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Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach

  • Dev Sureja , Soni Kumari , Basireddy Bhavani , Kumar Abhishek EMAIL logo , Rakesh Chaudhari , Mahendra Singh , Venkatachalam Revathi and Soumyashree M. Panchal EMAIL logo
Published/Copyright: April 4, 2024
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 43 Issue 1

Abstract

The automotive and aerospace sectors have a strong demand for Nitinol alloy machined parts; therefore, optimizing machining parameters is essential to achieving better process performance results in terms of cost and product quality. In general, the process variables that influence machining include feed (f), depth of cut (t), and spindle speed (S). Material removal rate (MRR), tool wear (TW), and surface roughness (Ra) are pertinent output performance indicators. Analysis of variance has been performed to assess the effect of process variables on the aforesaid output performance. It has been found that feed has a significant effect on MRR and surface roughness with a contribution of 50.65 and 33.62%, respectively, whereas spindle speed has a major contribution on TW with a contribution of 51.9%. This study assesses how well the Nitinol 56 machining process works overall. In this work, the Taguchi method has been used to determine the effect of aforesaid process variables on the output performance indices. To satisfy previously stated conflicting performance indices, a variety of multi-attribute decision-making approaches were used, such as utility, TOPSIS, and grey, to determine the optimal process variables. The optimal process variable combination has been achieved as f = 0.133 mm·rev−1, d = 0.06, and S = 835 RPM. This combination has been achieved using all methods.

Keywords: MADM; grey; utility; Nitinol 56; Taguchi; TOPSIS

1 Introduction

An essential group of shape memory alloy are NiTi alloys. The observation of the shape memory effect (SME) dates back to 1938, initially documented in copper–zinc alloys (Cu–Zn) and copper–tin alloys. However, the formal recognition of Nitinol, a nickel–titanium alloy with unique pseudoelasticity, strong damping capabilities, and an SME, came later in 1965 when the first patent application for this alloy developed by the Naval Ordnance Laboratory [1,2,3,4]. Renowned for their outstanding properties, including fatigue strength, thermal stability, and corrosion resistance under extreme conditions, Ni-based superalloys are employed in manufacturing critical components of aero-engines and gas turbines that frequently endure high velocity, high pressure, and high temperature [5,6,7,8]. Nevertheless, the presence of y′ and y′′ precipitates makes these materials challenging to machine, creating a persistent obstacle in the field of Ni-based superalloy machining [9,10,11].

Ni-based superalloys are categorized depending on their chemical makeup [12]. Buehler and Wang [13] successfully improved the drilling and mechanical cutting machinability of NiTi alloys. An optimal drilling performance was observed using a tungsten–carbide twist drill with a rotational speed of 163 rpm and a feed rate of 0.07 mm·rev−1. This configuration exhibited favorable values for machining time, cutting forces, and tool service life. Shyha et al. [14] conducted machinability improvement tests during the drilling of Kovar SMAS. To minimize cutting temperature and improve surface integrity, the experiments, which were conducted on unbacked and backed Kovar alloy workpieces at speeds ranging from 450 to 3,750 rpm in dry conditions, recommended using smaller drill sizes, lower feed rates, and reduced cutting speeds. These modifications result in lower burr size, lower thermal hardening, and a decreased risk of microcrack formation.

Kaynak et al. [15,16] investigated cryogenic machining as a viable tactic to improve NiTi SMAS machining performance in a different study. We used liquid nitrogen as the cryogenic coolant, which we kept at 1.5 MPa. Preheated machining involved a preheating temperature of 175°C, while minimum quantity lubrication (MQL) was applied with a flow rate of 60 ml·h−1 and an air pressure of 0.4 MPa at various machining parameter combinations. The study revealed that cryogenically machined samples exhibited superior surface integrity compared to dry samples at high speeds. Thermal distortion and reduced tool wear (TW) were found to be the primary factors contributing to this improvement, which resulted in smoother surface generation during cryogenic machining at elevated cutting speeds.

Grguraš et al. [17] carried out a comparative investigation with carbide end tools in milling SS 316L and Inconel 718 under varied lubrication conditions, and they introduced full-body ceramic tools for machining Inconel 718. The study evaluated cost, surface integrity, and tool life. The findings showed that ceramic tools outperform carbide tools in terms of material removal rate (MRR) and productivity; however, further investigation is necessary due to economic factors. Using an improved PVD-coated (AlTiN) carbide tool, Venkatesan et al. [18] accomplished dry turning on Inconel 718. Low cutting depths, low feed rates, and high cutting speeds produce the best results with the least amount of Ra and cutting forces, according to studies on surface finish, cutting forces, and tool life. A desirability function was used for parameter optimization.

