ORIGINAL ARTICLE

Impact performance prediction of injection-molded talc-filledpolypropylene through thermomechanical environmentassessment

Carlos N. Barbosa & Francisco Carvalho & Júlio C. Viana &

Markus Franzen & Ricardo Simoes

Received: 18 December 2013 /Accepted: 13 October 2014# Springer-Verlag London 2014

Abstract Due to the fact that different injection molding con-ditions tailor the mechanical response of the thermoplasticmaterial, such effect must be considered earlier in the productdevelopment process. The existing approaches implemented indifferent commercial software solutions are very limited in theircapabilities to estimate the influence of processing conditionson the mechanical properties. Thus, the accuracy of predictivesimulations could be improved. In this study, we demonstratehow to establish straightforward processing-impact propertyrelationships of talc-filled injection-molded polypropylenedisc-shaped parts by assessing the thermomechanical environ-ment (TME). To investigate the relationship between impactproperties and the key operative variables (flow rate, melt andmold temperature, and holding pressure), the design of exper-iments approachwas applied to systematically vary the TME of

molded samples. The TME is characterized on computer flowsimulation outputs and defined by two thermomechanical indi-ces (TMI): the cooling index (CI; associated to the core fea-tures) and the thermo-stress index (TSI; related to the skinfeatures). The TMI methodology coupled to an integratedsimulation program has been developed as a tool to predictthe impact response. The dynamic impact properties (peakforce, peak energy, and puncture energy) were evaluated usinginstrumented falling weight impact tests and were all found tobe similarly affected by the imposed TME. The most importantmolding parameters affecting the impact properties were foundto be the processing temperatures (melt and mold). CI revealedgreater importance for the impact response than TSI. Thedeveloped integrative tool provided truthful predictions forthe envisaged impact properties.

Keywords Injectionmolding parameters . Polymer flowsimulations . Polypropylene . Impact behavior . Structure–properties relationships . Mechanical properties prediction

1 Introduction

The use of thermoplastic materials in structural automotiveparts presents important advantages, such as reduced weightand therefore reduced CO2 emissions, high design flexibility,good balance of properties, superior level of integration offunctionalities, and low processing costs by means of high-throughput processes (e.g., injection molding) [1].

Automotive plastic components are often required to with-stand high dynamic impact loads and dissipate energy toprotect occupants and pedestrians within a collision event [2,3]. The impact strength of polymers may be evaluated byvarious test methods, as suggested elsewhere [4]. A relativelyhigh number of experimental tests have been developed or

C. N. Barbosa (*) : F. Carvalho : J. C. Viana :R. SimoesInstitute for Polymers and Composites IPC/I3N, Department ofPolymer Engineering, University of Minho, 4800-058 Guimarães,Portugale-mail: cbnuno@dep.uminho.pt

F. Carvalhoe-mail: infotech.fjbc@gmail.com

J. C. Vianae-mail: jcv@dep.uminho.pt

R. Simoese-mail: rsimoes@dep.uminho.pt

R. Simoese-mail: rsimoes@ipca.pt

M. FranzenFord Research and Advanced Engineering Europe, 52072 Aachen,Germanye-mail: mfranze6@ford.com

R. SimoesPolytechnic Institute of Cávado and Ave, IPCA Campus,4750-810 Barcelos, Portugal

Int J Adv Manuf TechnolDOI 10.1007/s00170-014-6495-y

adapted to evaluate the impact response of polymeric mate-rials, such as Charpy [5, 6] and falling weight [7–9].

Among the endless variety of thermoplastics commerciallyavailable, polypropylene (PP) has found a wide range ofapplications within the automotive industry. Accordingly, thekey factor of the commercial success of PP is its versatility,which is due to the fact that the structure and properties of PPcan be adjusted to the requirements [5]. Almost a third of PPproduction is processed by injection molding. This is anintricate process that induces a high degree of structural com-plexity due to the wide range of competitive phenomena thatoccurs during the different molding stages. In this way, theoptimization of injection-molded components for a givenapplication (e.g., crashworthiness) is still a tough engi-neering task.

