# Determination of the best materials for development and designing product using a multi-criteria decision-making

Rabia Hassan (Department of Management, Economics, and Industrial Engineering DIG, Politecnico di Milano, Milan, Italy) (Department of Electrical Engineering, College of Engineering and Sciences, Institute of Business Management Sindh, Karachi, Pakistan)
Zeeshan Ahmad Arfeen (Department of Electrical Engineering, The Islamia University of Bahawalpur Pakistan, Bahawalpur, Pakistan)
Mehreen Kausar Azam (Department of Industrial Manufacturing, NED University of Engineering and Technology, Karachi, Pakistan)
Zain ul Abiden Akhtar (Department of Information and Communication Engineering, The Islamia University of Bahawalpur Pakistan, Bahawalpur, Pakistan)
Abubakar Siddique (Department of Electrical Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan)
Muhammad Rashid (Department of Electrical Engineering, Faculty of Science, The Islamia University of Bahawalpur Pakistan, Bahawalpur, Pakistan)

ISSN: 2755-0907

Article publication date: 6 August 2024

122

## Abstract

### Purpose

Material selection, driven by wide and often conflicting objectives, is an important, sometimes difficult problem in material engineering. In this context, multi-criteria decision-making (MCDM) methodologies are effective. An approach of MCDM is needed to cater to criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase.

### Design/methodology/approach

The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system. The intermediate pipes are used to join the components of the exhaust systems. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on tensile strength (TS), hardness (H), elongation (E), yield strength (YS) and cost (C). A hybrid methodology combining entropy-based criteria weighting, with the TOPSIS for alternative ranking, is pursued to identify the optimal design material for an engineered application in this paper. This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes. After that, the authors searched for and considered eight materials and evaluated them on the following five criteria: (1) TS, (2) YS, (3) H, (4) E and (5) C. The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes, on their performance and on the cost. In this structure, the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment. This essentially measures the quantity of information each criterion contribution, indicating the relative importance of these criteria better. Subsequently, the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative. The results show that SUS 309, SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.

### Findings

The material matrix of the decision presented in Table 3 was normalized through Equation 5, as shown in Table 5, and the matrix was multiplied with weighting criteria ß_j. The obtained weighted normalized matrix V_ij is presented in Table 6. However, the ideal, worst and best value was ascertained by employing Equation 7. This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively. For the future work, it is suggested to select more alternatives and criteria. The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE).

### Originality/value

The results provide important conclusions for material selection in this targeted application, verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

## Citation

Hassan, R., Arfeen, Z.A., Azam, M.K., Akhtar, Z.u.A., Siddique, A. and Rashid, M. (2024), "Determination of the best materials for development and designing product using a multi-criteria decision-making", Railway Sciences, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/RS-12-2023-0050

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

This is a significant aspect used in the development and design of a product. Choice of materials in the same years has received significant importance due to encouraging new material developments (Balakrishna, Chandra, Gogulamudi, & Someswararao, 2011). Inappropriate material selection can lead to failure to meet manufacturer and customer requirements (Chen et al., 2019). Another research was done by Yashpalsinh, Molvi, and Student (2019), and the authors found out that the AISI 409 stainless steel pipe failed after 50,000 km with high-temperature exposure, corrosion and mechanical stress. The microstructural analysis detected large grain growth, carbide precipitation and oxidation and the chemical analysis found chromium depletion and significant carbon increase. The authors recommend using better quality materials, improving design and maintenance, etc. to avoid these failures. The failure of the de Havilland Comet in the 1950s is a notable example of improper material selection that has led to assembly failure. The de Havilland Comet Material choice hence plays an important role in enabling products to be competitive and successful in the market, combined with meeting efficiency and consumer requirements. It is estimated that there are more than 100,000 selections available for engineering materials (Rahim, Musa, Ramesh, & Lim, 2020). However, improper material selection may cause assembly failure or dissatisfaction and tremendously reduce the performance and efficiency of products, thereby adversely affecting the organization’s productivity, profitability and credibility (Ganesh Kumar, Meikandan, Sakthivadivel, & Vigneswaran, 2021). This involves four main categories of engineering materials: ceramics, metals, composites and polymers (Liu et al., 2021). Ceramics are known for their high hardness and temperature resistance, making them suitable for extreme environments. Metals are valued for their strength, ductility and conductivity, essential for structural and mechanical parts. Composites, which combine different materials for enhanced properties, are ideal for aerospace and automotive uses due to their excellent strength-to-weight ratio. Polymers are versatile, lightweight and cost-effective and are used widely for their corrosion resistance. The wide range of choices, combined with complex interactions, made the process selection challenging for the design of industries, as these usually involve stakeholders across various fields to check along with their requirements to find the correct choice for the application (Jahan, Ismail, Sapuan, & Mustapha, 2010). As engaging stakeholders in material selection involves effective communication and collaboration across various disciplines, it would be ideal to direct the process through a framework consisting of screening, comparison and best material selection (Emovon & Oghenenyerovwho, 2020). This may be done in four basic stages, i.e. method of translation of criteria, a screening process, a method of ranking and a search for an appropriate approach (Ashby, Bréchet, Cebon, & Salvo, 2004). During screening, materials are filtered based on fundamental criteria like mechanical properties and cost. The comparison stage employs multi-criteria decision-making (MCDM) methods such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to assess and rank materials using weighted criteria. The final selection involves in-depth analysis, prototyping and testing to determine the best material. This framework should be integrated into the design process, involve cross-functional team collaboration, utilize software tools, maintain thorough documentation and include a feedback loop for continuous improvement in material selection.