Using SEM analysis of chip roots, Parida [19] machined Inconel 718 and investigated the effect of heating temperature on the workpiece surface. The researched attributes decreased as the heating temperature increased, according to the results. In their exploration of the machining difficulties of shape memory alloys based on Ni and Ti, Huang [20] found that, at speeds lower than 200 m·min−1, greater cutting rates result in lower TW, improved surface smoothness, and lower cutting force. But after this point, the impact is negligible. Additional factors that affect machining characteristics are the feed rate and depth of cut.

In their investigation on the turning of Inconel 825, Gandhi et al. [21] measured the machining force, Ra, rate of metal removal, and chip thickness ratio. Better surface finishes were produced using C-type inserts, and machining force and the chip thickness ratio decreased with increased spindle speed, utilizing a CVD-coated tungsten carbide tool. Yadav et al. [22] machined Inconel 718 with an emphasis on MRR and TW, utilizing DEFORM 3D software for modeling [23,24,25,26].

The Taguchi method has been widely applied in previous state-of-the-art studies, offering efficiency by minimizing the number of trials through the application of the orthogonal array (OA) concept in the design of experiments (DOE). Renowned among academics and scientists, this approach excels in predicting the optimal configuration of machine parameters within a discrete domain. However, it falls short when confronted with multi-response optimization challenges [27,28,29,30,31,32].

To address these limitations, researchers have sought to enhance the Taguchi methodology by integrating it with various techniques, such as TOPSIS [33], utility concept [34], grey relation theory [35], MOORA [36], and desirability function [37]. The combination of these approaches resolves the optimization challenges associated with multiple responses. By generating a unified performance index that encompasses various performance characteristics, these methods leverage the strengths of the Taguchi method for direct optimization. Consequently, optimization methodologies like MOORA–Taguchi, grey–Taguchi, TOPSIS–Taguchi, Utility–Taguchi, desirability–Taguchi, MOORA–Taguchi, and others have gained substantial recognition for real-time optimization across diverse qualities of processes or products [38,39].

The present study aims to investigate TOPSIS, utility, and grey multi-attribute decision making (MADM) approaches in order to find the best machine setup for Nitinol 56.

Adin [40,41] explored the impact of drilling parameters and cryogenic treatment on the M35 HSS drill bit, employed for drilling AA2024-T3 aluminum alloy under dry conditions. The investigation centered on predetermined drilling parameters based on the Taguchi experimental design and conducted subsequent analyses using both analysis of variance (ANOVA) and regression analysis methodologies [42].

2 Experimentation

Nitinol 56 was produced using the Banka 40, a manual lathe shown in Figure 1. Samples having a length of 10 mm and a diameter of 16 mm were cut from a total length of 150 mm. The TNMG160408 from KYOCERA (single-point cutting tool) was used in the machining process.

Figure 1 Experimental setup for dry machining.
Figure 1

Experimental setup for dry machining.

The literature reveals that various parameters influence machining characteristics. Drawing from the literature and considering the adjustable parameters in turning – specifically the spindle speed, feed, and depth of cut, have been chosen. As indicated in Table 1, several arrangements of process variables, including S (RPM), t (mm), and f (mm·rev−1), were used for the machining. Table 2 shows how the experimental trials were methodically arranged using Taguchi’s L9 OA.

Table 1

Process variables and their corresponding levels

Level S (in RPM) f (mm·rev−1) t (mm)
1 247 0.067 0.2
2 557 0.111 0.4
3 835 0.133 0.6
Table 2

DOE employing L9 OA

Experiment trial no. S (RPM) f (mm·rev−1) t (mm)
(1) 247 0.067 0.2
(2) 247 0.111 0.4
(3) 247 0.133 0.6
(4) 557 0.067 0.4
(5) 557 0.111 0.6
(6) 557 0.133 0.2
(7) 835 0.067 0.6
(8) 835 0.111 0.2
(9) 835 0.133 0.4

Material removal rate was assessed by comparing the workpiece weight before and after machining, along with recording the duration of the operation. An electronic weighing scale was utilized to measure the weight, and a digital watch was employed to measure the time. Equation (1) was utilized to evaluate the MRR:

(1) MRR = M 1 − M 2 ρ · m t ( mm 3 / S ) ,

where M 1 is the initial wt. of the workpiece (g), M 2 is the weight after machining the workpiece’s weight (g), ρ is the workpiece density, and m t is the machining time (s). The surface roughness (Ra) of the machined workpiece was calculated using a Taylor Hobson’s surface roughness tester (Figure 2).

