3D Printing High Temperature Materials - Magigoo

When it comes to FDM 3D printing, successfully printing with high temperature materials is still one of the most demanding prospects. These materials require printing temperatures which are often markedly higher than those of regular FDM materials such as PLA, ABS and PET-G. High temperature materials often have exceptional thermo-mechanical and chemical properties and are usually reserved for high end applications in the aerospace, automotive, healthcare, research and manufacturing industries

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High Temperature Materials

High temperature materials are often termed as such due to their high temperature performance. This is often a result of a stronger and more rigid chemical backbone which is less susceptible to viscoelastic deformation as the temperature increases. As a result these materials often retain their stiffness at elevated temperatures where commodity and engineering plastics such as ABS and Nylon tend to become soft and pliable. There are currently a number of high temperature thermoplastic materials available for FDM 3D printing, these include:

PEEK

PEEK (Polyether ether ketone) is one of the more popular high temperature FDM thermoplastic since it has a vast number of useful properties. It is highly resistant to heat and can maintain its mechanical properties at temperatures close to 260 °C. This material shows excellent mechanical properties with high stiffness and toughness values and is highly resistant to wear. In fact it is sometimes used to replace metal components. Additionally PEEK also shows excellent chemical resistance an is sterilizable which expands its applicability in high performance medical applications. Lastly PEEK also has excellent dielectric and flame resistant properties.

On the downside PEEK materials tend to cost significantly more than other FDM materials. PEEK is also a semi-crystalline material, thus extra care must be taken during the processing of this material depending on the intended application of the printed part.

PEKK

PEKK (Polyether ketone ketone) is quite similar to PEEK as a material however it exhibits a few key differences which might make it more attractive for certain applications. Although the rate of crystallization of PEKK can be controlled by altering the molecular structure of the material during synthesis, PEKK generally tends to have a slower crystallization rate and thus can be processed more easily as an amorphous material. As a result PEKK tends to exhibit improved layer adhesion and less warping when compared to PEEK.

PEKK also tends to have a wider process-ability window and can thus be printed on a wider array of machines which might not be able to print PEEK due to its higher temperature requirements. Furthermore apart from showing comparable mechanical and chemical properties as PEEK, PEKK also offers superior strength under compressive loads and higher heat resistance when compared to PEEK.

ULTEM &

ULTEM is the brand name for PEI (Polyetherimide) a high performance thermoplastic resin produced by SABIC. PEI also offers similar albeit slightly inferior properties to PEEK at a significantly lower price point. Both materials in fact show exceptional chemical resistance, strength and stiffness, long term heat resistance, fatigue resistance and dimensional stability. Nonetheless unlike PEEK, ULTEM is an amorphous material and thus tends to show lower heat resistance, even though it has a glass transition temperature of around 210 °C (ULTEM ).

Similarly to PEEK, ULTEM can also be sterilized and is autoclavable. Certain grades are also biocompatible and certified for food use. Furthermore ULTEM shows excellent chemical and thermal resistance.

ULTEM is another version of the ULTEM resin produced by SABIC available for FDM printing. At first glance ULTEM offers inferior chemical resistance and thermomechanical properties to ULTEM . In fact ULTEM has an HDT of around 153 °C which is around 50 °C less than that of ULTEM . Furthermore ULTEM exhibits lower strength and stiffness than ULTEM , although this makes the former material more forgiving than the latter. On the other hand ULTEM offers significantly increased toughness and impact resistance when compare to ULTEM .

PPSU/PPSF

PPSU/PPSF (Polyphenylsulfone) is high perfomance thermoplastic in the the PSU (Polysulfone) family. Polysulfones describes polymers containing a sulfonyl (O=S=O) moiety in their repeat unit. These thermoplastics often used in often used in specialty applications as a superior replacement for polycarbonates. Other polymers from the same family include PSU (polysulfone), PPS (polyphenyl sulfide) and PSF poly(bisphenol-A sulfone).

