Challenges and Opportunities in Additive Manufacturing Polymer Technology: A Review Based on Optimization Perspective

Raja, S.; John Rajan, A.

doi:https://doi.org/10.1155/2023/8639185

Advances in Polymer Technology

On this page

Abstract Introduction Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Bio-Polymer Composites for High-Performance Applications

View this Special Issue

Review Article | Open Access

Volume 2023 | Article ID 8639185 | https://doi.org/10.1155/2023/8639185

Challenges and Opportunities in Additive Manufacturing Polymer Technology: A Review Based on Optimization Perspective

S. Raja¹and A. John Rajan¹

Academic Editor: Indran Suyambulingam

Received11 Nov 2022

Revised12 Dec 2022

Accepted27 Mar 2023

Published19 Apr 2023

Abstract

In the emerging modern technology of additive manufacturing, the need for optimization can be found in literature in many places. Additive manufacturing (AM) is making an object layer by layer directly from digital data. Previous works of literature have classified additive manufacturing processes into seven types. However, there is a lack of comprehensive review describing the optimization challenges and opportunities in the material extrusion process (polymer technology) and also the need for FDM polymer materials application in impeller making. In this review paper, a specific optimization method called multicriteria decision-making (MCDM) from the mathematical programming technique used in additive manufacturing polymer technology (AMPT) is discussed. The other topics such as different types of optimization techniques, applications of different MCDM tools and their applications in different fields including AM, and the optimization challenges and opportunities in AMPT particularly impeller application are discussed.

1. Introduction

Additive manufacturing (AM) has been used everywhere in the manufacturing industry in recent decades [1]. AM has many advantages such as less waste during production, lower production cost, and direct production from design data. In this, material extrusion process polymer technology is attracting more attention due to many features such as raw material availability, low cost of raw material, and low cost of production machinery [2]. The polymer raw materials are used to produce end products for many applications like automobile, medical, and civil engineering. A lot of research is being done to improve this technology, and one of the most important is operational research [3]. Operation research is a subject from the scientific mathematical technique used to make decisions in difficult situations [4]. It is divided into three categories and can be seen in Figure 1. Vijay et al. used optimization approaches to investigate the thermal conditions of several polymer composites and then ranked the composites as a result [5]. Jothibasu et al. explored the different polymer composite mechanical properties using optimization techniques, and the result selected the best composite for making an L-framed flower stand application [6].

Using the MCDM optimization technique, Singaravelu et al. performed the research on various polymer composite brake frictions. Their findings showed that the boron graphite composition is the best among the various compositions [7]. The natural fibre polymer composite was investigated by Manoharan et al. using the Taguchi optimization approach, with the goal of identifying the ideal process parameter for natural fibre [8]. With the aid of the Taguchi optimization approach, Binoj et al. examined the areca fruit natural polymer composite fibre process parameter optimization and determined the ideal process parameter [9]. Mannan et al. did research on natural polymer composite for construction applications, and their findings produced a composition that was suitable for achieving a high level of mechanical strength [10, 11]. Natrayan et.al investigated the soybean oil-reinforced polymer composite shear strength with different compositions using an optimization method and their result ranked the high-strength composition [12]. Based on the literatures, optimization is the method of choosing the best and is a part of our daily life. There are several steps involved in optimization such as describing a system mathematically, finding the variables and conditions that are satisfying, describing the properties of the system, and finding the state of the system.

1.1. Optimization Techniques in Operation Research

De Leon-Aldaco et.al [13] reviewed the power converters’ metaheuristic optimization methods and classified the operation research optimization techniques as follows.

1.1.1. Mathematical Programming Techniques

In this method, the decision maker’s opinions are converted into numerical values and solved with a decision matrix, for example, MCDM (multicriteria decision-making) and linear and nonlinear programming.

1.1.2. Stochastic Process Techniques

This method application is known by previous researchers to give an approximate solution, for example, queuing theory and renewal theory.

1.1.3. Statistical Method

This method is used to evaluate the experimental results and select the appropriate one, for example, DOE and the Taguchi method.

From this, the application of MCDM methods in additive manufacturing’ material extrusion process (polymer technology) has very less research only carried out. Therefore an extensive review of MCDM and also a few DOE methods are proposed to optimize problems in the additive manufacturing material extrusion process. In the first step, MCDM and DOE optimization methods, additive manufacturing, and, especially, the material extrusion process can be seen. Then, the optimization challenges and opportunities in additive manufacturing polymer technology are explained in detail. More specifically, the novelty of this research is focusing on optimization in additive manufacturing polymer technology in impeller applications. Finally, the summary and conclusion show how well the research purpose was accomplished.

1.2. MCDM (Multicriteria Decision-Making)

MCDM is a method of selecting a suitable alternative from more than one alternative [14]. Previous researchers have applied this method to complex decision-making situations in many fields. The MCDM technique has been used in many names in previous literature such as multicriteria decision analysis (MCDA) [15], multiobjective decision analysis (MODA) [16], and multiattribute decision-making (MADM) [17]. Stojcic et al.’s [18] review explored how the MCDM method has been widely used in two ways, like qualitative and quantitative research, by previous literature. Figure 2 describes the hierarchy of the MCDM method and more details are given in Section 3. MCDM tools are AHP (analytical hierarchy process) [19], TOPSIS (technique for order of preference by similarity to ideal solution) [20], ANP (analytical network process) [21], BWM (best worst method) [22], FAHP (fuzzy analytical hierarchy process) [23], COPRAS (complex proportional assessment) [24], and PROMETHEE (preference ranking organization method for enrichment of evaluations) [25]. In this, a pairwise matrix is created based on the opinions of the decision maker, and it is converted into numerical values from 0 to 9 (based on the MCDM tool/technique) [26]. Then, the created pairwise matrix is evaluated by basic steps like criteria weight, consistency ratio, and random index [27]. Finally, the alternatives are ranked based on the decision matrix and priority values. On the basis of ranking, the necessary alternative is selected by the decision-maker.

1.3. DOE (Design of Experiment)

The design of the experiment is considered as an optimization technique that helps to analyze the data by conducting an experiment easily through knowledge and techniques and to find its correlation [28]. DOE is a structured technique used to find the relationship between input and output variables [29]. Also, it is used to find which parameter most influenced the result. DOE is used in many fields like agriculture [30], engineering [31], and defense [32]. Three such DOE strategies have been used by previous researchers like examining multiple factors simultaneously [33] and examining multiple factors together [34], and one factor is examined at a time [35]. Anderson and McLean [36] have classified the DOE as the factorial design (finding main effects on prices), response surface design (finding the maximum and minimum response of various factors), mixture design (finding ideal proportion in mixture processes), and optimal design (used to find sufficient details).

Factorial design is divided into two categories such as full factorial design (experiment conducted for all factors and levels) and fractional factorial design (experiment conducted only for certain combination levels), and the Taguchi design is also a type of factorial design. It is also believed that sustainability can be achieved through the use of optimization techniques in many fields. Additive manufacturing is considered to be a growing field in the current manufacturing industry, and also, the optimization needs to find a lot of processes [37].

1.4. Additive Manufacturing

The contribution of additive manufacturing in the manufacturing sector has attracted a bit more attention in recent times as compared to conventional manufacturing [38]. The main reason for this is many advantages such as lightweight, low material wastage of material, low cost, less lead time, low emission, and facilities that can easily produce hard material [39]. As proof of this, the use of additive manufacturing in forming, castings, etc. industries has increased gradually [40]. It consists of seven methods as shown in Figure 3 with its modern technology and raw materials. Liquid polymer, discrete particle, molten material, and solid shield systems are several types of AM technology. In this binder jetting, 3D printing, ink jetting, S-print, and M-print technology are used in which metal polymer and ceramic raw materials are used. The vat photopolymerization process uses stereolithography and digital light processing technologies and uses photo polymer and ceramics raw materials. The sheet lamination method uses ultrasonic consolidation and laminated object manufacture technology and hybrid metallic ceramic raw material. The material extrusion process uses FDM technology and polymer raw material. Material jetting uses polyjet, ink jetting, thermojet technology, and wax raw material. The powder bed fusion process uses SLS, SLM, EBM, and DMLS technologies and raw materials like polymer, ceramic powders, metal powders, and ceramic. Finally, in direct energy deposition, technologies such as LP-DED, LW-DED, AW-DED, and EB-DED and metal, metal alloy, wire, powder, ceramic, and polymer raw materials are used. However, the material extrusion method only has the lowest technical cost and raw material costs. Although many researchers have conducted many researches on the material extrusion method, some research gaps can be seen in the optimization area. In particular, this review article describes current problems such as the selection of production machinery and supplier selection.

