka išsiųsta 2009 m. gegužės 22 d.Su disertacija galima susipažinti Kauno technologijos universiteto bibliotekoje(K. Donelaičio g. 20, Kaunas).

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IntroductionRelevance of thesisThe relevance of research is conditioned by the constantly growing

demand of plastics injection mold design systems and the increasing quantity ofparts made of plastic. There are more than 20 enterprises working with theplastics injection mold design and manufacture and production of plastic parts inLithuania. The most known of them: AB „Snaigė“, UAB „Almecha“, UAB„Vilniaus Vingio mechanika“, AB „Salinta“, etc.

The design automation of new products and manufacture processes has ahigh importance, when the life cycle of new products increasingly shortens. Theuse of intelligent design systems allows performing design works faster, whencomputer executes intelligent processes and iterative operations.

It is very important to decrease the time cost of new product design and touse all available resources optimally. For customers is important to design theproduct as fast as possible with minimal design expenses. The main implementfor the decrease of time cost is the integration of mold design and manufacturing.The development of these intelligent design modules makes easier theintegration of mold design and manufacturing, allows for user to quicklyevaluate constructional variants of plastics injection mold and decreases cost ofdesign and manufacture time. These modules allow forecasting design errors ofplastics injection mold and estimating design expenses still in early design stage.

The objective of the scientific researchThe objective of the dissertation is to develop the intelligent design model

for designing of injection mold, using knowledge base and artificial intelligence.

Tasks1. To review methods, which are used in the development of intelligent

design systems for plastics injection mold and to make the classificationof scientific research methodologies of this scope.

2. To develop the module for mold cavity number definition based on themolded part mass, manufacture time, output, clamping force of moldingmachine and qualitative parameters and to evaluate the precision of themodule by detecting the variation coefficient of practical data andtheoretical results.

3. To develop the module for the design and selection of molding systemand to evaluate the precision of it by detecting the variation coefficientof practical data and theoretical results.

4. To develop the module for the definition of plastics injection mold malecavity/female cavity plate dimensions, which is based on Fuzzy logicand knowledge base and to evaluate the precision of it by detecting thevariation coefficient of practical data and theoretical results.

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Research methodsMathematical sets, fuzzy logic theory and artificial intelligence (AI)

methods are applied in the research. Using these methods, the intelligent modelof plastics injection molding design was developed and experimentally tested.

Scientific noveltyThe developed intelligent model of plastics injection mold design allows

to evaluate possible alternatives of design and manufacturing technology in theearly design stage. Heuristic rules, Fuzzy logic and mathematical sets theorymethods are used for solutions finding.

Practical valueThe developed intelligent model of plastics injection mold design can be

applied in small and medium, either different purpose manufacture systems, inorder to make design works of plastics injection molds faster and low-costed,reducing manufacture cost and improving quality of parts to be molded.

Subsystems for the definition of cavity number, molding system and malecavity/female cavity plate dimensions were developed using the results of theperformed research. They acquire knowledge and plastics injection mold designrules for the selection of cavity number, molding system and male cavity/femalecavity plate dimensions and parameters.

Propositions to be defended1. The model of plastics injection mold design, which is based on

knowledge base and intelligent functional modules.2. The method for optimal mold cavity number definition.3. The model of mold system design.4. The module for the design of plastics injection mold male cavity/female

cavity plate dimensions, which is based on fuzzy logic.Publications and approval of the workOn the subject of the dissertation were presented 5 papers in international

scientific conferences in Lithuania and Germany.On the subject of the dissertation were published 7 articles: 1 article in the

master Journal List of the Institute for Scientific Information (ISI), 1 article inInstitute for Scientific Information (ISI) proceedings, 2 articles in editions fromthe list, approved by Science Council of Lithuania and 3 articles in other referrededitions.

1. CONCLUSIONS OF LITERATURE REVIEW AND SCIENTIFICJUSTIFICATION

The integrated definition of all manufacture parameters is one of the mostimportant objects in modern-day computer aided design (CAD) systems forplastics injection molds. It is known that the cost of plastics injection molddepends on design methods and technological processes. It is necessary to

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integrate the plastics injection molds design and manufacturing processes, inorder to design and produce cost-effective, high quality and competitive productsand calculate design and production costs at the same time. However, the CADsoftware does not associate product design with product manufacturing processesand their capabilities, product quality and manufacture costs.

Following from the analysis of the literature sources known to us (approx60), the following conclusions can be made:

1. Universal plastics injection mold design, cost of mold and partmanufacturing and various possibilities application to increase moldvariety considered 52%; separate mould design modules as molding,cooling and etc. is taken 26%; while the most rarely are analyzedproblems developing intelligent design of plastics injection molds,molding process, cavity number definition and cavity layout models22%.

2. The analysis of computerized and automated plastics injection molddesign and manufacture technology design systems, which are used inmodern integrated manufacture, showed that engineering labor costs formanufacturing technology design highly depend on the knowledge baseand the use of intelligent functional modules.

3. The analysis of literature sources indicates that disposable and knownto us systems for plastics injection mold design require a lot of qualifiedengineering work in all injection mold design stages. This deficiency isproposed to remove by developing universal, artificial intelligence (AI)based and easily adaptable to various plastic injection mold designintelligent systems, which require minimal time and effort of engineer,and minimal computer resources and equipment.

4. The analysis of plastics injection mold design systems showed that theintelligence level of the systems is not high enough, the quality andduration of designing are conditioned by the knowledge and experienceof the designer. The designer must rely on his own experience whenselecting one or another parameter, so using the standard systems,problems with cavity number, molding system, and injection molddimension definition arise.

According to the conclusions of scientific literature review it was decidedto develop the intelligent model of plastics injection mold design, by integratingthe following injection mold design main stages:

1. The definition of mold cavity number.2. The design of molding system.3. The definition of male cavity/female cavity plate dimensions.4. The estimation of plastics injection mold manufacture costs.

The structure of developed intelligent integrated model is presented inFigure 1.