There is extensive evidence to relate the mechanical prop-erties of plastic components to the morphology—thus processsetup conditions—developed on the polymer (e.g., crystallinestructure, degree of crystallinity, or skin ratio) during theinjection molding stages. Relationships between molding con-ditions and properties [6, 7, 10, 11], as well as betweenprocessing parameters and morphology [12–14] have beengenerally reported; some studies have shown the relationshipsbetween polymer structure and final properties [15–18] andother studies have focused on the relationships between thethermomechanical processing environment and the mechani-cal properties [19–22].

Assessing the microstructure by means of experimentaltechniques in order to determine the mechanical behavior isnot a cost-effective approach and therefore not applicablewithin the automotive industry. Thus, establishing straightfor-ward relationships between processing and the mechanicalbehavior formerly at the product development process—en-abling properties prediction—seems to be a valuabletool/methodology for the Ford Motor Company.

First, this study aims at identifying the most relevant oper-ative variables and assessing in detail their influence on theimpact behavior of injection-molded talc-filled PP disc-shapedparts. Second, we have employed the thermomechanical indi-ces (TMI) methodology, previously proposed [14, 19–21], andrecently expanded in Barbosa et al. [22], to consider the pack-ing phase. Currently, an integrated simulation program has beendevelop and applied as a tool to compute the TMI and predictthe impact response as a function of the thermomechanicalconditions imposed during processing.

Resuming, this contribution shows how to correlate theprocessing conditions with the developed microstructure andthe subsequent impact properties by coupling the TMI meth-odology and the integrated simulation program. The proposedapproach can be a valuable and powerful engineering tool forexisting computer-aided engineering (CAE) packages and isaimed at optimizing the processing conditions and enhancingthe prediction of material behavior at the design stage of

automotive vehicle components under real-world impactscenarios.

2 Thermomechanical environment in injection molding

The properties of a certain molded plastic product are depen-dent on the processing technology and its settings becauseboth affect the intrinsic characteristics (e.g., morphology anddegree/type of crystallinity) of the material. In other words,the thermomechanical conditions imposed during processingaffect the morphological development of a plastic part, thusdetermining its final mechanical properties.

In the case of injection molding process, the combinedeffect of the processing variables, the part geometry, the typeof flow, and the thermo-rheological properties of the materialshould be taken into account while characterizing thethermomechanical environment (TME), since they create spe-cific fields of pressure, temperature, shear rate, and stress [21].These are termed thermomechanical variables (TMV) and areresponsible for the development of different TMEs within thespatial domain of the molding parts.

2.1 Thermomechanical variables

The TMV can be estimated by computer simulations of theinjection molding process or by adequate mold instrumenta-tion (local pressures and temperatures). In this study, the localspecific profiles of pressure, temperature, shear rate and stress,as well as the frozen layer developed for the period of fillingand packing stages of injection molding process were com-puted, as previously reported [22], through the AutodeskMoldflow Insight (AMI) simulation package.

2.2 Thermomechanical indices

The use of TMI has been suggested to correlate the processingconditions with the developedmicrostructure and the resultingmechanical properties [14, 19–21]. In this study, we haveemployed the TMI methodology, recently expanded to con-sider the packing phase [22], which considers two indices:

(a) A cooling index that quantifies the thermal level of thecore and serves as an indirect evaluation of the crystalli-zation process of the core region; and

(b) A thermal-stress index that indirectly assesses the orien-tation level of the skin.

In a general manner, a low thermal level (that is, low coolingindex) corresponds to a high thermal-stress index and viceversa. This confirms that the degree of crystallinity of the coreand the level of orientation of the skin cannot be controlledindependently in conventional injection moldings [14]. The

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main goal of the TMI values estimation comes along with thenecessity of mechanical properties prediction (at developmentstage), ever nearing academic interest, and the needs of engi-neers involved with the day-to-day design of plastic parts.

2.2.1 Cooling index

The cooling index (CI), Yc, is defined as the ratio between theoverheating and the cooling temperature difference. The coolingindex was calculated as a weighted average, by the relativeduration of the filling and packing phases, according to Eq. 1.

