Research and development of a composite with transparent polypropene fiber Part I: a study of combining the Taguchi method with the analytic hierarchy process for masterbatch modification and toughening to enhance characteristics

Abstract

Polypropene (PP) has weak mechanical properties; relevant fibrous composite products are likely to bend under stress, becoming unavailable due to creases and folds. In order to maintain the product haze and tensile characteristic, the toughening property is enhanced greatly in this study. Firstly, the PP and enhanced rubber segment-styrene ethylene/butylene styrene (ERS-SEBS) are analyzed, and the two materials’ melting points and cracking points are confirmed. Three proportions of composite are made by single-screw mixing. The cost is reduced and the efficiency is confirmed by the Taguchi experiment. The single quality optimum combination is obtained by analysis of variance (ANOVA) and a factor response table. The optimum process parameters are designed according to the contribution degrees of quality weight and control parameters by using the analytic hierarchy process and ANOVA of the Taguchi method based on the reproducibility of the single quality optimum combination validation experiment. According to practical validation, in the ERS-SEBS modified optimum process of PP, the impact strength is 7.26 kJ/m², higher than that of regular PP by 142%. The tensile strength is 23.69 MPa, high than that of regular PP by 3%. The haze can be reduced to 5.7%. The developed composite of the PP/styrene triblock copolymer has better mechanical properties and retains its optical performance. It can be used in a fibrous composite to make a composite with transparent fiber to present the fiber line distribution of fabric in the composite.

Keywords

polypropene enhanced rubber segment-styrene ethylene/butylene styrene analysis of variance impact strength haze tensile strength

The fibrous composite is extensively used in different structural composites for its light weight, high strength and high design flexibility, such as automobile parts and components, sporting equipment components, luggage and geotextiles. As the subject of environmental protection has received attention from global industries, the lightweight and energy-saving composite has developed into mainstream technology.^1,2 Polypropene (PP) is a good choice of polymer composite. It is used extensively in the fibrous composite industry for its low density, light weight, linear polymer with high crystallinity, low processing temperature, toxic substance-free combustion and good material fusion. In recent years, the composite with transparent fiber has been able to present the composite lines perfectly, increasing the value of products, so it is accepted by consumers extensively. However, the PP material is likely to be semitransparent during molding for its linear structure and crystallinity, failing to present the transparency of the fibrous composite.³ The polymer mixing is a rapid and effective method to develop advanced polymer material. The properties of the blend can be controlled by changing the material composition and polymer addition level according to the user. For PP with natural material blending modification, Zhang et al.⁴ developed the composites based on the PP/polylactic acid (PLA) matrix and filler bamboo fiber, which led to changes in the process ability, morphology, and rheological properties of the raw thermoplastic elastomer. The good rheological, morphological and thermal properties were obtained when the ratio of PP/PLA/BF/MAH-g-PP was 48.75/13/35/3.25. Barkoula et al.⁵ focused on short flax fiber and long flax fiber-reinforced polypropylene composites, manufactured by the injection molding method. The reduction in fiber length did not affect the tensile properties significantly. For PP with chemical materials blending modification, Jazani et al.⁶ prepared ternary polymer blends using a PP/polyethylene terephthalate (PET)/SEBS triblock copolymer and a reactive maleic anhydride grafted SEBS (SEBS-g-MAH) at various compositions. Mechanical properties showed that the addition of SEBS/SEBS-g-MA caused an increase in the impact strength of these blends. The morphology result indicated that core–shell dispersion formed with increasing SEBS rubbery concentration, and the impact strength increased consequently. Srinivasan and Gupta⁷ improved the tensile strength and flexural property of the rubber copolymer, and the mechanical properties of PP/SEBS were improved when the proportions were 95/5 and 90/10. Sharma and Maiti⁸ investigated the mechanical properties of PP and SEBS-g-MA copolymer blending with adding the volume fraction of SEBS-g-MA. The crystallinity of PP composites decreased with increasing concentrations of the SEBS-g-MA copolymer. Tensile elongation and impact strength were enhanced because of good dispersion of SEBS-g-MA into PP with a weak level of interaction between the phases.

