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Abstract

The growing demand for sustainable engineering materials has driven interest in utilizing industrial waste to create functional composites. In this study, a polyurethane matrix was reinforced with three recycled fillers—rigid polyurethane foam waste (WRPU), ground tire rubber (RTW), and waste printed circuit boards (WPCB)—using methylene diphenyl diisocyanate (MDI) as a binder. The composites were fabricated via mechanical mixing and casting, and their low-velocity impact resistance was evaluated experimentally. Various compositions were tested, and the optimal formulation (27.58 wt% WRPU, 15 wt% RTW, 2 wt% WPCB) exhibited the highest total energy absorption (2.699 J), closely matching ANSYS-based numerical predictions with minimal error. Microstructural analysis (HR-SEM) confirmed uniform filler dispersion and strong interfacial bonding, while thermogravimetric analysis (TGA) demonstrated improved thermal stability. The results support the feasibility of converting e-waste and rubber waste into impact-resistant composites for automotive applications.

Keywords

Sustainable composites polyurethane foam waste low-velocity impact response surface methodology energy absorption automotive materials

Introduction

In the past few years, studies on low velocity impact (LVI) behavior in composite materials have been given more attention due to its central importance in numerous structural applications, especially aerospace, automotive, and defense industries. LVI damage, conventionally from lower impact velocities <10 m/s, has the potential to initiate internal damages including delamination, matrix cracking and fiber failure, without any perceived surface deformation, which reduce residual strength and life expectancy of the composite structure. Despite being frequently unobservable, damage has a decisive influence on parts’ load-bearing capacity and reliability over extended lifetimes. Damage from LVI can be cumulative, and hence it is essential to model its effects correctly for safety and durability considerations in engineering design. Due to these hazards, there has been considerable research aimed at predicting and minimizing LVI-induced damage using analytical, numerical, and theoretical models.^1–3 Due to the influence of LVI behavior on structural integrity^4–11 which can occur via mechanisms such as delamination, matrix cracking, and fiber breakage, this behavior has been studied for a long time. This most recent research highlights the problems of barely visible impact damage in composites^12–18 and its use in defense, automotive, and aerospace structures.^19–26 As of late, there is a developing trend of studies that have examined sustainable composites under LVI conditions, which look to assess and improve upon the impact performance of composites with recycled fillers or waste-derived constituents. In this study, a sustainable composite was prepared based on three forms of recycled fillers: Rigid Polyurethane Foam Waste (RPUW), Ground Tire Rubber (GTR), and waste Printed Circuit Boards (PCBs). Each one of these fillers has certain advantages, playing unique roles towards the mechanical functionality of the resulting material. Famous for their energy-saving properties, strength, economic viability, and versatility, PU polymers are among the most widely used materials in a wide range of industries.^27,28 Rigid PU foam, nevertheless, is valued for its great mechanical strength, thermal insulation, and sound-deadening capacity. Rigid PU foam is used in the automotive and aeronautical sectors because it has structural support with light weight and insulation.^28,29 With the speedy growth of the automobile sector, the number of vehicles worldwide has increased enormously, resulting in increasing environmental issues.

Apart from the widely discussed topic of CO₂ emissions, one of the serious concerns is the stockpiling of enormous amounts of end-of-life tires.³⁰ Truck tires contain natural rubber more than passenger car tires do, as the former have to withstand higher loads and travel longer distances, thus must be more resistant to wear and fatigue.³¹ Disposal of tire is an important environmental problem as it is non-biodegradable in nature and produced in large quantity. To address this, environmental institutions and government regulatory bodies have implemented guidelines towards encouraging environmentally friendly recovery and recycling processes for tire waste. The best practice to deal with waste tires would be environmentally friendly, minimize the use of virgin raw materials, and facilitate their conversion into higher value industrial products.³⁰ Printed Circuit Boards (PCBs) are the structural and operational center of most electronic systems.^32,33 They usually consist of composites of epoxy laminates as well as fiber-reinforced composites. As world demand for electronic products keeps on growing, requirements for efficient and accurate techniques to identify PCB manufacturing defects have also become a high priority.³⁴ The integration of RPUW, GTR, and waste PCBs brings together three distinct grades of recycled material—each with distinct advantages in terms of lightness, energy absorption, and stiffness. However, their compositional and structural differences call for a uniform process technique to facilitate compatibility and homogeneity of the final composite.³⁵

Polyurethane (PU) waste can be recycled either chemically or mechanically.³⁶ Chemical recycling methods like glycolysis and hydrolysis can recover useful materials, but they usually need high energy, expensive chemicals, and create unwanted by-products.²⁷ For this reason, we chose mechanical recycling in this study, as it offers a simpler, cleaner, and more cost-effective way to reuse PU waste as filler material.³⁵ As cited by way of the European Tires and Rubber manufacturers association, mechanical approaches (shredding and grinding) accounted for around 87.5% of the 2 strategies (in parallel with PU foams) which might be used to recycle rubber wastes from tires. When a waste tire is ground, there are generally three main streams that result: steel fibers, textile cords, and ground tire rubber (GTR).^37,38 The GTR is thereafter widely recycled as a filler in a large number of composite applications, including composites researched in the present study.³⁹ Electronic waste (e-waste) means discarded, old, or faulty electrical and electronic equipment that has become useless and is sent towards recovery, recycling, or destruction processes. Some of the broad products that are included are computer tackle and supplemental bias.⁴⁰