PPSU is an amorphous material having a very high glass transition temperature (>230 °C) and a heat deflection temperature around 190 °C making it ideal for high temperature applications. PPSU typically exhibits a high operating temperature (180°C), very high impact strength and high dimensional stability. The thermoplastic also shows very good chemical compatibility and a high resistance to hydrolysis. Interestingly PPSU also shows very high resistance to gamma radiation and can be sterilized using both autoclave and radiation techniques.

PVDF

PVDF (Polyvinyldene Flouride) is a highly inert thermoplastic related to PTFE (teflon). The fluorine atoms are very tightly bonded to the carbon atoms in the flouropolymer structure leading to the exceptional chemical and thermal resistance of this polymer. PVDF is compatible with a wide range of chemicals, including oil, gas, and lubricants, as well as halogenated hydrocarbons, alcohols, acids and bases. Furthermore PVDF can withstand nuclear radiation and is highly resistant to UV, gamma radiation and oxidation. PVDF also shows high mechanical strength and can be used continuously at temperatures up to 150 °C.

Table 1: Typical Mechanical properties of high temperature printing materials, source: 3dxtech.com

3D Printing High Temperature Materials

Since all of these materials have high melting point and high glass transition temperatures, more often than not specialized high temperature printers are required for a successful print. The table below shows the typical printing conditions required for printing various high temperature materials. These values reflect the general recommendations for printing high temperature materials. However the printing conditions will be highly dependent on other factors such as the printer, material, size and geometry of part and also the intended mechanical properties of the printed part, as discussed below.

Table 2: Typical printing conditions of high temperature printing materials

Heated chamber

An actively heated build-chamber is an absolute requirement for printing modestly sized parts with most high temperature materials. As a result of their high melting points and glass transition temperatures printing high temperature materials at ambient temperatures will lead to poor layer adhesion resulting in weakened parts. Furthermore a heated chamber will also not just help with ensuring maximum Z-strength but will also help reduce internal stresses and limit warping during printing. A heated chamber also helps increase the chances of a successful prints for larger sized parts.

Crystallinity

Semi-crystalline materials such as PEEK and PEKK are semi-crystalline, the total crystallinity of a printed part can be controlled by controlling the cooling rate of the extruded material. A slow cooling rate favors the formation of crystalline regions within a material. On the other hand parts which are cooled quickly tend to form an amorphous plastic.

Amorphous parts tend to have inferior chemical, fatigue and wear resistance but have better bonding characteristics and good form-ability. On the other hand crystalline parts tend to exhibit superior strength, wear resistance and chemical resistance albeit with poorer bonding characteristics. Lastly amorphous materials tend to be transparent while crystalline parts tend to be opaque.

Drying filament

Most high temperature materials also tend to absorb water whilst exposed to a humid environment. Thus extra care must be taken to store filaments appropriately and to dry them thoroughly when required. Filaments which have absorbed water tend to produce parts with inferior mechanical and inferior aesthetic properties.

Annealing

Parts printed in high temperature materials might significantly benefit from annealing. This process involves heating the part to a specified temperature and then cooling it at a controlled rate. Annealing serves to relieve internal stresses in amorphous plastics, but more importantly it can drastically increase the crystallinity in semi-crystalline materials leading to enhanced mechanical properties. This is especially significant for materials such as PEEK which are sometimes printed in the amorphous state, as this favors increased layer adhesion. Then the parts are annealed to increase crystallinity, since parts printed directly in the crystalline state tend to show poor layer bonding.

First Layer Adhesion

High temperature tribological applications play a vital role across industries like aerospace, power generation, manufacturing and automotive. In the automotive sector, tribological applications ensure the efficiency and safety of components such as engines components, brakes, and transmissions, etc., operating under extreme thermal conditions. However, these applications face challenges such as material degradation, friction, wear, oxidation, and thermal expansion variations. Components such as cylinders, pistons, and brake discs frequently reach operating temperatures of up to 300 °C. Therefore, materials used in these settings must possess exceptional thermal stability, wear resistance, and mechanical strength to ensure reliable performance and durability under extreme conditions [1].

