Multi criteria decision making using MARCOS method in optimization of Al 6063 turning process parameters with different weighting methods

ABSTRACT

The Measurement Alternative and Ranking according to COmpromise Solution (MARCOS) method is applied for multi-criteria decision making for the selection of optimal process parameters during turning of Al 6063. In this work, turning of 6063 Aluminium is done using a lubricant mixed with Titanium carbide additives. Turning of Aluminium 6063 is carried out as per the design of experiments and L₂₇ orthogonal array is selected according to the Taguchi technique. The input process parameters considered are cutting speed, feed rate, depth of cut and percentage of titanium carbide as additive in the lubricant. The minimization of surface roughness, cutting force and maximization of material removal rate are the objective functions of this research. Different weighting methods are used to determine the weights for the criteria in MARCOS method and ranking of the alternatives and ordering the criteria on decision making have been performed. The results addresses the objective of this research by determining the optimal machining conditions balancing among the conflicting performance measures. Taguchi – MARCOS approach provides a reliable and robust decision making tool for machining parameter optimization and thereby demonstrating the practical applicability of the proposed optimization process.

Keywords:
Aluminium 6063; MARCOS; Entropy method; RS Weight; MCDM

1. INTRODUCTION

Turning process is a widely used machining process to provide high dimensional accuracy, cost effective manufacturing, surface integrity, versatility of operations, and high relevance to the industrial practices. Turning operation produce round and cylindrical components used in several industrial applications in Automotives, Aerospace, Power plants, manufacturing industries and so on. Material removal rate, surface roughness, cutting forces, tool wear, tool life, dimensional accuracy, tolerances, surface integrity, heat generation, vibration and cost of machining are the common criteria used to evaluate the turning process. This work provides investigation of turning process parameters such as cutting speed, feed rate and depth of cut on machining response such as material removal rate, cutting force and surface roughness. Aluminium 6063 is known for its surface finish and offers superior material flow during extrusion. This alloy exhibits excellent corrosion characteristics and good weldability. Al 6063 is light-weight alloy and provides good machining characteristics and it is suitable for turning, drilling and milling. Al 6063 finds several applications such as Architectural and structural applications, tubes and channels, automotive components, heat dissipation components for electrical and electronics industry, marine structures and manufacturing industries. Aluminium 6063 is found in several applications in architectural and structural designs since it has good formality, excellent surface finish, good strength and good corrosion resistance. Aluminium 6063 is used in windows, door frames, trims and rails. However because of its ductility and tendency to form built up edges, it makes the machining of 6063 aluminium a tedious challenge. Therefore it is more important to study the influence of cutting parameters, tool geometry on the machinability studies of 6063 Aluminium. The work material is heat treated to provide improved strength, hardness and dimensional stability during the machining.

Several researchers observed that the surface roughness decreases with increase in cutting speed and material removal rate increases with higher speed and depth of cut. In this work, turning of 6063 Aluminium is done using a lubricant mixed with Titanium carbide additives. This is done because of its high thermal conductivity and low hardness. Since the heat quickly dissipates from the cutting zone, machining with lubricants mixed with additives are preferred. Turning of 6063 Aluminium is economical and saves cost with simplified experimental setup with reduced cleaning time and improves the long term sustainability, surface quality and cleaner finish. The high ductility and adhesive nature, turning leads to build-up edges on the cutting tool. This creates high friction between the toll and the chip interface and increases higher temperatures in the cutting zones. To overcome all these issues in turning 6063 aluminium, lubricants mixed with titanium carbide additives are used with several percentages. The titanium carbide is extremely hard ceramic compound and act as solid lubricant to resist abrasion. It has high thermal conductivity and reduces the tool chip interface and also it is chemically stable and inert and prevents reaction with aluminium. The titanium carbide additives reduces friction due to rolling and film formation, improves the heat dissipation, protects the tool interface layer and prevents adhesion and abrasion, reduces the tool wear, improves the surface roughness and minimizes in the tool chip interface. When Titanium carbide particles are mixed in the coolant, it forms a protective film at the tool-workpiece interface and reduces the wear of the cutting tool. This minimizes the metal to metal contact and reduces the cutting force and produces improved surface finish and machining stability. Water soluble coolant is mixed with TiC particles of size 5µm and concentration up to 15% by volume by mechanical stirring to ensure uniform dispersion of the particles with a controlled flood delivery of 5 L/min at the tool-workpiece interface.