Figure 2 Surface roughness tester (Taylor Hobson’s) with Stylus.
Figure 2

Surface roughness tester (Taylor Hobson’s) with Stylus.

Due to continuous usage, TW is the progressive degradation of cutting tools. The assessment of TW was conducted using a toolmaker microscope, specifically the Mitutoyo TM Series, as depicted in Figure 3.

Figure 3 Tool Maker’s microscope made by Mitutoyo.
Figure 3

Tool Maker’s microscope made by Mitutoyo.

3 Methodology

The methodology employed in this study involved a systematic approach (illustrated in Figure 4) to optimize the machining process for Nitinol alloy. First, Nitinol alloy was selected as the material. The identification of input parameters, including the spindle speed (S), feed (f), and depth of cut (d), has been based on their significance in the machining process. DOE, specifically the Taguchi methodology, was used to systematically vary these input parameters and analyze their effects on the output performance indices, namely TW, MRR, and surface roughness (Ra). The workpiece’s weight before and after machining were compared to calculate the MRR, while TW and Ra were measured using appropriate tools and devices. The utility function approach, TOPSIS, and grey analysis are three examples of MADM approaches that were used to achieve optimal process variables, which were taken into consideration during a detailed analysis and discussion of the experiment outcomes. The flowchart illustrating the aforementioned techniques is presented in Figures 5–7.

Figure 4 Experimentation methodology.
Figure 4

Experimentation methodology.

Figure 5 GRA steps [27].
Figure 5

GRA steps [27].

Figure 6 Utility function approach steps [26–27].
Figure 6

Utility function approach steps [26–27].

Figure 7 For multi-response optimization: the TOPSIS–Taguchi approach flow chart [25].
Figure 7

For multi-response optimization: the TOPSIS–Taguchi approach flow chart [25].

Within Figure 5, the grey relation analysis (GRA) is depicted as a systematic procedure presented in a step-by-step flow chart. The sequence initiates with data collection, followed by normalization to standardize the scale. A reference series is established for comparative analysis with other variables. Grey values are computed based on their proximity to this reference series. Subsequently, the calculation of grey relation coefficients exposes the strength of relationships. The ranking of variables provides insights into their relative significance. The ensuing results undergo a comprehensive analysis, leading to interpretations that contribute to decision-making processes.

In Figure 6, the utility function approach is depicted through a succinct flow chart detailing crucial steps. The process commences with the identification of decision criteria and the assignment of weights based on their significance. Subsequently, utility functions are crafted to quantify preferences, enabling the computation of overall utility scores for alternatives. A sensitivity analysis is conducted to evaluate the repercussions of variations in criterion weights. This method provides decision-makers with a structured and systematic approach to assess and compare alternatives grounded in utility considerations.

In Figure 7, the flow chart outlines the multi-response optimization using the TOPSIS–Taguchi approach. It begins with identifying multiple responses and their optimization goals, employing Taguchi’s experimental design to establish optimal factor levels. The TOPSIS method is then utilized to rank alternative factor combinations based on their proximity to the ideal solution. A subsequent sensitivity analysis evaluates the robustness of the optimized solutions. This systematic approach, as depicted in the flow chart, provides practitioners with a structured methodology for the simultaneous optimization of multiple responses, leveraging the synergistic combination of TOPSIS and Taguchi techniques.

4 Results and discussion

ANOVA was used in the study’s findings section to evaluate the impacts of three machining factors: feed (f), depth of cut (d), and spindle speed (S). An analysis of the effects of each variable and their interactions on the output performance measures was made easier by this statistical method, which also gave important insights into how these characteristics affect the machining process. Furthermore, a comprehensive examination of the chips generated throughout the machining procedure was carried out in order to understand the behavior of the material and the relationship between the tool and the workpiece. Since chip formation directly affects productivity, surface smoothness, and TW, understanding it is critical to improving machining results.