1.4.1. Material Extrusion Process (Polymer Technology)

In the material extrusion process, the filament (polymers) is passed through a hot extruder to form a final product layer by layer according to the given (.STL) design [42]. Material extrusion is a method with very low-cost raw material and machine costs compared to all other AM processes. The lower cost of raw materials and machinery gets more attention in the market for increased producers and users of AM. Thermoplastic polymers like PLA (polylactic acid) and ABS (acrylonitrile butadiene styrene) are used as raw materials in the form of filament and the standard filament wire diameter used as 1.75 mm [43]. FDM machine and their important components such as filament spoof, filament, extruder, melting zones, molten filament, and object in the build platform are shown in Figure 4. Each polymer raw material has its unique properties, for example, PLA is a biodegradable material [44], and ABS is a toxic material [45]. So, it is used in many applications like medical [46], pipe making [47], and impeller making [48] based on the raw material properties. A previous literature review revealed that the FDM-fabricated impeller of the rotodynamic hydraulic pump performed similarly to the original impeller.

Filament spoof is the roll of polymer filament in the wire format, and it is connected with extruders. The extruders have heated with the melting zone when the wire filament passes through the melting zone.

Printing parameters play an important role in the material extrusion 3D printing process. One of the most important printing parameters is the infill pattern such as hexagonal, line, and triangle printing in which Figure 5 shows the microstructure of the hexagonal pattern. In this, the printing shape and distance of the hexagonal pattern and the average gap between the two layers are calculated as 454.6 μm.

Figure 6 illustrates the microstructure of the line pattern and the 822.5 μm average spacing for two adjacent lines.

Finally, Figure 7 describes the microstructure of the triangle pattern and the 612.7 μm average spacing between the two layers. These three remarkable patterns were produced by an FDM printer using PLA filament and described with the help of a FESEM image for this research. Based on this result, the hexagonal pattern is recommended for production in the material extrusion method because it is observed only for a low construction gap between two layers. Also, the solid infill pattern is having more conductivity after the spatter coating. Therefore, the microstructure of the solid infill pattern is unable to find with spatter coating. However, the remaining patterns can be selected according to the infill percentage, user’s application, etc.

Figure 8 illustrates the entire additive manufacturing process and some important material extrusion raw materials and their applications.

2. Optimization Challenges in Additive Manufacturing Polymer Technology

Optimization is the process of selecting the desired or suitable one from among several alternatives [49]. As illustrated in Figure 9, resource, weight, cost, and process are chosen for the optimization ways. The current optimization problem identified in the material extrusion process is as follows.

2.1. Machine Selection

The sales of similar FDM machines with slightly different features are increasing day by day in the market [10, 16, 50, 51]. Also, choosing the most suitable machine for the users from the various machines that have got the same sales rating on the online platforms is considered to be the current challenge so commercial companies can use optimization techniques to reduce investment and end product costs. Figure 10 described the optimization problems in the material extrusion process and proposed optimization tools.

2.2. Supplier Selection

FDM machine users suffer a lot because after purchasing a machine, services such as repair or replacement of parts are not available properly from suppliers. Therefore, it is considered very important for wholesalers to choose distributors [10, 17, 52, 53] who provide proper sales and service. Solving this using any suitable MCDM method would be a novelty.

2.3. Logistic Selections

3D printing manufacturers are largely independent of production. So, logistics is a bit more expensive and takes delivery time [54–58]. Additive manufacturing can solve logistics problems when manufacturers combine production. Especially in India, known as small industries development corporations (SIDCO), governments adopt policies to consolidate clusters of similar industries. This makes logistics continuous and cost-effective. Therefore, integrating FDM commercial manufacturers is considered very important in choosing the right logistic partners.

2.4. Raw Material Selections

Large numbers of smaller molecules or repeating units, known as monomers, are joined together chemically to form polymers, which are referred to as macromolecules.

Within a single polymer molecule, the degree of order, the relative orientation, and the kind of monomer can all vary. The benefits of polymers, including their low price, flexibility of manufacture, water resistance, and suppleness, have led to their use such as industry. Depending on the manufacture, various types of polymers can be found as powders, granolas, filaments, and resins. Polymers called thermoplastic are used in the material extrusion. It is fusible when heated [59]. This review article describes the most important polymers and their properties and applications. It is a novelty to use MCDM methods to select the most suitable polymer for the user among polymers with similar uses. The various types of polymers and their applications are shown in Table 1.

2.4.1. PLA (Polylactic Acid)

PLA is made from organic source sugarcane or corn starch. Its molecules are renewable so it can also be known as biodegradable material. It is often used to make medical, scaffolds, and prototypes as shown in Figure 8. Also, its melting point is calculated to be 195°C to 220°C. It is priced from INR 869 onwards in the Indian market. Moreover, PLA is not ideal for high-temperature applications. According to results from tests on creep behaviour, PLA’s behaviour resembled that of a weakly cross-linked elastomer the most, which caused the creep curve to be held to a constant limit under light loads. Previous literature presented PLA as a material to consider when looking for long-term use based on their findings. Comparing this polymer to other polymers, its fair price, eco-friendly biocompatibility, and suitable physicomechanical characteristics have made it an excellent choice. [60–65].

2.4.2. ABS (Acrylonitrile Butadiene Styrene)

ABS filament is styrene and acrylonitrile derived from polybutadiene. It is a toxic filament and is used for external use only and is also considered more suitable for higher temperature applications than PLA. As mentioned in Figure 8, computer hardware, prototypes, and pipe-making purpose ABS are used. The ABS melting point is calculated to be 210°C to 240°C. Previous research on ABS’s mechanical characteristics in the manufacture of impeller pumps demonstrated that ABS can be thought of as a good choice for the manufacture of impellers [65–67].

2.4.3. PETG (Polyethylene Terephthalate Glycol)

Chemical impact resistance hardness, ductility, and transparency are considered the main properties of PETG. As mentioned in Figure 5, it is used to make packing, domestic products, impellers, etc. PETG melting point is used between 220°C and 240°C. Previous research examined the use of PTEG impellers in pump-jet modules (PJM). A PTEG impeller exhibited the necessary characteristics while operating for this application, taking into account the 1200 rpm rotational speed that produced a thrust of 14 N. [68–71].

2.4.4. PEEK (Polyether Ether Ketone)

PEEK is a colourless organic thermoplastic with excellent fire performance and excellent mechanical strength. As mentioned in Figure 8, PEEK is used for bearing, piston parts, pumps, oil, gas, etc. Moreover, its melting point is calculated at 230°C to 250°C. Extensive researches have focused on the use of PEEK impellers in centrifugal pumps for medical applications because of the enhanced strength and durability they provide [72–76].

2.4.5. PC (Polycarbonate)

Bisphenol A is a toxic substance in polycarbonate, so it is used for external use only. However, PC has slightly higher strength and stiffness than other polymer filaments. As mentioned in Figure 8, it is used for safety glass production, auto parts, and led light production. PC melting point is used between 250°C and 285°C in the FDM printers [2, 77–80].

2.4.6. PP (Polypropylene)

PP is lightweight, flexible, chemical resistant, and tough. Therefore, as mentioned in Figure 8, it is mostly used for rope, carpet, clothing, and packing, and its extruder melt temperature is calculated from 220°C to 250°C. Also, PP is slightly more flexible than PLA [81–83].

2.4.7. Nylon

Nylon filament has high flexibility and high toughness. Its extruder melting point is calculated at 250°C, and also, nylon is used in clothing, seat belt, and conveyor applications as mentioned in Figure 8 [88–90].

In all polymer filament materials, composite filaments are available in the market with 90% parent polymer and 10% another polymer according to the application, for example, ABS+, ABS Premium, PLA Pro, PLA+, PLA carbon fibre, and PETG carbon fibre [91–94].

2.5. Raw Material Manufacturer Selection

The same polymer raw material or filaments are prepared by many manufacturers of different qualities in the Indian market. It is considered difficult to choose the best polymer raw material based on online ratings alone. Therefore, it is considered a novelty to investigate a polymer raw material using the optimization MCDM method based on the experimental result for different manufacturers same filament [95–97].

3. Opportunities of Optimization Techniques in AMPT

The purpose of this review article is how to solve all the optimization problems mentioned above using MCDM and the statistical method which is a common mathematical programming technique. Accordingly, Figure 2 illustrates the general hierarchy of the MCDM method, and the aim/goal means the problem to be solved. It is considered the first step in MCDM methods.

