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Abstract

Growing concerns over resource depletion and rampant material pollution have sparked enormous interest in the use of natural, renewable fillers for the formulation of polymer composites. In this paper, Pistachio Shell Powder (PSP), an easily available agro-processing waste product, is comprehensively investigated as a biorenewable natural filler for the development of green and sustainable PLA biocomposites. Experimental design and analysis were performed using a statistical approach, Response Surface Methodology - Box-Behnken Design (RSM-BBD). This methodology was then applied for the optimization of the material properties by systematically altering the main input parameters, which are represented by the filler loading, melting temperature, pressure, and speed. The material characterization data obtained were optimized by using RSM for multifactor analysis, and the validity of the statistical design was carefully checked by the use of ANOVA. The key results obtained from this work clearly pinpoint PSP as a promising, high-potential, and eco-friendly reinforcement for biodegradable PLA biocomposites.

Keywords

Green composites thermal behaviour of PLA double melting peak pista shell filler morphology of filler

Introduction

With the increasing environmental awareness and pressure in today’s world, the quest for eco-friendly solutions to reduce waste and low-impact living has become more essential than ever.¹ With the exponentially increasing population of the world coupled with the fast progress of industry, the problem of domestic and industrial waste management has been greatly accelerated. But this increasing issue provides a highly promising challenge: the conversion of agro-waste into valuable, environmentally friendly composite materials.² This change is in line with growing focus on circular economy values and demand for sustainable material development.³ Composite materials are well known for their high mechanical and physical properties, but traditionally they have heavily depended upon synthetic reinforcement.⁴ The ecological impact of synthetic fibers has initiated a strategic evolution towards the utilization of natural fibers and fillers. Fibers like flax, jute, hemp, kenaf, and sisal have received significant interest from researchers and manufacturers because they are biodegradable, cheap, and possess beneficial mechanical properties.⁵ They are considered to be more of an application in lightweight structures, especially throughout the automotive industry, where it has replaced glass fiber composites in interior panels, door linings, and other non-structural parts. Depending on the type of application, natural fiber composites can be made with thermoplastic matrices or thermosetting polymers, or with hybrids thereof. Thermoplastics, for example, polypropylene, polyethylene, polylactic acid (PLA), and polyester, offer unique advantages, such as recyclability and, in some cases, biodegradability.⁶ Thermoplastics are flexible and allow tailored material properties to be designed to meet specific performance needs such as heat resistance, mechanical strength, and chemical stability. New research has focused on first the addition of natural fibers into polymer matrices and the examination of many factors that impact the mechanical and thermal properties of the resulting composites, including the orientation of fibers, stacking sequence, and loading percent.^7,8 Natural fibers are mostly used as reinforcement agents, which add to composites’ strength and stiffness, whereas natural fillers tend to act as modifying agents to alter the matrix phase. Fillers are generally added to minimize the cost of materials, improve certain properties, or processability.^9,10

Several studies have illustrated the positive impact of using agro-waste-based fillers in polymer composites. For instance, the reinforcement of sisal/hemp/epoxy composites with silica nanoparticles in different percentages of weight (0–9 wt%) reduced void content and significantly enhanced mechanical and wear properties.¹¹ Likewise, the addition of mango shell particles into jute/flax/epoxy composites resulted in improvements in strength as well as wear resistance, emphasizing the reinforcing capabilities of natural fillers.¹² Groundnut husk fillers, on incorporation at higher weight fractions, have also been reported to enhance tensile and flexural strength substantially up to an optimal limit (approximately 40 wt%), in addition to overall cost-effectiveness of material and the environment.¹³ In this context, a study investigated the valorization of Tunisian almond shells as micrometric reinforcements of biocomposites based on LLDPE. The impact of filler content on the resultant properties of the biocomposites was also investigated. Here, the benefits of using low amounts of ASP and their production on a low-cost scale were emphasized.¹⁴ Some of the other residues such as eggshells, coconut shells, rice husks, sawdust of wood, fish bones, fish scales, and powders of Samanea saman have also been extensively researched as fillers in polymer composites. In addition to improving the mechanical properties, strength, rigidity, and hardness, the materials also serve a purpose in making the composite manufacturing more environmentally friendly by recycling agro-waste, thereby making composite manufacturing less ecologically stressful.^15,16

Processing of Polylactic Acid (PLA) bio-composites using natural fillers remains a critical and evolving subject, specifically with reference to the optimization of the processing conditions, and achieving a crucial balance in the process. Recent publications and emerging findings reported insights into the intricate characteristics of bio-composites. For instance, the potential of natural filler, namely walnut shell powder as a biodegradable filler in PLA concluded that particulate shell fillers introduce a rigid interphase that significantly alters the glass transition characteristic.¹⁷ Another study¹⁸ established that the large filler loading significantly alters the thermal transition temperatures of the PLA matrix, a phenomenon needing a careful study of the crystallization behavior. The results suggest that the loading of fillers is a stiff constraint on polymer chains, which influences the glass transition temperature as well as dimensional stability of the molded part. In addition, application of statistical optimization methods, such as the Design of Experiments (DOE), has proved to be fundamental to establishing a defined processing window for PLA composites. The need for statistical process control is established, where it was demonstrated that the narrow processing window of PLA needs to be subject to multi-variable optimization.¹⁹ Bringing together these established methodologies, this study now extends this optimization framework to the injection molding process by isolating the thermal evolution and density variation of the composite.

While the research on agro waste valorization is ongoing, some agro-wastes such as pistachio shell powder (PSP) are still underutilized. Pistachio shells contain lignocellulosic fibers with high contents possessing better thermal stability. They possess the ideal properties for being excellent reagents for reinforcement in nanocomposites, providing structural stability at the expense of reducing thermal stress.²⁰ Other studies have explored the PSP behavior in thermosetting matrices such as epoxy,^21,22 there has been rather little research on bringing PSP into bio-based thermoplastic systems. Addressing this sector might reveal a whole new realm of potential when it comes to the design of fully sustainable, biodegradable composite materials.²³ This work aims at bridging this gap by investigating the feasibility of pistachio shell powder as a biosourced filler in thermoplastic composites. The originality of the present investigation relies on the valorization of waste PSP, which is not widely used, while simultaneously optimizing the most significant input parameters: filler loading and injection molding conditions. In addition, the research explores the interaction effects of these parameters on the physical and thermal properties of the composite, providing worthwhile insight into process-performance and material behavior relationships. Even though the same material system and general processing methodology have been used as in the previous study,²⁴ the research theme differs in its essential features. The previous work was primarily focused on elaborated analysis of the mechanical strength properties’ optimization of pistachio shell–filled PLA composite materials. In contrast, this study is towards a systematic investigation of how the filler and variation in processing conditions influences a broader range of composite properties which include bulk density, glass transition behavior, cold crystallization phenomena, and melting characteristics. These properties are essential in describing composite processing reliability in addition to providing an understanding beyond just composite strength properties. With an emphasis on these parameters, the present work provides a complementary view to the previous study and contributes to a better understanding of the thermal performance of composite. Lastly, by converting agricultural residues into high-strength green composites, the research facilitates the twin goals of waste reduction and material design innovation. It is a reflection of how sustainability and technology unite to establish the means of clean and green production and assist the world towards a circular economy. This study advances the research on green composites and illustrates the extensive range of applications agro-waste offers in developing sustainable materials for various industrial needs.

