*Article* **Effective Evaluation of Elastic Properties of a Graphene and Ceramics Reinforced Epoxy Composite under a Thermal Environment Using the Impact Hammer Vibration Technique**

**Nitesh Kumar 1,2, Ananda Babu 1, Alok Kumar Das 2,\* and Ashish Kumar Srivastava <sup>3</sup>**


**Abstract:** This paper presents an evaluation of the mechanical properties of nanocomposites when a lower concentration of nanoparticles graphene and ceramics are mixed with epoxy to determine the damping and stability characteristics of hybrid epoxy, using vibration techniques to extract accurate results. The effectiveness of the Impact hammer vibration technique is validated with mechanical testing such as three-point bending in terms of Young's modulus of the nanocomposite. The graphene nanocomposite carries nanoparticle 1 wt.% of epoxy, while the ceramic nanocomposite carries 3 wt.% of epoxy. It is observed that the reduction in frequency under a thermal environment is significantly less for graphene and ceramic reinforced hybrid nanocomposites, whereas the reduction in pure epoxy under a thermal environment is high. Thus, the results show that the addition of nanoparticles to composites shows improvement in the mechanical and thermal stability of elastic properties. The elastic properties obtained from the vibrational analysis are more consistent and economical than the three-point bending test for the evaluation of hybrid nanocomposites.

**Keywords:** graphene; ceramics; epoxy resin; elastic properties; vibration technique

#### **1. Introduction**

Composites are widely used in the field of aerospace, automotive and other highperformance structural applications due to their high stiffness and strength-to-weight ratios. They are a cheaper lightweight option than conventional materials such as metals. Despite the several advantages of using composites, they have several drawbacks, including high stress and strain development under load conditions. However, research has shown that the addition of nanoparticles in the polymer matrix is considered a highly effective technique to improve the mechanical properties of composites. Amendola et al. [1] investigated that the addition of nanoparticles in the polymer matrix results in nanocomposites with enhanced thermal and mechanical properties. It was shown that there is a good agreement with the matrix and high surface-to-volume ratio of the fine nanoparticles, which is the main reason behind the enhancement of the mechanical properties of the nanocomposites [2–4]. Firsov et al. [5] used filler in nanocomposites due to its improved physical properties such as high aspect ratio, low electrical resistivity, high thermal conductivity, high strength and elastic modulus. K B Kanchrela et al. [6] observed that the use of Yttria-stabilised zirconia (YSZ) nanoparticles improved some mechanical properties of glass fabric composites. Kaushal Kumar et al. [7] found that the use of TiO2 nanoparticles in an epoxy composite increased its tensile strength. T S Muthu Kumar et al. [8] found an increase in the thermal stability and tensile strength of the polymer matrix composites due to the presence of small coffee bean powder. Taqui ur Rehman et al. [9] observed that the use of fillers SiO2, TiO2

**Citation:** Kumar, N.; Babu, A.; Das, A.K.; Srivastava, A.K. Effective Evaluation of Elastic Properties of a Graphene and Ceramics Reinforced Epoxy Composite under a Thermal Environment Using the Impact Hammer Vibration Technique. *Coatings* **2022**, *12*, 1325. https:// doi.org/10.3390/coatings12091325

Academic Editor: Jiri Militky

Received: 12 July 2022 Accepted: 5 September 2022 Published: 12 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and TiO2@ SiO2 as fillers in Zepoxy have minimal value LC (leakage current) and PD (partial discharge) for the best insulation performance. P Venkateshwar Reddy et al. [10] investigated the mechanical properties of a Prosopis juliflora fibre reinforced hybrid composite, which increased when using Al2O3 as filler material. Wei et al. [11] investigated that the addition of graphene nanoparticles at lower concentrations (0.3%) showed increased tensile strength (12.6%) and increased flexural strength (10%). Srivastava et al. [12] investigated composites with graphene at lower weight ratios and with high aspect ratios, which improved the tensile strength by almost 30%. It was concluded that the mechanical properties of composite material can be increased with the addition of fillers such as graphene. Nanocomposites with lower concentrations of ceramic nanoparticles have high thermal conductivity and lower electrical conductivity, which is useful for industrial insulation and electrical packaging. Unnikrishnan [13] investigated the low concentrated epoxy-based nanocomposites with thermoplastic and particulate fillers. The toughening process increased fracture toughness and impact resistance.

#### *1.1. Composite Made of Natural Fibre/Filler*

Natural fillers may be preferred when bio-composites are required, according to P K Jagadeesh et al. [14]. These composites are referred to as renewable and eco-friendly composites. Up to a certain weight percentage, natural fillers perform well, but if you add more, the qualities of composite materials may suffer. When combined with hydrophilic fibres and hydrophobic matrices, fillers improve adhesion behaviour. The fillers can be added following the demands of the material's qualities, but they are typically added following the type of composite application.

As a more environmentally friendly, biodegradable, and renewable resource than petroleum-based synthetic polymers, biopolymers were suggested by A. Vinod et al. [15]. However, compared to synthetic polymers, the mechanical properties of materials are unsatisfactory and need additional exploitation. These days, adding plasticisers, nanofillers, and coupling agents to biopolymers and biopolymer blends is one of several approaches for improving the properties and structural integrity. Commercially available biopolymers include TPS, PVA, PLA, PHBV, Chitosan, epoxidized plant oils, and polysaccharides. However, these materials have significant drawbacks, including gas permeability, moisture sensitivity, short shelf lives, low mechanical strength, and susceptibility to bacteria and fungi. This is because the structural and physical characteristics of biopolymers can be specifically tailored by using nanoparticles as fillers.

According to MR Sanjay et al. [16], natural fibre composites have similar tensile strength, impact strength, interlaminar shear strength, thermal, water absorption, and tribological properties to synthetic fibre composites. However, several factors affect the properties of composites, including the type of resin used, the origin of the fibre (fruit, stem, leaf, etc.), the type of reinforcement used (powder form, short fibre, continuous fibre), the fibre orientation (unidirectional or multi-directional), the manufacturing method used (hand layup, compression moulding, injection moulding, etc.), the crystallinity index and crystallite size of the fibre, the chemical functional groups present in the fibre, and volume and weight (raw or surface treated).

#### *1.2. Significance of Nano Filler*

Ganapathy et al. [17] filled the fibres made from the aerial roots of banyans with graphene. To create better epoxy composites, he described the appropriate ratio of graphene powder to banyan fibres. He noted that the unfilled epoxy composite had a flexural strength of 155.51 MPa and tensile strength of 27.93 MPa, while the strongest hybrid composites in terms of tensile strength (40.6 MPa) and flexural strength were those that contain 4% graphene (163.23 MPa).

The impact of Al2O3 nanofillers on the mechanical, wear, and hardness properties of basalt/epoxy laminate composites were discovered by Vinay et al. [18]. By using the hand layup process, composite laminates of basalt/epoxy with varying amounts of Al2O3

nanofillers were created. According to ASTM standards, mechanical properties such as tensile strength, interlaminar shear strength (ILSS), flexural strength, impact strength, and hardness were examined. Flexural strength and ILSS were shown to increase for small percentages of nanofillers, whereas tensile strength declined for larger percentages of fillers, and hardness increased for larger percentages of fillers. As the content of nanofillers increased, the wear rate gradually decreased.

Cissus quadrangularis stem fibre (CQSF)/epoxy resin particulate with and without coconut shell ash (CSA) powder underwent mechanical characterisation by Jenish et al. [19]. The hand lay-up method was used to build the base material from epoxy and 30 wt.% CQSF with 40 mm fibre length, and CSA was added separately at 2.5, 5, 7.5, and 10 wt.%. The tensile test SEM image of the CQSF/epoxy with 5 wt.% CSA filler composite showed less matrix breakage and fibre/matrix bonding, which boosted the tensile strength of the composite material. At 10 wt.% CSA, the impact strength (20.03 J/cm2) and hardness (98 HRRW) values were greater in the CQSF/epoxy resin composite, indicating that impact and hardness steadily rise as CSA filler content rises.