Then, the second step is to identify common criteria for alternatives depending on the objective. Finally, priority values are determined using any of the MCDM methods for the alternatives based on each criterion. Through this ranking, the alternative with the highest value is recommended to the decision-makers. This process is known as multicriteria decision-making [98–103].

For example, the hierarchy is illustrated in Figure 11, considering the machine selection problem. In which the selection of a suitable FDM machine is the aim/objective. The price of the machine, extruder type, build platform, safety guards, etc. is common criteria for alternatives. Finally, alternatives are FDM machines, namely, X (Wanhao Duplicator 4S), Y (Flashforge Creator Pro), and Z (MakerBot Replicator Plus). Moreover, if the MCDM tool called AHP is used to calculate the priority value for the alternatives, then the criteria weight, consistency ratio, random index, and pairwise matrix are found through the Saaty scale.

Then, it is confirmed whether the nature of the pairwise matrix is correct. Then, depending on each criterion, separate priority values are found through another pairwise matrix for alternatives with the help of the Saaty scale. Finally, by summing all the identified priority values, the final priority values are obtained for the alternatives. A high-value alternative is recommended to the decision maker.

Various important and significant MCDM methods and their uses in different fields can be seen in Table 2, and this method can also be done analytically (data collection—collect the opinions based on a numerical scale and solved). Therefore, in similar problem situations in the material extrusion process, the decision-maker can ease the use of the appropriate MCDM tool.

According to the statistical method, based on the experimental result, any optimization tool is applied, and a suitable solution is given to the end user. For example, taking the machine selection problem, innovative research can be carried out by producing a product on more than one machine and depending on the results, using an appropriate statistical tool. What is necessary to use the statistical tool in this is that the results obtained have distinct characteristics from each other. Statistical tools are used to make the decision-makers choose the appropriate option easily. It is worth noting that experimental optimization has been extensively explored by previous researchers [142–148]. Figure 12 explores the experimental optimization procedure.

4. Material Extrusion Polymers in Impeller Application

An impeller is considered the main part of turbomachinery. The work of the impeller is to convert the velocity of the working fluid into pressure due to the very fast rotation [149]. Impeller application also plays an important role in many fields, for example, pumps [150], medical [151], automobile [152], and aerospace [153]. In injection moulding and other traditional impeller production techniques, raw materials such as polymeric, metal glass, stainless steel, titanium, aluminium, and nickel alloy are frequently utilized. Metal impellers achieve only slightly lower efficiency due to their heavy weight. Efficiency can be increased when using thermoplastic polymer impellers with low weight and high strength. The main kinds of thermoplastics are amorphous and semicrystalline. Different thermoplastic polymer forms like powders, granules, and filaments. The benefits of this class of polymer include its capacity to be recycled, high ductility, and impact resistance when compared to thermosets. The modulus of the thermoplastic item is typically less than 5 GPa, though this might vary based on the object’s chemical constitution and production process. At present, the AMPT method is used to produce the final product directly from the given design in less time and more accurately. Polymer impellers were first used in the heating ventilation and air conditioning (HVAC) and microorganic ranking cycle (mORC) and refrigeration systems. Polymers like PEEK, PLA, and ABS have been used for the first time for impeller application. The ABS impeller met the anticipated operating condition taking into consideration the working environment and safety factor (FoS), which is the ratio of elastic modulus to the maximum equivalent stress. One of the key benefits of employing this polymer was the ability to manufacture the impeller at a lower cost by using ABS, which enables the mass production of mORC. It is also noteworthy that only PEEK-GF30 [154] has been used in the composite rotary component. Impellers are manufactured using a minimal amount of polymers when using an additive manufacturing process.

PLA has been utilized in the manufacturing of impellers for pumps and marine applications. However, since PLA and ABS are both easily accessible, these two categories of thermoplastic have been investigated in various experiments as pump impellers. Pump and compressor applications for a variety of industries, including the automotive, aerospace, and medical sectors, place a high value on PEEK impellers. Pump and mORC applications have both utilized PETG impellers. For the production of pump blades, this polymer was chosen because of its excellent water resistance and biodegradability. Metals have been replaced by carbon fibre polymer-matrix composites, one of the most effective families of materials. These kinds of composites can be divided into categories based on the type of fibre condition, such as short or continuous. Epoxy, which has been frequently utilized as a matrix for these composites, is a thermoset, and PPS, PEEK, PI, and PEI are thermoplastics. In the case of a microturbine generator, a PEEK carbon-reinforced impeller was suggested as a suitable material to replace an aluminium impeller. However, the time required to make an impeller using many polymers (directly or compositely blended like PLACF and PETG-CF) in the market, changing the printing parameters, making changes in the manufacturing geometry, etc. will be an optimization challenge and opportunity in AMPT for impeller application.

Figure 13 shows the flash forge software for slicing the impeller design. Choosing optimal process parameters using the MCDM method will be a novelty for future researchers because each polymer filament has different process parameters like different melting temperatures, printing speed, and infill.

5. Summary and Conclusions

In this review article, mathematical programming MCDM and statistical method optimization problems in additive manufacturing material extrusion are presented. In more than 147 AM material extrusion-related 3D printing selection, supplier selection, logistic selection, raw material selection, main properties of raw material (polymers), and raw material manufacturer selection, interesting new novel problems through this conclusion provide useful information to the researchers. This review article also describes the optimization challenges and opportunities in the use of polymers, especially in impeller applications. Although material extrusion polymers have many sectoral applications, only significant research has been done on impeller applications. Likewise, optimization challenges such as selecting the appropriate FDM and selecting the appropriate raw material have been highlighted. Moreover, the optimization opportunity is described with an example based on mathematical programming techniques and statistical techniques. Many fields like transport, logistics, energy, civil engineering, and other engineering disciplines have achieved sustainability by using optimization methods. It describes several applications of MCDMs so that future researchers can easily find the appropriate technique to suit their application. Finally, today’s increasing use of optimization in all fields reflects its importance and nature of sustainable decision-making.

Data Availability

The data used to support the findings of this study are included in the article. Should further data or information be required, these are available from the corresponding author upon request.

Conflicts of Interest

There is no conflict of interest.

Acknowledgments

For their assistance in allowing us to conduct this research, we gratefully acknowledge and thank the Vellore Institute of Technology management. Additionally, we would like to extend our gratitude to the technician staff at the FESEM lab and the DIGI-MAN LAB in charge.