Among various recycling processes, mechanical recycling has been a convenient and common process for processing printed circuit boards (PCBs) due to its simplicity of operation and cost-effectiveness.⁴¹ In regular recycling operations, PCBs are first removed from other components and shipped to smelters, where precious metals such as gold, silver, and copper are extracted.⁴² One significant restriction is that good delamination, required for quality separation of internal components, requires very fine fragmentation, which may not be economical or energy saving.^43,44 Shredding is a critical first process in mechanical printed circuit board recycling where boards are disassembled to pieces to facilitate downstream processing or separation. Following the shredding of all these materials, what about the polyurethane adhesives that hold it all together as a single composite material? Polyurethane adhesives are essential in both domestic/hobbyist applications and industrial applications because they can create strong bonds and are generally more versatile than other adhesives. MDI (methylene diphenyl diisocyanate) is, example of an isocyanate which used on composite materials, also known as an isocyanate. MDI is considered very reactive; it is hygroscopic, meaning it will attract water from its environment to form very strong urethane linkages.⁴⁵ It is a useful adhesive to produce particleboard (PB), high-density fiberboard (HDF), and medium-density fiberboard (MDF), as it tends to improve the physical and mechanical quality of the resultant panels.⁴⁶ With the MDI adhesive playing a vital role in gluing recycled elements like PU foam, ground tire rubber, and shredded PCBs together, there is a need to identify the optimum combination of these fillers in order to provide the best composite performance. For this purpose, optimization methods are used to determine the optimal combination ratios of different alternatives. One of the maximum effective and commonly carried out strategies for such experimental optimization is the RSM. RSM is a collection of statistical and high-quality methods used to model and dissect problems involving a response of interest that is influenced low with more than one variable. It enables scientists to formulate empirical models based on a systematic series of experiments and to identify the exact input —i.e., the ratio of PU foam, waste tire, and PCB filler—leading to the most favorable result. The best filler blend, deemed ideal for achieving optimal LVI energy absorption, was determined in this study through the use of RSM, which then guided the formulation of a composite with certain enhanced properties.⁴⁷

Despite the growing interest in recycling polymeric and electronic wastes, the main research gap lies in the limited studies addressing the synergistic effects among recycled polyurethane waste (RPUW), ground tire rubber (GTR), and waste printed circuit boards (WPCB) within a single composite system. This gap constrains the development of sustainable, impact-resistant materials derived entirely from industrial waste streams. To overcome this limitation, the novelty of the present study lies in the development of an MDI-bonded hybrid composite incorporating these three wastes and in the systematic evaluation of its low-velocity impact behavior. Furthermore, the use of Response Surface Methodology (RSM) to optimize the filler composition introduces an additional novel aspect by providing a quantitative framework to maximize impact energy absorption and identify the most effective synergy among the waste constituents.

Experimental procedure

Materials & composite preparation

Rigid Polyurethane Foam Waste (RPUW) was obtained from ASL, DRDO Hyderabad, Telangana, India, and subsequently physically recycled through grinding into fine powders with an average particle size of 200-300 µm. Waste Printed Circuit Boards (WPCBs) were obtained from VIT Chennai Electronics lab., Chennai, India, and then mechanically recycled to a final average size of 30-40 µm. Ground tire rubber (GTR) waste was obtained from VIT Chennai Centre for Advanced Material, located in Chennai, India, and it was pre-shredded to an average size of 100 - 150 µm. Flexi bond, an MDI-based adhesive, was acquired from Manali Petrochemicals Limited in Chennai, India. Distilled water was obtained from the VIT Chennai Chemistry laboratory in Chennai, India.

Sustainable Recycled Composite was prepared by blending Rigid Polyurethane Foam Waste, Waste Printed Circuit Boards, and Ground Tire Waste in specific weight percentages as indicated in the table. The composite manufacturing process incorporated compression mold methods defined in ASTM D3763-18. The fillers were initially placed in a beaker based on their weight percentage and mixed for 10 minutes at 150 rpm to get a homogeneous mixture. For enhancing sustainability, a bio-based MDI adhesive is used; afterward, 30 g of the adhesive is added to the homogeneous mixture and blended thoroughly for 15 minutes, to cover all surfaces evenly. Distilled water is then added to the mixture as a hardener in the form of 50% of the mass of the adhesive, i.e., 15 g, and then blended for 5 minutes. The blend is placed into a silicon mold measuring 100 mm in diameter and 20 mm in height. The blend is pressed into the mold with a tool that matches the mold’s size with a pressure of 4 MPa. The sample is allowed to cure overnight, and the following day it is delicately taken out and then put in a hot air furnace at 100°C for 6 hours to eliminate any moisture in the sample. Table 1 outlines the characteristic properties of the individual fillers involved, while the levels of fillers induced in the eco-friendly foam are shown in Table 2. Schematic diagram of the manufacture of the composite are shown in Figure 1.

Table 1.

Details of reinforcement.