To improve vehicle performance, reduce emissions, and enhance fuel efficiency, automakers are adopting lightweight materials. Advanced substitutes for steel and cast iron enable significant weight reduction, optimize costs, and promote recycling. This strategy improves fuel economy, braking efficiency, and crashworthiness, while meeting both structural and environmental sustainability goals [2]. For high-temperature tribological applications, achieving robust friction performance and minimizing wear is essential to ensure operational efficiency and long-term durability, especially under demanding thermal and mechanical conditions [3].

Therefore, aluminum alloys are extensively utilized in the automotive industry as lightweight materials [4], due to their high strength-to-weight ratio, excellent mechanical properties, and corrosion resistance [5]. Aluminum alloy serve as ideal substitutes for heavier materials like steel or copper, meeting the industry’s need for weight reduction [6]. Additionally, their excellent mechanical properties, recyclability, and eco-friendly nature make aluminum alloys versatile for various applications, including electric modules, automotive structures, and renewable energy systems, providing sustainable solutions across industries [7].

High-strength aluminum alloys, especially the 6xxx and 7xxx series, are gaining prominence in automotive applications for their superior strength-to-weight ratio, facilitating lighter and more efficient vehicle designs [8]. Among aluminum alloys, the 6xxx series alloys, primarily incorporating magnesium and silicon [9], stands out for its precipitation hardening capability, enhancing strength through heat treatment [10]. These alloys combine a high strength-to-density ratio, corrosion resistance, affordability, ease production [11], excellent thermal and electrical conductivity, formability, and weldability [12, 13] makes them essential in military vehicles, rockets, missiles, aircraft, and automobiles for both defense and civilian uses [14].

The demand for lightweight and eco-friendly materials in automotive and aerospace industries has increased the need for heat-resistant aluminum alloys. Although these alloys perform well under normal conditions, their mechanical properties degrade significantly at elevated temperatures (200–300 °C or higher), limiting their use in high-performance applications [15]. Aluminum alloys’ thermal instability, influenced by service temperature and duration, remains a key challenge [16], necessitating strategies to overcome these limitations in high-temperature tribological applications.

Ujah and Von Kallon () emphasize that although monolithic aluminum alloys possess valuable properties for aerospace, automotive, and other sectors, their low strength and wear resistance restrict their use in high temperature working condition. However, the development of aluminum metal matrix composites (AMMCs) with added reinforcements has successfully overcome these challenges [17]. This has driven research towards composite materials as lightweight and efficient solutions for various applications [18].

AMMCs outperform other metal matrix composites (MMCs) in terms of performance [19]. AMMCs are advanced engineering materials that have high hardness, significant thermal conductivity, attractive yielding strength, beneficial strength-to-weight ratio, low coefficient of thermal expansion and outstanding wear resistance. These properties make AMMCs ideal for enhancing performance and durability across diverse engineering applications [20]. AMMCs are essential for producing lightweight structural components [21], due to their superior properties such as low density, high strength-to-weight ratio, excellent corrosion and wear resistance, and reliable high-temperature performance have garnered significant attention across industries [22]. AMMCs, with their superior strength-to-weight ratio and excellent mechanical and tribological properties, are extensively used in biomedical, aerospace, automotive, and marine sectors. Their performance depends on the type and quantity of reinforcement added to the base material [23, 24]. Furthermore, aluminum alloy composites are ideal for automotive parts like valves, pistons, and cylinder linings, where wear resistance is critical [25]. This combination of properties continues to drive their adoption in engineering and industrial applications.