References

B. Devarajan, R. Lakshmi Narasimhan, B. Venkateswaran, S. Mavinkere Rangappa, and S. Siengchin, “Additive manufacturing of jute fiber reinforced polymer composites: a concise review of material forms and methods,” Polymer Composites, vol. 43, no. 10, pp. 6735–6748, 2022.
View at: Publisher Site | Google Scholar
B. O. Samuel, M. Sumaila, and B. Dan-Asabe, “Modeling and optimization of the manufacturing parameters of a hybrid fiber reinforced polymer composite PxGyEz,” The International Journal of Advanced Manufacturing Technology, vol. 118, no. 5-6, pp. 1441–1452, 2022.
View at: Publisher Site | Google Scholar
M. Priyadharshini, D. Balaji, V. Bhuvaneswari, L. Rajeshkumar, M. R. Sanjay, and S. Siengchin, “Fiber reinforced composite Manufacturing with the aid of artificial intelligence–a state-of-the-art review,” Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 5511–5524, 2022.
View at: Publisher Site | Google Scholar
D. Balaji, J. Ranga, V. Bhuvaneswari et al., “Additive manufacturing for aerospace from inception to certification,” Journal of Nanomaterials, vol. 2022, Article ID 7226852, 18 pages, 2022.
View at: Publisher Site | Google Scholar
R. Vijay, D. L. Singaravelu, and R. Jayaganthan, “Development and characterization of stainless steel fiber-based copper-free brake liner formulation: a positive solution for steel fiber replacement,” Friction, vol. 8, no. 2, pp. 396–420, 2020.
View at: Publisher Site | Google Scholar
S. Jothibasu, S. Mohanamurugan, R. Vijay, D. Lenin Singaravelu, A. Vinod, and M. R. Sanjay, “Investigation on the mechanical behavior of areca sheath fibers/jute fibers/glass fabrics reinforced hybrid composite for light weight applications,” Journal of Industrial Textiles, vol. 49, no. 8, pp. 1036–1060, 2020.
View at: Publisher Site | Google Scholar
D. L. Singaravelu, R. Vijay, and P. Filip, “Influence of various cashew friction dusts on the fade and recovery characteristics of non-asbestos copper free brake friction composites,” Wear, vol. 426-427, pp. 1129–1141, 2019.
View at: Publisher Site | Google Scholar
S. Manoharan, R. Vijay, D. L. Singaravelu, S. Krishnaraj, and B. Suresha, “Tribological characterization of recycled basalt-aramid fiber reinforced hybrid friction composites using grey-based Taguchi approach,” Materials Research Express, vol. 6, no. 6, article 065301, 2019.
View at: Publisher Site | Google Scholar
J. S. Binoj, N. Manikandan, B. B. Mansingh et al., “Taguchi’s optimization of areca fruit husk fiber mechanical properties for polymer composite applications,” Fibers and Polymers, vol. 23, no. 11, pp. 3207–3213, 2022.
View at: Publisher Site | Google Scholar
K. T. Mannan, V. Sivaprakash, S. Raja, P. P. Patil, S. Kaliappan, and S. Socrates, “Effect of Roselle and biochar reinforced natural fiber composites for construction applications in cryogenic environment,” Materials Today: Proceedings, vol. 69, pp. 1361–1368, 2022.
View at: Publisher Site | Google Scholar
K. T. Mannan, V. Sivaprakash, S. Raja, M. Kulandasamy, P. P. Patil, and S. Kaliappan, “Significance of Si₃N₄/Lime powder addition on the mechanical properties of natural calotropis gigantea composites,” Materials Today: Proceedings, vol. 69, pp. 1355–1360, 2022.
View at: Publisher Site | Google Scholar
L. Natrayan, S. Kaliappan, S. B. Sethupathy et al., “Investigation on interlaminar shear strength and moisture absorption properties of soybean oil reinforced with aluminium trihydrate-filled polyester-based nanocomposites,” Journal of Nanomaterials, vol. 2022, Article ID 7588699, 8 pages, 2022.
View at: Publisher Site | Google Scholar
S. E. De Leon-Aldaco, H. Calleja, and J. Aguayo Alquicira, “Metaheuristic optimization methods applied to power converters: a review,” IEEE Transactions on Power Electronics, vol. 30, no. 12, pp. 6791–6803, 2015.
View at: Publisher Site | Google Scholar
M. Sugavaneswaran, B. Prashanthi, and J. Rajan, “A multi-criteria decision making method for vapor smoothening fused deposition modelling part,” Rapid Prototyping Journal, vol. 28, no. 2, pp. 236–252, 2022.
View at: Publisher Site | Google Scholar
Y. Zhijun, H. Wang, X. Wei, K. Yan, and C. Gao, “Multiobjective optimization method for polymer injection molding based on a genetic algorithm,” Advances in Polymer Technology, vol. 2019, Article ID 9012085, 17 pages, 2019.
View at: Publisher Site | Google Scholar
Z. Shan, W. Wu, Y. Lei, and B. Zhao, “A new fuzzy rule based multi-objective optimization method for cross-scale injection molding of protein electrophoresis microfluidic chips,” Scientific Reports, vol. 12, no. 1, article 13159, 2022.
View at: Publisher Site | Google Scholar
S. U. Sapkal and P. H. Warule, “Application of multi-attribute decision making methods for fused deposition modelling,” in Sustainability for 3D Printing. Springer Tracts in Additive Manufacturing, K. Sandhu, S. Singh, C. Prakash, K. Subburaj, and S. Ramakrishna, Eds., pp. 55–75, Springer, Cham, Switzerland, 2022.
View at: Publisher Site | Google Scholar
M. Stojčić, E. K. Zavadskas, D. Pamučar, Ž. Stević, and A. Mardani, “Application of MCDM methods in sustainability engineering: a literature review 2008-2018,” Symmetry, vol. 11, no. 3, p. 350, 2019.
View at: Publisher Site | Google Scholar
D. Schuhmann, M. Rupp, M. Merkel, and D. K. Harrison, “Additive vs. conventional manufacturing of metal components: selection of the manufacturing process using the AHP method,” Process, vol. 10, no. 8, p. 1617, 2022.
View at: Publisher Site | Google Scholar
S. Raja and A. J. Rajan, “A decision-making model for selection of the suitable FDM machine using fuzzy TOPSIS,” Mathematical Problems in Engineering, vol. 2022, Article ID 7653292, 15 pages, 2022.
View at: Publisher Site | Google Scholar
G. M. Magableh and M. Z. Mistarihi, “Applications of MCDM approach (ANP-TOPSIS) to evaluate supply chain solutions in the context of COVID-19,” Heliyon, vol. 8, no. 3, article e09062, 2022.
View at: Publisher Site | Google Scholar
R. Agrawal, “Sustainable material selection for additive manufacturing technologies: a critical analysis of rank reversal approach,” Journal of Cleaner Production, vol. 296, article 126500, 2021.
View at: Publisher Site | Google Scholar
T.-C. T. Chen and C.-W. Lin, “Assessing cloud manufacturing applications using an optimally rectified FAHP approach,” Complex & Intelligent Systems, vol. 8, no. 6, pp. 5087–5099, 2022.
View at: Publisher Site | Google Scholar
A. Baroutaji, A. Arjunan, G. Singh, and J. Robinson, “Crushing and energy absorption properties of additively manufactured concave thin-walled tubes,” Results in Engineering, vol. 14, article 100424, 2022.
View at: Publisher Site | Google Scholar
A. S. Bhaskar and A. Khan, “Comparative analysis of hybrid MCDM methods in material selection for dental applications,” Expert Systems with Applications, vol. 209, article 118268, 2022.
View at: Publisher Site | Google Scholar
S. Raja, A. J. Rajan, V. P. Kumar et al., “Selection of additive manufacturing machine using analytical hierarchy process,” vol. 2022, Article ID 1596590, pp. 1–20, 2022.
View at: Publisher Site | Google Scholar
R. Subramani, S. Kaliappan, P. V. Arul et al., “A recent trend on additive manufacturing sustainability with supply chain management concept, multicriteria decision making techniques,” Multicriteria Decision Making Techniques, vol. 2022, article 9151839, pp. 1–12, 2022.
View at: Publisher Site | Google Scholar
V. Kumar, R. Singh, and I. Singh, “Online health monitoring of repaired non-structural cracks with innovative 3D printed strips in heritage buildings,” Materials Letters, vol. 327, article 133033, 2022.
View at: Publisher Site | Google Scholar
M. Karthick, M. Meikandan, S. Kaliappan et al., “Experimental investigation on mechanical properties of glass fiber hybridized natural fiber reinforced penta-layered hybrid polymer composite,” International Journal of Chemical Engineering, vol. 2022, Article ID 1864446, 9 pages, 2022.
View at: Publisher Site | Google Scholar
T. Jiang, Z. Guan, H. Li et al., “A feeding quantity monitoring system for a combine harvester: design and experiment,” Agriculture, vol. 12, no. 2, p. 153, 2022.
View at: Publisher Site | Google Scholar