Particle	Size (microns)	Density (g/cm³)	Melting point (°C)
Rigid PU foam	200–300	0.03–0.06	120
PCB waste	30–40	1.8–2.2	180
Tyre rubber scrap	100–150	1.1–2.2	270

Table 2.

Different levels of fillers used with RPUF.

Code	Parameters	Level−2	Level−1	Level0	Level1	Level2
A	RPUF (wt.%)	6	12	18	24	30
B	WPCB (wt.%)	2	4	6	8	10
C	RTW (wt.%)	3	6	9	12	15

Figure 1.

Schematic diagram of the manufacture of the composite.

Low-velocity impact test setup

This study focuses on the influence of a foam disk by testing with low low-velocity impact device as shown in Figure 2. These discs are securely fitted to the testing device, ensuring proper alignment and stability. Low-velocity impact tests were carried out in compliance with ASTM D3763-18, which outlines an instrumented puncture-impact test procedure for polymer composites using a drop-weight system. Testing machines for low-velocity impacts, equipped with force transducers and accelerometers, are calibrated before performing experiments. The impactor used had a hemispherical shape with a diameter of 12 mm. Each test was conducted by releasing the impactor from a fixed height of 60 cm. The force and acceleration generated during the impact were measured using load cells and accelerometers, from which the total energy absorption was calculated. Influence factors such as force, deformation, and energy absorption are measured during impact testing. Performed at various speeds and energy levels according to a pre-defined testing scheme. Information collected from the equipment is processed and examined to obtain necessary conclusions.

Figure 2.

Experimental setup of Low velocity impact testing equipment.

Response Surface Methodology (RSM) & computational simulation

Response Surface Methodology (RSM) was employed to study the relationship between key process parameters and the resulting material properties. A Central Composite Design (CCD) was used to estimate a second-degree polynomial model, which provides an approximate but effective tool to optimize the response variables for desired performance.⁴⁸ In contrast to traditional methods, statistical techniques can be used to detect the interaction between process factors. In addition, computational simulations were performed using ANSYS Mechanical to validate the experimental findings. The software enabled finite element analysis (FEA) of structural behavior under various conditions, providing insights into stress distribution and deformation patterns that complemented the RSM optimization results.⁴⁹

Characterization technique

Using a high-resolution scanning electron microscope (HR-SEM; Zeiss EVO10), the morphology of the recycled fillers and the fracture surfaces of the composites were analyzed. Instrumentation was conducted via a LaB₆ electron emitter in secondary electron (SE) mode with an accelerating voltage of 30 kV. Samples were coated with a gold layer using a sputtering process prior to imaging to generate a conductive specimen. The application of Fourier Transform Infrared (FTIR) spectroscopy used an Agilent Cary 630 FTIR spectrometer in order to identify the chemical functional groups. FTIR spectra were collected in the range of 4000–400 cm⁻¹ at 4 cm⁻¹ resolution to validate and confirm a recognition of the formation of urethane linkages. Thermogravimetric Analysis (TGA) was performed on a TA Instruments-named SDT Q600 in order to measure the thermal stability of the composites. A sample of around 10 mg was placed in an alumina crucible and was heated from a temperature of 30°C to 900°C with a consistent heating rate of 10°C/min, while in a continuous flow of nitrogen gas at 50 mL/min.

Results and discussion

Experimental analysis

The test for experimental evaluation of total absorbed energy specifies a process by trying, in detail, to assess the impact resistance of disks of waste-reinforced polyurethane (WRPU) foam materials reinforced with fillers from RTW and WPCB wastes. The disk specimens were freely dropped from a given height of 60 cm with the aid of a disk-shaped impactor experimentally set to provide controlled and uniform impact conditions. There will be no difference in the analysis; hence, accurate measurements calibrated drop height verification impact velocity. Graphs were plotted for all the samples from A0 to A15 to find the highest total energy absorption against the time (shown in Figure 6(a)–(c)), and the data analyses of total energy absorption (TEA) indicated that sample A15, with 18 wt% waste rigid polyurethane (WRPU), 9 wt% rubber tire waste (RTW), and 6 wt% waste printed circuit board (WPCB), was determined to be the best sample with a more substantial TEA of 2.236 J. This filler combination inherently has a good balance of flexibility, toughness, and stiffness. This is because of a high level of synergistic action between its components. The elastomeric RTW particles behave like tiny shock absorbers, deforming when impacted to dissipate energy, while the rigid particulate fillers from WPCB act like rigid barriers to restrict crack propagation. Ultimately, these specific filler characteristics allow for the highest impact performance. Specifically, WRPU from rigid polyurethane provides flexibility to absorb and disperse impact energy, RTW acts as a fibrous elastomeric filler that improves toughness through a deflection and bridging mechanism of cracks, and WPCB acts as a rigid particulate filler that improves stiffness and impact resistance properties. The sample A15 uses WPCB at 6 wt% - enough for wreckage to reinforce the WRPU matrix while avoiding brittleness in the sample. The consistent distribution of filler particles throughout the matrix, evident from the SEM analysis, is also a key attribute of this property, dispersing impact energy throughout the material. These three types of fillers can transfer stress, have energy dissipation capacity, and allow for damage control during an impact. In contrast, sample A0 recorded the lowest TEA as the WRPU matrix was unreinforced and failed locally. Intermediate samples with higher WRPU and either significantly more (A10 with 30 wt%), or exclusively, RTW or WPCB - and lower TEA values. For example, products with low WRPU (e.g., S9), were probably too brittle as there was not enough matrix to effectively transfer stress among the fillers. On the other hand, formulations with high WRPU (e.g., S10) produced a softer, excessively porous composite, lacking the stiffness to be effective under high impact stresses. Using too much WRPU will make the matrix too soft to absorb impact energy, and too much RTW or WPCB individually can lead to poor dispersion, or worse brittle failure. From Table 3, it clearly shows that sample A15 repurposed industrial waste with the best projected balance of ductility and toughness with reinforcement to enable a higher impact energy absorption capacity.