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Fig. 1 Environment of the integration of plastics injection mold and manufacturecosts

2. DEVELOPMENT OF MODEL FOR PLASTICS INJECTION MOLDSDESIGN, APPLYING KNOWLEDGE BASE AND FUZZY LOGIC

2.1 Study formulation

The design of new plastic injection molds and their manufacturingprocesses is based on competitive engineering principles. The objective of thisprinciple is to reduce discrepancies and errors, and shorten the duration of theproduct creation in design stages of plastics injection mold. The main designtools for the acceleration of product design are CAD systems as Solidworks,Mechanical Desktop, Catia, and others. These systems can perform morefunctions, such as the selection of materials, standard components and the like, ifthey have additional databases. However, here ends the potentiality of themajority of CAD systems. Applying Fuzzy logic and mathematical formalizationmethods it is seeking: to try more plastics injection molds design options duringthe same time and find optimal variant.

2.1.1 Research methods

Theoretical and experimental researches based on the intelligent functionalmodels, computer aided design methods, using the knowledge base, themeasurement theory of mechanical dimensions, mathematical statistics,mathematical and Fuzzy logic, theory of sets methods, and probability theory areperformed in the dissertation. For the modeling and analysis of data these

User requirements

Design of plasticsinjection mold

Estimation ofplastics injection

mold manufacturecost

Definition of cavity number Cavity layout Selection of male cavity/female cavity

plate dimension Selection of molding system Selection of cooling system Selection of part ejection system

Injection mold technology and design Alternatives assessment according to

production costs and duration Assessment of manufacture systems

potential

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software packages were used: Microsoft Office Excel, MatLab, COSMOSworks,and OriginLab.

2.1.2 Scope of the investigation

The dissertation results are best suited for the design and manufacture ofmechanical components, electronics and mechatronics products made of plastics.The developed model can help for plastics injection molds designers andproducers to determine the number of cavities, molding system parameters andtype, and male cavity/female cavity plate overall dimensions. These are scopeswhere the injection molds designers and manufacturers can expect the greatestbenefits, using the obtained results of dissertation researches.

2.1.3 Research scheme

Fig. 2 Block diagram of theoretical research: 3D CAD - three-dimensional computeraided design system; DFA - Design for assembly; DFM - Design for manufacturability;

DFC - Design for cost; DFPC – Design for process capability

The block diagram of theoretical research is presented in Figure 2. Thetool for the aid of designer is developed, which solves problems with the help ofinterfaces of computer aided design and technological processes. Using thedeveloped theoretical methods and technical means, would be easier to find theoptimal variant of plastics injection molds design. The designing of newinjection mold and technological processes at the same time allows to anticipatewhat design variants are possible and to verify their suitability, so it is possible toavoid design errors in the early product design stage.

Equipmentpotential

Workingtime

Procesespotential

Evaluation of plasticsinjection mold order

Interfase 1

Design of plasticsinjection mold

3D CAD DFA DFM

DFC DFPC

Interfase 2

Manufacture ofplastics injection

mold

Manu-facture

expenditures

Machining Procesespotential

Others

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2.2 Intelligent model structure of plastics injections molds

The intelligent model for plastics injection molds design was developedaccording to the design course of injection molds. The structure of the model ispresented in Figure 3.

Fig. 3 Structure of intelligent model for plastics injection molds design

As it seen from Fig. 3, the developed model consists of three modules:1. The definition of mold cavity number.2. The design of molding system:

The layout of mold cavities; The selection of molding system type; The selection of runner type and the definition of runner

parameters; The selection of gate type and the definition of gate parameters.

Definition ofmold cavity

number (LLS)

Selection ofmolding

system (LS)

Selection of male/female cavity plate

(LPM)

Interface

Manufacturecost estimation

LLSGKB

LLSGDB

LLSKB

LLSDB

LPMDB

LSDB

LSKB

LPMKB

Expert Manual CAD

Interface

Manufacturecost estimation

Manufacturecost estimation

ExpertManual CAM

LPMGDB

LSGDB

LLSKB

LPMKB

Interface

Data input

Data input

Data input Data input

Results Results Results

Data input

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3. The definition of male cavity/female cavity plate overalldimensions:

The definition of distance between mold cavities; The definition of male cavity/female cavity plate length, width and

thickness.

2.2.1 Module for definition of mold cavity number

The number of mold cavity n is defined according to this inequality:

qt nnn , (1)

),,min( lmmfq nnnn ; (2)

where nt is the number of mold cavity, evaluating the product delivery deadline;nf is the number of mold cavity, evaluating molding machine clamping force andthe internal pressure; nm is the number of mold cavity, evaluating mass of thepart to be molded, respectively to appropriate molding machine; nlm is thenumber of mold cavity, evaluating maximal male cavity/female cavity plateoverall dimensions.

Before the evaluation of the results, the following note must be taken intoaccount: if the cavity number in accordance with product delivery time nt isgreater than the qualitative cavity number nq, then it is necessary to increase theproduction time, or change the molding machine.

The number of mold cavity, according delivery time to customer nt, iscalculated [1]:

d

cat t

LtKn ; (3)

where Ka is is defect factor; tc is the cycle time of the part to be molded, s; L isproduction volume, pcs; td is part production volume delivery time to customer.

The number of mold cavity nm, according mass of the part to be molded, iscalculated:

LD

DD

mLM

LM

m

km

mm

DDn

max

22

23

; (4)

where mD is the mass of the part to be molded, g; DLM – molding machine screwdiameter, mm; kL is the coefficient evaluating the mass of gates and runners;mDmax is the maximal mass of the part to be molded, g; m – density, g/mm3.

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mAL

LLKeAAk )/(

213 , (5)

2maxmax1 20501,07171,061795,0 ssA , (6)

2maxmax2 20866,072532,062494,0 ssA , (7)

2maxmax3 16,272,1149,281 ssA ; (8)

where A1, A2, A3 are experimental constants; smax is the maximal thickness of thepart to be molded, mm.