Materials and Methodology

The experimental study materials and various test procedures have been succinctly summarized in this section. Figure 1 shows the research methodology for the current study.

Figure 1.

Flow chart of the present experimental study.

Resin and Filler

In the present investigation, polylactic acid (PLA) was utilized as the matrix material. PLA is a biodegradable thermoplastic polymer obtained from renewable resources and was purchased from Natur Tec India Pvt. Ltd. It is found to be extremely sustainable and has good processing traits. The polymer has a glass transition temperature ranging from 50°C to 60°C and a melting temperature ranging from 145°C to 180°C, and hence it can be used for fabricating thermoplastic composites.

Pistachio shells, employed as the bio-filler in the composite material, were taken from a nearby marketplace. For purity and to eliminate any contaminant, the shells were subjected to a multi-step cleaning procedure. The shells were first soaked in distilled water for 3 hours followed by careful washing to remove surface dirt. Thereafter, the shells were subjected to a 5% w/v NaOH solution for 1 hour for removal of waxy material and residual surface impurities. The shells, after chemical treatment, were sun-dried for 10 days for complete moisture evaporation. The shells were ground and milled into fine powder after drying to obtain pistachio shell powder (PSP). To ensure a consistent particle size distribution, the ground lignocellulosic material was sieved through mesh sizes ranging from 20 to 100 μm mesh to offer uniformity for all the experimental samples.

Design of Experiments with RSM

RSM was used in an attempt to design and methodically evaluate the manner in which different input parameters influenced the composite properties. It is an efficient instrument for modeling, analyzing, and optimizing processes in complex systems in mathematics and statistics. Although it requires resources, it investigates the interactions of multiple variables with minimal experimental trials. This method helps develop an empirical model describing the interaction among input variables and response results of interest so as to optimize best process conditions. The method allows for the graphical representation of interaction effects of process parameters and predicts the system’s behavior on the alteration of operating conditions.

In this study, the Box–Behnken Design was chosen for the experimental design since four independent variables were investigated at three levels each, as illustrated in Table 1. The design enabled quadratic response modeling, which was further employed to explore how each parameter interacts and affects response characteristics. Using experimental responses, a regression predictive model was constructed and verified to quantitatively describe the parameter-response relationships, enabling the identification of the optimal processing conditions for composite manufacturing.

Table 1.

Design of experiments for composite fabrication by Box–Behnken Design (BBD).

Run	Std.	Filler percentage (%)	Melting temperature (^oC)	Injection pressure (MPa)	Injection speed (mm/s)
1	12	30	175	70	50
2	8	20	165	70	50
3	5	20	175	110	40
4	23	20	175	90	50
5	21	20	175	90	50
6	18	30	175	110	50
7	1	10	175	70	50
8	27	20	165	110	50
9	10	30	175	90	40
10	7	20	175	70	60
11	11	10	165	90	50
12	16	20	175	70	40
13	17	10	175	90	40
14	19	10	175	110	50
15	22	20	185	90	60
16	9	10	175	90	60
17	20	30	185	90	50
18	4	30	175	90	60
19	25	20	165	90	60
20	15	20	185	110	50
21	14	20	165	90	40
22	6	20	185	90	40
23	26	20	175	110	60
24	3	10	185	90	50
25	24	20	175	90	50
26	2	30	165	90	50
27	13	20	185	70	50

Fabrication of Composites

The PLA/PSP (pistachio shell powder) composites were fabricated with a 50-ton servo-controlled hydraulic injection molding machine. The processing conditions, especially the barrel temperature, were precisely controlled in accordance with the experimental design to provide the best possible processing conditions for each trial. While molding, PLA pellets and PSP filler were introduced directly in the injection molding machine hopper.

The molding process was performed with a 20-g shot weight and around 2 seconds of a cycle time. These conditions were chosen for the purpose of maintaining even dispersion of the filler in the polymer matrix and to avoid degradation of the PLA resin during processing. In order to identify the best formulation and processing conditions, a set of experimental runs were carried out. Such trials entailed systematic variation of the filler weight percentage (w/w), along with the most important injection molding parameters, according to the experimental matrix set by the Response Surface Methodology. The composite samples produced were then characterized to assess the impact of process variables on material performance.

Physical Properties

Density is one of the most important properties of materials, which refers to mass per unit volume. In injection-molded composites, density testing is essential to evaluate quality, strength, and performance. Density determines mechanical properties including strength, stiffness, and durability. Testing thus ensures that the composite is up to specified standards and performs its best in the intended application.

The composite sample is weighed on a precision balance, usually to 0.001 g. Then, the sample is submerged in water in order to measure the volume in terms of displaced fluid. The displaced water volume is equal to the sample’s volume, and it is determined by using a graduated cylinder.

After both mass and volume are established, density is determined by using the formula:

ρ = \frac{m}{V}

(1)

Where, ρ is for Density (in g/cm³), m is for mass (in g), and V is for Volume (in cm³)

Thermal Characteristics

The composite specimens that were fabricated were tested using Differential Scanning Calorimetry (DSC) with the aim of assessing the influence of input parameters on the glass transition temperature (T_g) and melting temperature (T_m) of the composite materials. The technique allows for the detection of thermal transitions with high accuracy and provides useful information regarding the stability and thermal behavior of the produced composites. DSC analysis focuses on the influence of fillers and machining parameters on the overall thermal characteristics of the materials by analyzing certain thermal events, e.g., glass transition and melting temperatures. The experiments were carried out on SHIMAZDU DCG-60.