The recommended cutting parameters for turning of aluminium 6063 is cutting speed varying from 800 to 1200 m/minute and feed rate 0.1 to 0.2 mm per revolution and depth of cut 0.1 to 0.3 mm. The lubricant concentration for water soluble coolant is typically between 5 to 15%. The optimal values depends on the machining parameters, tool wear, cutting force etc., To obtain an optimal better minimum surface finish, the cutting speed should be higher and feed rate should be lower. For having maximum amount of material removal rate, the cutting speed and feed rate is to be increased. In contrary, it increases the surface roughness, to avoid this a proper lubrication is very essential and in this research a cutting fluid lubricant with 5–15% titanium carbide additive is used. The concentration of the coolant is more essential because lower concentration always provides the risk of decreasing the tool life and increases the corrosion. The higher concentration will provide wastage of coolant and it is always recommended to have minimum quantity lubrication which supports the environment and also improve the surface finish.

1.1. Need for MCDM and different weighting methods

Conventional optimization focus on single response and cannot predict the actual machining performance of the turning Al 6063 alloy. Hence Multi Criteria Decision Making (MCDM) is implemented in this work to balance the optimization process by considering all responses together. In this work, maximization of material removal rate, minimization of surface roughness, minimization of cutting forces are considered as the objective function. These functions are highly conflicting and thus it facilitates the MCDM technique to provide the better optimal turning process parameters. MCDM provides a integration with experimental and modelling techniques and supports for sustainable machining decisions.

Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) is a MCDM technique developed to rank the alternatives based on their closeness to relative Ideal and non-Ideal solutions. Normalization, weighting, utility calculation are carried out to assess each alternative effectively. MARCOS can handle both maximization and minimization problem without complex transformations. MARCOS are integrated with AHP, CRITIC, and Entropy weighting methods and requires minimal computational efforts. MARCOS method integrates with different criteria methods easily and provides a stale and reliable ranking of the alternatives there by improving the decision reliability for Taguchi based experimental designs. MARCOS method have proved its effectiveness in several machining studies and hence selection of this method is more appropriate in optimizing the turning process parameters for turning of Al6063.

In this work, equal weight method, Rank Sum (RS) weighting method and Entropy weight are used for weighing the criteria. Equal weight method assigns same weight for each response and contributes equally to the final decision. This method is simple and does not require pairwise comparison and ensures high transparency and reproducibility. RS method is a subjective weighting method where criteria weight are determined based on their ranked importance and incorporates relative importance among criteria providing a simple and systematic way to in MCDM analysis. Entropy method is an objective weighting method in MCDM which determines weights based on the degree of information contained in the experimental data. Criteria with higher variability are assigned with higher weights while criteria with uniform values are assigned with lower weights. This weighting method is more robust and reproducible and is independent of expert preference. This work is focusing on MARCOS method for optimizing the turning process parameters for turning of Al 6063. A comparative assessment of different weighing methods are carried out to determine the influence of criteria importance. The equal weight method provides equal weight for all criteria’s and provides a neutral reference. Whereas the Entropy and Rank sum method provides practical preference based importance based on the experimental data.

OSORIO-PINZON et al. [¹] discussed the Multi objective particle swarm optimization to determine the optimal machining parameters while turning Aluminium 6063 with considering rake angel, cutting speed and velocity as the cutting process parameters. The finite element analysis is used to evaluate the objectives of the machining of aluminium 6063. The objective includes minimization of the cutting force, maximization of microstructure refinement and maximization of material removal rate. The input output relationship in process parameters are developed using response surface methodology and artificial neural network. Particle swarm optimization technique is used to determine the optimal machining conditions and they are compared with pareto front. SABRY, et al. [²] used MARCOS – Taguchi approach for multi-purpose optimization of friction stir welded AA6063 alloy integrated with silicon carbide and graphite. Taguchi L6 orthogonal array is used to conduct the experiments and the process parameters such as dwell time, tool shoulder diameter, tool pin length are considered. The tensile strength and flash height are selected as the response and multi objective optimization is performed using the MARCOS Taguchi technique. The analysis of variance (ANOVA) is also done to verify the optimality of the results. It is observed that the MARCOS approaches are highly reliable to determine the welding characteristics. Also it is observed that the maximum weld strength and the tool pin length are the major significant parameters which impact on the quality of the weld. The presence of silicon graphite improves the micro hardness and tensile strength.