Figure 6.

Total Absorbed Energy graph for (a) Sample A0 - A5 (b) A0, A6 - A10 (c) A0, A11 - 15.

Table 3.

TEA measurement of eco-friendly foam without and with fillers.

S. No	WRPU (wt.%)	RTW (wt.%)	WPCB (wt.%)	Actual TEA (J)
A0	–	–	–	0.849
A1	12	6	4	1.804
A2	24	6	4	1.767
A3	12	12	4	1.485
A4	24	12	4	2.335
A5	12	6	8	2.170
A6	24	6	8	1.891
A7	12	12	8	1.334
A8	24	12	8	2.015
A9	6	9	6	0.962
A10	30	9	6	1.738
A11	18	3	6	2.180
A12	18	15	6	1.942
A13	18	9	2	2.068
A14	18	9	10	2.134
A15	18	9	6	2.236
A16	18	9	6	2.236
A17	18	9	6	2.236
A18	18	9	6	2.236
A19	18	9	6	2.236
A20	18	9	6	2.236

High-resolution scanning electron microscope images

HR-SEM, or high-resolution scanning electron microscopy, is a specialized scientific imaging method that is used to observe the microstructure and surface morphology of materials at resolutions of usually less than 1 nm.^50–53 The imaging technique applies electron gun technology to offer spatial resolution by using a magnetic field, by directing an electrical source from a narrow electron beam produced by electron guns, and focusing the electron beam onto a particular position of the sample. The primary advantage of HR-SEM is that it images nanoscale surface features and lend towards a greater understanding of structures used in notable electronic material developments, science, characterization, and failure analysis. SEM analysis brings to light distinct morphological characteristics in various samples. Figure 3(b) shows a rigid PU foam, which is common for waste PU foam. It has a brittle fracture surface, with flakes that have broken into sharp edges. The texture became thick while maintaining an irregular consistency because of non-human structures. Two scientific sources, numbered⁵⁴ and,⁵⁵ confirm that rigid PU foam still maintains its closed-cell pattern. In Figure 3(d) the scanning electron microscope displays waste PCBs which exhibit fibrous and flaky textures of glass fiber-reinforced epoxy resin and rough fractured surfaces that signal mechanical wear. The tire scrap in Figure 3(c) shows an irregular and crumpled surface, which is rough-surfaced vulcanized rubber in general. The appearance of wrinkled and swollen structures in materials typically occurs in carbon black-filled rubber composites which manufacturers use to produce automotive tires. The microstructure of MDI-based adhesive with water addition appears in Figure 3(a) showing a granular, rough, agglomerated structure featuring bubble-shaped formations that develop from gas-evolving reactions during the CO₂ evolution which produces micron-sized pores as a standard feature in the synthesis of polyurethane from MDI-water interactions. The SEM image of powdered fillers in RPUF at Figure 3(e) demonstrates their uniform distribution throughout the matrix which indicates effective composite mixing and reinforcement.

Figure 3.

HR-SEM characterization of (a) MDI-based sample without filler, (b)WRPU, (c) RTW, (d)RPCB, and (e) MDI-based sample with filler.

Fourier transform infrared

The term FTIR, or Fourier Transform Infrared, refers to a major testing technique for analyzing organic and inorganic materials by studying their absorbance of infrared light.^56–58 Certain frequencies pass through the sample and get absorbed by molecular bonds to produce unique spectral patterns. This technique permits the identification of functional groups and the derivation of molecular structures through observation of changes in molecular vibrations. The FTIR spectra of two samples are displayed in Figure 4. The FTIR analysis was performed to identify the major chemical functional groups in the composite, verify the production of urethane linkages resulting from the MDI adhesive, and assess any changes that occurred from the addition of the recycled fillers. The green line represents a sample with rigid PU foam dust and MDI, while the blue line shows a sample that also includes the main fillers. These spectra reveal significant differences in the chemical makeup of the two samples.^59,60 The green spectrum shows how methylene diphenyl diisocyanate (MDI) and rigid PU foam dust interact, creating specific functional groups. A big peak at 3300 cm⁻¹ points to N-H stretching, showing urethane bonds have formed. The peak at 1724 cm⁻¹ means C = O is stretching, which backs up the presence of urethane links, while the band from 2270 to 2250 cm⁻¹ suggests unreacted isocyanate (-NCO) groups. Small peaks at 1648 and 1381 cm⁻¹ hint at tiny amounts of hydroxyl (OH) groups and maybe some leftover moisture. Certain aromatic C-C and C-O-C vibrations in MDI’s makeup show distinct peaks below 1000 cm⁻¹. The 1381 cm⁻¹ peaks link to phenolic OH groups, which you often see in tire materials, and appear more pronounced.⁶¹ Fillers change the spectral features—for example, extra OH groups can react with -NCO, making its peak less intense, and the C–O–C area (1100–1000 cm⁻¹) becomes clearer depending on how much filler is there. On the other hand, the blue spectrum shows several more or stronger peaks because of fillers at 3380 cm⁻¹ and 550 cm⁻¹, which point to Zr–O bonds, suggesting the inclusion of zirconium-based ceramics found in PCBs.⁶²

Figure 4.