Results and Discussion

Surface Morphology

SEM was employed in the study of the surface morphology of the natural filler at resolutions between 5,000X and 20,000X to identify the geometry of its particles and their microstructural features. From the SEM images shown in Figure 2(a)–(c), it is seen that, in essence, the particles of this filler are spherical to sub-spherical in nature, possessing surfaces which are relatively smooth and continuous. Some isolated platelet or flake-like entities are also found on the surfaces of the particles, probably arising from the inherent crystalline growth habits or morphological diversities of the source material in nature. Correspondingly, the size of these particles lies within the range of approximately 3-10 µm, featuring the micron-scale dimensionality required for applications as functional additives or reinforcing elements in composite materials. The tendency toward particle agglomeration can also be evidenced, mainly in lower magnifications. Such a phenomenon may be explained by van der Waals forces or surface-retained moisture, quite common in natural particulate matter. The filler exhibits a highly wrinkled, folded sheet-like structure with thin lamellae and significant overlap at higher magnification, indicating both layered structure and high specific surface area. The mixture of spherical and irregular fillers is most likely to affect the surface area, packing density, and interfacial adhesion upon blending into polymeric or ceramic matrices.

Figure 2.

SEM Micrographs of Pista shell filler.

The SEM micrographs in Figure 2(d)-(e) are at 24,000x and 4,000x magnifications, respectively. The surfaces look smooth but are actually corrugated and wrinkled with a clear elasticity that may delaminate on preparation. At lower magnifications, the filler particles look as irregular aggregates consisting of piled-up, crumpled sheets that form a very open porous structure with huge voids in between clusters. A hierarchical surface architecture is caused by this multi-scale texture, which is characteristic of natural fillers and consists of coarse aggregates and fine lamellar sheets. These morphological characteristics are beneficial for composite reinforcement since the rough and porous surfaces promote mechanical interlocking and interfacial adhesion to polymer matrices. Thus, the glass transition temperature is strengthened with rising filler and pressure, but the intermediate phase is a bridge where materials move from hard to softer in character. On the whole, the SEM examination attests that the natural filler has a well-characterized and morphologically different microstructure, and has a highly complex, layered structure with strong surface roughness and porosity, altogether which would play an important role in the improvement of mechanical, thermal, and physicochemical performance in composite formulations.

Determination of Input Parameter Effects on Density of Composites

The ANOVA table for density as presented in Table 2 shows that some terms were found to be statistically insignificant. However, for the density, model terms A, B, C, AB, AC, AD, BD, CD, B², C², D² are significant. Among the input parameters, melting temperature and percentage of filler are noted as the most significant contributors. It can also be noted from the coded equation (2) that the input parameters filler percentage, pressure and speed has a positive impact on density of the fabricated composites, while, melting temperature is noted to exert a negative influence. As for the significant quadratic terms, only C² exert a positive effect, while B², and D² are noted to show a depleting effects. Equations (2) and (3) are coded and actual equation versions, respectively, of the density of the composite. It can be noticed that the coded equation is easier to model because the variables are normalized and the actual equation is the one expressing the physics of the problem in the original measurement units.

Coded Equation : 2.11 + 0.0241667 * A - 0.0541667 * B + 0.0158333 * C + 0.000833333 * D + 0.045 * AB - 0.0875 * AC - 0.04 * AD + 8.70228 e - 18 * BC + 0.0475 * BD - 0.035 * CD - 0.00541667 * A^{2} - 0.0204167 * B^{2} + 0.0145833 * C^{2} - 0.0554167 * D^{2}

(2)

Actual Equation : - 0.673958 - 0.0147917 * A + 0.0332917 * B + 0.0117292 * C - 0.003875 * D + 0.00045 * AB - 0.0004375 * AC - 0.0004 * AD - 1.98228 e - 19 * BC + 0.000475 * BD - 0.000175 * CD - 5.41667 e - 05 * A^{2} - 0.000204167 * B^{2} + 3.64583 e - 05 * C^{2} - 0.000554167 * D^{2}

(3)

Table 2.

ANOVA Table for Density of composites.

Source	Sum of squares	df	Mean square	F-value	p-value		Fit statistics
Model	0.1282	14	0.0092	99.89	<0.0001	Significant	R ²	0.9915
A-Filler percentage	0.0070	1	0.0070	76.45	<0.0001		Adjusted R ²	0.9816
B-Melting temperature	0.0352	1	0.0352	384.09	<0.0001		Predicted R ²	0.9564
C-pressure	0.0030	1	0.0030	32.82	<0.0001
D-Speed	8.333E-06	1	8.333E-06	0.0909	0.7682
AB	0.0081	1	0.0081	88.36	<0.0001
AC	0.0306	1	0.0306	334.09	<0.0001
AD	0.0064	1	0.0064	69.82	<0.0001
BC	0.0000	1	0.0000	0.0000	1.0000
BD	0.0090	1	0.0090	98.45	<0.0001
CD	0.0049	1	0.0049	53.45	<0.0001
A²	0.0002	1	0.0002	1.71	0.2159
B²	0.0022	1	0.0022	24.25	0.0004
C²	0.0011	1	0.0011	12.37	0.0042
D²	0.0164	1	0.0164	178.68	<0.0001
Residual	0.0011	12	0.0001
Lack of fit	0.0009	10	0.0001	0.9000	0.6334	Not significant
Pure error	0.0002	2	0.0001
Cor Total	0.1293	26

The coloured 3D surface (Figure 3) are representations of various density values, employing a gradient in which the colours change from cooler (like blue and green) to warmer (like yellow). Cooler colours usually represent lower density values, while the warmer hues represent higher density. The surface slopes and curves visibly, showing how the variations in the input variables influence the density. Regions of higher density are indicated by yellow and light green colours, implying that these regions relate to the best conditions for achieving maximum density. Underneath the 3D surface, on the grey base plane of the plot, there is a two-dimensional contour plot. This section of the visualization plots the 3D surface down so the density response is displayed top-down. The contour lines join points on the surface with identical density value, similar to elevation lines on a topographic map. Each line indicates a constant density, and the spacing between these lines gives further information on how rapidly density varies with changing percentages of filler and melting temperatures. When the lines are far apart, it means that the slope is steep, or that even slight variations in input values cause large variations in density. When the lines are close together, the variation in density is gradual.

Figure 3.

3D interaction graph between Filler percentage and melting temperature for density.