The MACROS approach provides better results when compared to Grey Relational analysis and TOPSIS. A response surface methodology is employed along with the desirability function analysis to predict the machining characteristics during the dry turning of Al 6063 alloy. L₂₇ orthogonal is used to conduct the experiments. Cutting Speed, feed rate and depth of get our considered as the process parameters. The regression model are determined and it is found that there is a reduction in surface roughness, cutting force, energy consumption, cutting power and carbon emission which leads to environmental friendly machining parameters. Feed is found to be highly significant when compared to the other process parameters and mathematical model established with respect to process parameters and its responses. KANNAN and SIVARAM [³, ⁴], JAYARAMAN and KUMAR [⁵], CHOWDHARY et al. [⁶] demonstrated several multi objective optimization techniques for the process parameters optimization of aluminium 6063 alloy using Taguchi approach, Grey relational analysis, TOPSIS approach and so on. Several researchers investigated the multi criteria decision making for selection of cutting parameters in milling process, grinding process, drilling process and so on [⁷–⁸]. THIRUMALAI and SENTHILKUMAAR [⁹] investigated the Multi Criteria Decision Making technique in the determination of optimal process parameters in turning operation Material selection for the tool holder for milling operation is carried out using MCDM technique [¹⁰]. MARCOS technique is used in the investigation of EDM based on MCDM techniques [¹¹,¹²,¹³].

Multi criteria decision making technique is employed in turning of 150Cr14 steel with process parameters including workpiece, speed, nose radius and feed rate. Maximization of material removal rate and minimization of surface roughness are selected as the responses and eight MCDM techniques are employed to determine the optimal process parameters. The MCDM techniques such as SAW, WASPAS, TOPSIS, VIKOR, MOORA, COPRAS, PIV and PSI are used to determine the optimal experiment [¹⁴–¹⁵]. DUC TRUNG [¹⁶] employed MCDM technique MARCOS under different weighting methods is applied to milling, grinding and turning process. MCDM is employed to determine the best optimal solution and MARCOS method is used with different weighting criteria such as Equal weight method, RS method, ROC method and Entropy method. The researchers attempted combination method of several optimization techniques and Taguchi method to solve optimization of surface grinding process of steel. NGUYEN and TRUNG [¹⁷] used Multi objective optimization on basis of Ratio Analysis (MOORA) and Complex Proportionate Assessment (COPRA) to determine the optimal parameters. These combined methods are found to useful in improving the quality and effectiveness of the grinding process. NGUYEN et al. [¹⁸] combined Taguchi and VIKOR methods for determining the optimal turning process parameters of EN10503 steel for a multi objective optimization process. The Spearman’s rank correlation coefficient analysis is mandatory when applying MCDM methods. A novel approach for criteria weighting using MCDM for industrial equipment and material selection is carried out and Spearmen rank correlation coefficient between the methods are also calculated and discussed [¹⁹, ²⁰]. TRUNG et al. [²¹] used a new method R-RAM for the option ranking among the alternatives, which combines the weight calculation method and a ranking method. R-RAM calculates criteria weights considering both subjective and objective factors and then ranks the alternatives. Spearmen correlation coefficient between R-RAM and other methods are calculated and it is found that this novel method provides higher value when compared to the other methods. SEKAR et al. [²²] used MCDM technique in optimizing M-Sand material supplier selection in construction industry. The weight of each criterion are determined by Analytical Hierarchy Process (AHP) and fuzzy AHP and TOPSIS model is used to rank the alternatives. DEA model was used to integrate and balance the qualitative and quantitative sustainable suppliers. ANAND et al. [²³] used multi objective optimization to enhance surface integrity in WEDM of Al-matrix composite using MCDM with TOPSIS method. It is concluded that external weight integrated TOPSIS methods provides the exact optimal solutions. SAH et al. [²⁴] performed WEDM process parameters optimization for super alloy by different objective weight integrated with MCDM using Combined compromise solution and Combine compromise for Ideal solution methods. These methods are proven for MCDM methods for solving complex multi-variable assessment problems. ANAND et al. [²⁵] used L₂₇ Taguchi design to study the surface integrity characteristics and multi response optimization in wire EDM of Aluminium composites. Desirability approach is used to accomplish the multi response optimization and it is concluded that the surface integrity varies with the machining conditions.