The number of mold cavity nlm, evaluating male/female cavity plate maximaloverall dimensions, is calculated:

aL

LIs

yL

yIs

xL

xxPMlm kl

dabbkb

kabkl

kkln11

1

1

21

2)2(

,2

min

, (9)

LMPM ll 95,0 ; (10)

where lPM is the length of male/female cavity plate, mm; k1x is the minimalallowable distance between mold cavities in x direction, mm; k2x is the minimalallowable distance in x direction from the edge of male/female cavity plate to theedge of mold cavity, when cavities are laid by the row principle, mm; lL is thelength of mold cavity, mm; bIs is the width of ejector plate, mm; k1y is theminimal allowable distance between mold cavities in y direction, mm; bL is thewidth of mold cavity, mm; lLM is the allowable distance of plastics injection moldin the molding machine, mm; k1a is the minimal wall thickness, when moldcavities are laid by the circle principle, mm; ,3ka if 03 k , ,0a if 03 k ;

,5kd if 05 k , ,0d if 05 k .

yIsPMFF kbbbbk 23 2

2)))(((

, (11)

LMPM bb 95,0 , (12)

akbbk yLIs 15 22 ; (13)

where bF is the width of flange plate, mm; bPM is the width of male/female cavityplate, mm; k2y is the minimal allowable distance in y direction from the edge ofmale cavity/female cavity plate to the edge of mold cavity, mm.

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The number of mold cavity nf, according the clamping force of molding machineand the internal pressure of material injection, is calculated:

D

LLf S

Sn ; (14)

where LLS is the maximal projection area of mold cavities, mm2; calculated byreferring to dependencies of Fuzzy logic, presented in Figures 4 – 6; DS is theprojection area of mold cavities, mm2.

Fig. 4 Dependence of molding machines clamping force FLM on : FLM1 – moldingmachines with very low clamping force; FLM2 –– molding machines with low clampingforce; FLM3 – molding machines with medium clamping force; FLM4 – molding machines

with high clamping force; FLM5 – molding machines with very high clamping force

Fig. 5 Dependence of internal pressure on

00,10,20,30,40,50,60,70,80,9

1

0 8 16 24 32 48 50

pv

pv, MPa

µpv

00,10,20,30,40,50,60,70,80,9

1

0 500 1000 1500 2000 2500 3000 3500 4000 4500

FLM1

FLM2 FLM3

FLM4

FLM5

F, kN

µFL

14

Fig. 6 Molding area dependence on

The method of results scanning „F“ is used for the projection area of moldcavity calculation:

))(1(0max0 LLLLSLLLLLL SSSS ; (15)

where0LLS is the initial value of molding area of appropriate molding machine

clamping force interval, when = 1;maxLLS is the maximal value of molding

area of appropriate molding machine clamping force interval, when = 0;

SLL is the real value of , obtained from the dependencies (Fig. 4 – 6 ) andrules interaction.

2.2.3 Estimation of plastics injection mold manufacture costs

While analyzing plastics injection mold manufacturing costs, it is veryimportant to evaluate the complexity of injection mold. It is advisable to evaluatethe complexity of plastics injection mold on the basis of design features,presented in Table 1.

Table 1. Classification of the design features

Class 1 2 3

DesignFeature

Class 4 5 6

DesignFeature

00,10,20,30,40,50,60,70,80,9

1

0 2500 5000 7500 10000 12500 15000 17500 20000 22500 25000 27500 30000

µSLL

R1

R2

R3

R4

R5

SLL, mm

15

In general case the injection mold (PLF) is possible to describe by thisdependency structure [2]:

m

jji

k

ii DFRRPLF

11; (16)

where PLF is the plastics injection mold; R is the plastics injection moldcomponent ki ,...,1 , DF is the design feature, mj ,...,1 k is the number ofplastics injection mold components; m is the number of design feature.

The highest influence on the cost of production has the manufacturingtime of technology operation or stage, so the calculation of machining time forDF was performed in the work. The obtained data are presented in Figure 7.

Fig. 7 Design features time norms dependencies on removable volume

2.2.4 Module for definition of molding system

The molding system is one of the major mold element, on which definitiondepends the quality of the molded parts. The purpose of the molding system is toprovide molding mass to mold cavities. The gate, its shape and parameters affectthe process of filling cavities, and also the quality of parts, therefore the properselection of molding system components is very important. The selection processof molding system and its parameters is given in Figure 8.

The selection of molding system LS and its parameters is carried out inaccordance with this functional dependence:

00,831,662,503,334,175,005,836,667,508,339,1610,010,8311,6612,513,3314,106

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000V, mm3

A, h

DF 3

DF 6

DF 2

DF 1 DF 5 DF 4

16

),,,,,,,( LTLKTLMMISFGMnfLS LLLLLL ; (17)

where n is the number of mold cavity; GMLL are overall dimensions of moldcavity; FLL is the shape of mold cavity; ISLL is the variant of mold cavities layout;M is the molding material; LM is the molding machine; LKT is the type ofrunner; LT is the type of gate.

Appealing to the analysis of the literature sources [3-6] and practicalexperience in enterprises, runners and gates were detected, which are most oftenused in practice. Based on their advantages and disadvantages, and theirproduction cost, if...then rules were developed for their selection.

Fig. 8 Algorithm for molding system selection

2.2.5 Module for definition of plastics injection mold plate dimensions

The module for the selection of plastics injection mold male and femalecavity plate dimensions was developed using Fuzzy logic and Hasco standardparts database. The structure of the model is presented in Figure 9.

The selection of male/female cavity plate is performed by the followingcriteria:

1. The cost of male/female cavity plate → min.

No

Yes

Definition of runnertype and calculation of

its parameters

Definition of moldcavity layout

Definition of moldingsystem type

Definition of gate typeand calculation of its

parameters

Parts layout scheme;Molding system;Runner shape;Runner type and its parameters;Gate type and its parameters.