However, Kesteren et al. also concur with the similar basic method, which includes selecting criteria, setting up a range of candidates, comparison and selecting an appropriate candidate (Van Kesteren, De Bruijn, & Stappers, 2008). The approach expresses resemblances among steps like screening and rating, regardless if they are implemented in the phases of design or selection criteria (Kokaraki, Hopfe, Robinson, & Nikolaidou, 2019). On the other hand, Gharibi, Babazadeh, and Hasanzadeh (2024) used the TOPSIS method to rank various tests for identifying the best and worst trials for the developed multi-layer perceptron (MLP) neural networks and regression models to accurately predict the performance of polyethylene gasification. Mojaver, Jafarmadar, Khalilarya, and Chitsaz (2019) combine analytic hierarchy process (AHP) and the TOPSIS technique for the identification of best alternatives for the synthesis gas composition. Similarly, Hasanzadeh, Mojaver, Khalilarya, and Azdast (2023) conducted research addressed how gasification parameters interact to influence outcomes. It involved an MCDM study with eleven alternatives and five criteria focusing on energy, environmental and economic aspects of gasification performance. Criteria weights were determined using the AHP, the Shannon entropy technique and a composite weighting approach. The TOPSIS and VIKOR techniques were employed alongside these weighting methods to rank the alternatives effectively. Hasanzadeh, Azdast, Mojaver, and Park (2022) conducted a study using the MCDM technique to determine optimal conditions for gasifying waste polystyrene (PS). This paper presents the identification of the optimal design material for engineering applications, utilizing a hybrid approach that combines entropy-based criteria weighting with the TOPSIS for alternative ranking. The study addresses the knowledge gap in optimal material selection for exhaust intermediate pipes. Figure 1 presents a standard process stage in material selection.

Figure 2 illustrates the categories for the selection criteria of material for pipe products including six important criteria for selecting materials in engineering: cost, reliability, ease of joining, ease of fabrication, mechanical properties and electrical properties. These factors are crucial for assessing materials based on their economic viability, consistent performance, ease of assembly, workability, strength and electrical characteristics (Cornish, 1987). Considering these criteria helps engineers choose the most appropriate materials, ensuring a balance of performance, cost-effectiveness and manufacturability for specific engineering applications, ultimately leading to optimal design and functionality.

Table 1 summarizes the specifications required for the intermediate pipe. It describes that the pipe should be sustainable to high temperatures, durable and cost-effective.

The structure of the article is methodically organized, beginning with an introduction that sets the stage for the research. This is followed by a detailed discussion of the methodology, incorporating aspects of MCDM and entropy. The paper then presents the results of the application of this methodology, engages in a thorough discussion to interpret these results and concludes with a summary of findings and implications of the study.

## 2. Literature review

MCDM aims to focus on providing a finite number of alternatives to decision-makers with fair recommendations when assessing against various requirements or goals (Kumar et al., 2017). With the dynamic properties of the process’s selection of product materials, the consideration of MCDM is significant. Although the optimal material also depends on the experience of designers and previously known information, for a designer, it is quite challenging to decide if there are so many parameters from which some might not be well known (Morente-Molinera, Wu, Morfeq, Al-Hmouz, & Herrera-Viedma, 2020). The utilization of MCDM is significant for material selection where differentiation of product, different materials, complex applications and market opportunities are included (Hamzeh & Xu, 2019). Hence, a method that helps the designers to find the optimal solution is highly preferred. Researchers have similar thoughts in diminishing problems towards selection of material, and it is recommended that evaluation and selection of the process take into consideration several criteria/attributes in recommending an optimal choice (Mousavi-Nasab & Sotoudeh-Anvari, 2018). In addition, the essence of problems in material selection fits best with the domain of MCDM (Dotoli, Epicoco, & Falagario, 2020).

Various methods have been suggested in the literary works to tackle the challenge for the material selection and to enhance the effectiveness in the design procedure, like AHP, Entropy criterion, Fuzzy logic, Ashby Method, Gray relational analysis (GRA), Elimination Et Choice Translating Reality (ELECTRE), Complex Proportional Assessment (COPRAS), VIekriterijumsko KOmpromisno Rangiranje (VIKOR), Graph theory matrix approach (GTMA) and TOPSIS.