FTIR graph of the sample with and without fillers.

Thermogravimetric analysis

Thermogravimetric analysis (TGA) was carried out to assess and compare the thermal stability and degradation characteristics of the composite with and without the incorporation of recycled fillers. The sample - verified to be clean, dry, and homogeneous - is placed in an aluminum pan with a low mass and a holder for the thermogravimetric analyzer’s finely tuned balance. Next, the temperature increases linearly at 10°C minute-1 up to a set point in an inert atmosphere provided by nitrogen or argon. The decomposition is recorded in weight loss, as shown in Figure 5(a). The unfilled (pure) sample exhibits a sharp and discernible level of decomposition between 350 °C and 500°C, the decomposition being where urethane bonds and isocyanates break down. The filled sample starts decomposing more slowly, and its degradation is wider, indicating extra thermal stability via the further inclusion of thermally resistant materials from PCB components (e.g., silicates, metals, ZrO) and carbon-containing wastes from tire scrap.^63–65

Figure 5.

(a) TGA curves (weight loss %) of the sample with and without fillers. (b) DTG curves of the sample with and without fillers.

The higher final residue of the filled sample indicates the inorganic fillers are thermally stable and therefore led to an increase in char formation. This is consistent with the FTIR peaks at 550 cm⁻¹ and 3380 cm⁻¹ which represent ZrO and other stable oxides from the fillers. This puts into action the statement using the DTG curve (Figure 5(b)). The pure sample has a single sharp peak around 370 °C–400°C that indicates the on-set of rapid thermal degradation of urethane linkages, while the filled sample has two smaller peaks indicative of multi-stage decomposition. The first peak represents the degradation of the PU matrix while the second peak at 500 °C–550°C could represent the degradation of the rubber-based fillers or the organic matter in the PCB. The multi-stage thermal decomposition demonstrates that thermal stability impacts char formation and thermal destruction by delaying the thermal loss of the filled sample. Furthermore, it shows that the sample has areas which behave thermally differently, and these thermal behaviors could influence the degradation of a composite material. The lower DTG peak height of the filled sample as compared to the pure sample also confirms a slower degradation rate, meaning improved thermal stability.^65,66

Statistical analysis

Statistical analysis is very important in evaluating the total energy absorption of filler-reinforced foams-a priority application in impact resistance. This involves the application of cutting-edge statistical methodologies for interpreting experimental data acquired from low-velocity impact tests. Analysis of Variance (ANOVA), regression analysis, and Design of Experiments (DOE) are some of the tools that allow researchers to investigate how parameters such as filler composition and foam density affect energy absorption. Findings from statistical study help in material design towards feasible compositions, hence better foam properties towards impact resistance. It will also be useful for engineering applications through material design may help under this subtopic. Table 4 gives a full summary of how different things affect the low-velocity impact behavior of foam paper that is reinforced with fillers. This analysis takes into account the effects of various factors and their interactions on the response variable. The Model, in its beginning form, shows data to have high variability with an F-value of 64.81 and a corresponding P-value of 0.000, which can be interpreted to mean that at least one factor or interaction among factors has a significant effect on low-velocity impact performance. Further decomposition of the Model revealed that Linear components WRPU (wt.%)) RTW (wt.%), and WPCB (wt.%) play a role as F-value gives it an immense 34.62 against a p-value which is equivalent to 0.000 in evidence. Individually WRPU (wt.%)) and WPCB (wt.% give huge imprints by having F-values of 65.70 and 38.50 respectively with same strength in their P-values being 0.000 while RTW (wt.%) is not significant with an F-value of just 0.41 giving it a P-value of 0.534. Square component which takes care of the possible quadratic effect is highly significant with F-value 106.34 and P-value 0.000 mainly due to WRPU (wt.%) *WRPU (wt.%) having F value 318.57 and P-value 0.000.2-Way Interaction component shows meaningful influence with F-value 45.13 and P-value 0.000 which means that factors’ interactions are essential in how the low-velocity impact behavior is determined. Some specific interaction terms like WRPU (wt.%) *RTW (wt.%), and RTW (wt.%) *WPCB (wt.%) show very strong effects having F values equal to 102.56 and 27.78 respectively with p equals to 0.000. Error components analysis shows model lack of fit however not exact figure because of asterisks in Lack-of-Fit F-value but overall error variance is quite low indicating data fits model well.

Table 4.

ANOVA for TEA (J).