In this paper, a lower concentration of the nanoparticles was maintained with 1 wt.% of epoxy in the case of graphene nanocomposites and 3 wt.% of epoxy in the case of ceramic nanocomposites. To evaluate the properties of the nanocomposites, vibration techniques (ASTM E1876-15) were conducted. The validation of the elastic properties such as Young's modulus, Shear modulus and Poisson's ratio was carried out by comparing the results obtained from the above two methods.

#### **2. Materials and Methods**

In this study, a thermoset type of polymer matrix material epoxy LY556 was used. Araldite hardener HY 917 was used as a curing agent. Graphene nanoparticles and stoichiometric spinel (MgAl2O4) precursor prepared by solid-state synthesis (0.4 wt.%) were used as reinforcement. Pure Isopropanol (2-Propanol), C3H8O, M.W 60.10 and Acetone extra pure AR grade, C3H8O and M.W 58.08 were used to ease the sonication process by the addition of a volatile liquid. The lower concentration of nanoparticles was maintained with 1 wt.% of epoxy in the case of graphene nanocomposites and 3 wt.% of epoxy in the case of ceramic nanocomposites. The flow chart for preparation of Epoxy/Graphene Composite is shown in Figure 1.

**Figure 1.** Flow Chart for preparation of Epoxy/Graphene Composite.

#### *2.1. Sample Preparation of Graphene/Epoxy Nanocomposites*

The epoxy composite samples mixed with epoxy resin LY556 were combined with hardener Araldite HY917 in a 10:1 ratio. The solution was manually stirred for 5 min and then poured into dies. To cure the samples, they were kept at 75 degrees for 1 h. The samples were machined according to the requirement of the testing apparatus. The graphene nanocomposites were prepared by a powder process. The sonication process was carried out while the samples were prepared to properly disperse the nanoparticles in the resin system. Failure to carry out the sonication process can severely affect the mechanical properties. The Sonication process was conducted in a sonicator with a titanium probe running at 1 Amp, 0.1 mV for 1 h. Sonicated solution was mixed with graphene nanoparticles manually. For preparing the sonicated solution, epoxy was heated and then mixed with a solution of acetone and graphene. Sonication was carried out in 3 intervals of 1 h. The solution was then kept in the oven to evaporate acetone from the solution. To prepare solid samples, the solution was mixed with hardener Araldite HY917 in a 10:1 ratio and then poured into the die for the curing process. The solidified samples were machined according to the requirement of the testing apparatus.

#### *2.2. Sample Preparation of Ceramics/Epoxy Nanocomposites*

The ceramic nanocomposites were prepared by the powder process. Sonicated solution was mixed with ceramic nanoparticles manually. For preparing the sonicated solution, epoxy was heated and then mixed with a solution of isopropanol and ceramic powder. Sonication was carried out for 30 min; the solution was then kept in the oven to evaporate isopropanol from the solution. To prepare solid samples, the solution was mixed with hardener Araldite HY917 in a 10:1 ratio and then poured into the die for the curing process. The solidified samples were machined according to the requirement of the testing apparatus. Figure 2 shows three different developed samples of pure Epoxy, Epoxy/Graphene and Epoxy/ceramic.

**Figure 2.** (**a**) Pure Epoxy sample (**b**) Epoxy/Graphene sample (**c**) Epoxy/ceramic sample.

#### **3. Material Characterisation**

Characterisation of materials was carried out to identify the mechanical properties of the nanocomposites. It helps in the proper evaluation of its capabilities. The three-point bending test and the Impact hammer test were conducted to evaluate the elastic properties.

#### *3.1. Three-Point Bending Test-ASTM D7264*

The three-point bending test (based on ASTM D7264) was conducted [20] using an Universal Testing Machine (Manufacturer INSTRON, Model 8801, Indian Institute of Technology, Dhanbad, India). The specimens were tested at room temperature under a uniform strain rate of 1 mm/min and the span to depth ratio was maintained to be greater than 16:1. The maximum flexure stress, maximum flexure extension and elastic modulus were obtained from the test as tabulated in Tables 1–3.


**Table 1.** Young's Modulus Values of Epoxy and Nanocomposites.

**Table 2.** Elastic properties of epoxy and Nanocomposites.


**Table 3.** Epoxy sample readings at varying temperatures.


#### *3.2. Impact Hammer Test-ASTM E1876-15*

To validate the improvements, we conducted impact hammer testing as used in [21] to explore the changes in elastic modulus along with damping characteristics. By measuring the resonant frequencies in a different configuration, Young's modulus, Shear modulus and Poisson's ratio were calculated using the ASTM E1876-15 [11]. The test samples were created according to the ASTM E1876-15, where B/t > 5 and L/t > 20 (B = breadth, L = Length & t = Thickness). The samples were placed on the specific fixtures and a contact accelerometer was placed to read the vibrations. The electrical signals were then transferred to software DeweSoft, (Version 2020, creator, Dewesoft, Indian Institute of Technology, Dhanbad, India) which converted them into vibrational signals to highlight the resonant frequency. Figure 3 shows the different positions of the testing fixture for evaluating elastic constants Young's and Shear moduli in bending and torsion modes of vibrations.

The Dynamic Young's modulus using standards.

$$E = 0.9465 \left( \frac{mf\_f^2}{b} \right) \left( \frac{L^3}{t^3} \right) T\_1 \tag{1}$$

$$T\_1 = \left[1.000 + 6.585 \left(\frac{t}{L}\right)^2\right] \tag{2}$$

where: *E* = Dynamic Young's modulus, Pa; *m* = mass of the bar, g; *b* = width of the bar, mm; *L* = length of the bar, mm; *t* = thickness of the bar, mm; *f <sup>f</sup>* = fundamental resonant frequency of bar in flexure, Hz; *T*<sup>1</sup> = correction factor for the fundamental flexural mode to account for the finite thickness of the bar.

**Figure 3.** (**a**) Out of Plane frequency. (**b**) Torsional frequency setup.

Dynamic shear modulus used formulas provided under ASTM standards.

$$G = \frac{4\ L m f\_t^2}{bt} R\tag{3}$$

$$R = \left[\frac{1 + \left(\frac{b}{t}\right)^2}{4 - 2.521\frac{t}{b}\left(1 - \frac{1.991}{\epsilon^{\frac{\pi}{4}} + 1}\right)}\right] \left[1 + \frac{0.00851n^2b^2}{L^2}\right] - 0.060\left(\frac{nb}{L}\right)^{\frac{3}{2}}\left(\frac{b}{t} - 1\right)^2\tag{4}$$

where *G* = Dynamic shear modulus, Pa; *ft* = fundamental torsional resonant frequency of bar, Hz; *n* = the order of the resonance, here *n* = 1.

Poisson's ratio used formulas provided under ASTM standards.

$$
\mu = \left(\frac{E}{2G}\right) - 1\tag{5}
$$

where *μ* = Poisson's ratio; *E* = Dynamic Young's Modulus. Pa; *G* = Dynamic Shear Modulus, Pa.

#### **4. Results and Conclusions**

The Young's modulus, Shear modulus and Poisson's ratios were calculated and tabulated after obtaining the resonant frequencies. The effect of the nanoparticles on the elastic properties has been discussed. There was an increase in Young's modulus due to the inclusion of nanoparticles in the matrix, as shown in Table 1 measured from UTM for Young's modulus, and Table 2 measured from vibration techniques for all the elastic constants. The inclusion of graphene nanoparticles showed an increase of 7.1%, while the inclusion of ceramic caused an increase of 10.4%.