Y f pc ¼ Y f

c ⋅t f þ Ypc ⋅tp

t f þ tpð1Þ

Ycf and Yc

p are the filling and packing cooling indices,respectively. t f and tp are the time of the filling and packingphases, respectively. The derivation of Eq. 1 has been previ-ously reported [22].

2.2.2 Thermo-stress index

The thermo-stress index (TSI), τYs , it is associated to theflow-induced orientation of the melt (indirectly inferred by themaximum wall shear stress, τw) and to the molecular relaxa-tion occurring before Tc (polymer crystallization temperature)is reached. The thermo-stress index was calculated as aweighted average, by the relative shear stress at wall duringthe filling and packing phases, according to Eq. 2.

τY f ps

¼ τ f pw

eYf pc

ð2Þ

Ycfp is the weighted cooling index (1) and τw

fp is the weight-ed shear stress at wall of filling and packing phases. Thederivation (2) has been previously reported [22].

3 Integrated simulation tool

The integrated simulation tool is the developed software thatconsists in importing relevant results from computer polymerflow simulations and, accordingly, predicting the local mechan-ical properties of 2.5D finite-element model mesh. This appli-cation computes the mechanical properties (per element dualdomainmesh) based on the previous TMI equations and a set ofregression equations resulting from both simulations and ex-perimental analyses. It is simply required to import into a singleroot directory a set of mandatory outputs from AMI studyreports. The imported files are grouped in three distinct func-tional clusters: (i) the geometrical definition of the component;(ii) set of processing conditions, including material-specific

properties; and (iii) the TMV for all the time instants of theinjection molding process. The integrated computational flowchart of mechanical properties prediction is illustrated in Fig. 1.

4 Experimental

4.1 Raw material

In this study, we used a commercial 20 % talc-filled PPcopolymer. This material has a density of 1080 kg/m3 andmelt flow index of 25 g/10 min (physical properties measuredunder ISO 1183).

4.2 Experimental design of the molding process and AMIsimulations

A definition of the injection molding program (A, flow rate(Qi); B, injection or melt temperature (Tm); C, mold walltemperature (Tw); D, holding pressure (Ph)) was establishedbased on a design of experiments (DOE) approach, Taguchiorthogonal array. They varied in two extreme levels: mini-mum (−) and maximum (+). The experimental plan of injec-tion molding process and AMI simulations is presented inTable 1. Each experiment corresponds to a specific injectionmolding condition, consequently to a different TME and, thusto a particular developed microstructure.

4.3 Injection molding of lateral gated discs

All test specimens were prepared according to the previousDOE plan. Thirty discs per experiment were obtained; halfwas selected for the falling weight impact tests, based on thecriteria of lowest average mass deviation. The FerromatikMilacron K85-S/2F injection machine was employed to pro-duce discs with thickness of 3 mm and diameter of 115 mmusing edge (standard) gate cavities.

4.4 Mold filling simulations and TMI calculations

The simulation of the injection process was obtained fromcommercial software AMI that uses a finite-element modelmesh, a set of material properties, and processing parameters.The TMVwere defined along a timeline that encompasses thedifferent phases of injection molding cycle (filling and pack-ing). Observe the example given below in Fig. 2 for betterunderstanding of the data flow.

The TMI methodology eases the cross analysis of relationsbetween process conditions and mechanical properties. Tovalidate and test the integrated simulation software and theTMI methodology behind it, a number of flow analysis havebeen carried out using the AMI, following the predefinedDOE. These results are discussed in a further section.

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4.5 Falling weight impact tests characterization

The instrumented falling weight impact test (IFWIT) waschosen for this study with the purpose of characterizingthe impact behavior of injection-molded lateral-gated discsprocessed under different TMEs. This test provides fulldetails of impact event from initial contact to final break-ing of the specimen by recording the force/time curve ofthe entire impact. For this study, we have focused on threemain impact properties: peak force, peak energy, andpuncture energy.

The molded specimens were stored on the laboratory, usingstandard conditioning (atmosphere of 23±2 °C and 50±5 %relative humidity). Impact test parameters were fixed in justone level (mass=25 kg, velocity=2 m/s, dart diameter=20 mm, and friction disabled) to study the influence of theprocessing parameters on the aforementioned impact proper-ties. For each experiment, 15 discs were tested meaning a totalof 120 tests.