Numerous studies highlight the effectiveness of AMMCs in high-temperature tribological applications. Babalola et al. [26] highlighted the importance of brake pads for vehicle safety and performance. They suggested a composite of aluminum reinforced with silicon carbide, alumina, zinc, and calcium via stir casting, consisting of 70% aluminum, 20% SiC, and 10% Al2O3, as a superior alternative to conventional brake pads. Anoop et al. [27] studied the dry sliding wear behaviour of AA–SiC composite for brake pad application. Agbeleye et al. [28] investigated aluminum alloy–clay (Al–clay) composites for brake pad applications. According to Dinesh Kumar and Darius Gnanaraj [29] automotive brake discs significantly contribute to vehicle weight, making lightweight aluminum discs a promising alternative to traditional cast iron or steel. Although monolithic aluminum lacks the necessary temperature and wear resistance, AMMCs provide a viable solution. AMMC brake discs resist warping, improve heat dissipation, reduce brake fade, and extend brake pad life. Tan et al. [30] fabricated lightweight hybrid composite brake discs using friction-stir processing, combining an A357/SiC top layer with an AA aluminum alloy base. These discs exhibit excellent wear and friction performance, with thermally stable SiC particles enhancing wear resistance, particularly at high temperatures. Their superior performance demonstrates their readiness to replace heavier iron and steel brake discs. Vinoth Babu et al. [31] successfully fabricated an Al–SiC composite brake rotor disc using the centrifugal casting technique. Baig et al. [32] highlighted that the AA–Al2O3 composite offers superior wear resistance and enhanced friction stability, making it an excellent material for brake disc applications. Piston failure, often caused by mechanical and thermal loads, can be costly to repair or replace. Therefore, selecting the most suitable material for pistons is essential. According to Rashmita et al. [33] the AA–B4C–SiC composite is considered the ideal material for pistons due to its superior properties. Additionally, Siva et al. [34] studied spur gears are made from AA composite with varying weight percentages of titanium carbide (TiC) with a grain size of 40 µm. Previous studies demonstrate that AMMCs are highly effective in high-temperature tribological applications, such as brake pads, discs, pistons, and gears, owing to their excellent thermal, mechanical, and wear-resistant properties. However, to provide enhanced product output and functionality, the right material selection is crucial.

Selection of the right materials for high temperature tribological application is critical for ensuring excellent working performance, durability, noise reduction, and environmental friendly. However, the common reliance on trial and error by manufacturers for material selection introduces challenges like high costs, time inefficiencies, narrow focus, inefficient resource management, lack of systematic approaches, subpar decisions, and limited knowledge transfer [35]. These challenges stem from the intricate nature of multi-criteria decision-making (MCDM) problems, given the extensive material options and varied criteria involved [36]. To address these complexities, a systematic, efficient, and logical approach is essential to streamline material selection decisions. MCDM procedures provide an organized approach to selecting materials based on multiple criteria [37]. Therefore, adopting a methodical and effective strategy is crucial for identifying the best material alternatives for high temperature tribological application.