Similar results to that of the three-point bending test were produced from the impact hammer test, with an error of about 8%. However, the values from this test are much more dependable as the number of iterations is more. The ceramic nanocomposites showed the highest improvement in Young's modulus (8%) and shear modulus (13%), while Graphene nanocomposites showed less of a decrease in Poisson's ratio due to lower brittleness.

#### *4.1. Elastic Properties of Nanocomposites under Thermal Environment*

To evaluate the thermal stability of the elastic properties, the entire set-up was transferred to an industrial oven. The temperature was increased slowly and the resonant frequencies were recorded. The elastic properties were then evaluated from the formulas provided in the ASTM standard. The trends of Young's modulus, Shear modulus and Poisson's Ratio is shown in Figure 4 and their corresponding values are given in Tables 4 and 5.

**Figure 4.** (**a**) Trends of Young's modulus (**b**) Trends of Shear modulus (**c**) Trends of Poisson's Ratio.



**Table 5.** Ceramic/Epoxy sample reading at varying temperatures.


The selected nanocomposites were tested up to 75 degrees, as the flash point for epoxy LY556 is 80 degrees. The inclusion of nanoparticles showed constantly better properties, even at elevated temperatures. The graphene nanoparticles showed the best performance under thermal conditions and, on average, lost 5% of their Young's modulus and a 4% decrease in Shear modulus. This is because graphene tends to disperse easily under high temperatures in a low viscosity system. Even though the ceramic lost up to 20% of its properties, its values were higher than that of pure epoxy composites.

#### *4.2. Validation Conclusions*

The investigation of elastic properties was carried out by performing a three-point bending test and an Impact hammer test. The results from the three-point bending test were investigated. There was an increase in Young's modulus with the addition of graphene nanoparticles (an increase of 7.1%), while the addition of ceramic nanoparticles showed an increase of 10.4%. Nanocomposites showed lower maximum flexural loads (7% decrease) as the addition of nanoparticles increases the brittleness of the composites. The graphene nanocomposites showed the lowest stress values (6% decreases) compared to that of pure epoxy composites. This is due to the dispersion of graphene in the epoxy matrix, forming a perfect continuous structure. The results from the Impact hammer test were investigated. The ceramic nanocomposites showed the highest improvement in Young's modulus (8%) and shear modulus (13%), while graphene nanocomposites showed less of a decrease in Poisson's ratio. This is because nanoparticles tend to disperse easily and form a continuous system in a low viscosity medium. The results from the three-point bending test and Impact hammer test were compared and given in Table 6.


**Table 6.** Comparison between Three-point bending and Vibrational hammer test.

There was a difference of 10%–12% between the final results but the impact hammer test was considered to be a more dependable test, as Young's modulus values after several iterations were found to be more consistent when compared to the three-point bending test results. As the Impact hammer test is non-destructive, a detailed analysis of elastic properties under varied thermal conditions was also possible. At elevated temperatures the graphene nanocomposites showed only a 5% decrease in Young's modulus and a 4% decrease in Shear modulus. Even though the ceramic lost up to 20% of its properties, its values were higher than that of pure epoxy composites. This is because graphene and ceramic nanoparticles tend to disperse easily under high temperatures in a low viscous system. Thus, the results show that the addition of nanoparticles to composites shows improvement in the mechanical and thermal stability of elastic properties. The Impact hammer vibration test can be carried out to efficiently investigate the elastic properties of the composites under varied thermal conditions.

#### **5. Conclusions**

Two different samples of nanocomposites have been developed with graphene/1 wt.% epoxy and ceramic/3 wt.% epoxy. The mechanical properties of the developed nanocomposites are compared in terms of young's modulus, shear modulus and Poisson's ratio at a temperature of 45 degrees, 60 degrees and 75 degrees. Further three-point bending tests and Impact hammer tests were carried out to compare the elastic properties of both the developed nanocomposites. The inclusion of graphene nanoparticles showed an increase of 7.1%, while the inclusion of ceramic caused an increase of 10.4%. At an increased temperature, the graphene nanoparticles showed the best performance under thermal conditions and, on average, lost 5% of their Young's modulus and there was a 4% decrease in Shear modulus, even though the ceramic lost up to 20% of its properties. The results from the three-point bending test shows lower maximum flexural loads (7% decrease), as the addition of nanoparticles increases the brittleness of the composites. The graphene nanocomposites showed the lowest stress values (6% decreases) compared to that of pure epoxy composites. The results from the Impact hammer test showed the highest improvement in Young's modulus (8%) and shear modulus (13%), while graphene nanocomposites showed less of a decrease in Poisson's ratio. There was a difference of 10%–12% between the final results of the three-point bending test and impact hammer test. However, the impact hammer test was considered to be a more dependable test, as Young's modulus values after several iterations were found to be more consistent when compared to the three-point bending test results.

**Author Contributions:** N.K., writing, investigation, conceptualization and software; A.B., data validation, formal analysis; A.K.D., supervision; A.K.S., review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Tribological Characteristics of Al359/Si3N4/Eggshell Surface Composite Produced by Friction Stir Processing**

**Ashish Kumar Srivastava 1, Suryank Dwivedi 2, Ambuj Saxena 1, Deepak Kumar 2, Amit Rai Dixit 2,\*, Gyanendra Kumar Singh 3, Javed Khan Bhutto <sup>4</sup> and Rajesh Verma <sup>4</sup>**


**Abstract:** In the present study, the surface composite Al359/Si3N4/Eggshell is prepared by friction stir processing (FSP). The effect of reinforced particle volume fraction on the microstructural and tribological properties of the Al359/Si3N4/Eggshell surface composites was investigated and compared with the friction stir processed (FSPed) Al359 alloy. The microstructural properties were further investigated by light microscopy, FESEM, and EDS mapping. The tribological properties of the developed composite and FSPed Al359 were investigated using a reciprocating ball-on-plate universal tribometer. The microstructural results showed that defect-free composite surfaces are produced due to improved physical properties, severe plastic deformation, and better grain refinement. Moreover, the mean value of the friction coefficient (μ) for the developed composite and FSPed alloy are 0.36 μ and 0.47 μ, respectively. The obtained results indicated that Si3N4/Eggshell is a promising reinforced particle for improving microstructural and tribological performance in journal bearing, rotors, and machinery applications.

**Keywords:** Al359 alloy; friction stir processing; friction and wear; Si3N4; eggshell waste; composite structure

#### **1. Introduction**

In the past decade, researchers and modern industries have been continuously finding new quality materials that are lightweight, dimensionally accurate, have a high-quality surface finish, a high production rate, are cost-effective to produce, and are environment friendly [1–4]. For these reasons, aluminium alloys and their composites are the primary preference for the aerospace and automotive industries due to their lightweight, good mechanical, and tribological properties [5]. However, the need for specific engineering materials for specific engineering applications is still open to investigation. Aluminium metal matrix composites (AMMCs) have been developed as advanced engineering materials for weight-saving applications in both industries. AMMCs exhibit an excellent combination of high specific strength, hardness, and better wear resistance for various applications. Moreover, the enhancement of all desired properties, such as physical and mechanical, depends on reinforcement/particulates and microstructure [6]. Several researchers have experimented with using different reinforcement particles (SiC, Al2O3, B4C, Gr, TiC, Si3N4, and TiB2, etc.). Si3N4 is considered a standout reinforcement and one of the most promising ceramics because of its high levels of hardness. It also has other extraordinary characteristics, such as low density, high melting point, high thermal stability, and good chemical

**Citation:** Srivastava, A.K.; Dwivedi, S.; Saxena, A.; Kumar, D.; Dixit, A.R.; Singh, G.K.; Bhutto, J.K.; Verma, R. Tribological Characteristics of Al359/Si3N4/Eggshell Surface Composite Produced by Friction Stir Processing. *Coatings* **2022**, *12*, 1362. https://doi.org/10.3390/ coatings12091362

Academic Editor: Jinyang Xu

Received: 11 August 2022 Accepted: 14 September 2022 Published: 18 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

stability [7]. Si3N4 also has excellent ballistic and mechanical properties, making it a desirable material for several defence applications [8]. Waste eggshell is a new engineering reinforcement, containing around 95% calcium carbonate (CaCO3), 3% phosphorus, and signs of magnesium, zinc, sodium, potassium, iron, and copper [9,10]. It can be used as a bio-waste material to meet the different requirements of modern products, and also to create new value. It is an inexpensive reinforcement material with excellent properties, such as low density, hardness, compressive strength, high thermal stability, and it is renewable [11]. These mechanical properties qualify it as an excellent candidate to reinforce aluminium and its alloys, usually used in the automobile industry [12]. Kumar et al. examined the effect of different reinforcement microparticles (SiC, Al2O3, and Ti) with the addition of waste carbonized eggshell powder. Ti/eggshell base reinforcement in AMMCs obtained an excellent hardness compared to the other reinforcement particles [13]. However, other reinforced composites possess good hardness compared with the base material. The addition of commonly used reinforced particles, such as SiC, Al2O3, and B4C, in the metal matrix improves tensile strength, yield strength, and hardness, but reduces ductility.