Source	DF	Adj SS	Adj MS	F-value	p-value
Model	9	2.42400	0.26933	64.81	0.000
Linear	3	0.43161	0.14387	34.62	0.000
WRPU (wt.%)	1	0.27304	0.27304	65.70	0.000
RTW (wt.%)	1	0.00172	0.00172	0.41	0.534
WPCB (wt.%)	1	0.16000	0.16000	38.50	0.000
Square	3	1.32583	0.44194	106.34	0.000
WRPU (wt.%) *WRPU (wt.%)	1	1.32396	1.32396	318.57	0.000
RTW (wt.%) *RTW (wt.%)	1	0.06730	0.06730	16.19	0.002
WPCB (wt.%) *WPCB (wt.%)	1	0.04376	0.04376	10.53	0.009
2-Way interaction	3	0.56272	0.18757	45.13	0.000
WRPU (wt.%) *RTW (wt.%)	1	0.42625	0.42625	102.56	0.000
WRPU (wt.%) *WPCB (wt.%)	1	0.02101	0.02101	5.06	0.048
RTW (wt.%) *WPCB (wt.%)	1	0.11546	0.11546	27.78	0.000
Error	10	0.04156	0.00416
Lack-of-Fit	5	0.04156	0.00831	*	*
Pure error	5	0.00000	0.00000
Total	19	2.46556

The quadratic model’s adequacy was confirmed using model summary and ANOVA outputs from Minitab. The coefficient of determination (R² = 98.31%), adjusted R² = 96.80%, and predicted R² = 86.59% indicate a strong association between experimental outputs and predicted outputs. A residual standard deviation (S = 0.0645) and low coefficient of variation (C.V. = 3.45%) indicate the model fits data very precisely. Although Minitab does not specifically calculate Adequate Precision it is of note that the combination of the high R² values and low standard deviation indicated there is a sufficient signal-to-noise ratio. The lack-of-fit test indicated a non-significant F-value and p-value, indicating the model was statistically adequate and well fit to the data.

Three contour plots were produced in order to evaluate the effects of WRPU, RTW, and WPCB weight percentages (wt.%) on the absorption capacity of the foam samples. The dark green colors on the maps represent the greatest absorption rates while the light blue colors represent lower values. Figure 7(a) is the contour plot for the relationship between the WRPU and the RTW. It shows that for 24 wt% WRPU and 12 wt% RTW foam samples, the absorption is over 2.2 J. Figure 7(b) is the contour plot of WRPU versus WPCB. There was a reciprocal amount of absorption achieved by the foam samples at 24 wt% WRPU and 8 wt% WPCB. Figure 7(c) showed that there is a similar range of absorption levels with the 12 wt% RTW and 8 wt% WPCB. Through these contour plots, we get a visual representation of how two-way interactions between the factors influence TEA, as well as interaction effects that were observed in the ANOVA (Table 2).

Figure 7.

Contour Plots (a) WRPU (wt.%) versus RTW (wt.%) (b) WRPU (wt.%) versus WPCB (wt.%) (c) RTW (wt.%) versus WPCB (wt.%).

The combined effect of WRPU, RTW, and WPCB (wt.%) on the Average TEA is plotted in Figure 8 and described in the context of “total TEA” focus. From the plot, it is evident that TEA does quite a lot of change due to WRPU, with a peak of approximately 2.1 J at 24 wt% before dropping to 1.7 J at 30 wt%, which is consistent in the ANOVA with a substantial quadratic effect of WRPU (F = 318.57, p = .000). The RTW effect is less pronounced, having TEA between 1.8 J and 2.1 J. This is in accordance with its non–non-significant linear effect, F = 0.41, p = .534. WPCB exerts an intermediate effect between 1.8 J and 2.0 J with a maximum value at 10 wt%, supporting its significant linear effect F = 38.50, p = .000. This figure serves well to illustrate the distinct influences of all factors relating to energy absorption and reinforces the conclusions from the ANOVA.

Actual TEA (J) = - 0.523 + 0.1685 WRPU (wt . %) - 0.0268 RTW (wt . %) + 0.3870 WPCB (wt . %) - 0.006374 WRPU (wt . %) * WRPU (wt . %) - 0.00575 RTW (wt . %) * RTW (wt . %) - 0.01043 WPCB (wt . %) * WPCB (wt . %) + 0.01282 WRPU (wt . %) * RTW (wt . %) - 0.00427 WRPU (wt . %) * WPCB (wt . %) - 0.02002 RTW (wt . %) * WPCB (wt . %)

(1)

Error (%) = (TEA_actual - TEA_predicted) / TEA_actual

(2)

Figure 8.

Main effects plot for TEA (J).

The regression equation (1) shows the relationship between Total Energy Absorption (TEA) in Joules and the weight percentages of three different components: WRPU, RTW, and WPCB, as well as their interactions. When all predictor variables are equal to 0, the predicted TEA is given by the intercept of −0.523. The coefficients associated with each main effect reveal the direction and magnitude of their influence on TEA; for instance, a one-unit increase in WRPU (wt.%) corresponds to an increase of 0.1685 J in TEA, while a similar increase in RTW (wt.%) corresponds to a decrease of 0.0268 J. Furthermore, interaction terms among these variables show more complexities regarding the relationship: the coefficient of WRPU (wt.%) *RTW (wt.%) is positive, which suggests WRPU and RTW have a synergistic effect each contributing 0.01,282 J per unit increase in their interaction, and negative coefficients for interaction terms such WRPU (wt.%) *WPCB (wt.%) and RTW (wt.%) *WPCB (wt.%) have antagonistic effects that resulted in lower TEA when interacting with these components. In the end, this equation facilitated our understanding of how the components and interactions behaved collectively with respect to the Total Energy Absorption for the system of interest. The actual and predicted TEA values can be found in Table 5 for the RSM models considering regression equation (1) as demonstrated above. The error percentage we calculated equation (2) verifies that the maximum error percentage between actual TEA and RSM predicted TEA values is less than 10%.