5 Results and discussion

5.1 Relation between impact properties and moldingconditions

The average and the standard deviation results of the studiedimpact properties for each experiment were assessed and thevalues have been normalized (with respect to the maximumvalue), as we are interested in the effect of the parametersrather than absolute values. The terminology adopted to indi-cate normalized impact properties is: reduced peak force (Fp),reduced peak energy (Ub), and reduced puncture energy (Ub).

The results of Fig. 3 allow comparing the average and thestandard deviation results of the peak force (or maximum load(Fp)), peak energy (or energy to maximum load (Up)), andpuncture energy (or total energy (Ub)) for each experiment. Asexpected, different processing conditions lead to considerablydissimilar material responses. It is well perceived that allimpact properties are similarly affected by the molding

Fig. 1 Work flow integration for mechanical properties prediction through thermomechanical environment assessment

Table 1 Experimental design—factor levels

Experiment (A) Qi (cm3s−1) (B) Tm (°C) (C) Tw (°C) (D) Ph (bar)

a

Level Value Level Value Level Value Level Value

E1 − 7 − 200 − 20 − 3

E2 − 7 − 200 + 70 + 30

E3 − 7 + 250 − 20 + 30

E4 − 7 + 250 + 70 − 3

E5 + 71 − 200 − 20 + 30

E6 + 71 − 200 + 70 − 3

E7 + 71 + 250 − 20 − 3

E8 + 71 + 250 + 70 + 30

aHydraulic pressure

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conditions, stating a feasibility of improving all three proper-ties with the same processing settings.

The peak force is simply the highest point on the curve; oftenthis point corresponds to the onset of material damage or com-plete failure, meaning it should not be used directly by the designengineer. This property is magnified by using the injectionmolding conditions defined in E5 and E1. On the other hand,the peak force presents the lowest values when E4 is setup.

The peak energy is the energy absorbed by the specimen upto the point of maximum load. When the peak force corre-sponds to failure, the peak energy is the amount of energy thespecimen can absorb before failing. This property is magni-fied by using the injection molding conditions defined in E5and E1. As for Fp, the peak energy presents the lowest valueswhen E4 is predefined.

The puncture energy is the amount of energy that thespecimen can absorb during the complete test; this valuemay be the same as Up when the specimen abruptly fails atthe maximum load point. This property is magnified by usingthe injection molding conditions defined in E1 and E5. As forother properties, Up presents the lowest values using theconditions of E4.

Resuming, the studied impact properties are maximized bythe pair of injection molding conditions E1/E5 correspondingto low levels of injection and mold temperatures, and mini-mized for the conditions of E4 (high levels of processingtemperatures). The peak force had a maximum variation in-duced by processing of 25 %, the peak energy varied of 55 %,and the puncture energy presented the highest variationof ca. 61 %.

5.2 Analysis of variance

Analysis of variance (ANOVA) was employed to study thecontribution and the evolution trends of each injection mold-ing parameter on the impact properties. Table 2 shows theANOVA statistical results as well as the effect, in terms ofpercentage contributions (% p) and trend of variation (usingthe symbols ↓ and ↑), of themain processing variables on peakforce, peak energy, and puncture energy for all microstructures(experiments).

The model “F value” is used to test the significance ofadding new model terms to those terms already in the model.In this case, all models (last row in Table 2) are significant andthere is only less than 3.78 % (Fp), 0.03 % (Up), and 0.56 %(Ub) that a large F value could occur due to noise. “Prob>F”<0.1000 indicate significant model terms (for a confidence levelof 90 %) and values >0.1000 indicate that model terms are notsignificant. “Adeq P” measures the signal-to-noise ratio (ra-tios >4 are desirable); thus, the ratios of all models are greaterthan this value indicating an adequate signal. The percentageof contribution for each model term and their interactions isgiven by “% p.”

Plots of the interaction effect are provided for a betteroutlook and understandable view of the interaction betweenthe considered process parameters. Here, whereas non-parallellines in the plot illustrate an existent interaction between twoparameters, parallel lines imply no interaction between

Fig. 2 Data flow for the TMI calculation at the end of packing phase

Fig. 3 Average and the standard deviation results of Fp, Up, and Ub

(using reduced units)

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variables; the lower the parallelism, the greater the effect ofinteraction between both factors. Note that the values of theimpact properties were normalized.