Both phase fabrication methods, such as liquid and solid phases, have been successfully used to make the desired composite. Several studies presented the enhancement of the specific mechanical properties and modified the microstructure of the matrix material. However, liquid state processing presents major drawbacks, such as porosity, solute redistribution, and solidification cracking [12–14]. High temperature is required for solidto-liquid and vapour phase changing processes, but the reverse process (liquid-to-solid phase) reduces some of the special properties of the composites. Several research studies have found that friction stir processing (FSP) is a suitable process for fabricating composites that work in the solid-to-solid state phase, thus eliminating these drawbacks [15,16].

FSP is a technique that has become very popular in recent decades. FSP is a versatile method of solid-state processing, and it is an energy efficient technique that results in no residual stresses that refine the microstructure, densification, and homogeneity of the structure [17,18]. Figure 1a shows the schematic arrangement of FSP. In this process, the matrix material is processed by a non-consumable tool with a shoulder-pin arrangement that rotates at high speed. The friction between the tool and matrix material generates sufficient heat. The tool traverses to cover up the desired area and is stirred by the tool shoulder-pin arrangement in the coverage area. Due to the stirring of the material, severe plastic deformation occurs, which causes refined microstructure, densification, and homogeneity in the formed composites [19,20]. The process parameters can directly control the advantage of FSP on mechanical and microstructural properties. Thus, FSP has manifested a similar or better tribological performance than other conventional processes [21,22].

**Figure 1.** Schematic arrangement of the (**a**) friction stir processing and (**b**) aluminium alloy AL359 plate.

The focus of the present research is placed on tribological characteristics. Generally, composites demonstrate a high friction coefficient in the range of 0.5–0.8, except for those that slide in water or under other lubricants. In the present work, a vertical milling machine

is used to produce the surface composites. Al359 aluminium alloy was used as a matrix material, while Si3N4 and waste eggshell powder was used as reinforcement materials (6% by volume). The tribological tests are carried out to test the frictional properties of Al-6% Si3N4/Eggshell composites. The microstructural study was done with the help of light microscopy, FE-SEM equipped with EDS mapping.

#### **2. Experimental Procedure**

Commercially available Al359 aluminium alloy was used as a matrix material. It is a suitable material in applications including aerospace, automotive, and for highly stressed parts (gears, fuse parts, and structural components). Al359 plates were purchased with a dimension of 75 mm× 150 mm× 6 mm, as shown in Figure 1b. Before FSP, a square groove was cut transversely from the middle according to the 6% reinforcement volume. Si3N4 and ball-milled carbonized eggshell powder, each of 3% volume, were selected as the primary and secondary reinforcement. Figure 2a shows the Si3N4 particle size varying between 5 μm to 80 μm with an average diameter of 18 μm.

**Figure 2.** The particle size distribution of (**a**) Si3N4 particles and (**b**) eggshell particles.

Waste eggshells were collected from local shops to prepare the carbonized eggshell. They were cleaned to remove any dust and egg liquid. Next, they were solar-dried for 48 h to remove the moisture. Dry eggshells were preheated to a temperature of 1000 ◦C for 1 h. Carbonized eggshells were ball milled to obtain a fine powder. The obtained powder was passed through multiple sieves of the required size to ensure that particles in the correct range were obtained [23]. Figure 2b showed the eggshell particle size varying between 5 μm to 140 μm with an average diameter of 33 μm. Previous research and lab experimentations have found that the process parameters of FSP affect the mechanical and physical properties of the developed composites. Parameters, such as rotational tool speed, traverse speed, tilt angle, and axial force, can improve the properties of the composites. The preferred process parameters for fabricating Al359/Si3N4/Eggshell surface composites are given in Table 1, and are based on the pilot experiments and the authors' own previous published literature.


**Table 1.** Process parameters used in the fabrication of Al359/Si3N4/Eggshell surface composites.

A non-consumable HSS tool with a shoulder diameter of shoulder 18 mm, shoulder length of 70 mm, and a triangular profile pin with 4 mm edges were prepared as shown in Figure 3. To make the homogeneous mixture, 3% of both types of reinforcements were chosen and mixed manually after preheating to 350 ◦C. The homogeneous preheated mixture of Si3N4/Eggshell powder (6% by volume) was filled into the square groove (dimensions are decided based on the 6% volume of reinforcement material) made on the top surface of the base plate. A pin-less HSS tool was used to cover the reinforcement powder to prevent it from spurting out of the track during the FSP processing. Then the primary FSP process was carried out using a vertical milling machine at room temperature, and within the process parameters described in Table 1, and with 6% volume of the reinforcement material. Figure 4 shows Al359/Si3N4/Eggshell surface composite after the FSP.

**Figure 3.** Photographic view of the HSS tool used in the experiment.

**Figure 4.** Photographic view of Al359/Si3N4/Eggshell surface composite after FSP.

The friction stir processed (FSPed) samples were cut using the Wire-EDM (CNC wirecut electric discharge machine manufactured by Electronica Machine Tool Ltd, IIT ISM

Dhanbad, India) and polished in the YZ plane using SiC paper of grit size 600 grade (230 × 280 mm) to remove uneven surfaces.

For the microstructural study, rectangular cross-section of the specimens was obtained as per the ASTM-E3 standard. Further, it was polished with diamond paste and etched in a solution of Keller's reagent (15 mL HCL + 25 mL HNO3 + 10 mL HF + 50 mL H2O). The microstructure of the specimens was studied through inverted light microscope (manufacturer Leica- model- DMI3000 M, IIT ISM Dhnabad, India) and FE-SEM (Jeol jsm-7800 prime field emission scanning electron microscopy) coupled with an EDS detector (LN2 Free SDD X-max 80 energy dispersive detector). The sliding wear behaviour of the FSPed Al359/Si3N4/Eggshell sample and FSPed Al359 without any reinforcement were studied using a reciprocating-type ball-on-plate universal tribometer (MFT 5000, Rtec instruments, USA). The 2D schematic of the used tribometer setup is depicted in Figure 5. Before the tribological analysis, specimens were prepared as per the ASTM G99 standard procedures. The samples were cut and cleaned in isopropyl alcohol solution for 10 min, followed by deionized water. The ultrasonically cleaned samples were dried in a furnace at 100 ◦C to remove the moisture from the samples' surface. The reciprocating-type ball-on-plate wear mode was set to perform the tribological analysis, and a stainless steel ball (6 mm diameter) was used as a counter body. Thus, a sliding pair of Al359/Si3N4/Eggshell and a stainless steel 316 ball worked as a working pair. During the sliding wear tests, a 2 Hz frequency was set to achieve the sliding velocity of 10 mm/s. A 15 N load and 5 mm stroke length were applied for the test duration of 10 min. The average coefficient of friction (COF) and frictional force (Fx) for the Al359/Si3N4/Eggshell composite sample was obtained as per the ASTM standard G115 for the sliding wear test. Thereafter, to remove worn-out debris, the samples were ultrasonically cleaned and dried in the furnace (Stericox, India) at 100 ◦C temperature for 30 min. Further, the 3D images of the worn samples were captured and studied in detail to study the wear mechanism.