Ali, Lalji, and Ali et al. (2024) and Ali, Lalji, and Haider et al. (2024) present a unique risk prioritizing strategy that blends the Fuzzy VIKOR with the Shannon entropy. This integrated strategy enhances the handling of ambiguity and complexity in risk assessment for core preparation experiments. The case study proves the model’s usefulness, providing engineers with a reliable tool for improving safety and decision-making.

Several scholars used different decision-making attributes to solve problems related to the selection of the material. However, under several circumstances, because of the difficulty of material selection issue, precise data are insufficient to model actual life situation. In order to cope with the vagueness and process uncertainty of decision-making, fuzzy set theory was implemented. Elevli et al. imposed the MCDM method to determine and assess the contamination of heavy metals in the Dilovasi region Turkey. Elevli and Ozturk, (2019) and Lederer, Kotas, and Khatibi (2020) describe a new method for assessing and predicting the lifetime of large area solder connections in microelectronics. This approach overcomes the constraints of standard models by including thorough experimental data and failure analysis. The suggested model effectively forecasts the lifetime of solder junctions, which significantly improves the reliability of electronic assemblies. Ali, Lalji, and Ali et al. (2024) and Ali, Lalji, and Haider et al. (2024) expound an upgraded risk ranking model for rigging up operational tasks using fuzzy MCDM methodologies. This technique addresses the complexities and uncertainties inherent in such operations, providing a more effective and accurate risk management tool. The case study demonstrates the model’s applicability and benefits, emphasizing its ability to improve safety and operational efficiency in the oil and gas industry. Deshmukh et al. used three methodologies for selection of material that best suitable for switching components of shunt capacitive switches. The methodologies that are utilized for the best selection of material are VIKOR, Ashby and TOPSIS (Deshmukh & Angira, 2019). Srinivasan et al. utilized TOPSIS, GRA and Taguchi method to improve the welding parameters of steel 15CDV6 (Srinivasan, Khan, Kannan, Sathiya, & Biju, 2019). To obtain a suitable workability and strength combination in sic/al composite, Khorshidi et al. used the MCDM technique for comparison analysis among Preference Selection Index (PSI) and TOPSIS methods for material selection (Khorshidi & Hassani, 2013). Pankaj et al. focused on TOPSIS methodology for the material selection of cutting tools (Shelar & Lekurwale, 2016). Prasenjit et al. used to tackle the problem of selecting material through the method of preferential ranking (Chatterjee & Chakraborty, 2012). A multi-criteria assessment framework was proposed by Bai et al. that permits decision-makers to estimate the systematic efficiency of the cutting fluid application in their lines of production granite (Bai, Hua, Elwert, & Wang, 2018).

### 2.1 Entropy method

The entropy method is being used in different areas of research as it is relatively simpler and gives good results. The alternatives are allotted weights, which are based upon their entropy calculated using the indicator values (Saad, Darras, & Nazzal, 2021). Each indicator is defined by the amount of information provided, which is a statistical parameter (Azadfallah, 2020). The importance of the indicator is directly proportional to the amount of information provided for the indicator. In this research, the entropy approach is used to derive the indicator values for the TOPSIS input weights, making weight assessment less arbitrary and allowing a more efficient procedure of safety assessment.

### 2.2 Research gap

The literature review revealed that, as of previous literature, no efficient method is developed for the material selection. There are not many studies on material selection, as this is mainly addressed in material properties and is a part of mechanical engineering. Since TOPSIS is a statistical tool and there aren’t many mechanical engineers who relate material properties to statistics, an approach of MCDM is needed to cater criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature, this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using TOPSIS, the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase.

## 3. Methodology

The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust systems. The intermediate pipes are used to join the components of the exhaust system. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on Tensile Strength (TS), Hardness (H), Elongation (E), Yield Strength (YS) and cost (C).

Figure 3 illustrates the phases of suitable material selection for product design or development. In the first phase, the study was conducted to get an exploration idea after getting the idea. The literature review was done to identify how the MCDM approach is used to tackle such problems. The second phase involves the selection of material alternatives and the determination of the important criteria for the material. The third phase is based on the calculation of weight criteria by using the entropy method, and these weighted criteria are the most important to implement the TOPSIS technique, which helps to determine the Euclidean distance from the ideal worst and best values. The performance score is calculated to identify the highest and lowest ranking of the material. This ranking helps in the most suitable selection of material for product development.

### 3.1 Entropy method to determine criteria weighting

The knowledge is formulated through the theory of probability and uncertainty measures by entropy (Deng, 2020). It shows that a large distribution is often more unpredictable than a sharply peaked one (Ding, Chong, Bao, Xue, & Zhang, 2017). Following are the four steps of finding the weight by using the method of entropy.