Gate type: Tunnel Vertical Dotted RectangularFacts, rules for the selection of

molding system, runners andgates types and its parameters

Runner types Circular Parabolic Trapezium

Molding systemtypes:

Normal Hot Cold

Are allcavities filling ?

DB of moldingsystem, runnersand gates types

DB of cavitieslayout schemes

Data input

Results

17

2. The efficient consumption of male/female cavity plate material →max.

3. The mass of male/female cavity plate → min.4. The exploitation of male /female cavity plate, male/female cavity

plate cost → max.

Fig. 9 Algorithm for male/female cavity plate definition

Male cavity/female cavity plate overall dimensions are calculated usingthe dependences of Fuzzy logic presented in Fig. 10 – 12 and formulas:

)min(:soptopt LPPMP , (18)

TKvMvvKvLP PMNPMPMNoptk 43)21 )1( ; (19)

where PMPopt is the optimal sign of male cavity plate; LPopt is the coefficient ofmale cavity/female cavity plate selection; KPMN is the inefficient cost of malecavity/female cavity plate, Lt; MPMN is the minimal mass of male cavity/femalecavity plate, kg; TK is length of runner to one cavity, mm PM is the real valueof , obtained from the dependences (Fig. 10 – 12); v1, v2, v3, v4, are importancecoefficients; k is the number of male/female cavity plates; ms ,...1 , m – thenumber of cavity layout in database.

lL, bL,k1x k2x,k1y ,k2y, k1a

Are moldingcavities circular?

Calculation of occupiedcavity diameter dLL and

required male /female cavityplate thickness SRPM

Calculation of efficient male/female cavity plate diameter

dPMN

Selection of male /femalecavity plate

Yes No

DB of male/female cavity

plate

Facts,rules

Calculation of occupied cavitylength lLL, width bLL, thickness

and required malecavity/female cavity plate

thickness SRPM

Calculation of efficient male/female cavity plate length

lPMN and width bPMN

dLL ≤ dPMN; lLL ≤ lPMN;bLL ≤ bPMN; SLL≤ SPM;KPMN → min; mPMN →min

Male cavity/femalecavity plate overalldimensions, cost andmass

Results

Data input

18

Fig. 10 Dependences fragment of male cavity/female cavity plate efficientlength, when the parts are laid by the row principle

Fig. 11 Dependences fragment of male cavity/female cavity plate efficient width,when the parts are laid by the row principle

1PMNb2PMNb

3PMNb …jPMNb …

kPMNbBPMN

00,10,20,30,40,50,60,70,80,91

0 50 100 150 200 250 300 350 400 450 500 550 600 650bLN,, mm

0

0,10,2

0,30,40,5

0,6

0,70,80,91

0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 9501000

1PMNl2PMNl

3PMNl …jPMNl …

kPMNl

jPMNljPMNl

LPMN

lLN, , mm

19

Fig. 12 Dependences fragment of male cavity/female cavity plate thickness, mm

)min(: 65, jj PMNPMNjs MvKvPMP , (20)

)1(jjjj SPMPMPMPMN kKK , (21)

)1()( jjj SPMPMPMjPMN kMM ; (22)

where PMP is the sign of male cavity/female plate; KPM is the cost of malecavity/female cavity plate, Lt; MPM is the mass of male cavity/female cavityplate, kg; SPMk is the coefficient, which evaluates the ratio of efficient andinefficient volume of male /female cavity plate; kj ,...1 , v5, v6, are importancecoefficients;

jjj PMNPMSPM VVk / ; (23)

If mold cavities are laid by the row principle:

3

)(jjj

j

SPMBLPMNLPMNPM

; (24)

If mold cavities are laid by the circular principle:

2)(

jj

j

SPMDPMNPM

; (25)

where LPMN is the useful consumption coefficient of male cavity/female cavityplate efficient length; BPMN is the useful consumption coefficient of male

1PMS2PMS

3PMS …jPMS …

kPMSSPM

SRPM , mm

00,10,20,30,40,50,60,70,80,91

0 25 50 75 100 125 150 175 200

20

cavity/female cavity plate width; SPM is the useful consumption coefficient ofmale cavity/female cavity plate efficient thickness; DPMN is the usefulconsumption of male cavity/female cavity plate diameter.

The required length lLN and width bLN of mold cavities can be calculated,when the cavities are laid by the row principle as follows:

xxexskexskLLN kkddll 21.. 2)1()( , (26)

yeyskeyskLLN kddbb 1.. )1()( ; (27)

where lL is the length of mold cavity, mm; bL is the width of mold cavity, mm;dsk.ex is the number of mold cavity in x direction, pcs.; dsk.ey is the number of moldcavity in y direction, pcs.; k1x is the minimal allowable distance between thecavities in x direction, mm; k2x is the minimal allowable distance in x directionfrom the edge of male cavity/female cavity plate to the edge of mold cavity, mm;k1y is the minimal allowable distance between the cavities in y direction, mm.

The calculation of required mold cavity diameter, when they are laid bythe circular principle, is performed as follows:

LLNLMM bdd 2 ; (28)

where yLN kd 12 , if ykA 12 ; AdLN , if ykA 12 ; LMMd is required moldcavity diameter, when they are laid by the circular principle, mm; dLN is requiredminimal mold cavity diameter, when they are laid by the circular principle, mm;

Lb is maximal mold cavity width, mm, if the cavity is circular LL db ;

)2( 1aL klnA

; (29)

where n is the number of mold cavity; lL is the length of mold cavity, mm, if thecavity is circular LL dl ; ld is the diameter of mold cavity, mm; ak1 – is theminimal wall thickness, when the cavities are laid by the circular principle.

For the calculation of efficient length lPMN and width bLPN of male /femalecavity plate, when mold cavities are laid by the row principle calculate asfollows:

PMPMN ll , (30)

abb IsPMN ; (31)

21

where lPM is the length of male/female cavity plate, mm; bIs is the width ofejector plate, mm; ,3ka if 03 k , ,0a if 03 k .