G. Velmurugan, V. S. Shankar, S. Kaliappan et al., “Effect of aluminium tetrahydrate nanofiller addition on the mechanical and thermal behaviour of luffa fibre-based polyester composites under cryogenic environment,” Journal of Nanomaterials, vol. 2022, Article ID 5970534, 10 pages, 2022.
View at: Publisher Site | Google Scholar
P. Octorina, A. Böhm, D. Martin-Creuzburg, and D. Straile, “Morphological defences and defence–cost trade-offs in daphnia in response to two co-occurring invertebrate predators,” Freshwater Biology, vol. 67, no. 5, pp. 883–892, 2022.
View at: Publisher Site | Google Scholar
K. C. Sekhar, R. Surakasi, P. Roy et al., “Mechanical Behavior of Aluminum and Graphene Nanopowder-Based Composites,” International Journal of Chemical Engineering, vol. 2022, Article ID 2224482, 13 pages, 2022.
View at: Publisher Site | Google Scholar
R. Subramani, S. Kaliappan, S. Sekar et al., “Polymer filament process parameter optimization with mechanical test and morphology analysis,” Advances in Materials Science and Engineering, vol. 2022, Article ID 8259804, 8 pages, 2022.
View at: Publisher Site | Google Scholar
S. Raja, A. P. Agrawal, P. P. Patil et al., “Optimization of 3D printing process parameters of Polylactic acid filament based on the mechanical test,” International Journal of Chemical Engineering, vol. 2022, Article ID 5830869, 7 pages, 2022.
View at: Publisher Site | Google Scholar
V. L. Anderson and R. A. McLean, Design of Experiments: A Realistic Approach, CRC Press, 1st edition, 1974.
View at: Publisher Site
L. Auffray, P. A. Gouge, and L. Hattali, “Design of experiment analysis on tensile properties of PLA samples produced by fused filament fabrication,” International Journal of Advanced Manufacturing Technology, vol. 118, no. 11-12, pp. 4123–4137, 2022.
View at: Publisher Site | Google Scholar
V. Madhavadas, D. Srivastava, U. Chadha et al., “A review on metal additive manufacturing for intricately shaped aerospace components,” CIRP Journal of Manufacturing Science and Technology, vol. 39, pp. 18–36, 2022.
View at: Publisher Site | Google Scholar
A. Garcia-Colomo, D. Wood, F. Martina, and S. W. Williams, “A comparison framework to support the selection of the best additive manufacturing process for specific aerospace applications,” International Journal of Rapid Manufacturing, vol. 9, no. 2/3, pp. 194–211, 2020.
View at: Publisher Site | Google Scholar
S. M. S. Mukras, “Experimental-based optimization of injection molding process parameters for short product cycle time,” Advances in Polymer Technology, vol. 2020, Article ID 1309209, 15 pages, 2020.
View at: Publisher Site | Google Scholar
S. M. Yusuf, S. Cutler, and N. Gao, “Review: the impact of metal additive manufacturing on the aerospace industry,” Metals, vol. 9, no. 12, p. 1286, 2019.
View at: Publisher Site | Google Scholar
D. Popescu, A. Zapciu, C. Amza, F. Baciu, and R. Marinescu, “FDM process parameters influence over the mechanical properties of polymer specimens: a review,” Polymer Testing, vol. 69, pp. 157–166, 2018.
View at: Publisher Site | Google Scholar
N. G. Olaiya, C. Maraveas, M. A. Salem et al., “Viscoelastic and properties of amphiphilic chitin in plasticised polylactic acid/starch biocomposite,” Polymers, vol. 14, no. 11, p. 2268, 2022.
View at: Publisher Site | Google Scholar
G. Atakok, M. Kam, and H. B. Koc, “Tensile, three-point bending and impact strength of 3D printed parts using PLA and recycled PLA filaments: a statistical investigation,” Journal of Materials Research and Technology, vol. 18, pp. 1542–1554, 2022.
View at: Publisher Site | Google Scholar
J. Zhang, D. Chen, and S. Chen, “A review of emission characteristics and control strategies for particles emitted from 3D fused deposition modeling (FDM) printing,” Building and Environment, vol. 221, article 109348, 2022.
View at: Publisher Site | Google Scholar
A. Haryńska, J. Kucinska-Lipka, A. Sulowska, I. Gubanska, M. Kostrzewa, and H. Janik, “Medical-grade PCL based polyurethane system for FDM 3D printing-characterization and fabrication,” Materials, vol. 12, no. 6, 2019.
View at: Publisher Site | Google Scholar
E. V. Chary and G. A. Kumar, “Overfcview of Manufacturing PMC’s Using Traditional and 3D printing Technology (FDM),” Metal Matrix Composites Journal, Taylor and Francis Publisher, vol. 9, no. 1, pp. 6–10, 2022.
View at: Google Scholar
N. Zirak, M. Shirinbayan, M. Deligant, and A. Tcharkhtchi, “Toward polymeric and polymer composites impeller fabrication,” Polymers, vol. 14, no. 1, 2021.
View at: Publisher Site | Google Scholar
S. Raja, J. Logeshwaran, S. Venkatasubramanian et al., “OCHSA: Designing Energy-Efficient Lifetime-Aware Leisure Degree Adaptive Routing Protocol with Optimal Cluster Head Selection for 5G Communication Network Disaster Management,” Scientific Programming, vol. 2022, Article ID 5424356, 11 pages, 2022.
View at: Publisher Site | Google Scholar
K. Ransikarbum and P. Khamhong, “Integrated fuzzy analytic hierarchy process and technique for order of preference by similarity to ideal solution for additive manufacturing printer selection,” Journal of Materials Engineering and Performance, vol. 30, no. 9, pp. 6481–6492, 2021.
View at: Publisher Site | Google Scholar
M. Palanisamy, A. Pugalendhi, and R. Ranganathan, “Selection of suitable additive manufacturing machine and materials through best–worst method (BWM),” International Journal of Advanced Manufacturing Technology, vol. 107, no. 5-6, pp. 2345–2362, 2020.
View at: Publisher Site | Google Scholar
M. L. Tseng, T. D. Bui, M. K. Lim, M. Fujii, and U. Mishra, “Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity,” International Journal of Production Economics, vol. 245, article 108401, 2022.
View at: Publisher Site | Google Scholar
L. Peng, J. Lu, J. Luo, and Y. Wang, “A Combination of FDM, DEMATEL, and DANP for Disclosing the Interrelationship of Influencing Factors in Rural Homestay Business: Empirical Evidence from China,” Sustainability, vol. 14, no. 16, article 10341, 2022.
View at: Publisher Site | Google Scholar
I. Valtonen, S. Rautio, and M. Salmi, “Capability development in hybrid organizations: enhancing military logistics with additive manufacturing,” Progress in Additive Manufacturing, vol. 7, no. 5, pp. 1037–1052, 2022.
View at: Publisher Site | Google Scholar
S. Venkatasubramanian, S. Raja, V. Sumanth et al., “Fault diagnosis using data fusion with ensemble deep learning technique in IIoT,” Mathematical Problems in Engineering, vol. 2022, Article ID 1682874, 8 pages, 2022.
View at: Publisher Site | Google Scholar
T. Santos, A relação da logística reversa e manufatura aditiva com a sustentabilidade: revisão de literatura, Engineering Archive, 2022.
D. Version, AMHUB-Additive Manufacturing Hubs for Radical Innovation in Digital Part Delivery Logistics Final Report, VTT Technical Research Centre of Finland, 2022.
B. Debnath, M. S. Shakur, F. Tanjum, M. A. Rahman, and Z. H. Adnan, “Impact of additive manufacturing on the supply chain of aerospace spare parts industry—a review,” Logistics, vol. 6, no. 2, p. 28, 2022.
View at: Publisher Site | Google Scholar
A. Kumar, R. Agrawal, and V. Ashok, “Materials today: proceedings material selection for metal additive manufacturing process,” Materials Today: Proceedings, vol. 66, pp. 1744–1749, 2022.
View at: Publisher Site | Google Scholar
R. A. D. N. V. Rajakaruna, B. Subeshan, and E. Asmatulu, “Fabrication of hydrophobic PLA filaments for additive manufacturing,” Journal of Materials Science, vol. 57, no. 19, pp. 8987–9001, 2022.
View at: Publisher Site | Google Scholar
A. Rimkus, M. M. Farh, and V. Gribniak, “Continuously reinforced polymeric composite for additive manufacturing — development and efficiency analysis,” Polymers, vol. 14, no. 17, p. 3471, 2022.
View at: Publisher Site | Google Scholar
J. Beniak, Ľ. Šooš, P. Križan, M. Matúš, and V. Ruprich, “Resistance and strength of conductive PLA processed by FDM additive manufacturing,” Polymers, vol. 14, no. 4, p. 678, 2022.
View at: Publisher Site | Google Scholar
E. Brancewicz-Steinmetz and J. Sawicki, “Bonding and strengthening the PLA biopolymer in multi-material additive manufacturing,” Materials, vol. 15, no. 16, p. 5563, 2022.