The shape of the surface and the organization of the contour lines tell us about the character of the relationships between the variables. Raising the Filler Percentage from 10% to 30% produces an unmistakable rise in density, which suggests that filler loading has a positive impact on density. In contrast, raising the Melting Temperature from 165°C to 185°C seems to decrease the density or maintain it at a constant rate at lower levels of filler. This implies that, although increased filler content increases the density, increased processing temperatures might negate that advantage, possibly as a result of matrix degradation or alteration in compaction. Surface curvature and the irregular spacing of contour lines also suggest the interaction between the two factors, which means they do not act independently but interact. This interaction effect reveals that the effect of the percentage of filler on density is a function of the melting temperature and vice versa. For example, added content of filler could result in a higher percentage increase of the density at low melting temperature, while such an increase could be less effective or even negative at high temperature. These complex interactions are important to develop when trying to optimize material properties. The composite density was seen to increase with the addition of fillers by different researchers.¹²

In short, this response surface plot simply displays the way the density of a material system varies based on filler percentage levels and melting temperature levels. It reveals that, whereas increased filler content tends to increase density, rising melting temperature can result in decreasing returns or even decreases in density. The surface also indicates that these two variables are dependent, and the best combination for attaining the highest density seems to exist at increased filler percentages and decreased melting temperatures.

Determination of Input Parameter Effects on Thermal Characteristic

The thermal behavior of semi-crystalline polymer systems can be considered as a major source of information about their molecular organization, processing history, and structure property relationships. Glass transition temperature ( $T_{g}$ ), cold crystallization temperature ( $T_{c c}$ ), and melting transitions $T_{m 1}$ and $T_{m 2}$ , can be determined by DSC, an important analytical methodology, including in the case of PLA and composite materials, which can be seen from Figure 4. Such thermal events reflect the equilibrium between crystalline and amorphous domains, as well as reorganization processes upon heating. The polymer’s amorphous phase changes from a rigid, glassy state to a more flexible, rubbery one at a temperature known as the glass transition temperature, $T_{g}$ . Below $T_{g}$ , the mobility of the chains is severely restricted, resulting in a brittle material which stores mechanical energy elastically. As the temperature rises through $T_{g}$ , molecular motion becomes more intense and the segments of the chain start to move in concert to soften the material. $T_{g}$ often appears as a step change in the baseline of the heat flow curve in DSC thermograms. However, an enthalpy relaxation peak may appear immediately following $T_{g}$ in physically aged materials, such as materials stored for considerable periods below $T_{g}$ . This endothermic event is related to the release of excess enthalpy developed during molecular relaxation in the glassy state. This effect is eliminated during the initial heating, and subsequent scans yield a more distinct $T_{g}$ value that reflects the polymer’s inherent amorphous nature. The $T_{g}$ value is a crucial determinant of free volume, interfacial interactions between the polymer and filler, and polymer chain mobility in composite systems.

Figure 4.

DSC curve of pista shell filler – PLA composite.

The cold crystallization temperature ( $T_{c c}$ ,) reflects the exothermic process of amorphous or partially ordered regions of the polymer reorganizing into a more crystalline structure upon heating. This is because PLA chains may not possess sufficient mobility during processing to fully crystallize, such as when cooling rapidly from the melt. As the temperature increases above $T_{g}$ , chain mobility improves and crystallization can proceed. The position and magnitude of the $T_{c c}$ , peak are highly dependent on prior thermal history, cooling rate, and the presence of nucleating agents or fillers. Generally, a lower $T_{c c}$ , and higher exothermic enthalpy are indicative of improved crystallization kinetics, often resulting from heterogeneous nucleation at filler interfaces. The size of the cold crystallization peak also reveals the amount of amorphous material that is able to crystallize upon heating.

In semicrystalline PLA systems, two characteristic endothermic peaks have been commonly detected and defined as $T_{m 1}$ and $T_{m 2}$ during the melting process.^25,26 These correspond to the melting of crystals having different structural orders or thermal histories. The metastable α′-form of PLA is usually associated with the fusion of thinner or less ordered lamellae, which is generally responsible for the first melting peak ( $T_{m 1}$ ). This form has a higher defect density and looser chain packing, and it is often formed by melt or cold crystallization below 120°C. The second melting peak ( $T_{m 2}$ ) is caused by the more thermodynamically stable α-form, which melts at a higher temperature after α′-crystals melt and recrystallize when heated.²⁷ The lamellar reorganization and polymorphism that are intrinsic to PLA’s semicrystalline morphology are reflected in this double-melting behavior.

When α′ and α domains coexist in the same crystalline system, overlapping melting events occur, which are frequently seen in the 140-150°C range. The distribution of lamellar thickness, the degree of structural ordering brought about by processing conditions, and the ratio and separation of $T_{m 1}$ and $T_{m 2}$ can all be inferred from these data. In PLA-based composites, the delicate balance between amorphous and crystalline phases is demonstrated by the interaction of $T_{g}$ , $T_{c c}$ , $T_{m 1}$ and $T_{m 2}$ . The presence of a clear $T_{g}$ followed by a noticeable $T_{c c}$ and well-defined melting peaks indicates a semicrystalline shape capable of structural rearrangement upon heating. Among these, processing parameters, especially cooling rate, annealing temperature, and addition of fillers, have a great influence on such transitions due to changes in chain mobility and nucleation density. From a material point of view, higher amorphous content imparts greater impact strength and flexibility, while encouraging controlled crystallization by optimized processing or nucleating additives enhances stiffness, thermal resistance, and dimensional stability.

Determination of Input Parameter Effects on T _g

The ANOVA table for glass transition temperature (T_g) as presented in Table 3 shows that most terms were found to be statistically insignificant. However, for the glass transition temperature, model terms A, C, D, A², and D² as significant. Among the input parameters, pressure and percentage of filler are noted as the most significant contributors. It should also be noted from the coded equation (4) that while the two input parameters play a positive role with respect to the glass transition temperature, the other two exert a negative influence, indicating that pressure have a negative effect. As for the significant quadratic terms, both exert a positive effect, which in turn accentuates their contribution to high thermal behavior with respect to the variation of evaluated parameters. Equations (4) and (5) are coded and actual equation versions, respectively, of the glass transition temperature (T_g) of the composite. It can be noticed that the coded equation is easier to model because the variables are normalized and the actual equation is the one expressing the physics of the problem in the original measurement units.

Coded Equation : 58.4533 + 0.516083 * A - 0.0316667 * B - 1.23742 * C + 0.219833 * D - 0.105 * AB + 0.08775 * AC + 0.0695 * AD + 0.03 * BC + 0.16 * BD - 0.02 * CD + 0.18275 * A^{2} - 0.118875 * B^{2} + 0.0225 * C^{2} + 0.256375 * D^{2}

(4)

Actual Equation : 47.3573 + 0.0880208 * A + 0.340396 * B - 0.102021 * C - 0.519292 * D - 0.00105 * AB + 0.00043875 * AC + 0.000695 * AD + 0.00015 * BC + 0.0016 * BD - 0.0001 * CD + 0.0018275 * A^{2} - 0.00118875 * B^{2} + 5.625 e - 05 * C^{2} + 0.00256375 * D^{2}

(5)

Table 3.