Table 5.

Actual vs. Predicted TEA with Error %.

S. No	Actual TEA (J)	Predicted TEA (J)	Residual	Error (%)
1	1.804	1.83	−0.03	−1.548
2	1.767	1.82	−0.05	−2.907
3	1.485	1.49	−0.01	−0.495
4	2.335	2.40	−0.07	−2.890
5	2.170	2.19	−0.02	−1.095
6	1.891	1.98	−0.08	−4.451
7	1.334	1.37	−0.04	−3.013
8	2.015	2.08	−0.06	−3.146
9	0.962	0.96	0.01	0.641
10	1.738	1.65	0.09	5.279
11	2.180	2.13	0.05	2.285
12	1.942	1.89	0.05	2.487
13	2.068	2.03	0.03	1.686
14	2.134	2.07	0.06	2.958
15	2.236	2.22	0.02	0.779
16	2.236	2.22	0.02	0.779
17	2.236	2.22	0.02	0.779
18	2.236	2.22	0.02	0.779
19	2.236	2.22	0.02	0.779
20	2.236	2.22	0.02	0.779

Figure 9 provides residual plots to check the regression model overall equation (1) predicting TEA values as previously mentioned in the results section. The plots consist of four separate plots: a normal probability plot of residuals (small point scatter along a straight line for normality), residuals versus fitted values (where points are evenly scattered around zero for constant variance), a histogram of residuals (for normality this chart should display bell-shaped pattern), and residuals versus observation order (for independence, residuals should show no pattern or systematic order). The assumptions of the models of independence of residuals, equal variance, and normality are evaluated in part by each figure. The ANOVA (Table 4) indicates a significant Lack-of-Fit for the models, however, the residual plots indicate some merit in the model as the plots indicate a generally acceptable pattern with small deviations consistent with the Lack-of-Fit, and consequently, the predictive capacity of the model in Table 5 can be considered acceptable for practical implications.

Figure 9.

Residual plots for TEA regression model.

Figure 10 provides a comparison of actual TEAs and predicted TEA values and followed a linear regression with a strong R² value. This confirms how statistically significant the regression equation (1) from the response surface methodology model which will statistically determine the optimal range of fillers to use.

Figure 10.

Scatter Plot of Actual TEA (J) versus Predicted TEA (J).

The optimal solution presented in Table 6 shows the best formulation of the composite material to achieve the maximum Total Energy Absorption (TEA). The combination that was considered the optimal solution was 27.5758% WRPU (wt.%), 15% RTW (wt.%), and 2% WPCB (wt.%). The fitted TEA value for this combination was 2.78,305 J. The Table 6 displays the composite desirability column, indicating the level of desirability for this particular formulation. The desirability rating was given as a 1, which indicated that this formulation satisfies all variables and is highly desirable. Table 6 shows for manufacturing purposes what would be the best values or combinations in producing a composite material that exhibits a high material in terms of Total Energy Absorption.

Table 6.

Optimum wt.% of WRPU, RTW, and WPCB.

Solution	WRPU (wt.%)	RTW (wt.%)	WPCB (wt.%)	Actual TEA (J) fit	Composite desirability
1	27.5758	15	2	2.78305	1

The maximum curvature of TEA is shown in Figure 11 identifying the response surface to capture WRPU, RTW, and WPCB additive content by weight percentage to realize maximum TEA. Figure 11 serves as a complimentary depiction of Table 5 results since this depicts the maximum response surface conditions for maximum energy absorption.

Figure 11.

Optimum curvature of TEA (J).

Confirmation sample and results

The sample for confirmation purposes contained 27.5758 wt% WRPU and 15 wt% RTW and 2 wt% WPCB because the expected ideal filler weight proportions need verification. The test for Total Energy Absorption (TEA) used low-velocity impact testing with the same method as shown before for sample evaluation. The actual TEA measurement reached 2.699 J with a 3% error, and the predicted TEA value for this composition was 2.783 J. All the results are shown in Table 7. Comparison of TEA with other literature is mentioned in Table 8.

Table 7.

Confirmation sample and outcomes for simulation.

Solution	WRPU (wt.%)	RTW (wt.%)	WPCB (wt.%)	Predicted TEA results	Experimental TEA results	Experimental error %	Simulation TEA results	Simulation error %
1	27.5758	15	2	2.783	2.699	3	2.727	2%

Table 8.

Comparison of TEA with other literatures.