• Step 1: Determine the decision matrix

Equation (1) indicates the matrix of decision M of the dilemma of multi-criteria with alternatives “m” and criteria “n” (Lotfi & Fallahnejad, 2010).

C1C2C3Cn
(1)M=A1A2A3A4Am[X11X21X31X41Xm1X12X22X32X42Xm2X13X23X33X43Xm3Xn1Xn2Xn3Xn4Xnm]
where,

A1,A2,A3,Am are the available alternatives

C1,C2,C3,Cm are the criterions and

Xij(i=1,2,3,4m;j=1,2,3,4n) is the value of performance of the ith alternative to the jth criteria.

• Step 2: Normalization of the decision matrix

Pij is calculated through Equation (2). It is the normalized decision matrix and measured to evaluate weights by using the method of entropy (Hussain, Mandal, & Mondal, 2017). The normalization objective is to attain dimensionless values to equate them with different parameters.

(2)Pij=Xiji=1mXij
• Step 3: Determine the entropy value

The computation of entropy measure by using the underneath equation (Hashemi, 2020)

(3)ej=hi=1mPijlnPij
where,

h=1lnm is constant 0ej1

m=numberofalternativematerials
• Step 4: Determine the entropy weight

Thus, the entropy weight computed by using equation (4)

(4)βj=1ejj=1n(1ej)

where,

1ej is the degree of divergence.

### 3.2 TOPSIS method

Hwang established the TOPSIS method (Balioti, Tzimopoulos, & Evangelides, 2018) for selecting a spillway for a dam. This technique is considered when a simpler weighting solution is preferred by the user. The TOPSIS approach is implemented in this research to attain the solution that is near mostly idealist solution and most far away from the negative ideal solution (Ramdania, Manaf, Junaedi, & Hadiana, 2020). The procedure includes details on the comparative value of the characteristics that are included in the procedure of selection. The TOPSIS approach includes the following steps.

• Step 1: Determine the normalized matrix

Decision matrix normalization is carried out by using Equation (5) (Saqlain, Jafar, Hamid, & Shahzad, 2019).

(5)X¯ij=Xiji=1mXij2
where,
i=1,2,3,4m;j=1,2,3,4n
• Step 2: Determine the weighted normalized matrix

The normalized columns of the decision matrix are multiplied by corresponding the weights in Equation (4), and the normalized matrix of weight is attained via Equation (6) (Saqlain et al., 2019).

(6)Vij=X¯ijβj
• Step 3 Determine the ideal, best and worst value

The value of ideal, worst and best value is determine by Equations (7) and (8), respectively (Çalişkan, Kurşuncu, Kurbanoĝlu, & Güven, 2013).

(7)V1+,V2+,V3+,V4+,Vi+={(MaxVij|jk),(MinVij|jk)|i=1,2,3,4m
(8)V1,V2,V3,V4,Vi={(MinVij|jk),(MaxVij|jk)|i=1,2,3,4m
where.
• k = benefit criteria index;

• k = criteria of cost index and

• Step 4: Determine the Euclidean distance.

The Euclidean distance from the ideal, worst and best is determined by Equations (9) and (10) (Kumar, Sreebalaji, & Ganesan, 2015).

(9)Si+=j=1m(VijVj+)2
(10)Si=j=1m(VijVj)2

j = 1, 2, 3, 4…. n; i = 1, 2, 3, 4….m

• Step 5: Determine the performance score

The performance score is calculated by Equation (11) (Athawale & Chakraborty, 2011).

(11)Pi=SiSi++Si

The result of Equation (11) shows the best rank is the higher value, and the lowest value has the worst ranking, meaning that the rank is in decreasing order.

### 3.3 Implementation of proposed methodology

The intermediate pipe of automotive exhaust system material selection is done through alternative material and alternative criteria. Table 2 represents the eight alternative materials, which used for the manufacturing of intermediate pipe, whereas Table 3 illustrates the material criteria, and the matrix of decision is represented in Table 4.

## 4. Results and discussion

The selection of material problems was assumed to depict the pertinence of the TOPSIS technique in combination with the entropy weighting method. The various steps included in the method were discussed above. The MCDM approach was implemented to the problem after evaluating the weights of various parameters using the entropy method.