In the case of peak force, the main processing parametersinfluencing this impact property are, in descending order: in-jection temperature (% p=52.6), mold temperature (% p=19.1), and holding pressure (% p=15.0). No interactions amongprocessing factors were detected. Low processing temperaturesand high holding-pressure levels lead to a high Fp values,corresponding to the microstructure E5 (↑Qi, ↓Tm, ↓Tw, ↑Ph).

Regarding the peak energy, the main processing pa-rameters influencing Up are (sort by descending order):injection temperature (% p=65.1), mold temperature (%p=19.4), flow rate (% p=6.0), and holding pressure (%p=0.5). A significant interaction between flow rate (A)and mold temperature (C) was observed, which presentsa % p ca. 8.9 in terms of peak energy behavior. Lowprocessing temperatures and high flow rate and holdingpressure levels lead to high peak energy values,matching with microstructure E5.

(AC) is a significant model term and, their interactions andeffects on Up, are plotted in Fig. 4. In this case, a lowparallelism is evidenced. For high flow rate levels (71 cm3/s), the mold temperature should be lower (20 °C) in order tomaximizeUp. Additionally, the effect of the mold temperatureon peak energy is more pronounced when using low flow ratelevels (7 cm3/s).

As far as puncture energy is concerned, the mainprocessing parameters influencing Ub are, in descendingorder: injection temperature (% p=63.7), mold tempera-ture (% p=22.7), and flow rate (% p=3.6). Importantinteraction between flow rate (A) and mold temperature(C) was detected. (AC) presents a percentage of contri-bution (% p) of 8.7 in terms of puncture energy behav-ior. Despite a very small contribution (% p=1.2), theinteraction (AD) is also a significant model term. Low

processing temperatures and high flow rate levels leadto high peak energy values, corresponding to micro-structure E5.

The interaction effects of (AC) and (AD) and their effects onUb are considered in Figs. 5 and 6, respectively. In the case of(AC), for a high flow rate (71 cm3/s), the mold temperatureshould be lower (20 °C) in order tomaximizeUb. Moreover, theeffect of the flow rate on puncture energy is more pronouncedin the case of high mold temperatures (70 °C). Concerning(AD), the effect of such interaction on Ub is much smallerthan the effect of (AC) due to the relative higher par-allelism evidenced (see also ANOVA results of Table 2).

From Table 2, it is interesting to notice that theinfluence of the processing conditions on the impactproperties is akin to what had been found previouslyfor the tensile properties [22]. The major difference isrelated to the large influence decline of the Qi factor inthe material behavior under impact. Impact propertiesseem to be mainly controlled by the processing temper-ature parameters showing higher values for lower levelsof Ti and Tm.

Table 2 ANOVA results, processing parameters percentage contributions (% p), and trends on impact properties (the symbol “↑” indicates increase,while the symbol “↓” indicates decrease)

Factor ↑ Fp ↑ Up ↑ Ub

F value Prob>F % p Adeq P F value Prob>F % p Adeq P F value Prob>F % p Adeq P

(A) Qi 3.88 924.97 0.0011 ↑ 6.02 31.87 0.0300 ↑ 3.58

(B) Tm 25.16 0.0153 ↓ 52.59 10,005.01 <0.0001 ↓ 65.09 567.15 0.0018 ↓ 63.65

(C) Tw 9.16 0.0565 ↓ 19.14 2883.96 0.0003 ↓ 19.41 202.26 0.0049 ↓ 22.70

(D) Ph 7.19 0.0750 ↑ 15.02 82.05 0.0120 ↑ 0.53 0.025

(AB) 0.33 6.2E−3 0.20

(AC) 6.97 1372.85 0.0007 8.93 77.53 0.0127 8.70

(AD) 2.07 6.8E−3 10.21 0.0855 1.15

Model 11.21 0.0378 9.59 3073.77 0.0003 158.50 177.80 0.0056 38.25

Numbers in bold indicate significant model terms for a confident level of 90%

Fig. 4 Interaction effects between flow rate (A) and mold temperature(C), on Up

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5.3 Relation between the TMI and molding conditions

The variations of the TMI with the main molding conditionsare depicted by responses surfaces in the form of 3D graphswhich, in turn, were obtained by fitting the experimental datato simple polynomial equations, using a least-square minimi-zation (Tablecurve3D® software).