ANOVA table for glass transition temperature.

Source	Sum of squares	df	Mean square	F- value	p- value		Fit statistics
Model	23.12	14	1.65	72.03	<0.0001	Significant	R ²	0.9882
A- filler percentage	3.20	1	3.20	139.41	<0.0001		Adjusted R ²	0.9745
B- Melting temperature	0.0120	1	0.0120	0.5249	0.4827		Predicted R ²	0.9334
C- pressure	18.37	1	18.37	801.47	<0.0001
D- Speed	0.5799	1	0.5799	25.30	0.0003
AB	0.0441	1	0.0441	1.92	0.1907
AC	0.0308	1	0.0308	1.34	0.2690
AD	0.0193	1	0.0193	0.8428	0.3767
BC	0.0036	1	0.0036	0.1570	0.6989
BD	0.1024	1	0.1024	4.47	0.0562
CD	0.0016	1	0.0016	0.0698	0.7961
A²	0.1781	1	0.1781	7.77	0.0164
B²	0.0754	1	0.0754	3.29	0.0949
C²	0.0027	1	0.0027	0.1178	0.7374
D²	0.3506	1	0.3506	15.29	0.0021
Residual	0.2751	12	0.0229
Lack of fit	0.2676	10	0.0268	7.17	0.1285	Not significant
Pure error	0.0075	2	0.0037
Cor Total	23.39	26

A three-dimensional illustration demonstrating the way the two primary influencing factors-pressure and filler percentage, interact with the glass transition temperature is shown in Figure 5. This picture goes beyond the elements of the interaction and provides insight into how these parameters affect the composite’s overall thermal behaviour. The impact of filler percentage on the X-axis and pressure on the Y-axis on the glass transition temperature on the Z-axis is shown in this three-dimensional surface plot. The gradient transitioning from blue (low T_g) to orange (high T_g) indicates that the glass transition temperature has increased, at least slightly, as a result of the simultaneous increases in filler percentage, at lowest value of pressure. The impact of filler percentage on the composite glass transition temperature could be seen from the 3D surface plot. At the three stages, the glass transition temperature increases by 1°, which is comparatively very slight. This pattern suggests that fillers contribute to improving the composite’s thermal stability. A further decrease in molecular mobility within the matrix is indicated by this temperature increase with the addition of filler, which ultimately leads to improved thermal performance. These results are still very important for maximising the amount of filler used to create composite materials with certain thermal characteristics. Depending on the filler material, different researchers have reported slight variations in the glass transition temperature of composites, either increasing or decreasing. These consist of their compatibility with the matrix, size, shape, and chemical makeup. While some have the capacity to reduce T_g because they enhance the molecular motion of the fillers, others with a higher degree of thermal stability will contribute to higher T_g because they are likely to decrease polymer molecular motion.^28–32

Figure 5.

3D interaction graph between Filler percentage and pressure for glass transition temperature.

Determination of Input Parameter Effects on T _cc

The analysis of variance (ANOVA) for the cold crystallization temperature

(T_{c c}

) of the fabricated composites is summarized in Table 4.

Coded Equation : 101.76 - 1.62163 * A + 0.669931 * B - 0.910035 * C - 0.155 * D - 0.0975 * AB + 0.295104 * AC - 0.5275 * AD - 0.0025 * BC + 0.08 * BD + 0.1025 * CD - 0.110833 * A^{2} + 0.361615 * B^{2} - 0.430833 * C^{2} - 0.543385 * D^{2}

(6)

Actual Equation : 189.22 + 0.183748 * A - 1.21803 * B + 0.0954253 * C + 0.44726 * D - 0.000975 * AB + 0.00147552 * AC - 0.005275 * AD - 1.25 e - 05 * BC + 0.0008 * BD + 0.0005125 * CD - 0.00110833 * A^{2} + 0.00361615 * B^{2} - 0.00107708 * C^{2} - 0.00543385 * D

(7)

Table 4.

ANOVA table for cold crystallization temperature.

Source	Sum of squares	df	Mean square	F- value	p- value		Fit statistics
Model	53.03	14	3.79	4166.20	<0.0001	Significant	R ²	0.9998
A- filler percentage	31.56	1	31.56	34708.93	<0.0001		Adjusted R ²	0.9996
B- Melting temperature	5.39	1	5.39	5923.74	<0.0001		Predicted R ²	0.9989
C- pressure	9.94	1	9.94	10930.82	<0.0001
D- Speed	0.2883	1	0.2883	317.10	<0.0001
AB	0.0380	1	0.0380	41.82	<0.0001
AC	0.3483	1	0.3483	383.15	<0.0001
AD	1.11	1	1.11	1224.22	<0.0001
BC	0.0000	1	0.0000	0.0275	0.8711
BD	0.0256	1	0.0256	28.16	0.0002
CD	0.0420	1	0.0420	46.22	<0.0001
A²	0.0655	1	0.0655	72.06	<0.0001
B²	0.6974	1	0.6974	767.09	<0.0001
C²	0.9900	1	0.9900	1088.86	<0.0001
D²	1.57	1	1.57	1732.09	<0.0001
Residual	0.0109	12	0.0009
Lack of fit	0.0103	10	0.0010	3.44	0.2463	Not significant
Pure error	0.0006	2	0.0003
Cor Total	53.04	26

The model terms A, B, C, D, AB, AC, AD, BD, CD, A², B², C², D² are found to be significant model terms (values lesser than 0.1000), while filler percentage and pressure can be seen to be the most influencing input parameters. Equations (6) and (7) represents the coded equation and actual equation for the model. It can be noted from the coded equation that most of the terms, namely A, C, D, AB, AD, BC, A², C²and D² are showing a negative or reducing trend on the output parameter.

The three-dimensional response surface plot (Figure 6) provides a clear visual representation of how the independent variables jointly influence the response variable. In this plot, the curved surface represents the predicted response across different combinations of factor levels, allowing the identification of optimal conditions within the experimental range. The gradient and curvature of the surface imply a quadratic trend for the relationship of the factors and response, which is other than a linear relationship. Such curvature in the surface means that both main effects and their interactions are significant. Maximum or minimum points on the surface correspond to regions where the response reaches a maximum or minimum value, respectively, reflecting possible optimal conditions of the process.