The βj weights were calculated by the entropy method, and the result is displayed in Table 5. The outcome showed that the most significant criterion is cost ($0.469). ### 4.2 TOPSIS method The material matrix of the decision presented in Table 6 which was normalized through Equation (5) shown in Table 6 and the matrix was multiplied with weighting criteria βj. The obtained weighted normalized matrix Vij is presented in Table 7. However, the ideal, worst and best value was ascertained by employing Equations (7) and (8), and the result is shown in Table 8. The Euclidean distance from the ideal, worst and best value is determined by Equations (9) and (10). The performance score is calculated by Equation (11). The best rank is the higher value, and the lowest value has the worst ranking, meaning that the ranking is in decreasing order, as presented in Table 9. Table 10 presents the results of a MCDM analysis for selecting the best material for an intermediate exhaust pipe. The analysis utilizes both the COPRAS and the TOPSIS methods. A ranking of materials based on the both the COPRAS and the TOPSIS approaches, with a lower rank number indicating a superior material. Where Qi is a composite score generated using the COPRAS approach that represents the overall performance of each substance and Udi is the utility degree, given as a percentage, demonstrating each material’s relative utility in comparison to the best-performing material (SUS 309 at 100%). The evaluation findings of several stainless-steel alloys utilizing the VIKOR method are shown in Table 11, along with the TOPSIS method rankings. The lower rank indicates a higher quality of each material according to the VIKOR technique. Qi (0.5) is the overall VIKOR index, which includes both the group utility (Si) and individual regret (Ri) values. The value of Qi is determined using a weight (v) of 0.5, signifying equal relevance for both Si and Ri. According to the results depicted in Table 11, it can be seen that SUS309, SUS441L and SUS432L occupy the first three rank positions, respectively, whereas SUS304 comes last. The graphical illustration of results, and the TOPSIS ranking is portrayed in Figure 4. Figure 5 effectively compares the performance of various stainless steel alloys using three separate MCDM methodologies. The optimal choice for designing exhaust intermediate pipes includes SUS 309, SUS 432L and SUS 436LM due to their distinctive attributes. These materials provide a well-rounded blend of high-temperature endurance, resistance to corrosion, flexibility and cost-efficiency. They can effectively function within a broad temperature spectrum, spanning from 500°C to 1000°C, rendering them suitable for various exhaust system uses. Moreover, they exhibit resilience against corrosive elements found in exhaust emissions, such as sulfuric acid, water and nitrogen oxides. Furthermore, their widespread acceptance and availability in the industry streamline the sourcing and fabrication of components. ## 5. Conclusion This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively, due to their balanced properties of high-temperature resistance, corrosion resistance, formability and cost-effectiveness. For future research, it is recommended to broaden the selection scope by incorporating more alternatives and criteria. Additionally, the comparison process can benefit from employing various MCDM techniques such as ELECTRE, DEMATEL and PROMETHEE. Diversifying material options may involve exploring alternatives like titanium or ceramics and considering factors like environmental impact. MCDM methods provide better analysis: ELECTRE for uncertain data, DEMATEL for identifying critical factors and PROMETHEE for systematic ranking. By combining these strategies, you can give facets importance, handle interdependencies and help decision-making as necessary. As a result, the overall evaluation of materials to be used for the exhaust system's performance, cost and sustainability is achieved. The study underscores the need to extend material considerations and employ MCDM methods in exhaust system design. This approach enables managers to prioritize decision-making on multiple options and drivers and, in so doing, achieve savings, performance enhancements and more sustainable development. Among others, organizations can benefit from competitive advantages and increased material choice security by considering environmental matters and utilizing advanced decision-making approaches. Ultimately, the research contains critical information for managers interested in refining the design of an actual system to enhance its operational performance and meet rigorous regulatory and industrial standards. ## Figures ### Figure 1 Process of selecting material ### Figure 2 Selection criteria for the mechanical products ### Figure 3 Suitable material selection for product design phases ### Figure 4 Graphical representation of ideal and nadir solution Pi and ranking ### Figure 5 Comparison of materials using VIKOR, COPRAS and TOPSIS methods ## Table 1 Properties required for intermediate pipe Sr. #PropertyUtility 1Thermal expansionEndures constant exposure to heat from exhaust normally from 500oC to 1000oC 2Fatigue strengthCould manages vibrations and exhaust pressure 3Rust resistanceWithstands damage from road salt, moisture and exhaust fumes 4WeldabilityShould permits simple shaping to occur during production 5WeightShould have enhanced fuel efficiency (best if lightweight) 6CostAcquire a cost-effective balance between necessary attributes Source(s): Authors’ own work ## Table 2 Alternative materials S. No.Material 1SUS 441L 2SUS 439L 3SUS 430 4SUS 432L 5SUS 436 LM 6SUS 304 7SUS 309 8SUS310 Source(s): Authors ’ own work ## Table 3 Material criteria CriteriaUnit Yield strength (YS)(N/mm2) Tensile strength (TS)(N/mm2) Elongation (E)(%) Hardness (H)(HV) Cost (C)$/Kg

Source(s): Authors’ own work

## Table 4

Material decision matrix

MaterialYSTSEHC
SUS 441L270470381663.2
SUS 439L260490361654.5
SUS 430205450312003.2
SUS 432L260456321522.4
SUS 436 LM281479331522.9
SUS 304345605551645.6
SUS 309310621452252.7
SUS310212532432105