The cooling index, Yfp, quantifies the thermal level of thecore and serves as an indirect evaluation of the crystallizationprocess of the core region; a high thermal level of the coremaypossibly corresponds to a higher degree of crystallinity[14, 21].

The thermal (cooling) index, at the end of packing phase, isextremely influenced by the processing temperatures, such asmold and injection temperatures, in spite of a tiny contributionof the flow rate. High levels of these factors lead to high Yfpvalues. The mold temperature (Tw) is the most importantparameter regarding the response of thermal index as observedin Fig. 7.

The thermal-stress index (τYs ) intends to quantify the finallevel of the skin layer molecular orientation; a high thermo-stress index might lead to a higher molecular orientation onthe skin layer [14, 21]. The main processing parametersinfluencing τY f p response are the flow rate (Qi) and injection

temperature (Ti). Low levels of these parameters lead to highτY f p values (see Fig. 8). This is consistent with the highest

shear stress and the lowest thermal levels of the skin. In fact,the maximum level of orientation of the skin layers is obtainedfor the low settings of these processing variables, as reportedelsewhere [14, 20].

5.4 Relation between the TMI and the impact properties

The relationships between the TMI and the impact properties(normalized values) were also analyzed through 3D responsesurfaces obtained by polynomial equations. The evolution ofthe Fp,Up, andUbwith both TMI is depicted in Fig. 9 where itis noticeable that the cooling index is more relevant for theimpact response; on the other hand, the role of thermo-stressindex revealed relative less significance.

The most overriding factor which controls the fracturebehavior in semi-crystalline polymers is their morphology[4]. A high degree of crystallinity is generally detrimental

Fig. 5 Interaction effects between flow rate (A) and mold temperature(C), on Ub

Fig. 6 Interaction effects between flow rate (A) and holding pressure(D), on Ub

Fig. 7 Variations of Yfp (end of packing) with the main processingvariables

Fig. 8 Variations of τY f p (end of packing) with the main processingvariables

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for the capability of the material to absorb energy in very shorttime intervals [8]. Indeed, our results reflect those statements:high cooling index (which serves as an indirect evaluation ofthe crystallization process of the core region) values lead tolow impact properties results thus, to a more brittle material.

In this study, and by means of differential scanning calo-rimetry, the degree of crystallinity of the core and skin wasassessed. We have verified that the degree of crystallinity washigher in the core than in skin region, as expected, due to thedifferent cooling rates on both regions. No great differenceswere found among all microstructures/experiments: the vari-ation of the degree of crystallinity among E1 to E8 was ca.1.85 and 3.33 % for skin and core regions, respectively. Theselow variations are resulting from high induced degree ofcrystallinity due to the presence of talc particles which mayact as a nucleating agent thus influencing the kinetics ofcrystallization.

Despite these small changes, the highest degree of crystal-linity was found for the injection molding conditions of E4 (↓Qi, ↑ Tm, ↑ Tw, ↓ Ph), corresponding to the poorest impactbehavior. Moreover, preliminary ANOVA results concerning

the influence of processing conditions on crystallinity re-vealed that the degree of crystallinity is maximized by usinghigh levels of processing temperatures and low holding pres-sure levels; an important interaction between the melt andmold temperatures was observed.

Actually, Fp values are nearly independent on the τY f p

levels (however, high τY f p levels lead to high Fp values).

This property seems to be rather controlled by the coolingindex (low Yfp conducts to high Fp values). Up is mainlycontrolled by the cooling index (low Yfp leads to high Up

values). A slight contribution of the thermo-stress index isobserved (high τY f p lead to high Up values). In spite of a

slight contribution of the thermo-stress index (high τY f p lead

to highUp values) on theUb, this property is mainly controlledby the cooling index (low Yfp leads to high Up values).