Figure 6.

3D interaction graph between filler percentage and pressure for cold crystallization temperature.

The contour plot, which is projected underneath the 3D surface, provides a two-dimensional view that supplements the surface visualization. Contours connect points of equal response; elliptical shapes indicate important interactions between the factors studied, meaning that for one variable changing, the response changes based on the level of the other. Smooth and well-spaced contours represent a region of stability in response, while packed contours indicate steep change and thus sensitivity of the response to variation in the factor. These plots together not only confirm the adequacy of the model in capturing the factor interactions but also serve as a powerful tool for visual optimization and interpretation of process behaviour. Statistically, the model exhibits a very high degree of reliability and reproducibility. The F-value of the model was found to be 4166.20 with a p-value less than 0.0001, and hence the regression model is highly significant. This confirmed that the variation in response was well explained by the fitted model rather than random error. The respective significant terms are A, B, C, D, AB, AC, BC, BD, CD, A², B², C², and D², showing both linear and quadratic components make significant contributions to the determination of the response.

The addition of natural fillers to PLA composites significantly affects the cold crystallization process of the polymer, which mainly changes the cold crystallization temperature, T_cc. Such influence is, in fact, related to the balance between two competing influences: one that favors heterogeneous nucleation and one that restricts chain mobility. The influence of the former, heterogeneous nucleation, almost usually predominates due to the introduction of a great number of solid interfaces with high surface area into the PLA matrix. These surfaces act as energetically favorable sites for crystal initiation, enabling the formation of ordered structures at a lower thermal driving force than required for homogeneous nucleation in neat PLA. As a result, the cold crystallization peak observed in Differential Scanning Calorimetry (DSC) analyses shifts toward a lower temperature, indicating that the presence of the filler effectively accelerates the crystallization kinetics. However, this nucleation-driven decrease in $T_{c c}$ can be partially offset by the restricted chain mobility that occurs at the polymer–filler interface. In contrast, strong interfacial adhesion or high filler loading may impede the necessary rearrangement of chains for crystal growth and, thus, delay crystallization and, occasionally, slightly shift upward or broaden the $T_{c c}$ peak.^33–37 In general, therefore, the overall thermal response of PLA composites reflects a balance between these two competing effects, with the nucleating action of the filler generally dominating and producing a net decrease in $T_{c c}$ . Such reduction not only confirms the capability of natural fillers to act as nucleators but also represents an improvement in crystallization efficiency, a desirable outcome for the enhancement of thermal stability and processing performance of PLA-based biocomposites.

Determination of Input Parameter Effects on T _m1

The analysis of variance (ANOVA) for the quadratic model describing Response

T_{m 1}

of the fabricated composites is summarized in Table 5. The obtained Model F-value of 23,531.32 with a p-value < .0001 clearly demonstrates that the developed regression model is highly significant, and the probability of this level of correlation occurring due to random noise is less than 0.01%. This confirms that the model efficiently explains the variability in

T_{m 1}

as a function of the selected process parameters. All the input factors (A: filler percentage, B: melting temperature, C: pressure, and D: speed) and their interactions (AB, AC, AD, BC, BD, CD), along with the quadratic components (A², B², C², D²), were found to be statistically significant with p-values below 0.05. This clearly indicates that both first-order and second-order effects substantially influence the

T_{m 1}

response, reflecting the complex interdependence between material composition and processing variables. Among these, pressure (C) exhibited the strongest effect, followed by injection speed followed by filler percentage in the composite. Also, Equations (8) and (9), representing the coded and actual models, respectively.

Coded Equation : 146.417 + 0.185 * A - 0.1 * B - 1.13667 * C + 0.216667 * D - 0.56 * AB - 1.27 * AC + 0.15 * AD + 0.3525 * BC - 0.7375 * BD - 0.5475 * CD - 0.252917 * A^{2} - 0.0779167 * B^{2} - 0.482917 * C^{2} - 0.290417 * D^{2}

(8)

Actual Equation : 31.2946 + 1.59617 * A + 0.584833 * B + 0.115917 * C + 1.81908 * D - 0.0056 * AB - 0.00635 * AC + 0.0015 * AD + 0.0017625 * BC - 0.007375 * BD - 0.0027375 * CD - 0.00252917 * A^{2} - 0.000779167 * B^{2} - 0.00120729 * C^{2} - 0.00290417 * D^{2}

(9)

Table 5.

ANOVA table for lower melting temperature.

Source	Sum of squares	df	Mean square	F- value	p- value		Fit statistics
Model	29.74	14	2.12	23531.32	<0.0001	Significant	R ²	1.0000
A- filler percentage	0.4107	1	0.4107	4549.29	<0.0001		Adjusted R ²	0.9999
B- Melting temperature	0.1200	1	0.1200	1329.23	<0.0001		Predicted R ²	0.9998
C- pressure	15.50	1	15.50	1.717 E+05	<0.0001
D- Speed	0.5633	1	0.5633	6240.00	<0.0001
AB	1.25	1	1.25	13894.89	<0.0001
AC	6.45	1	6.45	71463.88	<0.0001
AD	0.0900	1	0.0900	996.92	<0.0001
BC	0.4970	1	0.4970	5505.51	<0.0001
BD	2.18	1	2.18	24099.23	<0.0001
CD	1.20	1	1.20	13281.51	<0.0001
A²	0.3412	1	0.3412	3778.96	<0.0001
B²	0.0324	1	0.0324	358.66	<0.0001
C²	1.24	1	1.24	13777.24	<0.0001
D²	0.4498	1	0.4498	4982.66	<0.0001
Residual	0.0011	12	0.0001
Lack of fit	0.0010	10	0.0001	3.05	0.2721	Not significant
Pure error	0.0001	2	0.0000
Cor Total	29.74	26

The Lack of Fit F-value of 3.05 and a p-value of 0.2721 indicate that the lack of fit is not significant relative to the pure error, confirming that the quadratic model provides an excellent fit to the experimental data. Such a non-significant lack of fit is desirable, as it implies that the residual variation is primarily due to random experimental error rather than systematic model inadequacy. The model also exhibits strong statistical performance, with a high Predicted R² value greater than 0.9, all in close agreement.