Based on the aforementioned literatures, different proportion of SEBS blended with PP will influence the mechanical properties of the blends more than natural material blending. Therefore, this study modifies the material formula according to the transmittance and impact resistance of the fibrous composite. PP is used as the principal part, and the deficient PP performance is improved by the mixing process with enhanced rubber segment-styrene ethylene/butylene styrene (ERS-SEBS). The crystal floating white and impact resistance of PP during molding are overcome, so that it has excellent transmittancy, good low-temperature impact resistance, excellent normal temperature impact resistance and higher tensile strength. This material can be the principal part of a fibrous composite. The directional tear and tensile strength are improved by reinforcing fabric or cross-woven fabric, implementing the application of PP to the field of high-class fibrous composites.

This study uses a new type of material ERS-SEBS, the material addition level is reduced, and the good mechanical properties of PP and the optical performance of the product are maintained. The Taguchi method with an orthogonal array was applied to reduce the number of experiments. The control factors that have a profound effect on the quality characteristics were confirmed using analyses of variance (ANOVAs) and main effect analysis, since the Taguchi method was for the single quality analysis. A well-established decision-making technique, the analytic hierarchy process (AHP)^9–11 with its mathematical simplicity and flexibility, is conducted to optimize the multi-parameter combination. The quality changes in the mixing ratio interval are analyzed in detail according to the variations of melting temperature, injection speed, packing pressure, packing time and cooling time. The results are compared and investigated theoretically and experimentally. The signal-to-noise (S/N) ratios are predicted under the optimal conditions by addition to investigate the total anticipated improvement. The confirmation experiments are carried out to verify reproducibility and feasibility through the proposed approach.

Materials

Polypropene

PP is a representative synthetic polymer made by stereo regular polymerization from thermoplastic polymer. The PP material has low specific gravity (0.9 g/m³), is resistant to acid and alkali and it has poor dyeability and heat resistance. This study selects the PP polymer from Li Chang-Rong Co. to develop the modified substrate; the specifications are shown in Table 1.

Table 1.

The specification of polypropene

Property	Value	Test method
Specific gravity (g/cm³)	0.903	ASTM D792
Hardness (Shore A)	109	ASTM D785
Melt index (g/10 min)	14.5	ASTM D1238
Heat distortion temperature (℃)	137	ASTM D648
Breaking elongation (%)	14	ASTM D638
Impact strength-notch (KJ/m²)	2.2	ASTM D256
Pyrolysis temperature (℃)	>300	ASTM D648

Deviation: 1%.

ERS-SEBS

ERS-SEBS is a styrene triblock copolymer, a linear triblock copolymer with polystyrene as the end block and an ethylene-butylene copolymer generated by mixing polybutadiene with hydrogen as the intermediate elastic block. This polymer is free of unsaturated double bonds, so it has good stability and aging resistance, and the distribution lengths of soft and hard segments are improved by increasing the proportion of butylene, so as to enhance its performance. The ERS-SEBS in this study is a Kraton SEBS improved product, the specifications of which are shown in Table 2.

Table 2.

The specification of enhanced rubber segment-styrene ethylene/butylene styrene

Property	Value	Test method
Specific gravity (g/cm³)	0.9	ASTM D792
Hardness (Shore A)	52	ASTM D785
Melt index (g/10 min)	18	ASTM D1238
Solution viscosity (cps)	210	ASTM D455
Styrene/rubber ratio	20/80	–
Breaking elongation (%)	>600	ASTM D638

Deviation: 1%.

ERS-SEBS degrades rapidly at 370℃ according to a thermogravimetry analyzer. This is the pyrolysis temperature of material. The melting point is 153℃ according to a differential thermal analyzer. The schematic diagram of the chemical structure is shown in Figure 1.

Figure 1.

Enhanced rubber segment-styrene ethylene/butylene styrene chemical structure.

Taguchi quality method

The Taguchi method is combined with industrial experiment design, quality control technology and the statistical method, so that the product design and manufacturing process can reach the optimum condition, and conform to mass production reproducibility.

Signal-to-noise ratio

The S/N is the indicator for measuring the process quality level; different level indexes are determined according to different quality characteristic forms. This study hopes to reduce the failure rate of products by enhancing the physical properties; letting the products retain color (high haze) can reduce the consumption of stains. Therefore, the impact strength, tensile strength and haze qualities of the samples produced by the injection molding machine are measured by using the S/N computing mode of the larger-the-better quality characteristic

S / N = - 10 \times {log}_{10} (MSD) = - 10 \times (\frac{1}{n} \sum_{i = 1}^{n} \frac{1}{y_{i}^{2}})

(1)

where MSD is the mean square deviation of the target value, n is the measured total number and

00 A 0 y_{i}

is the measured value of quality.