Researchers have widely applied MCDM methods for various optimization purposes across different fields. Satapathy et al. [38] utilized the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) technique to achieve optimal design of friction materials in fly ash-reinforced composites. Tidjani et al. [39] suggested the use of the Vlse kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), AHP, and TOPSIS approaches for appropriate material selection in the manufacturing process of micro Pelton turbine buckets. Panda et al. [40] employed the Analytic Hierarchy Process—Complex Proportional Assessment (AHP-COPRAS) and Analytic Hierarchy Process—Evaluation based on Distance from Average Solution (AHP-EDAS) methodologies to improve the performance of aluminum/rice husk and ash aluminum/fly ash and metal matrix composites by optimizing abrasive wear loss. Maity and Chakraborty [41] applied Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) to address material selection problem of tool steel. Zhu et al. [42] employed AHP and PROMETHEE to optimize the non-metallic friction material reinforced with aramid fiber and CaSO4 whisker tribological properties. Goswami et al. [43] utilized the entropy-TOPSIS decision-making techniques to identify the best AISI steel grades and the appropriate heat treatment process for them. Subba and Shabbiruddin [44] employed the TFN (Triangular Fuzzy Number) in conjunction with the COPRAS (Complex Proportional Assessment) or Fuzzy- COPRAS technique to achieve optimal harnessing of solar energy by selecting the appropriate phase changing material. Khargotra et al. [45] applied the Best Worst Method—Compromise Solution (BWM—CODAS) technique to optimize the design parameters of V-shaped perforated blocks in a rectangular duct for a solar air heater. Bachchhav et al. [46] used AHP, TOPSIS, and Simple Additive Weighting (SAW) techniques for the selection of spot welding electrode materials. Singh [47] utilized the Criteria Importance Through Intercriteria Correlation—Multi-Expression Programming Weighting (CRITIC-MEW) approach to achieve optimum design in the context of manufactured natural fiber reinforced automotive brake friction composites. Jahan et al. () focused on selecting composite materials for automotive brake pads. They determined the criteria weights using the Linear Goal Programming Model for Best Worst-Method (LGPMBWM) and ranked using various MCDM techniques, including CoCoSo (Combined Compromise Solution), WPM (Weighted Product Method), WSM (Weighted Sum Method), PIV (Proximity Indexed Value), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and MABAC (Multi-Attributive Border Approximation Area Comparison) [48]. Kassa et al. [49] employed CoCoSo technique to identify and select airfoil designs that best meet the specific performance and operational requirements of small wind turbine. Bulut et al. () conducted a study on selecting thermoplastic materials for hybrid vehicle battery packs in the automotive industry. The research focused on identifying materials that improve battery efficiency, lifespan, and overall vehicle performance, using methods such as SWARA (step wise weight assessment ratio analysis)-ARAS (additive ratio assessment), SWARA-EDAS, and SWARA-TOPSIS [50]. Dev et al. [51] conducted a study on material selection for automotive pistons using the Entropy-VIKOR, which focuses on optimizing and balancing various criteria.

According to Hassan et al. (), 6xxx series aluminum alloy-based metal matrix composites are increasingly being used in industries such as marine, aerospace, and automotive. The wide range of available ceramic particles and hybrid reinforcement combinations enhance their versatility, offering superior properties and broad application potential [52]. Due to its low density, high strength, low electrical resistance, good corrosion resistance, and superior machinable qualities, AA is the most widely used matrix material [53]. With its low cost of AA aluminum alloy is an excellent choice as a matrix material due to high corrosion resistance, exceptional surface finish capabilities, and excellent extrudability and weldability [54]. The medium-strength, heat-treatable alloy AA is renowned for its superior formability and strong resistance to corrosion [55]. In automotive engineering, choosing the right matrix materials for high-temperature tribological components is essential for ensuring performance and durability, and environmental impact. This research aims to explore the use of MCDM techniques, Entropy -TOPSIS and COCOSO to identify the optimal matrix material for high-temperature tribological application, with an emphasis on the aluminum 6xxx series known for its strength and corrosion resistance. By analyzing the properties of 6xxx materials, this study seeks to improve high-temperature tribological application in the automotive sector. The evaluation criteria and alternative materials are identified for optimum matrix material selection. The major evaluation criteria which have weightage for the appropriate material selection are: hardness, ultimate tensile strength, thermal conductivity, and melting point, coefficient of friction, wear rate, and thermal degradation. The alternative materials: Aluminum alloy (AA), Aluminum alloy (AA), and Aluminum alloy (AA). The properties of aluminum alloy 6xxx series are indicated in the Table 1 (Fig. 1).

3.1 Applying MCDM technique for selecting material for high temperature tribological application

To evaluate different materials for high temperature tribological application, a MCDM approach was implemented. This method involves assigning weights to selected criteria and ranks alternatives based on their computed final scores. The entropy method was employed to determine the relative importance of each criterion, ensuring an objective weighting process.

3.1.1 Procedure for determining weight of alternatives through entropy method

Assigning accurate weights to criteria is a crucial yet challenging aspect of multi-criteria decision-making (MCDM). The entropy method offers an effective solution by objectively calculating weights based on data variability, ensuring unbiased rankings. As one of the earliest objective approaches in MCDM, the entropy method is highly versatile and widely applicable across different decision-making scenarios, thanks to its data-driven and impartial nature [68]. The entropy method was employed to assess the weight of criteria. The process for determining attribute weights using the entropy method is outlined as follows [69].