The three-dimensional response surface plot (Figure 7) illustrates the combined influence of the most influential parameters on $T_{m 1}$ . The surface plot illustrates a smooth and gently curved topology and indicates a non-linear interaction between the two parameters. It can be seen that an increase in pressure lead to deccrease in output response and increase in speed resulted in a progressive rise in $T_{m 1}$ value, while the maximum response was obtained at lower pressure levels combined with moderate to high speed. The curvature in the surface indicates that the relationship of the factors and response follows a quadratic trend: $T_{m 1}$ changes with a change in process parameters and then attains a plateau. It means that beyond threshold levels of these variables, their effect is not pronounced, and probably an optimum operating zone exists within the range of the variables studied. This variation is further dramatized by the color gradient over the surface from green to red, and it has to be remembered that red areas reflect higher $T_{m 1}$ values while the blue-green reflects lower response areas. The projected contour plot below the 3D surface gives further detail on the interactive behaviour of pressure with speed. The elliptical contour lines confirm significant interaction between the two parameters, where a change in one factor causes the response to vary depending on the level of the other. The smooth and equally spaced contours towards the mid-region indicate a stable and predictable response, while the closely packed contours around the high-pressure-high-speed region indicate steep gradients reflecting the increased sensitivity of $T_{m 1}$ .against small changes in these variables. This can be explained by the increased compactness and greater ordering of the polymer matrix at the right amount of pressure, providing better interfacial contact between fillers and the matrix, besides uniformity in structure. While increased pressure results in faster filling as well as high shear, resulting in the polymer chains having less time to organize into crystalline regions resulting in lower melting temperature. Similarly, the increase in the speed of processing promotes better heat transfer and orientation of the polymer chains, both of which contribute to the increase in $T_{m 1}$ . However, excessive combinations of pressure and speed could impose stress on the polymer network, leading to a reduction or stabilization in response.

Figure 7.

3D interaction graph between pressure and speed for lower melting temperature.

While filler percentage also have a significant effect on the lower melting curve of the PLA Composites, the effect can be viewed in Figure 8. The addition of natural fillers to poly lactic acid (PLA) composites has a profound influence on the intensity of the lower melting peak $T_{m 1}$ and a subtle, though quantifiable, impact on its position. Within the investigated range of contents (10-30 wt% filler), the temperature of $T_{m 1}$ increased by about 1°C. This slight positive displacement of $T_{m 1}$ is the result of a balance between increased nucleation kinetics and slight improvements of lamellar stability due to the filler. At low to moderate filler loadings, the natural filler acts as an effective heterogeneous nucleating agent, driving fast cold crystallization and the growth of many small, imperfect α′-form crystals.³⁸ Although these lamellae are typically less ordered, the presence of well-dispersed filler surfaces facilitates slightly improved crystal organization, yielding a marginally higher melting temperature. The limited magnitude of this shift aligns with the Gibbs–Thomson relationship, which dictates that melting temperature depends primarily on lamellar thickness and perfection, parameters that are only modestly affected under non-isothermal conditions. At higher filler concentrations, the reinforcing particles begin to restrict polymer chain mobility, reducing the overall degree of crystallinity but not significantly altering the thermal stability of the imperfect crystals formed.^39,40

Figure 8.

Effect of filler percentage on percentage of lower melting temperature.

Determination of Input Parameter Effects on T _m2

As seen in Table 6 which summarize Higher Melting Temperatures’ (

T_{m 2}

) ANOVA test, the majority of the terms are significant at the given level of significance. The significant main effects and interactions include A, B, C, D, AB, BD, CD, A², B², C², and D². From the input parameters assessed for their contribution to the model, melting temperature and filler content are listed as the most significant contributors to the model. From the coded equation (10), it has been identified that although the filler percentage has a positive impact on melting temperature of composite, other three parameters A, B, and C have a negative impact. The main focus for significant interaction term is most of them are positive except for a few but negative. It further suggests that the optimization of other parameters along with filler percentage will raise the melting temperature for the composite material. Equations (10) and (11) are representations of the coded and actual equations, respectively, for melting temperature,

T_{m 2}

, of the composite material. Coded equation expresses the melting temperature in a coded form.

Coded Equation : 154.587 + 0.9775 * A - 0.395 * B - 0.0991667 * C - 0.351667 * D + 0.4725 * AB + 0.03 * AC - 0.15 * AD + 0.1525 * BC + 0.735 * BD - 0.685 * CD + 0.71375 * A^{2} + 0.605 * B^{2} - 0.18625 * C^{2} - 0.5325 * D^{2}

(10)

Actual Equation : 409.018 - 0.953125 * A - 2.68763 * B + 0.113667 * C - 0.450667 * D + 0.004725 * AB + 0.00015 * AC - 0.0015 * AD + 0.0007625 * BC + 0.00735 * BD - 0.003425 * CD + 0.0071375 * A^{2} + 0.00605 * B^{2} - 0.000465625 * C^{2} - 0.005325 * D^{2}

(11)

Table 6.

ANOVA table for melting temperature.

Source	Sum of squares	df	Mean square	F- value	p- value		Fit statistics
Model	29.13	14	2.08	94.69	<0.0001	Significant	R ²	0.9910
A- filler percentage	11.47	1	11.47	521.86	<0.0001		Adjusted R ²	0.9806
B- Melting temperature	1.87	1	1.87	85.21	<0.0001		Predicted R ²	0.9493
C- pressure	0.1180	1	0.1180	5.37	0.0389
D- Speed	1.48	1	1.48	67.54	<0.0001
AB	0.8930	1	0.8930	40.64	<0.0001
AC	0.0036	1	0.0036	0.1638	0.6928
AD	0.0900	1	0.0900	4.10	0.0658
BC	0.0930	1	0.0930	4.23	0.0620
BD	2.16	1	2.16	98.35	<0.0001
CD	1.88	1	1.88	85.42	<0.0001
A²	2.72	1	2.72	123.66	<0.0001
B²	1.95	1	1.95	88.85	<0.0001
C²	0.1850	1	0.1850	8.42	0.0133
D²	1.51	1	1.51	68.83	<0.0001
Residual	0.2637	12	0.0220
Lack of fit	0.2556	10	0.0256	6.34	0.1439	Not significant
Pure error	0.0081	2	0.0040
Cor Total	29.39	26

From Figure 9, the 3D plot showcases the interaction between two major influential parameters, melting temperature and filler percentage, on the melting point temperature ( $T_{m 2}$ ) of the composite. The X-axis represents the filler percentage, ranging from 0% to 30%, while the Y-axis displays the melting temperature within the range of 165°C to 185°C. The Z-axis represents the response variable, $T_{m 2}$ . It can be observed from the plot that as the filler percentage increases and melting temperature increases, the composite melting point temperature increases, indicated by the upward slope on the surface plot, and both factors contribute to higher $T_{m 2}$ values. While in the case of the input parameter, Melting Temperature, there is a clear trend evident in the graph. It can be clearly noticed in the graph that the melting temperature, ( $T_{m 2}$ ) decreases when its level increases from Level 1 to Level 2. When its level increases further from Level 2 to Level 3, T_m tends to increase, which infers that with the rise in temperature from level 2 to level 3, it has a positive effect on the composite melting point. Furthermore, at the bottom, the contour lines give an overt indication of how $T_{m 2}$ varies with different percentages of fillers and melting temperatures. These contour plots enable straightforward recognition of certain values of $T_{m 2}$ for various parameter combinations, with the ability to optimize the material properties more efficiently.