Analysis of variance

The ANOVA uses a statistical test to recognize the effect of individual factors, and applies the F-test to remedy the deficiency in the Taguchi experiment in that it cannot analyze the effect of various experimental parameters on the quantity characteristic. This study uses the ANOVA to find out the significant factor that influences the quality characteristic, so that the quality confidence intervals can be calculated to guarantee the reproducibility of experiment. The ANOVA is expressed as follows.

Total sum of squares (SS_T)

The SS_T is the sum of squares of all control factors minus correction

{SS}_{t} = \sum_{i = 1}^{n} y_{i}^{2} - CF

(2)

and

CF = (\sum_{i = 1}^{n} y_{i})^{2} / n

(3)

where y_i is the S/N ratio of various experiments, n is the total number of experimental observations and CF is the correction factor.

Sum of squares (SS_factor)

SS_factor is the variation of various factors. Factor A has p levels, and each level has m observed values; the sum of squares of the factor j is expressed as follows

{SS}_{j} = (A_{1}^{2} + A_{2}^{2} + \dots + A_{p}^{2}) / (m - CF)

(4)

Error sum of squares (SS_e)

SS_e is the total sum of squares minus the sum of squares of the main effect and the sum of squares of all interaction effects, expressed as follows

{SS}_{e} = {SS}_{t} - \sum_{j = 1}^{n} {SS}_{j}

(5)

Degrees of freedom (DOFs)

The DOF is the measurement of experimental information magnitude. A larger DOF represents more experimental information; the equations for DOFs of various factors, total DOFs and DOF of error are

DOF of the factor j

{DOF}_{j} = numberoflevels - 1

(6)

Total DOF

{DOF}_{t} = totalnumberof experiments - 1

(7)

DOF of error

{DOF}_{e} = {DOF}_{t} - \sum_{j = 1}^{n} {DOF}_{j}

(8)

Mean square (MS)

The MS is the sum of squares of various factors divided by the DOF of various factors, expressed as

MS = {SS}_{j} / {DOF}_{j}

(9)

The MSE is the sum of square error divided by DOF of error, expressed as

MSE = {SS}_{e} / {DOF}_{e}

(10)

F-ratio

The ANOVA uses the F value to represent the relationship between the factorial effect and error variance. The larger the F value is, the greater the effect of the factor on the system, so the F value can be used to arrange the order of importance of factors, and to improve the Taguchi experiment, which cannot analyze the effect of various experimental parameters on characteristics. The combination is required to avoid overrating the factorial effect.

The F value is defined as follows

F = MS / MSE

(11)

Percent contribution ( $CN$ )

The proportion of individual factors to the total sum of squares can be found from the CN, which is the relation of the factor to reduce variance, defined as

{CN}_{j} = {SS}_{j} / {SS}_{t}

(12)

Analytic hierarchy process

The AHP is used in uncertain conditions and in decision problems with multiple evaluation criteria, and the layer structure with interaction is established for studying the interaction between various elements in the layer and the contribution to the objective.^9–11 The AHP has a special concern with departure from consistency, its measurement and on dependence within and between the groups of elements of its structure. It has found its widest applications in multicriteria decision making, planning and resource allocation and in conflict resolution. In its general form the AHP is a nonlinear framework for carrying out both deductive and inductive thinking by taking several factors into consideration simultaneously and allowing for dependence and for feedback, and making numerical tradeoffs to arrive at a synthesis or conclusion.¹² In this study, the hierarchical structure can be built from the relationship among the multi-quality optimum parameter combination (ultimate objective), three product qualities (impact strength, tensile strength and haze as a major factor layer) and six control factors (ERS-SEBS addition, melting temperature, injection speed, packing pressure, packing time and cooling time as the minor layer). The levels are connected completely to form multiple levels. The importance of the control factor to quality is determined by the factor response table of the Taguchi method. The importance matrix is obtained from the importance. The factor weights with consistency are found by calculation and the single quality optimum parameter group is obtained. The weight is determined according to the requirement of the product for three qualities, multiplied by single quality optimum weight to obtain the multi-quality optimum parameter group; the specific computing process is described below.

Principle of hierarchical structurization

The elements that influence the system are resolved into several groups; the ultimate objective of this study is multi-quality parameter optimization group, the major factor layer, is the three qualities measured in the experiment; the minor layer is six control factors, and the relationship is shown in Figure 2.