View at: Publisher Site | Google Scholar
J. Sasse, L. Pelzer, M. Schön, T. Ghaddar, and C. Hopmann, “Investigation of recycled and coextruded PLA filament for additive manufacturing,” Polymers, vol. 14, no. 12, p. 2407, 2022.
View at: Publisher Site | Google Scholar
A. B. Stefaniak, L. N. Bowers, G. Cottrell et al., “Towards sustainable additive manufacturing: the need for awareness of particle and vapor releases during polymer recycling, making filament, and fused filament fabrication 3-D printing,” Resources, Conservation and Recycling, vol. 176, 2023.
View at: Publisher Site | Google Scholar
Y. Hong, M. Mrinal, H. Si, V. Dung, X. Liu, and C. Luo, “In-situ observation of the extrusion processes of acrylonitrile butadiene styrene and polylactic acid for material extrusion additive manufacturing,” Additive Manufacturing, vol. 49, article 102507, 2022.
View at: Publisher Site | Google Scholar
A. Goulas, J. R. Mcghee, T. Whittaker et al., “Synthesis and dielectric characterisation of a low loss BaSrTiO₃/ABS ceramic/polymer composite for fused filament fabrication additive manufacturing,” vol. 55, Article ID 102844, 2022.
View at: Publisher Site | Google Scholar
R. Anandkumar and S. R. Babu, “FDM filaments with unique segmentation since evolution: a critical review,” Progress in Additive Manufacturing, vol. 4, no. 2, pp. 185–193, 2019.
View at: Publisher Site | Google Scholar
K. Singh, C. Herrmann, P. Stief, J. Dantan, A. Etienne, and A. Siadat, “A Comparative Study on the Life Cycle Assessment of a 3D Printed Product with PLA, ABS & PETG Materials,” Procedia CIRP, vol. 107, pp. 15–20, 2022.
View at: Publisher Site | Google Scholar
S. Rijckaert, L. Daelemans, L. Cardon, M. Boone, W. Van Paepegem, and K. De Clerck, “Continuous fiber-reinforced aramid/PETG 3D-printed composites with high fiber loading through fused filament fabrication,” Polymers, vol. 14, no. 2, p. 298, 2022.
View at: Publisher Site | Google Scholar
M. A. Caminero and J. M. Chac, “Effects of carbon fibre reinforcement on the geometric properties of PETG-based filament using FFF additive manufacturing,” Composites Part B: Engineering, vol. 235, 2022.
View at: Publisher Site | Google Scholar
H. Spece, P. M. Desantis, and S. M. Kurtz, “Development of an architecture-property model for triply periodic minimal surface structures and validation using material extrusion additive manufacturing with polyetheretherketone (PEEK),” Journal of the Mechanical Behavior of Biomedical Materials, vol. 133, article 105345, 2022.
View at: Publisher Site | Google Scholar
G. Ramesh, J. Logeshwaran, and K. Rajkumar, “The smart construction for image preprocessing of mobile robotic systems using neuro fuzzy logical system approach,” Neuro Quantology, vol. 20, no. 10, pp. 6354–6367, 2022.
View at: Google Scholar
W. Freudenberg, F. Wich, N. Langhof, and S. Schaff, “Additive manufacturing of carbon fiber reinforced ceramic matrix composites based on fused filament fabrication,” Journal of the European Ceramic Society, vol. 42, no. 4, pp. 1822–1828, 2022.
View at: Publisher Site | Google Scholar
A. Diouf-lewis, R. D. Farahani, F. Iervolino et al., “Design and characterization of carbon fiber-reinforced PEEK/PEI blends for fused filament fabrication additive manufacturing,” Materials Today Communications, vol. 31, article 103445, 2022.
View at: Publisher Site | Google Scholar
A. Lee, M. Wynn, L. Quigley, M. Salviato, and N. Zobeiry, “Effect of temperature history during additive manufacturing on crystalline morphology of PEEK,” Advances in Industrial and Manufacturing Engineering, vol. 4, article 100085, 2022.
View at: Publisher Site | Google Scholar
M. Petousis, N. Vidakis, N. Mountakis et al., “Silicon carbide nanoparticles as a mechanical boosting agent in material extrusion 3D-printed polycarbonate,” Polymers, vol. 14, no. 17, p. 3492, 2022.
View at: Publisher Site | Google Scholar
N. Vidakis, M. Petousis, S. Grammatikos, V. Papadakis, A. Korlos, and N. Mountakis, “High performance polycarbonate nanocomposites mechanically boosted with titanium carbide in material extrusion additive manufacturing,” vol. 12, no. 7, p. 1068, 2022.
View at: Publisher Site | Google Scholar
S. J. Mohan, P. S. S. Devasahayam, I. Suyambulingam, and S. Siengchin, “Suitability characterization of novel cellulosic plant fiber from Ficus benjamina L. aerial root for a potential polymeric composite reinforcement,” Polymer Composites, vol. 43, no. 12, pp. 9012–9026, 2022.
View at: Publisher Site | Google Scholar
A. Gupta, S. Hasanov, and I. Fidan, “Thermal characterization of short carbon fiber reinforced high temperature polymer material produced using the fused filament fabrication process,” Journal of Manufacturing Processes, vol. 80, pp. 515–528, 2022.
View at: Publisher Site | Google Scholar
N. Vidakis, M. Petousis, E. Velidakis et al., “Fused filament fabrication 3D printed polypropylene/ alumina nanocomposites: effect of filler loading on the mechanical reinforcement,” Polymer Testing, vol. 109, article 107545, 2022.
View at: Publisher Site | Google Scholar
D. Divya, I. Suyambulingam, M. R. Sanjay, and S. Siengchin, “Suitability examination of novel cellulosic plant fiber from Furcraea selloa K. Koch peduncle for a potential polymeric composite reinforcement,” Polymer Composites, vol. 43, no. 7, pp. 4223–4243, 2022.
View at: Publisher Site | Google Scholar
A. N. Solodov, J. Shayimova, D. Balkaev et al., “High-throughput, low-cost and "green" production method for highly stable polypropylene/perovskite composites, applicable in 3D printing,” Additive Manufacturing, vol. 59, article 103094, 2022.
View at: Publisher Site | Google Scholar
S. Ramasamy, A. Karuppuchamy, J. J. Jayaraj, I. Suyambulingam, S. Siengchin, and S. Fischer, “Comprehensive characterization of novel Robusta (AAA) banana bracts fibers reinforced polylactic acid based biocomposites for lightweight applications,” Polymer Composites, vol. 43, no. 11, pp. 8569–8580, 2022.
View at: Publisher Site | Google Scholar
F. Jiang and D. Drummer, “Analysis of UV curing strategy on reaction heat control and part accuracy for additive manufacturing,” Polymers, vol. 14, no. 4, p. 759, 2022.
View at: Publisher Site | Google Scholar
M. M. Zerankeshi, R. Bakhshi, and R. Alizadeh, “Polymer/metal composite 3D porous bone tissue engineering scaffolds fabricated by additive manufacturing techniques: a review,” Bioprinting, vol. 25, article e00191, 2022.
View at: Publisher Site | Google Scholar
A. Pajonk, A. Prieto, U. Blum, and U. Knaack, “Multi-material additive manufacturing in architecture and construction: a review,” Journal of Building Engineering, vol. 45, article 103603, 2022.
View at: Publisher Site | Google Scholar
V. Mishra, S. Negi, S. Kar, A. K. Sharma, Y. N. K. Rajbahadur, and A. Kumar, “Recent advances in fused deposition modeling based additive manufacturing of thermoplastic composite structures: a review,” p. 089270572211028, 2022.
View at: Publisher Site | Google Scholar
R. Somasundaram, R. Rajamoni, I. Suyambulingam, D. Divakaran, S. Mavinkere Rangappa, and S. Siengchin, “Utilization of discarded Cymbopogon flexuosus root waste as a novel lignocellulosic fiber for lightweight polymer composite application,” Polymer Composites, vol. 43, no. 5, pp. 2838–2853, 2022.
View at: Publisher Site | Google Scholar
L. Di and Y. Yang, “Towards closed-loop material flow in additive manufacturing: Recyclability analysis of thermoplastic waste,” Journal of Cleaner Production, vol. 362, article 132427, 2022.
View at: Publisher Site | Google Scholar
M. Chandra, F. Shahab, K. E. K. Vimal, and S. Rajak, “Selection for additive manufacturing using hybrid MCDM technique considering sustainable concepts,” Rapid Prototyping Journal, vol. 28, no. 7, pp. 1297–1311, 2022.
View at: Publisher Site | Google Scholar
Y. Abderrafai, M. Hadi, F. Sosa-rey et al., “Additive manufacturing of short carbon fiber-reinforced polyamide composites by fused filament fabrication: formulation, manufacturing and characterization,” Materials & Design, vol. 214, article 110358, 2022.
View at: Publisher Site | Google Scholar
J. Logeshwaran, “AICSA - an artificial intelligence cyber security algorithm for cooperative P2P file sharing in social networks. ICTACT journal on data science and machine,” Learning, vol. 3, no. 1, pp. 251–253, 2021.