2. EXPERIMENTAL METHODOLOGY

Turning of Aluminium 6063 is carried out as per the design of experiments and L₂₇ orthogonal array is selected according to the Taguchi technique. The input process parameters considered are cutting speed, feed rate, and depth of cut and percentage of titanium carbide additive in the lubricant. The surface roughness, cutting force and material removal rate are selected as the responses while turning the Al6063 aluminium. The minimization of surface roughness, minimization of cutting force and maximization of material removal rate are the objective functions of this research. The Figure 1 shows the workpiece material and the experiments are conducted on a CNC lathe as shown in the Figure 2. Turning operations are performed on a CNC turning centre powered with 3HP motor with height of centre of 185 mm, swing over cross slide 200 mm and swing over bed 350 mm and the feed mechanism allows 24 longitudinal and transverse feeds. Cemented carbide cutting tool inserts with high positive rake angle of 12 degrees, nose radius of 0.8 mm and minimum land width are used for the turning process, which facilitates minimum adhesion, adequate toughness and reduced friction.

Figure 1
Workpiece material.

Figure 2
CNC turning centre.

Surface roughness is measured using surface roughness tester and cutting force is measure using lathe tool dynamometer and metal removal rate is calculated by weighing the material before and after turning operations. For every experiment is conducted using fresh cutting inserts and continuous tool wear was considered in this work to maintain controlled experimental consistency for all the experiments. Including the tool wear will make it difficult to analyze the parameters performance on the responses. Mitutoyo surface tester is used to measure the surface roughness of the work material with a cut-off length of 0.8 mm and an evaluation length of 4 mm in compliance with ASME standards. The roughness values are located at three locations along the machine part and the average of the three values is reported here. Cutting force is measured using three component Kistler type piezo-electric dynamometer with an accuracy of 1% of full scale. Cutting force, feed force and radial force are reported as separate resolved components and this allows the multi criteria decision making process to weigh each force according to its impact on all the responses considered in this work. The density of the work material Al6063 is taken as 2.7 g/cm³ and the material removal rate is calculated using the formula.

$$\text{MRR}=\text{Volume}/\text{Time};cubic.mm/min$$

The process parameters and its levels are shown in Table 1. Three trials are conducted for every run and the average of the three is recorded here in the experimental data presented in Table 2.

Table 1
Process parameters and its levels.

CUTTING PARAMETERS	LEVEL 1	LEVEL 2	LEVEL 3
Cutting speed (vc), m/min	800	1000	1200
Feed rate (f), mm/rev	0.12	0.14	0.16
Depth of cut (d), mm	0.1	0.2	0.3
% of TiC additive in Coolant	5	10	15

Table 2
Experimental data.

TRIAL	CUTTING SPEED, m/min	FEED, mm/REV	DEPTH OF CUT, mm	TIC %	MATERIAL REMOVAL RATE (mm3/min)	CUTTING FORCE (N)	SURFACE ROUGHNESS (µm)
1	800	0.12	0.1	5	6031	117	5.12
2	800	0.12	0.1	5	5813	117	5.12
3	800	0.12	0.1	5	6128	121	5.48
4	800	0.14	0.2	10	6452	119	6.12
5	800	0.14	0.2	10	6499	120	5.37
6	800	0.14	0.2	10	6173	124	6.07
7	800	0.16	0.3	15	6206	117	5.39
8	800	0.16	0.3	15	6029	121	4.85
9	800	0.16	0.3	15	5967	118	5.47
10	1000	0.12	0.2	15	5660	126	5.58
11	1000	0.12	0.2	15	5891	127	5.02
12	1000	0.12	0.2	15	5637	122	5.91
13	1000	0.14	0.3	5	5748	118	4.73
14	1000	0.14	0.3	5	6032	117	5.11
15	1000	0.14	0.3	5	5833	112	5.17
16	1000	0.16	0.1	10	5237	126	5.12
17	1000	0.16	0.1	10	5512	119	5.33
18	1000	0.16	0.1	10	5582	124	5.15
19	1200	0.12	0.3	10	6086	113	5.09
20	1200	0.12	0.3	10	6078	110	5.33
21	1200	0.12	0.3	10	5793	107	5.17
22	1200	0.14	0.1	15	5694	118	6.4
23	1200	0.14	0.1	15	5639	117	5.97
24	1200	0.14	0.1	15	5807	119	5.82
25	1200	0.16	0.2	5	6194	127	6.28
26	1200	0.16	0.2	5	6018	124	5.68
27	1200	0.16	0.2	5	6003	122	6.07