Material	Thickness (mm)	Achieved value (J)	References
Flax/MAPP composites, with high twist twill architecture	2.00	2.55	⁶⁷
Aluminum + Glass/Epoxy (M4-C3)	2.50	5.6	⁶⁸
Bio-PU foam + CMC, MgO, BC	10.0	1.894	⁶⁹
WRPU + RTW + WPCB	20.00	2.236	Current study

Simulation results

A software simulation was established in ANSYS Workbench to ensure the fidelity of the anticipated filler weight percentages using an impact response study. Two geometries were imported: one being a disc and the other an impactor (inductor). The geometries and the simulated sample can be seen in Figure 12(a) and (b). The disc was assigned custom material with a density of 1.5 × 10⁻⁶ kg/mm³, Young’s Modulus of 22.63 MPa, and Poisson’s Ratio of 0.35. From these parameters, the software computed the bulk modulus of 25.144 MPa and the shear modulus of 8.3815 MPa. The impactor was modeled as standard Structural Steel. Mesh assignments were made with the Quad Dominant method for sheet bodies and sweep for sweepable areas. Mesh familiarity is important for modeling. The global mesh had a default element size of 17.027 mm, with a minimum edge length of 11.781 mm and no adaptive sizing. A separate body sizing control was assigned to the disc, with element size control refined to 16.0. Curvature capture was enabled in order to represent important overall features. Initial conditions were defined using the Drop Height method, where the inductor was assigned a drop height of 600 mm, which corresponds to an impact velocity of 3430.4 mm/s, in the +Y direction. Standard Earth Gravity was used to account for the gravity, and a fixed support was given to the disc, restricting all degrees of freedom during impact, and the simulation result was compared with the predicted result. Table 7 confirms the validity. The research demonstrates that using three recycled fillers together with RSM optimization produces sustainable impact-resistant composites. As a point of comparison for these results, the Total Energy Absorption (TEA) of the ideal composite was compared against the performance of different sustainable composite systems documented recently in the literature.

Figure 12.

a) Geometry of the inductor and sample (b) Simulated geometry in ANSYS workbench.

Conclusion

The study successfully demonstrates the production of a sustainable polyurethane composite using a hybrid mix of three industrial waste streams: rigid polyurethane foam, ground tire rubber, and printed circuit boards. A robust methodology based on Response Surface Methodology (RSM) was employed to determine the optimal filler formulation for maximizing low-velocity impact strength. The optimized composite was thoroughly validated, with experimental impact performances correlating well with both the statistical model and computational simulations, providing confidence in the design and testing methods. Characterization techniques, including HR-SEM, FTIR, and TGA, were used to examine the morphology, chemical interactions, and thermal stability of the composites, supporting the interpretation of the impact performance results. It is important to note the limitations of the RSM model used. While effective as an empirical tool, it is applicable only within the specific ranges of filler concentrations assessed in this study. The model captured statistical relationships between factors and impact absorption but did not clarify the underlying physical failure mechanisms. Additionally, the ANOVA results indicated a significant Lack-of-Fit, suggesting that higher-order or more complex interactions could influence the material response. Therefore, broader filler ranges and physics-based modelling could be explored in future studies to supplement these findings. This research focused specifically on low-velocity impact performance, and conclusions are limited to the materials and test conditions studied; performance under different loading conditions remains to be investigated. Future work could include compression-after-impact (CAI) testing and evaluation of durability under varying environmental and loading conditions to provide further insight into practical applicability. Nonetheless, the developed composite shows impressive energy absorption characteristics suitable for automotive safety applications, such as bumper fillers, interior trim panels, and knee bolsters. Beyond mechanical performance, this work contributes to the circular economy by providing a constructive pathway to convert electronic waste and waste rubber into engineered materials of value, supporting the development of safer and more sustainable vehicles.

Footnotes

Author contributions

Vinoth Kumar Selvaraj: Conceptualization, Software, Validation, Writing - review & editing. Jeyanthi Subramanian: Conceptualization, Methodology, Supervision. Aadithya Narayanan S: Software, Writing - original draft. Nitesh Verma Kannammaraju: Data curation, Validation. Vijayakumar Rajendran: Methodology, Writing - review & editing.

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.

Acknowledgement

The authors extend their gratitude to the Department of Science and Technology, India, particularly DST-SERB with File no: SPG/2021/003522, and DST, Ministry of Science and Technology, India (Grant No.: SR/FST/ET-I/2022/1080) for providing the essential facilities necessary to carry out this study. And, the authors are grateful to VIT - Chennai for providing us with the seed fund and a fully operational laboratory for carrying out the experiments.

ORCID iDs

Vinoth Kumar Selvaraj

Jeyanthi Subramanian

Aadithya Narayanan S

Nitesh Verma Kannammaraju

Vijayakumar Rajendran

Data Availability Statement

Data will be made available on request.*

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Development of sustainable polyurethane composites from e-waste and tire rubber for low-velocity impact applications in automotive structures

Abstract

Keywords

Introduction

Experimental procedure

Materials & composite preparation

Low-velocity impact test setup

Response Surface Methodology (RSM) & computational simulation

Characterization technique

Results and discussion

Experimental analysis

High-resolution scanning electron microscope images

Fourier transform infrared

Thermogravimetric analysis

Statistical analysis

Confirmation sample and results

Simulation results

Conclusion

Footnotes

Author contributions

Declaration of conflicting interests

Funding

Acknowledgement

ORCID iDs

Data Availability Statement

References