Evaluation scales of AHP

Figure 2.

Analytic hierarchy process layer hierarchical structure. ERS: enhanced rubber segment.

The variations of different control factors influence the quality more or less. In order to calculate the importance matrix, the AHP converts the influence into an evaluation scale table. The evaluation scale table contains equal importance, weak importance, essential importance, very strong importance and absolute importance, and the nominal scales are given measurement values of 1, 3, 5, 7 and 9; four items within five cardinal scales are given measurement values of 2, 4, 6 and 8. The meanings of various scales are shown in Table 3.

Build the importance matrix

Table 3.

The meanings of various scales^13,14

Evaluation scale	Definition	Description
1	Equal importance	The contribution degrees of two alternative schemes are of equal importance ˙Equally
3	Weak importance	Experience and judgment slightly prefer a scheme ˙Moderately
5	Essential importance	Experience and judgment strongly prefer a scheme ˙Strongly
7	Very strong importance	Reality very strongly prefer a scheme ˙Very Strong
9	Absolute importance	Enough evidence for absolute preference for a scheme ˙Extremely
2, 4, 6, 8	Adjacent scale intermediate value (intermediate values)	When tradeoff required

There are three quality items measured in this study, so a 3 × 3 pairwise comparison is required. The importance of quality is analyzed by means of the concept and actual result of this study during pairwise comparison; the importance is arranged corresponding to the evaluation scale table of the AHP, as shown in Table 3. The corresponding result is placed in the upper triangle of importance matrix A (the principal diagonal is a comparison of elements, so it is 1); the values in the lower triangle are the reciprocals of the values in relative positions of the upper triangle, that is, $a_{ji} = 1 / a_{ij}$ . The matrix is expressed as in Equation (13)

\begin{matrix} A = [a_{ij}] = [\begin{matrix} 1 & \dots & a_{1 n} \\ : & : \\ 1 / a_{1 n} & \dots & 1 \end{matrix}] \end{matrix}

(13)

AHP expression

When the importance matrix of Layer 2 quality items is built, the feature vector value can be found by the common feature value algorithm of the numerical analysis, so as to work out the weight of quality corresponding to the control factor. The AHP uses the vector mean standardization of the first line to calculate the vector value, and the weight is calculated by Equation (14)

W_{i} = \frac{1}{n} \sum_{j = 1}^{n} a_{ij} / \sum_{i = 1}^{n} a_{ij} i, j = 1, 2, \dots, n

(14)

To calculate the consistency index (C.I.) of the matrix, the aforesaid weight W_i is used to figure out the consistency vector, represented by ν, so as to calculate the λ value, expressed as

V_{i} = (\sum_{j = 1}^{n} W_{i} a_{ij}) / W_{i} i, j = 1, 2, \dots, n

(15)

When the consistency vector is obtained, it is averaged to obtain the λ value, expressed as in Equation (16), and the λ value is substituted in Equation (17) to obtain the C.I. value. C.I. = 0, meaning the former and latter judgments are completely consistent, failing analysis, expressed as

λ = (\sum_{i = 1}^{n} ν_{i}) / n

(16)

C . I . = (λ - n) / (n - 1)

(17)

Each system has different random index (R.I.) under different layer numbers; the multi-quality parameter optimization system of this study has three levels, that is, the R.I. value is 0.58.¹⁵ The R.I. values are shown in Table 4.

Table 4.

The random indexes

Layer number	1	2	3	4	5	6	7	8
R.I.	0.00	0.00	0.58	0.90	1.12	1.24	1.32	1.41

The C.R. is the ratio of the C.I. value to the R.I. value. C.R. < 0.1 can be regarded as better consistency¹⁶

C . R . = C . I . / R . I .

(18)

The optimum parameter combination is calculated by multiplying the weight of three qualities of Layer 2 by the ANOVA contribution degree of three qualities of the Taguchi method. Finally, the maximum value of various control factor weights is the top layer multi-quality optimum parameter group, represented by S_i, expressed as

S_{i} = W_{i} α_{ij} i = 1, 2, \dots r; j = 1, 2, \dots n

(19)

where W_i is the weight of various qualities and

α_{ij}

is the control factor weight corresponding to quality.