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Step 1: Normalization of performance indices in the decision matrix to obtain the project outcomes pij:

$$P_{ij} = \frac{{x_{ij} }}{{\mathop \sum \nolimits_{i = 1}^{m} x_{ij} }}$$ (1)

where xij is the performance value of the i-th alternative under the j-th criteria.

Step 2: Computation of the entropy measures of project outcoes using the following equation:

$$E_{j} = - k\mathop \sum \limits_{i = 1}^{m} p_{ij} {\text{ln}}\left( {p_{ij} } \right)$$ (2)

where k = 1/ln (m) and m is the number of alternatives. L is natural algorithm base, Pij is normalized project outcome.

Step 3: Define the objective weight (wj) based on the entropy concept:

$$w_{j} = \frac{{1 - E_{j} }}{{\mathop \sum \nolimits_{j = 1}^{n} \left( {1 - E_{j} } \right)}}$$ (3)

This ensure that the sum weight to 1.

3.1.2 TOPSIS method

After determining the weight of each criterion, the TOPSIS method was applied to select the most suitable materials. The TOPSIS model is well-suited for solving multi-criteria material selection problems because it enables clear trade-offs and interactions between attributes. In this approach, a change in one attribute can be directly balanced or offset by changes in other attributes, making it highly effective for comprehensive decision-making [70]. The TOPSIS, introduced by Hwang and Yoon in , has evolved into a classic method for MCDM [71]. TOPSIS was utilized to determine the best option utilizing the compromise solution approach. The compromise solution can be defined as selecting the solution with the shortest Euclidean distance from the ideal solution and the farthest Euclidean distance from the negative ideal solution. The steps involved in TOPSIS are as follows [72].

Step 1: Define the decision criteria and alternative using Eq. (4).

$$x = \left[ {\begin{array}{*{20}c} {x11} & {x12} & \cdots & {x1n} \\ {x21} & {x22} & \cdots & {x2n} \\ \vdots & \vdots & {} & \vdots \\ {xm1} & {xm2} & \cdots & {xmn} \\ \end{array} } \right]$$ (4)

Step 2: The choice matrix has been normalized using Eq. (5).

$$X_{ij}^{*} = \frac{{X_{ij} }}{{\sqrt {\mathop \sum \nolimits_{j = 1}^{n} \left( {X_{ij} } \right)^{2} } }}$$ (5)

Step 3: The weighted normalized decision matrix using Eq. (6). Where wj is the weight of the jth criteria, where i = 1, 2, 3, … m, and j = 1, 2, 3, …, n and and \(\sum\nolimits_{j = 1}^{n} {wj = 1}\)

$$v_{ij = } w_{j} x_{ij}^{*}$$ (6)

Step 4: Find out the ideal best and ideal worst. In this paper the criteria for thermal conductivity, the maximum value is the ideal worst, and the minimum value is the ideal best value. For hardness, tensile strength, and melting point, the maximum value is the ideal best value, and the minimum value is the ideal worst value.

Step 5: Euclidean separation distance for each material type. Equation (7) was used to calculate the Euclidean distance from the ideal best solution.

Where \({\text{The }}\,{\text{Euclidean}}\,{\text{distance}}\,{\text{from}}\,{\text{the}}\,{\text{ideal}}\,{\text{best}}\,{\text{is}}\,{\text{S}}_{{\text{i}}}^{ + }\)

$$S_{i}^{ + } = \sqrt {\mathop \sum \limits_{j = 1}^{m} \left( {v _{ij} - v_{j}^{ + } } \right) ^{2} }$$ (7)

Euclidean distance from the ideal worst calculated using Eq. (8).

$$S _{i}^{ - } = \sqrt {\mathop \sum \limits_{j = 1}^{m} \left( {v _{ij} - v_{ j}^{ - } } \right)^{2} }$$ (8)