3. MULTI CRITERIA DECISION MAKING FOR MILLING PROCESS (MARCOS METHOD)

In this work, several performance measures such as materials removal rate, tool wear rate, surface roughness are evaluated. Improvement in single objective function may not consider the other objectives and leads to provide poor optimal results. Hence to have balanced trade-offs and to find best optimal solution considering all constraints, MCDM should be used. Also, MCDM incorporates the criteria importance (weight allocation) for the objective functions using Entropy method, RS method, ROC method and so on. The weights of the criteria differ will differ since they do not contribute equally to decision making process.

Multi-criteria decision making is used to determine the optimal objective function with maximum material removal rate and minimum cutting force and minimum surface roughness. The stability assessment of multi-criteria decision making is carried out by MARCOS method corresponding to the identified methods different weights. In this work equal weight method, RS weight method and Entropy method are used to determine the weights for the criteria and it is presented in Table 3. It is observed that the ranking results of the alternatives for different weighing methods are also different. In equal weight method, the weights of the each criteria are assigned equally and in this work three responses are carried and the weights of each criteria for surface roughness, cutting force and material removal rate are assigned as 0.33. In RS (Rank sum) method, the weight drops linearly with rank, the highest criteria gets the highest rank. In this work, weight of the criterion for the material removal rate, cutting force and surface roughness are assigned as 0.611, 0.2778 and 0.111 respectively. In entropy method, larger weights to the criteria shows more variation across the alternatives and the entropy method does not depend on the order of the criteria. In this work, the weight of the criterion for the material removal rate, cutting force and surface roughness are assigned as 0.6766, 0.0727 and 0.2508 respectively.

Table 3
Weighing methods.

WEIGHTING METHODS	CRITERIA
	MRR	Fc	Ra
Equal weight method	0.33	0.33	0.33
RS weight method	0.5	0.333	0.1667
Entropy method	0.6766	0.0727	0.2508

Measurement of Alternatives and Ranking according to the compromised solution (MARCOS) is a new MCDM technique to rank the alternatives. Ideal and Anti-Ideal solution are compared with the alternatives and it is ranked accordingly. The weights of the criterion are considered to build the normalized matrix with weights. The MARCOS parameters ($K_i^{-}$), ($K_i^{+}$), f ($K_i^{-}$), f ($K_i^{\mp }$), f (K_i) are then calculated and the alternatives are ranked. The ranking of alternatives using Equal weight method, RS method and Entropy method are determined. The ranking of alternatives using different weighting methods are determined as shown in Table 4.

Table 4
Ranking of alternatives using different weighting methods.

EXPT. NO	RANK
	EQUAL WEIGHT METHOD	RS METHOD	ENTROPY METHOD
1	22	17	21
2	17	12	12
3	21	22	20
4	10	24	25
5	25	27	27
6	11	20	16
7	19	21	24
8	27	26	26
9	14	14	13
10	13	10	6
11	26	25	19
12	3	6	4
13	24	15	17
14	23	18	22
15	9	8	11
16	15	5	2
17	7	4	5
18	18	11	8
19	20	16	23
20	8	9	18
21	5	3	10
22	1	1	1
23	2	2	3
24	4	7	7
25	12	23	15
26	16	19	14
27	6	13	9

RS method assigns weight based on the variation of alternatives across each criterion and in this work, MRR exhibits larger variation resulting in the highest weight assignment. In this work, entropy method assigns low weight for cutting force (0.0727) since it exhibits low variation among the trials. The MARCOS ranking is sensitive to small weight changes and minor adjustments in weight alters the ranking.