**Figure 5.** 2D Schematic arrangement of the ball-on-plate tribometer for the friction and wear test of Al359/Si3N4/Eggshell sample against the SS316 steel ball.

#### **3. Results and Discussion**

The microstructures of the FSPed specimen were observed using light microscopy, as shown in Figure 6a,b taken at two different locations (a) stir zone (SZ) (b) thermomechanical affected zone (TMAZ). The matrix phase of the alloy is shown by the brighter regions, and the reinforcement phase is shown by the darker regions in the micrograph. From Figure 6, it can be seen that reinforced particles are dispersed homogeneously with a minor porosity due to the high tool rotation. Moreover, the distribution of the reinforced particles is more homogeneous with the reduced density, which decreases the porosity in the sample. When compared to the base alloy, the larger grains break into smaller sized grains with the increased number of the grain boundary. The result shows that the FSPed zone produces a

defect-free composite that can offer excellent mechanical, physical, and plastic deformation and better grain refinement.

**Figure 6.** Optical photomicrograph of Al359/Si3N4/Eggshell surface composites. (**a**) stir zone (SZ) (**b**) thermomechanical affected zone (TMAZ).

Morphological studies of the FSPed sample were done using FESEM, which was coupled with EDS mapping. This was used to produce a detailed study of the microstructure, plastic deformation, grain refinement, and chemical composition of the composite. The SEM micrograph of the FSPed composite at different magnifications and different locations of stir zone at 10 μm, TMAZ location at 1 μm and 10 μm) is presented in Figure 7a–d, respectively. The obtained micrograph depicted the severe plastic deformation of the base alloy and the presence of a reinforcement phase in the stir zone region and TMAZ. A finegrained structure with a large number of grain boundaries was formed, which is attributed to the dynamic recrystallization during the FSP process. The optimized parameters were used to produce the equiaxed and refined grain structure, which highlights the capability of FSP processing. It is revealed from the FESEM images that a uniform distribution of the Si3N4/Eggshell reinforced particles was obtained. The desired composites, consisting of matrix material and reinforced particles, have fine and smooth surfaces caused by the FSP with proper shoulder design. However, few defects, such as clustering of the reinforcement phase and micro-pores, can be seen in the micrographs. These defects can be considered negligible due to the enhanced microstructural features. The chemical composition of the Al359/Si3N4/Eggshell developed surface composites is presented in Figure 8a,b.

**Figure 7.** FE-SEM micrograph of Al359/Si3N4/Eggshell surface composites. (**a**) stir zone location 1 at 10 μm; (**b**) stir zone location 2 at 10 μm (**c**) TMAZ location 1 μm (**d**) TMAZ location 10 μm.

**Figure 8.** (**a**) EDS spectrum of Al359/Si3N4/Eggshell surface composites. (**b**) EDS phase mapping of Al359/Si3N4/Eggshell surface composites.

Figure 8a shows the EDS spectrum and Figure 8b shows the element mapping of the composition of surface composite Al359/Si3N4/Eggshell. The EDS spectrum of all the major constituents of the developed composite, such as Al, Si, and Ca, can be seen in the elemental list along with their significant weight percentage. The small contribution of other constituents of base alloy Al359, such as Zn, Cu, and Mg, are also present in the selected phase of the EDS mapping. It is evident from Figure 8b that the major elements of Al359/Si3N4/Eggshell surface composite, such as Zn, Cu, Mg, and Ca, are completely diffused with the Al matrix material. This is because, during the FSP, the frictional heat plastically deformed the aluminium matrix material below its melting temperature, resulting in a softening of the matrix. In this region, it can be concluded that proper wettability formed between the reinforced particles and matrix material. However, the Si phase is seen in low density. This is attributed to the fact that the Si3N4 particles are not completely diffused into the matrix material due to their low density and high melting temperature. Some other elements, i.e., carbon, oxygen, and fluorine, also showed they can exist in the matrix material due to chemical and metallurgical reactions during the preparation of the composites by FSP.

Further, the sliding wear test was carried out using the ball-on-plate reciprocating-type universal tribometer. During the test, a constant normal load of 15 N and sliding speed of 10 mm/s were used to evaluate the friction and wear performance of the developed surface composites. A frictional force (Fx) and normal force (Fz) were measured during the test run. The 2D load cell was used to maintain the normal force (Fz) of 15 N ± 1 N during the test, which can be seen in Figure 9. Furthermore, a coefficient of friction (COF) value was calculated as a function of time from the force data set and presented in Figure 10.

**Figure 9.** Generation of normal force (Fz) graph for Al359/Si3N4/Eggshell surface composites.

**Figure 10.** Generation of the friction force (Fx) graph for Al359/Si3N4/Eggshell surface composites.

Figure 9 highlights the generation of the frictional force (Fx) value for the composite surface. At the beginning of the process, the generation of the frictional force value is low, but later on it increases and stabilizes (Figure 10). The average Fx value for the composite surface is 6.9 N. Further, Figure 11 depicts the COF (μ) values for both the FSPed Al359 without reinforcement and Al359/Si3N4/Eggshell surface composite. During the test, the COF value for the surface composite steadily increased during the first 80 s, and then continued to rise until approximately 170 s, where it reached roughly 0.42 μ. Afterwards, the COF value was reduced and stabilized at 0.39 μ. The mean value of the COF for the produced composite was 0.37. Furthermore, for the FSPed Al359 without reinforcement, the COF increases at a higher rate than the composite surface, and attains an average COF value of 0.48 μ (Figure 11), which dictates the poor tribological performance of the FSPed material. The tribological results show that the COF value for the surface composite is 23% lower than the FSPed Al359 without reinforcement. Moreover, the variation in friction coefficient with time was observed for the FSPed sample, with greater variation during the first 80 s, influenced by the "stick-slip" phenomena. This effect can occur when objects are analysed while in the dynamic contact between two surfaces, resulting in a spontaneous jerking motion and unstable movement along the sliding track. However, during the dynamic contact, a significant increase in the friction force and friction coefficient was observed due to the unevenness that comes from the developed surface material and the counter body (SS316L ball). This unevenness causes a specific roughness value, piloting to an insignificant contact surface between the ball and the plate, which leads to the separation of the soft particle from the mating surface. However, it should also be noted that the decrease in friction coefficient is mainly caused by a growth of an adhesion layer of the reinforced particles, and due to the formation of the uniform fine grain structure in the stir zone.

**Figure 11.** Generation of COF with sliding time for FSPed and Al359/Si3N4/Eggshell surface composites.

The EDX (Figure 8a) and elemental mapping (Figure 8b) results show the presence of oxygen and carbon elements that dictate the presence of oxides and carbides over the composite surfaces. The modified elemental composition helps to increase the hardness of the prepared surface composite [24]. However, according to the Pascal Law, it is known that the wear resistance property is directly proportional to the hardness value of that sample [25]. The increased surface hardness leads to improved tribological performance, as can be seen in Figure 11. Therefore, the obtained results help to understand the tribological performance of the composite surfaces and presents the relationship between the mechanical, microstructural, and tribological performance of the Al359/Si3N4/Eggshell composite and FSPed Al359 alloy.