While for the calculation of efficient diameter dPMN of male /female cavityas follows:

abd IsPMN , (32)

yIsPMFF kbbbbk 23 2

2)))((

; (33)

where bF is the width of flange plate, mm; bPM is the width of male/female cavityplate, mm; k2y is the minimal allowable distance in x direction from the edge ofmale/female cavity plate to the edge of mold cavity.

The required thickness of male cavity/female cavity plate is calculated bythis expression:

zLspRPM khkS ; (34)

where SRPM is the required thickness of male cavity/female cavity plate, mm; kspis the coefficient of correction; hL is the height of mold cavity, mm; kz is theminimal required thickness of male cavity/female cavity plate, mm.

2.2.6 Calculation of minimal allowable distance between mold cavities andminimal allowable distance from the edge of male cavity/female cavity plateto the edge of mold cavity

Functional dependences of minimal male cavity/female cavity platethickness, minimal allowable distance between mold cavities and minimalallowable distance from the edge of male cavity/female cavity plate to the edgeof mold cavity are expressed as [7,8]:

k1x, k2x, k1y, k2y, k1a ),,,,,,( tfmphblf PLFvLLL , (35)

),,,,( tfmpSfk PLFvdz ; (36)

where pv is the internal pressure, Pa, mPLF is the material of plastics injectionmold; t is the temperature load, °C; Sd is the projection area of mold cavity, mm2.The dependences for the calculation of minimal distance between mold cavitiesand minimal male cavity/female cavity plate thickness were created on the basisof the structural scheme presented in Figure 13.

22

Fig. 13 Modeling scheme of minimal male/female cavity plate thickness, minimalallowable distance between mold cavities and minimal allowable distance from the edge

of male cavity/female cavity plate to the edge of mold cavity: a – mold cavities arecircular; b – mold cavities are rectangular; c – mold cavities are circular; d – mold cavities

are rectangular

3. EXPERIMENTAL INVESTIGATION OF DEVELOPEDINTELLIGENT MODEL FOR PLASTICS INJECTION MOLD DESIGN

The purpose of experimental investigation was to verify and substantiatethe theoretical investigations and claims, and evaluate their accuracy.

Performing the experimental investigation the following were done:1. The precision analysis of mold cavity definition module by detecting

the variation coefficient of practical data and theoretical results.2. The precision analysis of plastics injection molds manufacture costs

estimation by detecting the variation coefficient of practical data andtheoretical results.

3. The precision analysis of the module for molding system selection bydetecting the variation coefficient of practical data and theoreticalresults.

4. The precision analysis of the module for injection mold malecavity/female cavity plate dimensions by detecting the variationcoefficient of practical data and theoretical results.

pv

bL or lL

k1x pv

bL or lL

hL

a b

kz

lL

pVpV

dL

kz

c d

23

3.1 Testing of module for mold cavity number definition

In the dissertation, for the analysis of mold cavity definition, evaluatingthe submission of the product, 18 parts and 5 molding machines were used. Theobtained results are presented in Figure 14 and Table 2.After the testing of mold cavity number definition module, it was found that:

1. The average data variation coefficient of theoretical and practical resultsof mold cavity number is 6 %, and the average variation coefficient oftheoretical and practical results of molding machine selection is 12%.

2. The mismatch of mold cavity number occurs due to the differentselection of spoilage factor. The expert of each enterprise selects thisfactor in accordance to the methodology nominated in his enterprise andintuition.

3. The mismatch of mold cavity number occurs due to the part cycle time,worker brakes and the calculation of molding machine rest time.

4. The mismatch of mold cavity number can occur due to the calculationmethodology of safe distance between mold cavities, when mold cavityheight is higher than 41 mm.

5. Selecting the type of molding machine our module considers only thequalitative parameters of molding machine, while in the data receivedfrom the expert, there were evaluated factors of quality, economy andmolding machine employment, therefore, the mismatch of selection hasoccurred.

Fig. 14 Comparison of practical and theoretical mold cavity number: 1 – mold cavitynumber calculated by the intelligent module; 2 – mold cavity number calculated by the

expert

0123456789

101112131415161718

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1

2

Part number

n

24

Table 2 Comparison of molding machine selection by the module and the expert

Molding mashine Total MatchThe

variationcoefficient

Battenfeld HM 600/130 18 13 23 %Allrounder 420s 1000 - 150 18 17 4 %

D55 18 13 23 %KD100 18 16 8 %

Battenfeld HM 1600/350 18 18 0,00 %The average variation coefficient 12%

3.2 Reliability analysis of plastics injection mold manufacture cost

The precision analysis of plastics injection mold manufacturing timeestimation was performed using nomograms of design features timedependencies (Section 2, Fig 2.23) by detecting the variation coefficient ofpractical data and theoretical results.

For this purpose 7 parts were used. The parts were separated to designfeatures according to Table 1 presented in Section 2. The testing of manufacturetime cost estimation was based on the manufacture time of mold cavity in femalecavity plate. Simulation results were compared with the practically calculatedtimes of mold cavity in female cavity plate. The obtained results are presented inFigure 15.

Fig. 15 Comparison of mold cavities manufacture time calculation: 1 – mold cavity infemale cavity plate manufacture time calculated by the module; 2 – mold cavity in female

cavity plate manufacture time calculated by the expert

The investigation showed that the manufacture time cost, modeled usingthe developed model, well matches the practical unitary time, the average

0,000,17

0,330,500,67

0,831,00

1,171,33

2 4 6 7 9 15 18Part number

A, min

12

25

variation coefficient of practical and theoretical data is 10%. The highestvariation coefficient of these data 17 and 23 % is obtained on the case of the 9th

and 15th part due to the incorrect or simplified separation of mold cavities todesign features. For example, when calculating the volume of the 9th part, it wasseparated to the features of the first and second class, when calculated thevolume of the 15th part, it was separated as the design feature of the 4th class.