Experimental details

The Taguchi method uses an orthogonal table as the basis of experimental design; the orthogonal table can reduce the number of required experiments greatly based on the characteristic that the levels of various factors in the table are orthogonal to each other. The influence of the levels of various factors on the end product quality can be calculated by the orthogonal table. The orthogonal table structure is represented by L_a (b^c), where a represents the number of experiments, b represents the level number of the factor and c represents the number of factors of the level. The L₁₈ (3⁶) orthogonal table used in this study is shown in Tables 5 and 6. Three groups of samples are made for each experiment to make sure the quality measured has considerable stability.

Table 5.

Polypropene composite injection molding processing parameter

Control factor	Exp. no	Level 1	Level 2	Level 3
ERS-SEBS addition (%)	A	10	30	50
Melting temperature (℃)	B	190	200	210
Injection speed (mm/s)	C	30	40	50
Packing pressure (MPa)	D	40	60	80
Packing time (s)	E	0.5	1	1.5
Cooling time (s)	F	10	13	15

ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

Table 6.

L₁₈ orthogonal table

	A	B	C	D	E	F
1	1	1	1	1	1	1
2	2	2	2	2	2	2
3	3	3	3	3	3	3
4	1	1	2	2	3	3
5	2	2	3	3	1	1
6	3	3	1	2	2	2
7	1	1	1	3	2	3
8	2	2	2	1	3	1
9	3	3	3	2	1	2
10	1	2	3	2	2	1
11	2	3	1	3	3	2
12	3	1	2	1	1	3
13	1	3	3	1	3	2
14	2	1	1	2	1	3
15	3	2	2	3	2	1
16	1	2	2	3	1	2
17	2	3	3	1	2	3
18	3	1	1	2	3	1

Impact strength optimality analysis

The experimental data of various experiments of Taguchi design L₁₈ are calculated by using the Taguchi method. The response table of impact strength optimality analysis is shown in Table 7 and the response figure is shown in Figure 3.

Table 7.

The response table of impact strength

	ERS-SEBS addition	Melting temperature	Injection speed	Packing pressure	Packing time	Cooling time
Level 1	18.152	20.533	20.681	21.428	19.893	20.336
Level 2	23.414	21.457	19.254	20.593	20.534	20.727
Level 3	19.120	19.207	21.263	19.176	20.771	20.134
Max	23.414	21.457	21.263	21.428	20.771	20.727
Min	18.152	19.207	19.254	19.176	19.893	20.134
Effect	5.262	2.250	2.009	2.252	0.878	0.593

ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

Figure 3.

The response figure of impact strength. ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

The response table and response figure show that the optimum factor levels are A₂, B₂, C₃, D₁, E₃ and F₂, which are ERS-SEBS addition 30%, melting temperature 200℃, injection speed 50 mm/s, packing pressure 40 MPa, packing time 1.5 s and cooling time 13 s, respectively.

The impact strength ANOVA is shown in Table 8. The influencing efficiency of various control factors for the impact strength can be judged, along with the effect of various control factors on the quality characteristic impact strength, followed by ERS-SEBS addition, packing pressure, melting temperature, injection speed, packing time and cooling time. The increase in the ERS-SEBS addition level can enhance the impact strength effectively, meaning the ethylene-butylene molecule segment can be compatible with PP, the styrene chain segment in the molecular chain can enhance the impact strength, in the formation of composite with fabric in next stage, and the mechanical properties of the fibrous composite can be enhanced effectively.

Table 8.

The analysis of variance of impact strength

	DOF	SS	MS	F ratio	CN (%)
A	2	94.14	47.07	26.10	75.75
B	2	9.91	4.95	2.75	7.97
C	2	8.58	4.29	2.38	6.90
D	2	10.29	5.15	2.85	8.28
E	2	1.24	0.62	0.34	1.00
F	2	0.12	0.06	0.03	0.10
Error	2	9.02	1.80
Total	17	124.28			100

Tensile strength optimality analysis

The tensile strength experimental data of various experiments of Taguchi design L₁₈ are calculated by the Taguchi method. The response table and response figure of tensile strength optimality analysis are shown in Table 9 and Figure 4.

Table 9.

The response table of tensile strength

	ERS-SEBS addition	Melting temperature	Injection speed	Packing pressure	Packing time	Cooling time
Level 1	28.044	23.720	23.775	23.824	23.676	23.814
Level 2	24.020	23.689	23.733	23.679	23.755	23.668
Level 3	19.053	23.708	23.609	23.614	23.686	23.634
Max	28.044	23.720	23.775	23.824	23.755	23.814
Min	19.053	23.689	23.609	23.614	23.676	23.634
Effect	8.992	0.031	0.166	0.211	0.079	0.180

ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

Figure 4.