Figure 12a–d shows the 3D images of the tribological wear track on Al359/Si3N4/Eggshell surface composites during the reciprocating ball-on-plate test. The worn-out depth profile was captured, as per the given ASTM standard G133-05. The 3D profiles were captured at five different locations to study the generation of the wear depth. During the wear test, the applied normal force (15 N) ensured continuous contact between the mating surfaces, thus producing friction and leading to the generation of the wear-out profiles. Figure 12a shows the lack of uniformity of the wear track due to increased amplitude values, which refers to an increase in volatility during the initial timestamp. A tribolayer is formed with a discontinuous layer, with a small amount of debris and material ruptures because these particles have mechanically adhered to the surface. Figure 12b shows the formation of a continuous layer due to the rapid growth of the temperature in the contact area. Both friction forces and temperature conditions favour the Al alloy's adhesive phenomena to reinforcement material. Figure 12c shows the adhered layered became unstable and possibly detached due to the reaction of friction forces on the wear surface. Figure 12d reveals the continuous layer and the dynamic behaviours of the adhesion wear mechanism.

**Figure 12.** 3D image of the tribological wear track on FSPed composites during the ball-on-plate wear test as per the ASTM standard G133-05 at five different locations. (**a**) location 1; (**b**) Location 2 (**c**) Location 3 (**d**) Location 4.

The red colours in Figure 13 shows the formed valley in the wear track. The reinforced (Si3N4/Eggshell) particles are adhered by the Al alloy layer's wear, which accelerates the worn particles over the surface. Due to this impact, debris from Al alloy and reinforced particles (hard particles) is again deposited on the wear track and produces improved frictional properties. In recent years, individually customized products have gained favour, and the design of suitable composite materials has become more flexible for various applications, such as aerospace and automobiles. The results show that friction stir processing can be a promising approach for producing such components using Al359/Si3N4/Eggshell composites with improved physical properties.

**Figure 13.** 3D image of the tribological wear track on FSPed composites during the reciprocation ball-on-plate wear test.

#### **4. Conclusions**

In this study, an Al359/Si3N4/Eggshell surface composite was fabricated by friction stir processing at room temperature. The microstructural results confirm the uniform and homogeneous distribution of Si3N4/Eggshell throughout the stir zone. The FESEM images show that the FSPed region has refined grains and a large number of grain boundaries due to severe plastic deformation, leading to better tribological properties. The tribological study reveals that the mean friction coefficient values for the FSPed Al359 without reinforcement and Al359/Si3N4/Eggshell surface composite specimens are 0.48 μ and 0.37 μ, respectively, which proves the improved frictional properties of the composite surface. The tribological results show that the COF value for the surface composite is 23% lower than that of FSPed Al359 without reinforcement. In addition to being a replacement for surface modification, the surface composite may be used to improve friction performance above and beyond traditional surface modification techniques. The obtained results indicate that Si3N4/Eggshell is a promising reinforced particle for the improvement of microstructural and tribological performance in journal bearing, rotors, and machinery applications.

**Author Contributions:** Conceptualization, A.K.S., and S.D.; methodology, S.D., A.S.; software, A.S., D.K.; validation, A.R.D., A.K.S., and G.K.S.; formal analysis, A.K.S., and S.D.; writing—A.K.S., and S.D., supervision, A.R.D.; funding acquisition, J.K.B. and R.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Javed Khan Bhutto and Rajesh Verma, grant number RGP 2/91/43. The APC was funded by [King Khalid University, Saudi Arabia].

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Saudi Arabia, for funding this work through the Research Group Program under Grant No: RGP 2/91/43.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength**

**Maha M. A. Lashin 1, Ali M. Al Samhan 2, Ahmed Badwelan <sup>2</sup> and Muhammad Ijaz Khan 3,4,\***

	- Riyadh 11421, Saudi Arabia

**Abstract:** Solid-state welding is a derivative of the friction stir spot welding (FSSW) technique, which has been developed as a new method for joining aluminum alloys. FSSW is a variant of linear friction stir welding intended to deal with lightweight alloy resistance spot welding (RSW) and riveting. Tensile strength refers to a material's ability to withstand excessive stress when being stretched or pulled before necking; it is expressed in terms of force per unit area. The tensile strength in stir spot welding is affected by dynamic and static parameters. The control of dynamic parameters and static parameters is studied in this paper to optimize the friction stir spot welding strength. A fuzzy logic control system is used to optimize the process as a new approach that can be used in this field. The obtained results prove that the fuzzy logic control system is an easy and inexpensive technology that can be used in prediction and optimization for the strength of FSSW. Furthermore, the results show the efficacy and adequacy of the proposed fuzzy logic control system.

**Keywords:** friction stir spot welding; dynamic parameters; static parameters; fuzzy logic control system

#### **1. Introduction**

Friction stir spot welding (FSSW) is a pressure welding method that operates below the workpieces' melting temperature [1]. FSSW is implemented on the welded sheet through steps such as plunging, stirring, and retracting, as shown in Figure 1. A welding tool, rotating with a high angular speed, enters the workpiece to form a weld spot (tool shoulder contacts the upper workpiece surface). Expelling of the material occurs during the plunging step; then, the tool reaches a predetermined depth in the stirring step. The frictional heat that is generated during the plunging and stirring steps causes heating, softening, and mixing in materials adjacent to the tool. Retraction of the tool from the workpiece occurs when acceptable bonding is obtained [2].

The main speeds that must be taken into consideration during the friction stir spot welding process are the speed of tool rotation and the speed of the tool traversing along the interface. An increasing tool rotating speed and decreasing tool traversing speed have a good effect on the quality of the welding process and the welded surface. The friction that is implemented by the tool and traversal speeds produces heat around the tool to minimize the forces acting on the tool [3].

The rotational speed of the tools and the welding speed are the parameters that are controlled to achieve the correction of heat and pressure when forming the weld. They are adjusted to heat the interface to the temperature of the plastic state. Vickers hardness tests showed a strong relation between the weld strength and tool and welding speeds [4].

**Citation:** Lashin, M.M.A.; Al Samhan, A.M.; Badwelan, A.; Khan, M.I. Control of Static and Dynamic Parameters by Fuzzy Controller to Optimize Friction Stir Spot Welding Strength. *Coatings* **2022**, *12*, 1442. https://doi.org/10.3390/ coatings12101442

Academic Editor: Ashish Kumar Srivastava

Received: 5 September 2022 Accepted: 27 September 2022 Published: 30 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Figure 1.** FSSW process: (**a**) plunging, (**b**) stirring, and (**c**) retracting [5].

Increasing the feed rate leads to a reduction in friction heating, grain, ductility, and the strain hardening exponent of the joint. Increasing the feed rate also causes increases in fragmentation and homogenization [6].

The tool rotation speed and feed rate are affected by the surface appearance, microstructure, and microhardness of the weld. In the friction stir spot welding process, the high rotational speed and feed rate cause the more uniform distribution of the steel particles in the stir zone [7].

Plunge depth is defined as the depth of the lowest point of the shoulder below the surface of the welded plate [8], or the contact between the tool shoulder and the workpiece [9]. Plunge depth affects the heat generation and the proper consolidation of the material without defects. It affects the force required during the plunging operation. Defect-free welds can be obtained with a zero plunge depth. An increase in plunge depth increases excessive flash and voids. The tensile properties of welds with zero plunge depth match with the properties of the base material. An increase in plunge depth decreases the hardness value and tensile properties [10].

Several factors can influence the FSSW process, including the tool material, tool rotation speed, tool head angle, pin length, pin profile, workpiece material and thickness, temperature input, and welding speed. These parameters are distinct, yet they each have an impact on the others [11]. To obtain products with the best mechanical performance while keeping costs to a minimum, the most appropriate process conditions should be chosen while considering the intervention requirements among these factors [12]. Considering new advances in artificial intelligence (AI) technology, its applications have grown significantly in numerous industrial domains [13–17]. Some other techniques used in the field of material engineering provide precise formulations for strength prediction; however, the accuracy is compromised [18–22]. The accuracy of the developed model depends on the optimization parameters, the number of input variables, and the number of entries being used while modeling [23,24].