3.3 Testing of molding system design module

Performing the testing of molding system design module the followingwere done:

1. The investigation of gate type selection reliability in accordance to thegiven rules.

2. The precision analysis of tunnel gate parameters calculation bydetecting the variation coefficient of practical data and theoretical results.

3. The precision analysis of runner parameters calculation by detectingthe variation coefficient of practical data and theoretical results.

Fragments of the obtained results are presented in Figures 16 and 17.

Fig. 16 Comparison of runner diameter bLK: 1 – calculated using methodology presentedin [1]; 2 – calculated using methodology presented in [9]; 3 – calculated using developed

intelligent model; 4 – selected by the expert

Fig. 17 Comparison of tunnel gate diameter dT: 1 – calculated using methodologypresented in [6]; 2 – calculated using methodology presented in [8]; 3 – calculated using

developed intelligent model; 4 – selected by the expert

00,5

11,5

22,5

33,5

44,5

55,5

66,5

7

1 2 3 4 5 6 7 8 9

12 3 4

Part number

dLK , mm

00,20,40,60,8

11,21,41,6

2 3 5 6 7 8 9

1 2 3 4

Part number

dT,mm

26

The investigation revealed that:1. When selecting the type of gate, the priority is set for tunnel gate in the

rules of gate selection, if it is indicated the automatic separation ofmolded part, regardless to its shape. The automatic separation of thepart was not emphasized in all the requirements of parts to be molded inour case of analyzing parts, therefore the module selects the type ofgate, which is acceptable for the part shape, while the expert selects thetype of tunnel gate by the intuition.

2. The average variation coefficient of theoretical and practical data ofrunner parameters is 10 %, compared with others methodologies [1, 9],the variation coefficient is obtained similar, respectively 11 and 12 %.

3. The average variation coefficient of theoretical and practical data oftunnel gate parameters is 25 %, but compared with other methodologies[96, 92] the variation is obtained even higher – 29 and 52 %.

4. The average variation coefficient of theoretical and practical data ofangle between gate and runner is 17 %. Compared with otherliterature sources, the angle ranges from 30 to 45°, if material isbrittle – stiff, = 30°, while for ductile and elastic = 45°. Anotherobserved trend is that designers have been selected different values ofangle for the same material.

5. The average variation coefficient of theoretical and practical data ofgate tilt is 2,5% except the case of the 5th part reaching 11 %.

3.4 Testing precision’s of male cavity/female cavity module definition

The precision analysis of male cavity/female cavity plate dimensionsselection module by detecting the variation coefficient of practical data andtheoretical results is presented in this chapter.

18 orders for the design of plastics injection mold were brought to thefore. The definition of male cavity plate was based on the following criteria:molding plate losing cost; male cavity plate losing mass and material flow path.All these criteria were considered equally, their interest rate was 0.25. Theinfluence of mold cavities filling time on the selection of male cavity plate wasnot taken into account. The obtained theoretical dimensions of male cavity platewere compared with the dimensions selected by the expert. Fragments of theobtained results are presented in Figures 18 – 20.

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Fig. 18 Comparison of theoretical and practical male cavity plate length: 1 – calculatedusing the module; 2 – selected by the expert

Fig. 19 Comparison of theoretical and practical male cavity plate width: 1 – calculatedusing the module; 2 – selected by the expert

Fig. 20 Comparison of theoretical and practical male cavity plate thickness: 1 – calculatedusing the module; 2 – selected by the expert

050

100150200250300350400450500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 2

lPM,mm

Part number

050

100150200250300350400450

1 2 33

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 2

bPM,mm

Part number

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

SPM,mm

Part number

1 2

28

After the analysis of male/female cavity plate dimensions accuracy, it wasstated that the average variation coefficient of theoretical and practical results ofmale cavity plate lengths is 13%, the distribution of the results of male cavityplate width is 11% and the distribution of the results of male cavity platethickness is 27%.

The main factors of the mismatch of male cavity plate dimensions are1. The mismatch occurs due to the different layout of mold cavities, under

different safety distance between mold cavities and safe distancebetween mold cavity and male cavity plate edges.

2. The mismatch occurs due to the calculation methodology of requiredmold cavity diameter, the mismatch of practical and calculated diameteroccurs; this mismatch decreases when the number of mold cavities isincreasing.

3. When the number of mold cavities is higher than 8, designers ofinjection mold select higher the first distance between the cavities andlater they start lowering this distance, while the module keeps the samesafety distance.

4. The filling time of the part was not taken into account while selectingmold cavities layout variant.

5. The designers not always take into account factors such as the flowpath, the molding plate price, the effective male cavity / female cavityplate exploitation, when selecting plastics injection mold dimensions.

After the comparison of minimal distance between mold cavities thefollowing can be stated:

1. The average variation coefficient of theoretical and practical data ofminimal distance between mold cavities is 26 %, while the averagedata variation coefficient of the safe distance calculated in theliterature source [10] is even 40 %. This difference occurs because thedesigners take higher distance between mold cavities and highersafety factor.

2. It is not always possible to calculate minimal allowable distancebetween mold cavities when the height of mold cavities is higher than5 mm. Comparing theoretical and practical values the mismatch wasup to 73 %. According to the experience of plastics injection moldsdesigners, it was determined that when the height of mold cavities is 5mm, the distance between them must be not less than 10 mm.

3. The data matches closest when the height of mold cavities rangesfrom 23 to 28 mm and the width or length of mold cavities rangesfrom 13 to 60 mm, in this case the average data variation coefficient isup to 10 %.

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4. CONLUSIONS

1. According to the review of the available plastics mould design referencesis stated that universal plastics injection mold design, quotation of moldand part manufacturing and various possibilities application to increasemold variety; considered 52%; separate mould design modules asmolding, cooling and etc. is taken 26%; while the most rarely are analyzedproblems developing intelligent design of plastics injection molds,molding process, cavity number definition and cavity layout models 22%.