The response figure of tensile strength. ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

The response table and response figure show that the optimum factor levels are A₁, B₁, C₁, D₁, E₂ and F₁, which are ERS-SEBS addition 10%, melting temperature 190℃, injection speed 30 mm/s, packing pressure 40 MPa, packing time 1 s and cooling time 10 s, respectively.

The ANOVA of tensile strength is shown in Table 10. The order of effects of various control factors on the quality tensile strength is ERS-SEBS addition, packing pressure, cooling time, injection speed, packing time and melting temperature. The addition of ERS-SEBS can enhance the tensile strength effectively, meaning the ethylene-butylene molecule segment can extend the molecular chain during elongation, the styrene chain segment in the molecular chain enhances the stiffness, enhancing the tensile property of fibrous composite. However, the styrene chain segment is incompatible with PP; when the addition level is higher than 20%, the molecular chain structure of the composite has micro face separation, failing to further enhance the tensile property.

Table 10.

The analysis of variance of tensile strength

	DOF	SS	MS	F ratio	CN (%)
A	2	243.45	121.72	4256.67	99.85
B	2	0.003	0.0001	0.05	0.001
C	2	0.09	0.04	1.56	0.04
D	2	0.14	0.07	2.45	0.06
E	2	0.02	0.01	0.39	0.01
F	2	0.11	0.05	1.92	0.05
Error	2	0.14	0.03
Total	17	234.18			100

Haze optimality analysis

The haze experimental data of various experiments of Taguchi design L₁₈ are calculated by the Taguchi method. The response table and response figure of haze optimality analysis are shown in Table 11 and Figure 5.

Table 11.

The response table of haze optimality analysis

	ERS-SEBS addition	Melting temperature	Injection speed	Packing pressure	Packing time	Cooling time
Level 1	38.942	38.344	38.262	38.346	38.415	38.374
Level 2	38.389	38.352	38.395	38.346	38.307	38.284
Level 3	37.789	38.418	38.456	38.421	38.391	38.452
Max	38.942	38.418	38.456	38.421	38.415	38.452
Min	37.783	38.344	38.262	38.345	38.307	38.287
Effect	1.158	0.074	0.195	0.075	0.109	0.164

ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

Figure 5.

The response figure of haze optimality analysis. ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

The response table and response figure show that the optimum factor levels are A₁, B₃, C₃, D₃, E₁ and F₃, which are ERS-SEBS addition 10%, melting temperature 210℃, injection speed 50 mm/s, packing pressure 80 MPa, packing time 0.5 s and cooling time 15 s, respectively.

The haze ANOVA is shown in Table 12. The order of effects of various control factors on the quality haze is ERS-SEBS addition, injection speed, cooling time, packing time, packing pressure and melting temperature. The ERS-SEBS addition keeps the composite haze at 88% effectively, meaning the styrene-ethylene-butylene- styrene block polymer segment damages the PP molecule chain arrangement orientation, the composite crystallinity declines and the material remains transparent as the ERS-SEBS and PP materials have different refraction indexes. In the formation of composite with fabric in the next stage, the mechanical properties of the fibrous composite can be enhanced effectively, and the composite with transparent fiber is formed, presenting the fiber line distribution of fabric in the composite.

Table 12.

The analysis of variance of haze

	DOF	SS	MS	F ratio	CN (%)
A	2	4.03	2.01	231.21	93.48
B	2	0.02	0.01	1.15	0.46
C	2	0.12	0.06	6.80	2.75
D	2	0.02	0.01	1.28	0.52
E	2	0.04	0.02	2.25	0.91
F	2	0.08	0.04	4.66	1.88
Error	2	0.04	0.01
Total	17	4.31			100

Experimental verification

According to Figures 4 and 5, the effect of factor A (ERS-SEBS addition) is much more significant than the others. To prove the importance of these factors (melting temperature, injection speed, packing pressure, packing time and cooling time), the L₁₈ experiment was designed as shown in Table 13.

Table 13.

Experimental verification without factor A

Control factor	Exp. no	Level 1	Level 2	Level 3
Melting temperature (℃)	B	19	200	210
Injection speed (mm/s)	C	30	40	50
Packing pressure (MPa)	D	40	60	80
Packing time (s)	E	0.5	1	1.5
Cooling time (s)	F	10	13	15

The enhanced rubber segment-styrene ethylene/butylene styrene addition is 10%.