Step 6: Performance score calculated using Eq. (9).

$$P_{i} = \frac{{S _{i}^{ - } }}{{S _{i}^{ + } + S_{ i}^{ - } }}$$ (9)

3.1.3 CoCoSo method

After determining the weight of each criteria, the CoCoSo method developed by Yazdani et al. [73] was applied to select the most suitable materials. This method combines simple additive weighting (SAM) and exponentially weighted product model (WPM) to identify compromise solution that balance the trade-offs among various criteria [56]. The CoCoSo method aims to find an optimal alternative by considering both additive and multiplicative approaches, ensuring a more comprehensive evaluation of the materials. The method follows several key steps to integrate these approaches and reach the best possible decision.

Step 1: Formulating the initial decision matrix xij as follows:

$$Xij = \left[ {\begin{array}{*{20}c} {X11} & {X12} & \cdots & {X1n} \\ {X21} & {X22} & \cdots & {X2n} \\ \vdots & \vdots & {} & \vdots \\ {Xm1} & {Xm2} & \cdots & {Xmn} \\ \end{array} } \right];\quad {\text{i}} = 1,2, \ldots ,{\text{m}}\;{\text{and}}\;{\text{j}} = 1,2, \ldots {\text{n}}$$ (10)

where Xij is the performance value of i-th alternative with j-th criteria, m and n are the number of alternatives and criterion respectively.

Step 2: Normalize the criteria values according to Zelany [74] compromise normalization method (i.e. Equations 11, 12) to make the element of the decision matrix dimensionless and comparable.

$$r_{ij} = \frac{{x_{ij} - {\text{min}}\left( {x_{ij} } \right)}}{{\max \left( {x_{ij} } \right) - {\text{min}}\left( {x_{ij} } \right) }};\quad{\text{For}}\,{\text{benefit}}\,{\text{criteria}}$$ (11) $$r_{ij} = \frac{{{\text{max}}\left( {x_{ij} } \right) - x_{ij} }}{{\max \left( {x_{ij} } \right) - {\text{min}}\left( {x_{ij} } \right) }};\quad{\text{For}}\,{\text{non - benefit}}\,{\text{criteria}}$$ (12)

where rij and xij respectively represents the normalized and original value of the performance of alternative i with respect to criteria j.

Step 3: Based on the WSM and WPM methods, corresponding performance indices Si and Pi for each of alternative are evaluated using Eqs. (13) and (14).

$$Si = \mathop \sum \limits_{j = 1}^{n} \left( {r_{ij} \,x \,w_{j} } \right)$$ (13) $$Pi = \sum\limits_{j = 1}^{n} {\left[ {(r_{ij} )^{wj} } \right]}$$ (14)

Step 4: Determine the three different appraisal scores for the alternatives based on the following aggregation strategies.

$$k_{ia} = \frac{{s_{i} + p_{i} }}{{\mathop \sum \nolimits_{j = 1}^{n} \left( {s_{i} + p_{i} } \right)}}$$ (15) $$k_{ib} = \frac{{s_{i} }}{{\min s_{i} }} + \frac{{p_{i} }}{{\min p_{i} }}$$ (16) $$k_{ic} = \frac{{\lambda \left( {s_{i} } \right) + \left( {1 - \lambda } \right)\left( {p_{i} } \right)}}{{\left( {\lambda x max\left( {s_{i} } \right) + \left( {1 - \lambda } \right) x max\left( {p_{i} } \right)} \right)}};\quad 0 \le \lambda \le 1$$ (17)

Step 5: Evaluate the ranking of alternatives Ki based on Eq. (18).

$$k_{i} = \left( {x_{ia} k_{ib} k_{ic} } \right)^{1/3} + \frac{1}{3}\left( {k_{ia} + k_{ib} + k_{ic} } \right)$$ (18)