View at: Google Scholar
G. S. Sivagnanamani, S. R. Begum, R. Siva, and M. S. Kumar, “Experimental investigation on influence of waste egg Shell particles on polylactic acid matrix for additive manufacturing application,” Journal of Materials Engineering and Performance, vol. 31, no. 5, pp. 3471–3480, 2022.
View at: Publisher Site | Google Scholar
H. Sonar, V. Khanzode, and M. Akarte, “Additive manufacturing enabled supply chain management: a review and research directions,” Vision, vol. 26, no. 2, pp. 147–162, 2022.
View at: Publisher Site | Google Scholar
A. Mecheter, S. Pokharel, and F. Tarlochan, “Additive manufacturing technology for spare parts application: a systematic review on supply chain management,” Applied Sciences, vol. 12, no. 9, p. 4160, 2022.
View at: Publisher Site | Google Scholar
M. K. Jha, S. Gupta, V. Chaudhary, and P. Gupta, “Material selection for biomedical application in additive manufacturing using TOPSIS approach,” Materials Today: Proceedings, vol. 62, pp. 1452–1457, 2022.
View at: Publisher Site | Google Scholar
Ž. Stević, S. Miškić, D. Vojinović, E. Huskanović, M. Stanković, and D. Pamučar, “Development of a model for evaluating the efficiency of transport companies: PCA–DEA–MCDM model,” Axioms, vol. 11, no. 3, p. 140, 2022.
View at: Publisher Site | Google Scholar
X. Wang, C. Zhang, J. Deng, C. Su, and Z. Gao, “Analysis of factors influencing miners’ unsafe behaviors in intelligent mines using a novel hybrid MCDM model,” International Journal of Environmental Research and Public Health, vol. 19, no. 12, p. 7368, 2022.
View at: Publisher Site | Google Scholar
A. I. Yoris-nobile, E. Lizasoain-arteaga, E. Blanco-fernandez, and S. Alonso-cañon, “Life cycle assessment (LCA) and multi-criteria decision-making (MCDM) analysis to determine the performance of 3D printed cement mortars and geopolymers,” Journal of Sustainable Cement-Based Materials, vol. 11, pp. 1–18, 2022.
View at: Publisher Site | Google Scholar
J. Logeshwaran, M. J. Rex, T. Kiruthiga, and V. A. Rajan, “FPSMM: fuzzy probabilistic based semi morkov model among the sensor nodes for realtime applications,” in 2017 International Conference on Intelligent Sustainable Systems (ICISS), pp. 442–446, Palladam, India, 2017.
View at: Publisher Site | Google Scholar
C. N. Wang, T. T. Dang, N. A. T. Nguyen, and J. W. Wang, “A combined data envelopment analysis (DEA) and Grey based multiple criteria decision making (G-MCDM) for solar PV power plants site selection: a case study in Vietnam,” Energy Reports, vol. 8, pp. 1124–1142, 2022.
View at: Publisher Site | Google Scholar
Z. C. Wang, Y. Ran, Y. Chen, X. Yang, and G. Zhang, “Group risk assessment in failure mode and effects analysis using a hybrid probabilistic hesitant fuzzy linguistic MCDM method,” Expert Systems with Applications, vol. 188, article 116013, 2022.
View at: Publisher Site | Google Scholar
M. R. Sanjay, S. Siengchin, J. Parameswaranpillai, M. Jawaid, C. I. Pruncu, and A. Khan, “A comprehensive review of techniques for natural fibers as reinforcement in composites: preparation, processing and characterization,” Carbohydrate Polymers, vol. 207, pp. 108–121, 2019.
View at: Publisher Site | Google Scholar
S. Inti and V. Tandon, “Application of fuzzy preference-analytic hierarchy process logic in evaluating sustainability of transportation infrastructure requiring multicriteria decision making,” Journal of Infrastructure Systems, vol. 23, no. 4, article 04017014, 2017.
View at: Publisher Site | Google Scholar
S. Zecevic, S. Tadic, and M. Krstic, “Intermodal transport terminal location selection using a novel hybrid MCDM model,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 6, pp. 853–876, 2017.
View at: Publisher Site | Google Scholar
J. Jasmine, N. Yuvaraj, and J. Logeshwaran, “DSQLR-A distributed scheduling and QoS localized routing scheme for wireless sensor network,” Recent Trends in Information Technology and Communication for Industry, vol. 4, pp. 47–60, 2022.
View at: Google Scholar
D. S. Pamucar, S. P. Tarle, and T. Parezanovic, “New hybrid multi-criteria decision-making DEMATEL-MAIRCA model: sustainable selection of a location for the development of multimodal logistics centre,” Economic Research-Ekonomska istraživanja, vol. 31, no. 1, pp. 1641–1665, 2018.
View at: Publisher Site | Google Scholar
D. Baric, H. Pilko, and J. Strujic, “An analytic hierarchy process model to evaluate road section design,” Transport, vol. 31, no. 3, pp. 312–321, 2016.
View at: Publisher Site | Google Scholar
A. Awasthi, S. S. Chauhan, and H. Omrani, “Application of fuzzy TOPSIS in evaluating sustainable transportation systems,” Expert Systems with Applications, vol. 38, no. 10, pp. 12270–12280, 2011.
View at: Publisher Site | Google Scholar
R. K. Mavi, M. Goh, and N. Zarbakhshnia, “Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry,” International Journal of Advanced Manufacturing Technology, vol. 91, no. 5-8, pp. 2401–2418, 2017.
View at: Publisher Site | Google Scholar
S. Jones, M. Tefe, and S. Appiah-Opoku, “Proposed framework for sustainability screening of urban transport projects in developing countries: a case study of Accra, Ghana,” Transportation Research Part A: Policy and Practice, vol. 49, pp. 21–34, 2013.
View at: Publisher Site | Google Scholar
H. Wang, Z. G. Jiang, H. Zhang, Y. Wang, Y. H. Yang, and Y. Li, “An integrated MCDM approach considering demands-matching for reverse logistics,” Journal of Cleaner Production, vol. 208, pp. 199–210, 2019.
View at: Publisher Site | Google Scholar
A. Formisano and F. M. Mazzolani, “On the selection by MCDM methods of the optimal system for seismic retrofitting and vertical addition of existing buildings,” Computers and Structures, vol. 159, pp. 1–13, 2015.
View at: Publisher Site | Google Scholar
P. O. Akadiri, P. O. Olomolaiye, and E. A. Chinyio, “Multi-criteria evaluation model for the selection of sustainable materials for building projects,” Automation in Construction, vol. 30, pp. 113–125, 2013.
View at: Publisher Site | Google Scholar
J. Logeshwaran, “The topology configuration of protocol-based local networks in high speed communication networks,” Multidisciplinary Approach in Research, vol. 15, pp. 78–83, 2022.
View at: Google Scholar
Z. Stevic, D. Pamucar, M. Subotic, J. Antucheviciene, and E. K. Zavadskas, “The location selection for roundabout construction using rough BWM-rough WASPAS approach based on a new rough Hamy aggregator,” Sustainability, vol. 10, no. 8, p. 2817, 2018.
View at: Publisher Site | Google Scholar
M. Marzouk and L. Elmesteckawi, “Analyzing procurement route selection for electric power plants projects using SMART,” Journal of Civil Engineering and Management, vol. 21, no. 7, pp. 912–922, 2015.
View at: Publisher Site | Google Scholar
Y. T. Birgani and F. Yazdandoost, “An integrated framework to evaluate resilient-sustainable urban drainage management plans using a combined-adaptive MCDM technique,” Water Resources Management, vol. 32, no. 8, pp. 2817–2835, 2018.
View at: Publisher Site | Google Scholar
S. Akhtar, B. Reza, K. Hewage, A. Shahriar, A. Zargar, and R. Sadiq, “Life cycle sustainability assessment (LCSA) for selection of sewer pipe materials,” Clean Technologies and Environmental Policy, vol. 17, no. 4, pp. 973–992, 2015.
View at: Publisher Site | Google Scholar
W. H. Tsai, S. J. Lin, Y. F. Lee, Y. C. Chang, and J. L. Hsu, “Construction method selection for green building projects to improve environmental sustainability by using an MCDM approach,” Journal of Environmental Planning and Management, vol. 56, no. 10, pp. 1487–1510, 2013.
View at: Publisher Site | Google Scholar
A. De la Fuente, O. Pons, A. Josa, and A. Aguado, “Multi-criteria decision making in the sustainability assessment of sewerage pipe systems,” Journal of Cleaner Production, vol. 112, pp. 4762–4770, 2016.
View at: Publisher Site | Google Scholar
V. Palevicius, G. M. Paliulis, J. Venckauskaite, and B. Vengrys, “Evaluation of the requirement for passenger car parking spaces using multi-criteria methods,” Journal of Civil Engineering and Management, vol. 19, no. 1, pp. 49–58, 2013.