The ANOVA of tensile strength and haze verified analysis are shown in Tables 14 and 15.

Table 14.

The analysis of variance of tensile strength verified analysis

	DOF	SS	MS	F ratio	CN (%)
Melting temperature	2	0.379	0.1895	9.9736842	9.600668
Injection speed	2	0.667	0.3335	17.552632	17.70915
Packing pressure	2	1.299	0.6495	34.184211	35.50276
Packing time	2	0.594	0.297	15.631579	15.65388
Cooling time	2	0.794	0.397	20.894737	21.28477
Error	7	0.133	0.019		0.249
Total	17	3.552			100

Table 15.

The analysis of variance of haze

	DOF	SS	MS	F ratio	CN (%)
Melting temperature	2	1.212	0.606	13.728	13.017
Injection speed	2	2.864	1.432	32.440	32.152
Packing pressure	2	1.244	0.622	14.091	13.387
Packing time	2	1.451	0.726	16.435	15.785
Cooling time	2	1.553	0.777	17.591	16.966
Error	7	0.309	0.044		8.693
Total	17	8.633			100

According to Tables 14 and 15, factors B–F (melting temperature, injection speed, packing pressure, packing time and cooling time) have significant contributions for the qualities (tensile strength and haze) when factor A (ERS-SEBS addition) is not present. Factors B–F are important in injection process optimization as well.

Multi-quality analysis

In terms of the evaluation scales of Layer 2 quality items, the effects of the impact strength, tensile strength and haze quality characteristics are compared according to the evaluation scale rule in Table 3. There are two fundamental purposes of using PP composite in fibrous composite; the first one is to enhance the quality strength to reduce the product replacement rate. To attain this goal, the physical properties of the product must be enhanced to increase the product strength, so as to reduce the probability of product damage resulting from misapplication. The second one is to keep the fibrous composite transparent, so a transparent lightweight fibrous composite is made. Therefore, the three qualities are of the same importance. According to Table 3, we set the haze importance as 1, the impact strength importance as 1.1 and the tensile strength importance as 1.2 to make a slight difference, because according to the ANOVA, the tensile strength has a greater contribution degree than the impact strength. The comparison results are shown in Table 16.

Table 16.

Quality importance matrix

	Impact strength	Tensile strength	Haze
Impact strength	1	1.09	0.91
Tensile strength	0.92	1	0.83
Haze	1.1	1.2	1

The C.R. value is calculated, the level element weights are calculated by Equation (14) and the consistency vector is obtained by Equation (15), as shown in Table 17, and the results are added up and averaged to obtain the feature value λ. The random indexes are shown in Table 4, the R.I. value of level number 3 is 0.58, substituted in Equations (16)–(18) to obtain C.R. = 0.00073, as shown in Table 18.

Table 17.

Quality characteristic weights and consistency vector

Ratio	Weight	consistency vector
Impact strength	0.34334	3.00087
Tensile strength	0.35344	3.00089
Haze	0.30322	3.00077

Table 18.

C.R. value

Feature value	3.00084
C.I. value	0.00042
C.R. value	0.00073

As the consistency ratio C.R. value is lower than 0.1, the weights represented by various qualities are reliable. Finally, the optimum processing parameter combination is calculated by Equation (19). The quality importance weights are shown in Table 16, multiplied by the factor contribution degree of single quality in the Taguchi ANOVA. The levels are combined according to single quality optimization, and the results are shown in Table 19. The optimum parameter combination is ERS-SEBS addition 10%, melting temperature 200℃, injection speed 50 mm/s, packing pressure 40 MPa, packing time 1.5 s and cooling time 15 s. The comparison results of product quality and unmodified PP are shown in Table 20. The optimum condition shows when the ERS-SEBS addition level is 10%, the impact strength of PP composite is 7.26 kJ/m² (142% higher than PP raw material), the tensile strength is 23.69 MPa (3% higher than PP raw material) and haze is 84.88% (5.7% lower than PP raw material).

Table 19.