Artificial intelligence (AI) approaches are increasingly being employed in FSSW investigations due to their remarkable performance, ease of implementation, as well as flexibility in any discipline [25–28]. Numerous factors in the FSSW technique are optimized and estimated using AI techniques [29]. The fuzzy logic meta-heuristic technique, artificial neural networks (ANN), heuristic fuzzy, wavelet, and heuristic-ANN are among the most prominent AI techniques being used for FSSW [30]. These techniques are employed interchangeably, although they are also recommended for distinct reasons due to their benefits and drawbacks over one another.

Fuzzy logic (FL) is an extremely viable approach for regulating systems that are quasi, complicated, challenging to describe, and have questionable or precise data reliability. It functions in the same way as human logic does, with intermediate variables such as

extremely long, short, and so on. There is currently no work published in the literature that predicts FSSW properties by employing only a fuzzy logic control system (FLCS). Furthermore, fuzzy control is divided into the Mamdani and Sugano categories, and it is employed in a variety of applications, including control and prediction [31,32].

Mohanty et al. [33] studied the impact of tool probe diameter, tool type, and shoulder interaction area on the strength of welds. They developed an ANN architecture and Mamdani FLCS to produce seven distinct triangular fuzzy memberships. The authors found that the fuzzy logic outperformed the ANN structure in modeling the connections of each FSSW characteristic with the output results.

A Mamdani fuzzy system was used for predicting and exploring the influence of friction stir spot process parameters on the tensile strength of AA1100 joints. The fuzzy model showed the increasing tensile strength of friction stir spot-welded joints with increasing pin diameter, tool rotating speed, welding speed, and feed rate. This methodology is a useful tool to assess the tensile strength of friction stir-welded AA1100 [34].

Mamdani fuzzy models implemented at forward and rotational speed as inputs and mechanical properties as outputs based on experimental data have been proposed. The results indicate the appropriate of the fuzzy method [35].

In this ongoing study, Mamdani FLCS was employed to build a model for the estimation and evaluation of the impact of FSSW workflow conditions on the tensile strength of Al 1050 joints, considering dynamic welding parameters (DWP) as a novel approach to achieve increasing weld strength. With the welding stroke, FSSW variables including tool feed rate and spindle speed fluctuate. The tensile strength improves substantially when DWPs are applied in the FSSW technique, in comparison with static welding parameters.

#### **2. Material Specifications (Friction Stir Spot Welding of Al 1050)**

Al 1050 exhibits outstanding corrosion resistance, higher electrical conductivity, higher ductility, and strength and can be produced with highly reflective finishing. Due to its lower weight and non-toxic nature, it is most suitable to be used for architectural flashings, industrial containers, lamp reflectors, and cable sheathings. Furthermore, it can also be used effectively in chemical processing plants. Strips of Al 1050 with the dimensions, chemical composition, and mechanical properties shown in Figure 2 and Tables 1 and 2, respectively, were used to study the strength of friction stir spot welding [36].

**Figure 2.** Dimensions of Al 1050 strips.

**Table 1.** Chemical composition of Al 1050.



**Table 2.** Mechanical properties of Al 1050.

Overlapping Al 1050 strips were welded at the German Computer Numerical Control (CNC) Vertical Machining Center with a developed welding fixture for maintaining the welding spot in the middle of the overlapping region, as shown in Figure 3. The fixture's holding tray was subsequently placed on top of the load cell. A circular cylinder-shaped temperature sensor was installed in the middle of the grip plate, with a 1.5 mm gap between the Al 1050 welding strip and the temperature sensor. A welding strip, holding bracket, grip plate assembly, and holding screws comprised the holding tray. The designed welding fixture's primary goal was to keep the welding point in the central overlapped area of the Al 1050 strip. The NI-USB-6341 data capture device was employed to gather temperature and welding force measurements corresponding to the welding stroke.

**Figure 3.** Friction stir spot welding machine setup.

Spindle speed (SS), tool feed rate (FR), and plunging depth (PD) were the static friction stir spot welding parameters, while the fixed values of feed rate (FR) and spindle speed (SS) throughout the welding stroke were considered as dynamic parameters, as shown in Figure 4.

**Figure 4.** Schematic representation of friction sir spot welding process.

Ninety experiments were performed with different values of SS, FR, and PD to study the influence of welding parameters on FSSW strength. Values of FR, SS, and PD during experiments on friction stir spot welding are shown in Table 3. The complete experimental procedure included two different phases. In the first phase, during the welding process, the welding parameters were kept constant, while in the second phase, the welding parameters such as SS and FR were varied for the period of welding stroke, which were then labeled as dynamic welding parameters (DWP). In the DWP method, the SS or FR was varied (decreased or increased) for the period of welding stroke, in comparison with the original (initial) value.

**Table 3.** FSSW parameters for experimental study.


All the weld strength tests were carried out via the Instron-3300 mechanical testing setup, having a tension rate equal to 5 mm per minute, while the microhardness was checked via a DuraScan-10 computer. The ISO standard 6507-1:2018 was followed by applying a 100 (g) load for the duration equal to 15 s, with a space between the consecutive grooves. Following the same ISO standard, for the chosen FSSW samples, the microhardness was measured twice [37], i.e., vertically with the starting point as the tool pin area (bottom to top of welded sheets) and horizontally across the welded samples' seam line.

Changes in FSSW strength with changing welding parameters during the welding process are summarized in Table 4. From the table, it can be noticed that the FSSW strength increased with an increase in plunging depth. Increasing spindle speed causes a decrease in welding strength at the same values of feed rate and plunging depth. Moreover, a decreasing feed rate causes an increase in welding strength.

**Table 4.** Strength of FSSW with FR, SS, and PD.


The desirability approach is a common method for assigning a "score" to a group of responses and selecting factor settings to optimize this score. One of the most used approaches in industry for optimizing multiple response processes is the desirability feature approach [38].

An individual desirability function was used as an optimizing technique to optimize the friction stir spot welding process parameters. The maximum value of strength throughout the 90 experiments shown in Table 5, with optimized values of static parameters.

**Table 5.** Optimum values of FSSW parameters.


#### **3. Fuzzy Logic Control System (Optimization, Results and Discussion)**

A fuzzy logic control system (FLCS) was used to optimize the strength of FSSW by controlling the static parameters of the welding process. The results of the fuzzy system were compared with individual desirability function results to determine the feasibility of using the FLCS technique to optimize the welding strength by controlling the dynamic parameters' values for the same welding material (Al 1050). A flow chart of the FLCS used is shown in Figure 5.

**Figure 5.** Flow chart of FLCS to optimize FSSW strength.

#### *3.1. Fuzzy Logic Control System*

The fuzzy logic control framework breaks down parameters into simple values regarding either 1 (true) or 0 (false) qualities. Fuzzy sets classify objects easily by relying upon enrollment, making them useful for estimation models [39]. Fuzzy logic control systems rely upon the guidelines of appointing the yield and are contingent upon the likelihood of the condition of the information. If–Then rules are utilized due to their great benefits in planning FLCS [40]. The fuzzy logic control system in the presented paper is used as an artificial intelligence tool, to optimize the strength of friction stir spot welding by controlling the welding parameters (static and dynamic parameters).

#### 3.1.1. Architecture of Fuzzy Logic Controller

The fuzzifier (fuzzification step), information base, fuzzy principal base (fuzzy knowledge base), and defuzzifier (defuzzifization step) are the fundamental components in the construction of a fuzzy system regulator for any controlled framework, as shown in Figure 6.