2. The analysis of plastics injection mold design systems showed that theintelligence level of the systems is not high enough for the definition ofmold cavity number, the design of molding system and the calculation ofmale cavity/female cavity plate overall dimensions, besides the quality andduration of designing are conditioned by the knowledge and experience ofthe designer.

3. Theoretical studies have proved that the intelligent model of plasticsinjection molds design, applying artificial intelligence and knowledgebase, helps to select the optimal design variant, and create interfacesbetween the different modules based on allows the sharing of source andwork data in all stages of a design.

4. The developed module for mold cavities detection calculates the numberof mold cavities evaluating the mass of part to be molded, the duration andoutput of manufacture, the clamping force of molding machine and thequalitative parameters; the average data variation coefficient of theoreticaldata and practical results of mold cavity number is 6 % and the averagevariation coefficient of theoretical data and practical results of moldingmachine selection is 12%.

5. The developed module for the design of molding system allows theselection of molding system and the calculations of system parameters; theaverage variation coefficient of runner diameter theoretical and practicaldata is 10% and compared with other methodologies the data variation issimilar, respectively are 11% and 12 %; the average variation coefficientof practical and theoretical data of tunnel gate diameter because thevariation coefficient is 25%, but compared to other methodologies thiscoefficient is higher – 29 and 52%.

6. The developed module for the selection of plastics injection mold malecavity/female cavity plate dimensions based on Fuzzy logic andknowledge base calculates male cavity/female cavity plate optimal overalldimensions, evaluating the cost, mass, exploitation rate and material flowpath of the plate; the average variation coefficient of theoretical andpractical results of male cavity plate lengths is 13%, the average variation

30

coefficient of the results of male cavity plate width is 11% and the averagevariation coefficient of the results of male cavity plate thickness is 27%.

7. The main factor of the mismatch of male cavity plate dimensions is themismatch of the distance between mold cavities; the average variationcoefficient of theoretical and practical data of this distance is 26 %, whilethe data variation of the safe distance calculated using methodologypresented in the literature source is even 40 %.

8. The male cavity plate data matches closest when the height of mold cavitiesranges from 23 to 28 mm and the width or length of mold cavities rangesfrom 13 to 60 mm, in this case the average variation coefficient is 10 %.

REFERENCES

1. Menges, G.; Michaeli, W.; Mohren, P. Anleitung zum Bau vonSpritzgießwerkzeugen. München: Hanser, 1999, 669 p.

2. Mankutė, R. Mechaninių komponentų technologinio gamybos paruošimointegruotas projektavimas: disertacija. Kaunas: Kauno technologijosuniversitetas

3. Rao, N.S.; O'Brien, K.T. Design data for plastics engineers: Munich:Hanser, 1998, 207 p.

4. Lichius, U.; Schmidt, L. Rechnergestütztes Konstruieren vonSpritzgießwerkzeugen: systematisches Entwickeln von Betriebsmitteln,Aufbau und Funktion von Spritzgießwerkzeugen. Würzburg: Vogel, 1986,296 p.

5. Runner system. [interaktyvus] [žiūrėta 2007.-03-22]. Prieiga per internetą:http://www.scudc.scu.edu/.

6. Unger, P. Gastrow Spritzgießwerkzeugbau in 130 Beispielen. München:Hanser, 2007, 330 p.

7. Niemann, K.; Heidemann, D. Maschineneinstellstrategie für Thermoplast-Spritzgießmaschinen. Heidelberg: Hüthig, 1995, 68 p.

8. Rees, H. Mold Engineering. Munich:Viena 2002, 688 p.9. Runner system. [interaktyvus] [žiūrėta 2007.-03-22]. Prieiga per internetą:

http://www.scudc.scu.edu/.10. Louis, T. Applications of computer aided engineering in injection molding

Hanser, 1987, 302 p.11. Linß, G. Statistiktraining im Qualitätsmanagement Hanser, 2006, 445 p.

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LIST OF AUTHOR‘S PUBLICATIONS

Articles in publications from the master Journal List of theInstitute for Scientific Information (ISI)

1. Rutkauskas, Žydrūnas; Bargelis, Algirdas. Knowledge-based method forgate and cold runner definition in injection mold design. // Mechanika /Kauno technologijos universitetas, Lietuvos mokslų akademija, VilniausGedimino technikos universitetas. ISSN 1392-1207. 2007, nr. 4(66), p. 49-54. [ISI Web of Science].

Articles in other journals referred in ISI databases[Proceedings and oth.]

1. Rutkauskas, Žydrūnas. Knowledge based method for injection mold design// Mechanika 2008 : proceedings of 13th International Conference, 2008,April 3-4, Kaunas, Lithuania / Kaunas University of Technology,Lithuanian Academy of Science, IFTOMM National Committee ofLithuania, Baltic Association of Mechanical Engineering. ISSN 1822-2951. 2008, p. 445-450. [Proceedings].

Articles referred in International Databases List approved byScience Council of Lithuania

1. Rutkauskas, Žydrūnas; Bargelis, Algirdas; Linss, Gerhard. Knowledge-based method for cavity number definition in injection mold design //Mechanika / Kauno technologijos universitetas, Lietuvos mokslųakademija, Vilniaus Gedimino technikos universitetas. Kaunas. ISSN 1392-1207. 2006, nr. 6(62), p. 46-53.

2. Rutkauskas, Žydrūnas; Bargelis, Algirdas; Linss, Gerhard. Peculiarities ofautomation design moulds for micro parts // Mechanika 2006 : proceedingsof the 11th international conference, 2006, April 6-7, Kaunas, Lithuania /Kaunas University of Technology, Lithuanian Academy of Science,IFTOMM National Committee of Lithuania, Baltic Association ofMechanical Engineering. ISSN 1822-2951. 2006, p. 300-304.

Articles in Proceedings of referred International Scientific Conferences

1. Rutkauskas, Žydrūnas; Bargelis, Algirdas. Knowledge based method forprecision products design // 53. Internationales WissenschaftlichesKolloquium [elektroninis išteklius]: 2008, September 8-12, Ilmenau,Deutschland. 2008, ISBN 978-3-938843-40-6. p. 1-10.