Globally, the impact properties are controlled by the com-bined effect of Yfp and τY f p . All the impact properties increase

with the increase of τY f p and decrease of Yfp, suggesting that

a less crystalline (low thermal level) core has a better impactresistance, as reported elsewhere [8, 21]. The variations ofpuncture energy, Ub, are similar to those of the strain atbreak, reported in our previous study [22], indicating

Fig. 9 Relationships between the TMI and the impact properties (Fp,Up,and Ub)

Fig. 10 Scatter plot of measured and predicted values for Fp over thepredefined design of experiments (linear regression equation: PV=0.1904+0.7714×MV)

Fig. 11 Scatter plot of measured and predicted values for theUp over thepredefined design of experiments (linear regression equation: PV=0.1299+0.7814×MV)

Fig. 12 Scatter plot of measured and predicted values for theUb over thepredefined design of experiments (linear regression equation: PV=0.0838+0.8496×MV)

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the significant contribution of the total deformation tothe global absorbed energy.

The evolution of Fp with the TMI (polymer microstructuralstate) is less sensitive than for Up or Ub. The peak force had amaximum variation induced by processing of 25 %, the peakenergy varied of 55 %, and the puncture energy presented thehighest variation ca. 61 %. Therefore, the effects of the TMEimposed by different processing conditions on the proposedimpact properties are identical to the relationships found betweenthe TMI and the referred properties. This reveals that cooling andthermo-stress indices (computed by assessing the specific fieldsof pressures, temperatures, shear rates, and stresses) are properly

characterizing the different thermomechanical conditionsimposed.

5.5 Mechanical properties prediction through TMEassessment

The prediction of the envisaged impact properties of disc-shaped specimens as a function of the TME developed duringthe injection molding process is assessed by coupling the TMImethodology, a set of regression equations and the integratedsimulation program. A set of injection molding simulationswere carried out according to the equivalent molding condi-tions over the predefined DOE. It must be highlight thatdifferences always exist between molding simulations andthe real molding environment. The predicted values of theimpact properties under study have been normalized for eachexperiment (following the same procedure for the resultsobtained experimentally).

Figures 10, 11, and 12 and Table 3 compare the normalizedresults of the measured values (MV) and predicted values(PV) for all impact properties. As shown in Table 3, the Pvalue, for the respective slope coefficient, is indeed significantwhich means a strong evident relationship between the

Table 3 Comparison between the experimental and predicted results for the envisaged impact properties and the main regression statistics over thepredefined DOE

Experiment Peak force (Fp) Peak energy (Up) Puncture energy (Ub)

Meas. Pred. Dev. Meas. Pred. Dev. Meas. Pred. Dev.

E1 0.9629 0.9730 1.04 % 0.9903 0.9441 4.66 % 1.0000 0.9432 5.68 %

E2 0.9389 0.8945 4.73 % 0.7573 0.7502 0.94 % 0.6532 0.7132 9.18 %

E3 0.9274 0.8995 3.01 % 0.7362 0.7553 2.59 % 0.6606 0.7130 7.94 %

E4 0.7535 0.8063 7.00 % 0.4456 0.5264 18.13 % 0.3872 0.4430 14.41 %

E5 1.0000 1.0000 0.00 % 1.0000 1.0000 0.00 % 0.9818 1.0000 1.86 %

E6 0.9411 0.8813 6.36 % 0.9269 0.7127 23.11 % 0.8594 0.6646 22.68 %

E7 0.8714 0.8434 3.21 % 0.6886 0.6011 12.71 % 0.5937 0.5169 12.94 %

E8 0.8813 0.8383 4.89 % 0.6614 0.5992 9.40 % 0.5702 0.5239 8.12 %

R square 0.7643 0.7851 0.8314

St error 0.0349 0.0836 0.0888

P value 0.0045 0.0034 0.0016

Fig. 13 Mapping distribution of the Fp over specific sets of meshelements for the experiment E5 (the horizontal arrow represents theinjection location)

Table 4 Impact properties predicted results of a given set of meshelements for experiment E5

Elements set Predicted Fp (kN) Predicted Up (J) Predicted Ub (J)