**Figure 6.** Structure of fuzzy logic controller.

Changing estimations of the contribution of fuzzy qualities are obtained through the fuzzification step in a fuzzy logic control system. Data sources and yield familiarities of fuzzy connections result in different participation capacities for every one of them [41]. The If–Then rule is the basic rule used in fuzzy systems to join the membership functions of inputs and outputs. The inference engine is the center of any FLCS since it performs inexact thinking [42]. The defuzzification process or step performed through the defuzzifier serves to transform the fuzzy values of the fuzzy inference engine into new values [43]. The design and implementation of the fuzzy logic control system is achieved by using the fuzzy logic toolbox of MATLAB.

#### 3.1.2. Fuzzy System for Static Parameters

A three-input–one-output fuzzy logic control system is designed and implemented to optimize the friction stir spot welding strength of Al 1050 samples by controlling the values of static parameters (SS, FR, PD) of the welding process. Spindle speed (SS), feed rate (FR), and plunging depth (PD) were the inputs and welding strength was the output of the fuzzy system. The structure of the FSSW strength fuzzy system is shown in Figure 7.

**Figure 7.** FSSW strength (static parameter) fuzzy logic control system (**a**) inputs and outputs (**b**) Mamdani FLCS.

#### 3.1.3. Inputs and Outputs of Membership Function

The fuzzy set's function is to reflect the pointer work for old-style sets. It produces a a graphical fuzzy set representation (A) for debate (X) as μA:X → [0, 1], and this means that the value between 0 and 1 is mapped to (X). The (x) axis is the universe of debate, while the (y) axis is the degree of membership in the [0, 1] set. The mathematical form for a triangular membership function is shown in Figure 8, where a and b are the lower and upper limits, respectively [44].

**Figure 8.** Triangular membership function for fuzzy system inputs and outputs.

Through the fuzzification step in the fuzzy system, the quantity of inputs is converted to a fuzzy quantity through identifying the deterministic quantities as completely nondeterministic. The triangular membership function used to fuzzify inputs to three levels (low, medium, and high) of fuzzy input values, as shown in Figure 9. The range of fuzzy system inputs (SS, FR, PD) with three levels, low, medium, and high, is shown in Figure 10 and Table 6, respectively.

**Figure 9.** Fuzzification of FSSW strength fuzzy system inputs.


**Table 6.** Membership functions of FSSW strength fuzzy system inputs.

The defuzzification step in a fuzzy system is performed through a number of rules that transform several variables into fuzzy results to define fuzzy sets and membership function degrees, as shown in Figure 11.

To defuzzify the fuzzy output into low, medium, and high levels through defuzzification, the triangular membership function is used, as shown in Figure 12. Levels of fuzzy system output are detailed in Table 7.

**Figure 11.** Defuzzification process in FSSW strength fuzzy system.

**Figure 12.** Outputs of FSSW strength fuzzy system.

**Table 7.** Membership functions of FSSW strength fuzzy system outputs.


#### 3.1.4. FLCS Base Rules

The If–Then rule is a fuzzy system-based rule used to join fuzzy system inputs and outputs. Some of the rules that are used in the friction stir spot welding strength fuzzy logic control system are shown in Table 8.

**Table 8.** FSSW strength fuzzy system's If–Then rules.



Figure 13 and Table 9 present the optimum values of friction stir spot welding strength that result from using the designed FSSW fuzzy system. Figure 13 presents the effects of changes in SS and PD values on the strength of welding.

**Figure 13.** Three-dimensional surface result of FSSW fuzzy system.

**Table 9.** Output results of FSSW fuzzy system.


A comparison was performed between the individual desirability function results and the results of the FSSW fuzzy system, as shown in Table 10, to verify the effectiveness of the designed FLCS in optimizing the welding strength value. The deviation between the two techniques used is 7%, and this means that we can use the fuzzy logic control system to control the static welding parameters to obtain the optimum value of FSSW strength.

**Table 10.** The optimum values of static welding parameters of desirability function and fuzzy system.


After using the fuzzy controller to optimize the strength of stir welding through controlling the static parameters of the welding process, another fuzzy model was designed to

optimize the welding strength but this time through the control of the dynamic parameters of the welding process.

#### *3.2. Fuzzy System for Dynamic Parameters*

A two-input–one-output fuzzy system was designed to be implemented on the dynamic parameters (spindle speed and feed rate) to optimize the strength of friction stir spot welding for Al 1050 alloy samples. The model of the FSSW strength (dynamic parameters) fuzzy system is shown in Figure 14.

**Figure 14.** FSSW strength (dynamic parameters) fuzzy logic control system (**a**) inputs and outputs (**b**) Mamdani FLCS.

The triangular membership function used to fuzzify the inputs to five levels (very low, low, medium, high, and very high) of fuzzy input values is shown in Figure 15, with ranges given in Figure 16 and Table 11, respectively.

**Figure 15.** Fuzzification of FSSW strength fuzzy system inputs.

**Table 11.** Membership functions of FSSW strength fuzzy system inputs.


**Figure 16.** Triangular membership functions for fuzzy system inputs (**a**) SS, (**b**) FR.

The final decision or fuzzy model result obtained from defuzzification by the number of rules to defined fuzzy sets and membership function degrees is as shown in Figure 17. The range of triangle membership functions that are used to defuzzify the fuzzy output into very low, low, medium, high, and very high levels is shown in Figure 18 and Table 12, respectively.

**Figure 17.** Defuzzification step in FSSW strength (dynamic parameters) fuzzy system.

**Table 12.** Membership functions of FSSW strength fuzzy system output.


Table 13 shows the If–Then rules that are used in the FSSW strength (dynamic parameters) fuzzy system to join the fuzzy system inputs and output.

**Table 13.** If–Then rules of FSSW strength fuzzy systems.

#### **Fuzzy Rules (If–Then Rules)**

If (Spindle-Speed(rev./min) is V Low) and (Feed-Rate(mm/min) is Medium) then (Welding-Strength (N) is V High) If (Spindle-Speed(rev./min) is Medium) and (Feed-Rate(mm/min) is V Low) then (Welding-Strength (N) is Medium) If (Spindle-Speed(rev./min) is Medium) and (Feed-Rate(mm/min) is High) then (Welding-Strength (N) is Low) If (Spindle-Speed(rev./min) is High) and (Feed-Rate(mm/min) is Medium) then (Welding-Strength (N) is Medium)

> The optimum value of friction stir spot welding related to the controlled dynamic parameters (SS, FR), explained in terms of the surface view for this fuzzy system, is as shown in Figure 19.

**Figure 19.** Welding strength variation with spindle speed and feed rate.

Table 14 shows the output result of the designed fuzzy model of the dynamic parameters of friction stir spot welding.

**Table 14.** Results of FSSW strength fuzzy system (dynamic parameters).


A comparison was performed between the results of experiments and the designed FSSW fuzzy system's results, as shown in Table 15. The deviation between the two results was around 7%, and this mean that using the fuzzy logic control system as a technique to control the dynamic welding parameters will optimize the strength of friction stir spot welding.

**Table 15.** The optimum values for dynamic welding parameters of experiment and fuzzy system.


#### **4. Conclusions**

A fuzzy logic controller was designed and used to optimize the strength of friction stir spot welding by controlling the welding parameters. Two fuzzy models, one for controlling the static welding parameters and another for the dynamic welding parameters, were designed to optimize the strength of the friction stir spot welding of Al 1050 alloy samples. The results obtained from fuzzy logic, which is considered a type of artificial intelligence, proved that the fuzzy logic control system is an easy and inexpensive technology that can be used in the prediction and optimization of the strength of friction stir spot welding (FSSW). The results obtained show the efficacy and adequacy of the proposed fuzzy logic control system.

**Author Contributions:** M.M.A.L. wrote the paper and completed the experimental design; A.M.A.S. supervised and provided project funding; A.B. revised the manuscript, performed validation and visualization, and helped with results; M.I.K. performed supervision, writing—review and editing, and software implementation. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors express their gratitude to the Princess Nourah bint Abdulrahman University Researchers Supporting Project (Grant No. PNURSP2022R152), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All the data are clearly presented in the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