The 6xxx series aluminum alloys (Al–Mg–Si) consist of a group of aluminum alloys that primarily contain magnesium and silicon as the main alloying elements have been identified as recyclable materials. One of the notable characteristics of 6xxx series alloys is their precipitation hardening ability. Through heat treatment processes, the alloying elements form fine precipitates that enhance the mechanical properties and further improve the strength of the material. 6xxx alloys, which are distinguished by their remarkable strength-to-weight ratio, go through a number of processes after direct chill (DC) casting. Hot working, cold working, process annealing, and age-hardening heat treatments are some of the techniques used to homogenize and further process them. All of these procedures improve the 6xxx series alloys’ mechanical qualities, which makes them essential for a variety of industrial uses. One of the most important lightweight materials for reducing energy consumption and CO2 emissions in the automobile sector is the 6xxx alloy [75]. Aluminum-magnesium-silicon alloys are extensively used across various industries including aerospace, defense, railway, automotive, aircraft, marine, shipbuilding, and construction. This popularity stems from their lightweight nature, good thermal and electrical conductivity, flexibility, affordability, excellent strength-to-weight ratio, exceptional corrosion resistance, low density, excellent formability, and weldability, ease of fabrication, and favorable physical properties.

The aim is to employ Entropy for determining criteria weights and TOPSIS and CoCoSo methods ranking the alternatives. Table 1 displays the decision matrix for this matter, presenting three altenatives against eight decision criteria.

4.1 Attribute weight calculation

Determining attribute weights is a critical precursor in the decision making process prior to ranking the alternatives.

4.1.1 Entropy weight method

In the entropy weighting method, the decision matrix was normalized using Eq. (1). Using Eqs. (2) and (3), the entropy measure value (Ej) and attribute weights (Wi) were calculated and presented in Table 2. The highest attribute weight was assigned to WR or Wear rate at 0., followed by UTS or ultimate tensile strength at 0., and next to UTS, H or hardness at 0.. Conversely, CR or Corrosion resistance and D or Density were assigned weights of 0.0, indicating they are the least preferred attribute in the evaluation.

4.2 Alternative ranking method

For checking the impact of objective weighting method (entropy), TOPSIS and CoCoSo model was used for ranking analysis.

4.2.1 TOPSIS method

After establishing the weights of the criterion using entropy, the TOPSIS approach was used to rank the selected alternatives. Choice matrix was created based on the many factors presented in Table 1. Table 3 shows a normalized decision matrix produced using Eq. (5). Each criteria weight obtained from the entropy approach is used to create a weighted normalized decision matrix using Eq. (6), as illustrated in Table 4. Table 5 shows the weighted decision matrix, which determines the positive and negative ideal solutions for each criteria. The value of the separating between each alternative is used to calculate the relative proximity of the alternatives. The Eucidean distance from the ideal best and the ideal worst, as well as the performance score, were used to rank the alternatives in Table 6.

The examination of the optimal material for high temperature tribological application using the TOPSIS method reveals that AA has the greatest performance value of 0.. Following AA (0.) and AA (0.). After reviewing the data, it is clear that AA is the best material for high temperature tribological application, due to its superior performance value in the TOPSIS assessment.

4.2.2 CoCoSo method

Following the construction of the decision matrix, normalize the values using Eq. 11 for beneficial criteria and Eq. 12 for non-beneficial criteria for each data point from Table 1. In Table 7, the initial criterion is beneficial (max), where higher values are favored. Conversely, the last three criteria are non-beneficial (min), where least values are prefered.

Equation 13 was utilized to compute the weight sum value (Si), and Eq. 14 was employed to calculate the exponentially weighted product (pi) value. The resulting values are presented in Table 8, which also encompasses the aggregated evaluation scores, kia, kib, and kic, computed using Eq. 15 through Eq. 17, and ki value by Eq. 18. In the context of the CoCoSo technique, the result of this study are presented in Table 8, showcasing the performance score (ki) of all three alternatives. The analysis indicates that alternative AA achieve the highest performance score, with AA and AA following in subsequent order.

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