From the Table 4, it is observed that the experiment number #22 is ranked as “1” and it provides the best optimal result during turning of Al6063 Aluminium alloy. The Parameters corresponding to this experiment are; cutting speed 1200 m/min; Feed rate 0.14 mm/rev; Depth of cut 0.1 mm and 15% of TiC additive is mixed in the lubricant. 15% TiC concentration outperforms lower configuration since it acts a solid lubricant providing better contact area coverage and improved heat dissipation at the tool-workpiece interface. This will lead to minimal built-up edge formation, reduced cutting force, and improved surface finish. Surface finish is a primary functional requirement when compared to the cutting force. Surface roughness influences dimensional accuracy, Fatigue life, wear and corrosion resistance. Since the experiments are conducted within the capacity of the spindle power of the CNC machine, there is no chatter, overload, tool failure and hence the cutting force influence are less significant when compared to the surface roughness. Radar Graph based on weighted normalized matrix is presented in Figure 3. The radar graph is constructed using the average values of the weighted normalized decision matrix for MRR, cutting force (Fc), and surface roughness (Ra). The plot highlights the relative contribution of each criterion after considering both normalization and assigned weights.

Figure 3
Radar graph based on weighted normalized matrix.

The validation test is also conducted according to this results and the percentage of error is in the acceptable level as shown in the Table 5. For multi objective machining optimization studies, the reported deviation are accepted and found to be realistic since the responses are influenced by uncontrollable factors, material inhomogeneity, and process instability. For multi objective optimization problems, these deviations reflect the inherent variability of the turning process and thereby confirming the practical validity of the optimal solution. GHANI et al. [²⁶] have reported the confirmation errors with 10–15% are acceptable for machining optimization due to compromise nature of the multi objective optimization.

Table 5
Validation test.

ITEM	MRR	SURFACE ROUGHNESS	CUTTING FORCE
Actual result	5694	6.4	118
Validation Result	6378	5.52	109
% Deviation	12.01%	13.7%	7.67%

A novel approach for criteria weighting using MCDM for industrial equipment and material selection is carried out and Spearmen rank correlation coefficient between the methods are also calculated and discussed. Spearmen correlation coefficient between RS weight method and Entropy method is 0.867, indicating a very strong positive correlation. Similarly the Spearmen correlation coefficient between equal weight method and RS weigh method is 0.747, indicating strong correlation and Spearmen correlation coefficient between equal weight method and entropy method is 0.75, indicating strong correlation between them.

4. CONCLUSION

This work integrates Taguchi technique, MCDM and MARCOS method to optimize turning process parameters of Aluminium alloys contributing robust optimization strategy and integration of TiC additives mixed with the coolant as an explicit variable in the machining conditions. Measurement of Alternatives and Ranking according to the compromised solution (MARCOS) is a new MCDM technique to rank the alternatives. Ideal and Anti-Ideal solution are compared with the alternatives and it is ranked accordingly. It is observed that experiment number #22 is ranked with 1 and it provides the best optimal result during turning of Al6063 Aluminium alloy. The minimization of surface roughness, minimization of cutting force and maximization of material removal rate are the objective functions of this research. The experiments are conducted on a CNC lathe and Surface roughness is measured using surface roughness tester, Cutting force is measured using lathe tool dynamometer and metal removal rate is calculated by weighing the material before and after turning operations. The Measurement Alternative and Ranking according to COmpromise Solution (MARCOS) method is applied for multi-criteria decision making for the selection of optimal process parameters during turning of Al 6063. In equal weight method, the weights of the each criteria are assigned equally, RS (Rank sum) method, the weight drops linearly with rank, the highest criteria gets the highest rank and in entropy method, larger weights to the criteria shows more variation across the alternatives and the entropy method does not depend on the order of the criteria. Spearmen rank correlation coefficient between the methods are also calculated and discussed.

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Publication Dates

• Publication in this collection
  27 Feb 2026
• Date of issue
  2026

History

• Received
  05 Nov 2025
• Accepted
  19 Jan 2026