32

2. Rutkauskas, Žydrūnas; Linss, Gerhard; Bargelis, Algirdas. MechanicalEngineering from Macro to Nano // 50. Internationales WissenschaftlichesKolloquium 2005 [elektroninis išteklius] 2005 September 19-23, Ilmenau,Deutschland: Tagengsunterlagen / Proceedings. 2005, p. 1-6.

3. Rutkauskas, Žydrūnas; Bargelis, Algirdas. DFQ metodo taikymas preciziniųkomponentų gamyboje // Mechanika - 2005: 10-osios tarptautinėskonferencijos pranešimų medžiaga, 2005, balandžio 7-8 d., Kaunas / Kaunotechnologijos universitetas, Lietuvos mokslų akademija, Tarptautinės mašinųir mechanizmų teorijos federacijos (IFToMM) Lietuvos nacionaliniskomitetas, Baltijos mašinų gamintojų asociacija (BAME). ISBN 9955-09-850-3. 2005, p. 237-241

ACKNOWLEDGEMENTS

I sincerely thank the scientific supervisor Habil. Dr. Prof. Algirdas Bargelisfor valuable scientific guidance, consistent supervision, assistance andpatience in the preparation of this dissertation;

I thank Habil. Dr. Prof. Gerhard Linß for the opportunity to performscientific research in Ilmenau Technical University, the Department ofQuality Management (Germany), and its staff for helpful advices and sincerehelp during the research internship;•

I thank the staff of the Department of Manufacturing Technology for helpfuladvices and friendly assistance;

I thank the close people for their support during the entire period of doctoralstudies;

INFORMATION ABOUT THE AUTHOR

Žydrūnas Rutkauskas was born in September 25th, 1980. In 1986 he began toattend Varnėnai primary school, from 1990 to 1998 was Veisiejai high schoollearner. In 1998 he entered Kaunas University of Technology, MechanicalEngineering Faculty. In 2002 obtained bachelor degree of MechanicalEngineering, specialization Mechanical Information Systems. In 2002, beganMaster level studies at the same faculty. In 2004 he graduated the MechanicalEngineering Master's degree with honors, of Engineering ComputerTechnologies specialization. During Master's studies he has been attended forone semester study in Technical University of Ilmenau (Germany) in accordancewith the Socrates/Erasmus study exchange program. Since 2004 he is studyingthe Doctoral studies course in Mechanical Engineering Science in KaunasUniversity of Technology. During Doctoral studies from 2004.10.01 to2005.01.31 in accordance with the DFG (German Research Foundation) grant heperformed scientific research in Ilmenau Technical University (Germany). From

33

2005.10.01 to 2007.07.31 in accordance with the DAAD (German AcademicExchange Service) studies exchange program he performed scientific research ofdissertation in the same Ilmenau Technical University (Germany).

REZIUMĖ

Šiuolaikinėje gamyboje labai svarbu mažinti laiko sąnaudas, atliekantnaujo gaminio projektavimą bei optimaliai panaudoti visus turimus resursus.Užsakovui yra svarbu, kad gaminys būtų suprojektuotas per kuo trumpesnį laiką,o projektavimo išlaidos būtų kiek įmanoma mažesnės. Pagrindinė priemonėsumažinti laiko sąnaudas yra integruoti konstruktoriaus ir technologo darbą.

Darbe sukurtas plastikų liejimo formų projektavimo modelis, kurį sudarotrys atskiri vienas su kitu susieti moduliai: liejimo lizdų skaičiaus nustatymo,liejimo sistemos parinkimo bei puansono/matricos plokštės matmenųapskaičiavimo. Liejimo lizdų skaičius apskaičiuojamas įvertinant liejamogaminio masę, pagaminimo trukmę, gamybos apimtį, liejimo formos uždarymojėgą ir kokybinius parametrus; Maksimalaus užliejamo ploto apskaičiavimuibuvo panaudoti neraiškiosios logikos metodai. Skaičiuojant liejimo lizdų skaičiųįvertinant liejamos detalės masę buvo sukurtos liejimo sistemos masėsprognozavimo priklausomybės. Kuriant liejimo sistemos tipo parinkimo ir josparametrų apskaičiavimo modulį buvo surinkti dažniausiai Lietuvos ir Vokietijosįmonėse naudojami liejimo kanalai bei liečių tipai. Remiantis literatūros šaltiniųapžvalga bei inžinierių patirtimi buvo sudarytos taisyklės liejimo sistemos,liejimo kanalų ir liečių tipų parinkimui bei sudarytos priklausomybės jųparametrų apskaičiavimui. Puansono/matricos plokštės gabaritiniai matmenysapskaičiuojami remiantis standartinėmis Hasco plokštėmis. Apskaičiuojantpuansono/matricos gabaritinius matmenis buvo taikoma neraiškiosios logikosmetodai. Šio modulio sukūrimui remiantis baigtinių elementų metodu buvosudaromos priklausomybės saugiam atstumui tarp liejimo lizdų apskaičiuoti.Plastikų liejimo formų gamybos sąnaudų prognozavimui liejimo forma buvoskaidoma į konstrukcinius elementus. Buvo sukurti tipiniai konstrukciniaielementai iš kurių sudaromi liejimo lizdai. Šiems konstrukciniams elementamsbuvo apskaičiuotos gamybos laiko priklausomybės nuo nuimamo medžiagostūrio. Sukurto modelio patikimumo tyrimas buvo atliekamas testuojant jįLietuvos įmonėje projektuojančioje plastikų liejimo formas.

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SL 344. 2009-05-15. 2,25 leidyb. apsk. 1. Tiražas 70 egz. Užsakymas 294.Išleido leidykla „Technologija“, K. Donelaičio g. 73, 44029 KaunasSpausdino leidyklos „Technologija“ spaustuvė, Studentų g. 54, 51424 Kaunas