1 4.03 39.1 44.8

2 3.66 30.4 33.4

3 3.82 33.6 36.9

4 3.92 36.2 40.7

5 3.92 36.0 40.4

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measured and the predicted values. The relative high R squarevalue and the low residual standard deviation (st error), alsoshown in Table 3, which represents the typical fluctuationsaround the deviation “line,” make the three linear regressionmodels very reliable. The trends observed for the results ob-tained experimentally are identical to those detected for thecomputed results, i.e., the highest predicted values correspondto the highest measured results (E1 and E5); on the other hand,the lowest predicted values correspond to the lowest measuredresults (E8). For all impact properties under study, the pointsare clustered along a line with a positive slope.

For all impact properties, the biggest differences betweenmeasured and predicted values were found for E4 and E6: Fp

shows a deviation of 7 %, and Up and Ub present a deviationof 23 %. Generally, the largest deviations were found for thepeak and puncture energies which, in turn, presented thehighest standard deviation results (seen in Fig. 3). The relativedeviation has been calculated according to Eq. 3):

Dev:% ¼ Valuesmeas−Valuepred��

��

Valuesmeas� 100 ð3Þ

The developed predictive software reports data in a specificoutput file defined format or in an analytical user interface thatshows mechanical properties estimated values and allows themapping (graphic module) of their distribution over the meshgeometry of the component under study. The exploring resultspanel lets the user refine queries by specifying a set of ele-ments (or all imported elements from AMI) and eventually aspecific time gap in the simulation window. Figure 13 illus-trates five different mesh regions: (1) center, (2) near the gate,(3) far from the gate, (4) left of the gate, and (5) right of thegate. Table 4 reports the predicted (non-normalized) values ofall impact properties for the considered mesh regions.

6 Concluding remarks

In this study, three main impact properties and their relation-ships with processing parameters and TMI of injection-molded PP composite were investigated. All impact propertieswere shown to be similarly affected by the molding condi-tions, indicating the feasibility of improving all the threeproperties with the same processing settings. They are maxi-mized for low levels of injection and mold temperatures.

According to ANOVA analyses, the most important pa-rameters affecting the impact properties are the injection tem-perature, followed by the mold temperature. Regarding Fp, theholding pressure plays also a significant role, while forUp andUb, it is the interaction between the flow rate and moldtemperature that governs the resulting properties. For highflow rate levels, the mold temperature should be lower in

order to maximize Up and Ub. In both cases, the effect of themold temperature is more pronounced when using low flowrate levels.

The TME imposed during the molding process, and itsdependence on the molding conditions was also evaluated.The processing temperatures have a significant effect on thecooling index; high levels of Tm and Tw lead to high Yfp valuesand resulted in poor impact behavior (brittle material). On theother hand, the main processing parameters influencing thethermo-stress index response are the flow rate and injectiontemperature; low levels of these parameters lead to high τY f p

values. Correlation analyses confirmed that all impact proper-ties increase with the decrease of Yfp and increase of τY f p ; the

impact response is mainly governed by the Yfp (related withthe core features).

The presented methodology foresees an important step inlinking the results from flow simulations to structural model-ing approaches. It enables establishing relationships betweenprocessing conditions and the mechanical behavior of ther-moplastic parts under load, minimizing the need for expensiveand time-consuming trial and error approaches. This is mostimportant at the early design stage of injection-molded poly-mer-based products, since it helps engineers/designers to re-duce costs, to shrink development time (and thus, time tomarket), and assure better product quality.

Despite the good results observed, the TMI methodologyand the mechanical properties predictive tool are still underrefinement. This integrative approach should be extended toinclude the cooling stage of the injection molding process andthe analysis outspreaded to different geometries, materials,properties, and should encompass as well the anisotropiccharacter of semi-crystalline polymers (molecular orientationat skin layer). Moreover, a methodology to integrate/combinethe approach presented in this paper (already extend to includethe cooling stage) with structural mechanical simulations hasbeen defined and is being developed.

Acknowledgments Foundation for Science and Technology, Lisbon,through the 3° Quadro Comunitário de Apoio, and the POCTI andFEDER programs, and project PEst-C/CTM/LA0025/2013.

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