Source(s): Authors’ own work

## Table 5

Criteria weighting by entropy method

YSTSEHC
βj0.1420.0780.1970.1140.469

Source(s): Authors’ own work

## Table 6

Normalized matrix decision

MaterialYSTSEHC
SUS 441L0.351760.321620.337020.323930.29390
SUS 439L0.338730.335300.319280.321980.41330
SUS 4300.267080.307930.274940.390280.29390
SUS 432L0.338730.312040.283810.296610.22042
SUS 436 LM0.366090.327780.292680.296610.26635
SUS 3040.449470.414000.487800.320030.51432
SUS 3090.403870.424950.399110.439060.24798
SUS3100.276200.364040.381370.409790.45922

Source(s): Authors’ own work

## Table 7

Weighted and normalized decision matrix, Vij

MaterialYSTSEHC
SUS 441L0.049940.025080.066400.036990.13779
SUS 439L0.048090.026140.062910.036770.19377
SUS 4300.037920.024010.054170.044560.13779
SUS 432L0.048090.024330.055920.033870.10335
SUS 436 LM0.051970.025560.057670.033870.12488
SUS 3040.063810.032280.096110.036540.24114
SUS 3090.057340.033130.078640.050140.11626
SUS3100.039210.028390.075140.046790.21530

Source(s): Authors’ own work

## Table 8

Ideal, best and worst value

 V+ 0.06381 0.033134 0.09611 0.050135 0.103346 V− 0.037916 0.02401 0.054171 0.033869 0.24114

Source(s): Authors’ own work

## Table 9

Distance from ideal and nonideal solution and Pi

MaterialSi+Si−PiRank
SUS 441L0.0499940.1048110.6770544
SUS 439L0.0987640.0493600.3332346
SUS 4300.0610760.1038980.6297825
SUS 432L0.0469530.1381810.7463832
SUS 436 LM0.0490270.1171730.7050143
SUS 3040.1384660.0500490.2654917
SUS 3090.0226750.1300670.8515491
SUS3100.1166750.0359870.2357318

Source(s): Authors’ own work

## Table 10

Comparison of materials using COPRAS and TOPSIS methods

MaterialsS+F-CiMin (FCi)/F-CiQiUdi (%)COPRAS rankTOPSIS rank
SUS 441L0.178410.1380.7500.34782.80344
SUS 439L0.173910.1940.5330.29470.11486
SUS 4300.160660.1380.7500.32978.56555
SUS 432L0.162210.1031.0000.38792.33222
SUS 436 LM0.169070.1250.8280.35584.72933
SUS 3040.228740.2410.4290.32577.59067
SUS 3090.219250.1160.8890.419100.00011
SUS3100.189530.2150.4800.29770.98578

Source(s): Authors’ own work

## Table 11

Comparative analysis of different materials using VIKOR and TOPSIS techniques

MaterialsSiRiQi (0.5)VIKOR rankTOPSIS rank
SUS 441L0.493832270.139556740.4001317624
SUS 439L0.70338640.307769980.8670006966
SUS 4300.573289740.19700470.5668570555
SUS 432L0.464262230.188784420.4528916432
SUS 436 LM0.497529510.180564150.4702210943
SUS 3040.571581080.4691.0075438887
SUS 3090.161496260.0820618011
SUS3100.678455620.381050160.9631504978

Source(s): Authors’ own work

## References

Ali, S. I., Lalji, S. M., Ali, S. M., Zia, Y. K., Husain, N., Yousaf, N., … Awan, Z. (2024). Risk ranking of rig up operation activities using multi-criteria decision-making methods based on fuzzy environment. Journal of Engineering Research, 12(1), 289298. doi: 10.1016/j.jer.2024.01.011.

Ali, S. I., Lalji, S. M., Haider, S. A., Haneef, J., Husain, N., Yahya, A., … Arfeen, Z. A. (2024). Risk prioritization in a core preparation experiment using fuzzy VIKOR integrated with Shannon entropy method. Ain Shams Engineering Journal, 15(2), 102421. doi: 10.1016/j.asej.2023.102421.

Ashby, M. F., Bréchet, Y. J. M., Cebon, D., & Salvo, L. (2004). Selection strategies for materials and processes. Materials and Design, 25(1), 5167. doi: 10.1016/S0261-3069(03)00159-6.

Athawale, V. M., & Chakraborty, S. (2011). A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection. International Journal of Industrial Engineering Computations, 2(4), 831850. doi: 10.5267/j.ijiec.2011.05.002.

Azadfallah, M. (2020). Improving the entropy weighting method performance by using alternative normalization procedures. International Journal of Society Systems Science, 12(3), 231249. doi: 10.1504/ijsss.2020.111347.

Bai, S., Hua, Q., Elwert, T., & Wang, Q. (2018). Development of a method based on MADM theory for selecting a suitable cutting fluid for granite sawing process. Journal of Cleaner Production, 185, 211229. doi: 10.1016/j.jclepro.2018.03.053.

Balakrishna, A., Chandra, G. R., Gogulamudi, B., & Someswararao, C. (2011). Fuzzy approach to the selection of material data in concurrent engineering environment. Engineering, 03(09), 921927. doi: 10.4236/eng.2011.39113.