The optimum parameter combination of the multi-quality combination

	Impact strength		Tensile strength		Haze
ERS-SEBS addition	A2	0.26008	A1	0.35291	A1	0.28345
Melting temperature	B2	0.02736	B1	0.00001	B3	0.00139
Injection speed	C3	0.02369	C1	0.00014	C3	0.00834
Packing pressure	D1	0.02843	D1	0.0021	D3	0.00158
Packing time	E3	0.00343	E2	0.0003	E1	0.00276
Cooling time	F2	0.00034	F1	0.0002	F3	0.00037

ERS-SEBS: enhanced rubber segment-styrene ethylene/butylene styrene.

Table 20.

Comparison of this study and the unmodified polypropene (PP)

Quality	Test method	Unit	Normal PP	This study	Gain (%)
Impact strength	ISO 180	kJ/m²	3.0	7.26	142%
Tensile strength	ASTM D638	MPa	23	23.69	3%
Haze	ASTM D1003	%	90	84.88	5.7%^a

The property of haze is the smaller the better.

Conclusions

This study aims to enhance the quality process of modified PP composite using a styrene triblock copolymer as the copolymer mixed in three proportions by a single-screw extruder. The physical properties of PP/ERS-SEBS are better than those of regular PP, and the optical performance is maintained. The optimization parameter combination is 10% ERS-SEBS adding amount, process temperature 200℃, injection speed 50 mm/s, holding pressure 40 MPa, holding time 1.5 s and cooling time 15 s. The optimum parameter shows the PP/ERS-SEBS composite impact strength is 7.26 kJ/m², higher than the regular PP by 142%. The tensile strength is 23.69 MPa, higher than the regular PP by 3%. The haze can be reduced to 5.7%. This indicates that the new kind of SEBS can disperse into the PP phase very well and it is only necessary to add 10%. The next phase, this optimum parameter of PP/ERS-SEBS, will be used to study the PP/ERS-SEBS/fiber composite and discuss the physical properties.

This study modifies the material formulation for the transmittancy and impact resistance of fibrous composites. The PP as the principal part is mixed with ERS-SEBS for remedying the deficiencies in PP performance, overcoming the crystal floating white and impact resistance problems of PP in the molding process, so that it has excellent transmittancy, good impact resistance at low temperature, excellent impact resistance at normal temperature and stressless whitenization. This material can be used as the principal part of fibrous composites, reinforced by fabric or cross-woven fabric to improve directional tear and tensile strength, so as to implement the application of PP to the field of high-class fibrous composites.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflict of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Science and Technology of the Republic of China (Grant no. MOST 105-2221-E-011-156).

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	A	B	C	D	E	F
1	1	1	1	1	1	1
2	2	2	2	2	2	2
3	3	3	3	3	3	3
4	1	1	2	2	3	3
5	2	2	3	3	1	1
6	3	3	1	2	2	2
7	1	1	1	3	2	3
8	2	2	2	1	3	1
9	3	3	3	2	1	2
10	1	2	3	2	2	1
11	2	3	1	3	3	2
12	3	1	2	1	1	3
13	1	3	3	1	3	2
14	2	1	1	2	1	3
15	3	2	2	3	2	1
16	1	2	2	3	1	2
17	2	3	3	1	2	3
18	3	1	1	2	3	1

	A	B	C	D	E	F
1	1	1	1	1	1	1
2	2	2	2	2	2	2
3	3	3	3	3	3	3
4	1	1	2	2	3	3
5	2	2	3	3	1	1
6	3	3	1	2	2	2
7	1	1	1	3	2	3
8	2	2	2	1	3	1
9	3	3	3	2	1	2
10	1	2	3	2	2	1
11	2	3	1	3	3	2
12	3	1	2	1	1	3
13	1	3	3	1	3	2
14	2	1	1	2	1	3
15	3	2	2	3	2	1
16	1	2	2	3	1	2
17	2	3	3	1	2	3
18	3	1	1	2	3	1

	A	B	C	D	E	F
1	1	1	1	1	1	1
2	2	2	2	2	2	2
3	3	3	3	3	3	3
4	1	1	2	2	3	3
5	2	2	3	3	1	1
6	3	3	1	2	2	2
7	1	1	1	3	2	3
8	2	2	2	1	3	1
9	3	3	3	2	1	2
10	1	2	3	2	2	1
11	2	3	1	3	3	2
12	3	1	2	1	1	3
13	1	3	3	1	3	2
14	2	1	1	2	1	3
15	3	2	2	3	2	1
16	1	2	2	3	1	2
17	2	3	3	1	2	3
18	3	1	1	2	3	1