Figure 9.

3D interaction graph between Filler percentage and pressure for melting temperature.

The figure provides a visual overview of the interaction between filler content and melting temperature and its effect on composite higher melting temperature ( $T_{m 2}$ ). It has previously been established in research that increasing the amount of filler material leads to higher composite melting temperatures.^41–44 However, the rise and overall behaviour significantly rely on the nature of filler material used. Various fillers are responsible for their varying thermal characteristics, chemical structures, and interaction with the matrix material in governing the melting point differently. The nature, size, type, and concentration of the filler reinforced into the matrix also have significant roles to determine how the filler level will function in affecting the general melting behaviour of the composite.

Conclusion

The present study demonstrates the valorization of high-crystallinity agro-waste, pistachio shell powder, hitherto rendered unusable, into a functional bio-filler for polylactic acid composites through injection molding. The Response Surface Methodology with the Box-Behnken Design provided us not only with an efficient mapping of the processing space but also helped in accomplishing a multi-factor optimization of the properties of the ensuing composite. The experimental results establish the efficiency of PSP filler in two ways: as an efficient physical reinforcement and as an active modifier of the polymer’s thermal properties. Some key findings support this claim, as it is shown that with increasing filler content, the composite performance increases, providing higher density, a higher glass transition temperature $T_{g}$ , and and increased higher melting temperature $T_{m 2}$ . This evidences the capability of PSP to enhance the overall thermal stability of the composite. The filler also showed a significant effect on the cold crystallization temperature $T_{c c}$ , as well lower melting temperature $T_{m 1}$ of the fabricated composites.

It was also established through detailed ANOVA analysis that the interaction of filler loading, temperature, pressure, and speed, with the PSP inclusion, imposed synergistic effects on composite properties, indicating complex quadratic and interactive relationships, which required careful control of the process parameters. The validity of RSM models, shown in this research, therefore confirms that the statistical approach can be used to determine the best manufacturing conditions toward maximum property enhancement. In summary, this work presents not only a feasible, green, and economic route for the valorization of agro-waste but also secures a place for PSP as a promising and functional candidate for sustainable bio-composites, hence supporting the transition toward a circular material economy.

Footnotes

Declaration of conflicting interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Avinash Petta

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Run	Std.	Filler percentage (%)	Melting temperature (^oC)	Injection pressure (MPa)	Injection speed (mm/s)
1	12	30	175	70	50
2	8	20	165	70	50
3	5	20	175	110	40
4	23	20	175	90	50
5	21	20	175	90	50
6	18	30	175	110	50
7	1	10	175	70	50
8	27	20	165	110	50
9	10	30	175	90	40
10	7	20	175	70	60
11	11	10	165	90	50
12	16	20	175	70	40
13	17	10	175	90	40
14	19	10	175	110	50
15	22	20	185	90	60
16	9	10	175	90	60
17	20	30	185	90	50
18	4	30	175	90	60
19	25	20	165	90	60
20	15	20	185	110	50
21	14	20	165	90	40
22	6	20	185	90	40
23	26	20	175	110	60
24	3	10	185	90	50
25	24	20	175	90	50
26	2	30	165	90	50
27	13	20	185	70	50

Run	Std.	Filler percentage (%)	Melting temperature (^oC)	Injection pressure (MPa)	Injection speed (mm/s)
1	12	30	175	70	50
2	8	20	165	70	50
3	5	20	175	110	40
4	23	20	175	90	50
5	21	20	175	90	50
6	18	30	175	110	50
7	1	10	175	70	50
8	27	20	165	110	50
9	10	30	175	90	40
10	7	20	175	70	60
11	11	10	165	90	50
12	16	20	175	70	40
13	17	10	175	90	40
14	19	10	175	110	50
15	22	20	185	90	60
16	9	10	175	90	60
17	20	30	185	90	50
18	4	30	175	90	60
19	25	20	165	90	60
20	15	20	185	110	50
21	14	20	165	90	40
22	6	20	185	90	40
23	26	20	175	110	60
24	3	10	185	90	50
25	24	20	175	90	50
26	2	30	165	90	50
27	13	20	185	70	50

Valorization of pistachio shell filler: Optimization of filler content and injection molding parameters on physical and thermal characteristics of PLA based green composites

Abstract

Keywords

Introduction

Materials and Methodology

Resin and Filler

Design of Experiments with RSM

Fabrication of Composites

Physical Properties

Thermal Characteristics

Results and Discussion

Surface Morphology

Determination of Input Parameter Effects on Density of Composites

Determination of Input Parameter Effects on Thermal Characteristic

Determination of Input Parameter Effects on T g

Determination of Input Parameter Effects on T cc

Determination of Input Parameter Effects on T m1

Determination of Input Parameter Effects on T m2

Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Data Availability Statement

References

Determination of Input Parameter Effects on T _g

Determination of Input Parameter Effects on T _cc

Determination of Input Parameter Effects on T _m1

Determination of Input Parameter Effects on T _m2

Run	Std.	Filler percentage (%)	Melting temperature (^oC)	Injection pressure (MPa)	Injection speed (mm/s)
1	12	30	175	70	50
2	8	20	165	70	50
3	5	20	175	110	40
4	23	20	175	90	50
5	21	20	175	90	50
6	18	30	175	110	50
7	1	10	175	70	50
8	27	20	165	110	50
9	10	30	175	90	40
10	7	20	175	70	60
11	11	10	165	90	50
12	16	20	175	70	40
13	17	10	175	90	40
14	19	10	175	110	50
15	22	20	185	90	60
16	9	10	175	90	60
17	20	30	185	90	50
18	4	30	175	90	60
19	25	20	165	90	60
20	15	20	185	110	50
21	14	20	165	90	40
22	6	20	185	90	40
23	26	20	175	110	60
24	3	10	185	90	50
25	24	20	175	90	50
26	2	30	165	90	50
27	13	20	185	70	50