Balioti, V., Tzimopoulos, C., & Evangelides, C. (2018). Multi-criteria decision making using TOPSIS method under fuzzy environment. Application in spillway selection. Proceedings, 2(11), 637. doi: 10.3390/proceedings2110637.

Çalişkan, H., Kurşuncu, B., Kurbanoĝlu, C., & Güven, S. Y. (2013). Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods. Materials and Design, 45, 473479. doi: 10.1016/j.matdes.2012.09.042.

Chatterjee, P., & Chakraborty, S. (2012). Material selection using preferential ranking methods. Materials and Design, 35, 384393. doi: 10.1016/j.matdes.2011.09.027.

Chen, Z. S., Martínez, L., Chang, J. P., Wang, X. J., Xionge, S. H., & Chin, K. S. (2019). Sustainable building material selection: A QFD- and ELECTRE III-embedded hybrid MCGDM approach with consensus building. Engineering Applications of Artificial Intelligence, 85(August), 783807. doi: 10.1016/j.engappai.2019.08.006.

Cornish, E. H. (1991). Materials and the designer. Cambridge University Press. ISBN: 9780521385800. Available from: https://www.cambridge.org/pk/universitypress/subjects/engineering/materials-science/materials-and-designer?format=PB&isbn=9780521385800

Deng, Y. (2020). Uncertainty measure in evidence theory. Science China Information Sciences, 63(11), 119. doi: 10.1007/s11432-020-3006-9.

Deshmukh, D., & Angira, M. (2019). Investigation on switching structure material selection for RF-MEMS shunt capacitive switches using Ashby, TOPSIS and VIKOR. Transactions on Electrical and Electronic Materials, 20(3), 181188. doi: 10.1007/s42341-018-00094-3.

Ding, X., Chong, X., Bao, Z., Xue, Y., & Zhang, S. (2017). Fuzzy comprehensive assessment method based on the entropy weight method and its application in the water environmental safety evaluation of the Heshangshan drinking water source area, three Gorges reservoir area, China. Water (Switzerland), 9(5), 329. doi: 10.3390/w9050329.

Dotoli, M., Epicoco, N., & Falagario, M. (2020). Multi-criteria decision making techniques for the management of public procurement tenders: A case study. Applied Soft Computing Journal, 88, 106064. doi: 10.1016/j.asoc.2020.106064.

Elevli, B., & Ozturk, H. (2019). Multi-criteria assessment of heavy metals contaminations in waters and ranking the sites by using PROMETHEE/GAIA method. Journal of Environmental Health Science and Engineering, 17(1), 7584. doi: 10.1007/s40201-018-00328-9.

Emovon, I., & Oghenenyerovwho, O. S. (2020). Application of MCDM method in material selection for optimal design: A review. Results in Materials, 7(June), 100115. doi: 10.1016/j.rinma.2020.100115.

Ganesh Kumar, P., Meikandan, M., Sakthivadivel, D., & Vigneswaran, V. S. (2021). Selection of optimum glazing material for solar thermal applications using TOPSIS methodology. International Journal of Ambient Energy, 42(3), 274278. doi:10.1080/01430750.2018.1542626.

Gharibi, A., Babazadeh, R., & Hasanzadeh, R. (2024). Machine learning and multi-criteria decision analysis for polyethylene air-gasification considering energy and environmental aspects. Process Safety and Environmental Protection, 183, 4658. doi: 10.1016/j.psep.2023.12.069.

Hamzeh, R., & Xu, X. (2019). Technology selection methods and applications in manufacturing: A review from 1990 to 2017. Computers and Industrial Engineering, 138, 106123. doi: 10.1016/j.cie.2019.106123.

Hasanzadeh, R., Azdast, T., Mojaver, M., & Park, C. B. (2022). High-efficiency and low-pollutant waste polystyrene and waste polystyrene foam gasification: Comprehensive comparison analysis, multi-objective optimization and multi-criteria decision analysis. Fuel, 316, 123362. doi: 10.1016/j.fuel.2022.123362.

Hasanzadeh, R., Mojaver, P., Khalilarya, S., & Azdast, T. (2023). Air co-gasification process of LDPE/HDPE waste based on thermodynamic modeling: Hybrid multi-criteria decision-making techniques with sensitivity analysis. International Journal of Hydrogen Energy, 48(6), 21452160. doi: 10.1016/j.ijhydene.2022.10.101.

Hashemi, S. Z. (2020). Environmental education in social sciences textbooks of senior high schools in Iran. Pertanika Journal of Social Sciences and Humanities, 28(1), 441451.

Hussain, S. A. I., Mandal, U. K., & Mondal, S. P. (2017). Entropy based MCDM approach for selection of material. In National Level Conference on Engineering Problems and Application of Mathematics (pp. 17), May 2016.