View at: Publisher Site | Google Scholar
D. Pamucar, I. Badi, K. Sanja, and R. Obradovic, “A novel approach for the selection of power-generation technology using a linguistic neutrosophic CODAS method: a case study in Libya,” Energies, vol. 11, no. 9, p. 2489, 2018.
View at: Publisher Site | Google Scholar
D. Pamucar, L. Gigovic, Z. Bajic, and M. Janosevic, “Location selection for wind farms using GIS multi-criteria hybrid model: an approach based on fuzzy and rough numbers,” Sustainability, vol. 9, no. 8, p. 1315, 2017.
View at: Publisher Site | Google Scholar
H. Z. Al Garni and A. Awasthi, “Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia,” Applied Energy, vol. 206, pp. 1225–1240, 2017.
View at: Publisher Site | Google Scholar
J. M. Sanchez-Lozano, M. S. Garcia-Cascales, and M. T. Lamata, “GIS-based onshore wind farm site selection using fuzzy multi-criteria decision making methods. Evaluating the case of southeastern Spain,” Applied Energy, vol. 171, pp. 86–102, 2016.
View at: Publisher Site | Google Scholar
Y. N. Wu, C. B. Xu, and T. Zhang, “Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: a case in China,” Energy, vol. 147, pp. 1227–1239, 2018.
View at: Publisher Site | Google Scholar
P. Skobalj, M. Kijevcanin, N. Afgan, M. Jovanovic, V. Turanjanin, and B. Vucicevic, “Multi-criteria sustainability analysis of thermal power plant Kolubara-A unit 2,” Energy, vol. 125, pp. 837–847, 2017.
View at: Publisher Site | Google Scholar
A. Fallahpour, E. U. Olugu, S. N. Musa, K. Y. Wong, and S. Noori, “A decision support model for sustainable supplier selection in sustainable supply chain management,” Computers and Industrial Engineering, vol. 105, pp. 391–410, 2017.
View at: Publisher Site | Google Scholar
H. R. Zhao and S. Guo, “Selecting green supplier of thermal power equipment by using a hybrid MCDM method for sustainability,” Sustainability, vol. 6, no. 1, pp. 217–235, 2014.
View at: Publisher Site | Google Scholar
J. Logeshwaran, N. Adhikari, S. S. Joshi, P. Saxena, and A. Sharma, “Deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing,” International Journal of Health Sciences, vol. 6, no. S5, pp. 9364–9375, 2022.
View at: Publisher Site | Google Scholar
S. Luthra, K. Govindan, D. Kannan, S. K. Mangla, and C. P. Garg, “An integrated framework for sustainable supplier selection and evaluation in supply chains,” Journal of Cleaner Production, vol. 140, pp. 1686–1698, 2017.
View at: Publisher Site | Google Scholar
J. F. F. Barata, O. L. G. Quelhas, H. G. Costa, R. H. Gutierrez, V. D. Lameira, and M. J. Meirino, “Multi-criteria indicator for sustainability rating in suppliers of the oil and gas industries in Brazil,” Sustainability, vol. 6, no. 3, pp. 1107–1128, 2014.
View at: Publisher Site | Google Scholar
N. Nag, M. Chandra, K. H. Kazmi, A. Shukla, and S. K. Sharma, “Selection of suitable powder bed fusion technique for medical applications using MCDM techniques,” https://ssrn.com/abstract=4192942.
View at: Google Scholar
C. M. Sivaraja and G. Sakthivel, “Compression ignition engine performance modelling using hybrid MCDM techniques for the selection of optimum fish oil biodiesel blend at different injection timings,” Energy, vol. 139, pp. 118–141, 2017.
View at: Publisher Site | Google Scholar
A. Debnath, J. Roy, S. Kar, E. K. Zavadskas, and J. Antucheviciene, “A hybrid MCDM approach for strategic project portfolio selection of agro by-products,” Sustainability, vol. 9, no. 8, p. 1302, 2017.
View at: Publisher Site | Google Scholar
C. Y. Huang, P. H. Chung, J. Z. Shyu et al., “Evaluation and selection of materials for particulate matter MEMS sensors by using hybrid MCDM methods,” Sustainability, vol. 10, no. 10, p. 3451, 2018.
View at: Publisher Site | Google Scholar
H. Alwaer and D. J. Clements-Croome, “Key performance indicators (KPIs) and priority setting in using the multi- attribute approach for assessing sustainable intelligent buildings,” Building and Environment, vol. 45, no. 4, pp. 799–807, 2010.
View at: Publisher Site | Google Scholar
E. K. Zavadskas, R. Bausys, and M. Lazauskas, “Sustainable assessment of alternative sites for the construction of a waste incineration plant by applying WASPAS method with single-valued neutrosophic set,” Sustainability, vol. 7, no. 12, pp. 15923–15936, 2015.
View at: Publisher Site | Google Scholar
N. R. Khalili and S. Duecker, “Application of multi-criteria decision analysis in design of sustainable environmental management system framework,” Journal of Cleaner Production, vol. 47, pp. 188–198, 2013.
View at: Publisher Site | Google Scholar
S. Mohammad, D. Tezerjani, M. S. Yazdi, and M. H. Hosseinzadeh, “The effect of 3D printing parameters on the shape memory properties of 4D printed polylactic acid circular disks: an experimental investigation and parameters optimization,” Materials Today Communications, vol. 33, article 104262, 2022.
View at: Publisher Site | Google Scholar
J. C. Kurnia, L. A. F. Haryoko, I. Taufiqurrahman, L. Chen, L. Jiang, and A. P. Sasmito, “Optimization of an innovative hybrid thermal energy storage with phase change material (PCM) wall insulator utilizing Taguchi method,” Journal of Energy Storage, vol. 49, article 104067, 2022.
View at: Publisher Site | Google Scholar
G. Yang, Y. Tao, P. Wang, X. Xu, and X. Zhu, “Optimizing 3D printing of chicken meat by response surface methodology and genetic algorithm: feasibility study of 3D printed chicken product,” LWT, vol. 154, article 112693, 2022.
View at: Publisher Site | Google Scholar
R. Vijay, J. D. James Dhilip, S. Gowtham et al., “Characterization of natural cellulose fiber from the barks ofVachellia farnesiana,” Journal of Natural Fibers, vol. 19, no. 4, pp. 1343–1352, 2022.
View at: Publisher Site | Google Scholar
B. Sampath, N. Naveenkumar, P. Sampathkumar, P. Silambarasan, A. Venkadesh, and M. Sakthivel, “Experimental comparative study of banana fiber composite with glass fiber composite material using Taguchi method,” Materials Today: Proceedings, vol. 49, pp. 1475–1480, 2022.
View at: Publisher Site | Google Scholar
M. Sutharasan and J. Logeshwaran, “Design intelligence data gathering and incident response model for data security using honey pot system,” International Journal for Research & Development in Technology, vol. 5, no. 5, pp. 310–314, 2016.
View at: Google Scholar
M. Jayasudha, M. Elangovan, M. Mahdal, and J. Priyadarshini, “Accurate estimation of tensile strength of 3D printed parts using machine learning algorithms,” Processes, vol. 10, no. 6, p. 1158, 2022.
View at: Publisher Site | Google Scholar
M. R. Sanjay, P. Madhu, M. Jawaid, P. Senthamaraikannan, S. Senthil, and S. Pradeep, “Characterization and properties of natural fiber polymer composites: a comprehensive review,” Journal of Cleaner Production, vol. 172, pp. 566–581, 2018.
View at: Publisher Site | Google Scholar
M. Liu, L. Tan, and S. Cao, “Performance prediction and geometry optimization for application of pump as turbine: a review,” Frontiers in Energy Research, vol. 9, pp. 1–16, 2022.
View at: Publisher Site | Google Scholar
M. Isametova, N. Dishovsky, P. Velev, and A. Duisengali, “Properties of glass-fiber reinforced polycarbonates for centrifugal pumps impellers,” Journal of Chemical Technology and Metallurgy, vol. 57, no. 2, pp. 224–231, 2022.
View at: Google Scholar
A. J. Humaidi, S. K. Kadhim, and A. S. Gataa, “Optimal adaptive magnetic suspension control of rotary impeller for artificial heart pump,” Cybernetics and Systems, vol. 53, no. 1, pp. 141–167, 2022.
View at: Publisher Site | Google Scholar
X. Song, H. Y. Li, Y. Li, and X. Luo, “The development of a high-speed miniature pump with dynamic bearing,” Journal of Physics: Conference Series, vol. 2217, no. 1, p. 012049, 2022.
View at: Publisher Site | Google Scholar
M. Isametova, R. Nussipali, D. Karaivanov, Z. Abilikhair, and A. Isametov, “Computational and experimental study of the composite material for the centrifugal pump impellers manufacturing,” Journal of Applied and Computational Mechanics, vol. 8, no. 4, pp. 2383–4536, 2022.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2023 S. Raja and A. John Rajan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

1103

Downloads

459

Citations