Jahan, A., Ismail, M. Y., Sapuan, S. M., & Mustapha, F. (2010). Material screening and choosing methods - a review. Materials and Design, 31(2), 696705. doi: 10.1016/j.matdes.2009.08.013.

Khorshidi, R., & Hassani, A. (2013). Comparative analysis between TOPSIS and PSI methods of materials selection to achieve a desirable combination of strength and workability in Al/SiC composite. Materials and Design, 52, 9991010. doi: 10.1016/j.matdes.2013.06.011.

Kokaraki, N., Hopfe, C. J., Robinson, E., & Nikolaidou, E. (2019). Testing the reliability of deterministic multi-criteria decision-making methods using building performance simulation. Renewable and Sustainable Energy Reviews, 112(October 2018), 9911007. doi: 10.1016/j.rser.2019.06.018.

Kumar, K. R., Sreebalaji, V. S., & Ganesan, V. (2015). Analysis of Kerf Width and cutting speed characteristics of Alumi- nium/Tungsten carbide composites. Asian Journal of Applied Science and Technology, 1(JANUARY 2015), 4756.

Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69(June 2016), 596609. doi: 10.1016/j.rser.2016.11.191.

Lederer, M., Kotas, A. B., & Khatibi, G. (2020). A lifetime assessment and prediction method for large area solder joints. Microelectronics Reliability, 114, 113888. doi: 10.1016/j.microrel.2020.113888.

Liu, G., Zhang, X., Chen, X., He, Y., Cheng, L., Huo, M., … Lu, J. (2021). Additive manufacturing of structural materials. Materials Science and Engineering R: Reports, 145, 100596. doi: 10.1016/j.mser.2020.100596.

Lotfi, F. H., & Fallahnejad, R. (2010). Imprecise Shannon’s entropy and multi attribute decision making. Entropy, 12(1), 5362. doi: 10.3390/e12010053.

Mojaver, P., Jafarmadar, S., Khalilarya, S., & Chitsaz, A. (2019). Study of synthesis gas composition, exergy assessment, and multi-criteria decision-making analysis of fluidized bed gasifier. International Journal of Hydrogen Energy, 44(51), 2772627740. doi: 10.1016/j.ijhydene.2019.08.240.

Morente-Molinera, J. A., Wu, X., Morfeq, A., Al-Hmouz, R., & Herrera-Viedma, E. (2020). A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures. Information Fusion, 53, 240250. doi: 10.1016/j.inffus.2019.06.028.

Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466484. doi: 10.1016/j.jclepro.2018.02.062.

Rahim, A. A. A., Musa, S. N., Ramesh, S., & Lim, M. K. (2020). A systematic review on material selection methods. In Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 234(7), 1032-1059. doi: 10.1177/1464420720916765.

Ramdania, D. R., Manaf, K., Junaedi, F. R., & Hadiana, A. (2020). TOPSIS method on selection of new employees’ acceptance. In 6th International Conference on Wireless and Telematics (ICWT), Yogyakarta (pp. 14). doi: 10.1109/ICWT50448.2020.9243658.

Saad, M. H., Darras, B. M., & Nazzal, M. A. (2021). Evaluation of welding processes based on multi-dimensional sustainability assessment model. International Journal of Precision Engineering and Manufacturing - Green Technology, 8(1), 5775. doi: 10.1007/s40684-019-00184-4.

Saqlain, M., Jafar, N., Hamid, R., & Shahzad, A. (2019). Prediction of cricket World Cup 2019 by TOPSIS technique of MCDM-A mathematical analysis. International Journal of Scientific and Engineering Researc, 10(2), 789792.

Shelar, P. R., & Lekurwale, R. R. (2016). Selecting appropriate cutting tool insert for turning using analytical Hierarchy and processweighted product method. International Journal of Mechanical And Production Engineering, 4(5), 47.

Srinivasan, L., Khan, M. C., Kannan, T. D. B., Sathiya, P., & Biju, S. (2019). Application of genetic algorithm optimization technique in TIG welding of 15CDV6 aerospace steel. Silicon, 11(1), 459469. doi: 10.1007/s12633-018-9862-8.

Van Kesteren, I., De Bruijn, S., & Stappers, P. J. (2008). Evaluation of materials selection activities in user-centered design projects. Journal of Engineering Design, 19(5), 417429. doi: 10.1080/09544820701716248.

Yashpalsinh, S., Molvi, P. I., & Student, P. G. (2019). Failure analysis of exhaust system of diesel engine using SolidWorks modelling. Journal of Emerging Technologies and Innovative Research, 6. Available from: www.jetir.org

## Acknowledgements

The authors are grateful to KTDMC for the material specifications. The contribution of Engr. Muhammad Ahsan for providing materials details is also very much acknowledged by the authors.

## Corresponding author

Zeeshan Ahmad Arfeen can be contacted at: zeeshan.arfeen@iub.edu.pk