Furthermore, employing MADM techniques, the study descended into the optimization phase. The best mix of machining variables that concurrently satisfy competing performance indices was found by using a variety of MADM approaches, such as utility, TOPSIS, and grey. Among these measures were TW, the MRR, and surface roughness (Ra) (Table 3). The study combined the findings of ANOVA with the insights from chip formation analysis to identify the most productive and effective machining settings (Table 3).

Table 3

Experimental data

No. Ra (μm) MRR (mm3·s−1) TW
1 1.1 1.3457 0.029
2 1.4 5.2852 0.0094
3 0.9 3.8682 0.0029
4 1.6 3.5767 0.0206
5 1.6 11.0976 0.0267
6 1.1 15.4124 0.034
7 1.0 3.2246 0.0028
8 1.3 8.8837 0.0023
9 1.3 19.9069 0.016

4.1 Effects of machining variables on output characteristics

To explore the individual impact of all input variables on the output responses, the ANOVA technique was implemented. Tables 4–6 list the percentage contributions of each variable for MRR, Ra, and TWR. Figure 8 represents the percentage contribution of the spindle speed, depth of cut, and feed with respect to the MRR, Ra, and TWR. It is obvious that the MRR is highly impacted by the feed, with a share of 50.65%., followed by the depth of cut (6.18%) and spindle speed (29.69%). The same has been calculated for other output characteristics, namely Ra and TW, as presented in Figure 8(b) and (c), respectively. For Ra, feed tops the chart, with a share of 33.62%, followed by the depth of cut (28.31%) and spindle speed (29.64%). For TW, the percentage share of the spindle speed is highest (51.9%) and the depth of cut (15.09%) and feed (3.73%).

Table 4

ANOVA technique for the MRR

Source DOF Adj-SS Adj-MS F-value % contribution
Spindle speed 2 94.48 47.238 2.21 29.69
Feed 2 161.15 80.577 3.76 50.65
DOC 2 19.69 9.845 0.46 6.18
Error 2 42.82 21.410 13.45
Total 8 318.14
Table 5

ANOVA technique for surface roughness

Source DF Adj SS Adj MS F-value % contribution
Spindle speed 2 0.14889 0.07444 3.53 29.64
Feed 2 0.16889 0.08444 4.00 33.62
DOC 2 0.14222 0.07111 3.37 28.31
Error 2 0.04222 0.02111 8.4
Total 8 0.50222
Table 6

ANOVA technique for TW

Source DF Adj SS Adj MS F-value % contribution
Spindle speed 2 0.000626 0.000313 1.77 51.9
Feed 2 0.000045 0.000023 0.13 3.73
DOC 2 0.000182 0.000091 0.52 15.09
Error 2 0.000353 0.000176 29.27
Total 8 0.001206
Figure 8 Percentage contribution of machining parameters for (a) the MRR and (b) surface roughness. (c) TWR of all input variables.
Figure 8

Percentage contribution of machining parameters for (a) the MRR and (b) surface roughness. (c) TWR of all input variables.

4.2 Chip formation

The study highlighted several crucial observations related to chip formation and its impact on the machining process. First, the depth of cut emerged as a pivotal factor, as it directly influenced the chip thickness; higher depths of cut led to an increase in the chip diameter, underscoring the importance of this parameter in determining the size and shape of the generated chips, which is illustrated in Figure 9. Additionally, the study revealed that chip thickness was positively correlated with cutting velocity and feed amount in dry conditions, indicating that these factors played a significant role in chip formation dynamics. Moreover, the research findings indicated that chips produced under all dry-cutting conditions exhibited a continuous pattern, emphasizing consistency in the machining process. It was also noted that during dry-turning operations, the generation of chips was accompanied by the dissipation of heat energy, contributing to heat-related losses. Importantly, the study emphasized the critical implications of chip characteristics on the lifespan of tools and the overall surface quality of the machined components, highlighting the need for a comprehensive understanding of chip formation for optimizing tool longevity and product quality in machining processes.

Figure 9 Chip formed during different machining conditions.
Figure 9

Chip formed during different machining conditions.

4.3 Optimal process combination using different MADM techniques

4.3.1 Grey analysis

First of all, experimental data will be normalized to exclude all the diversity of data as well as conflict requirements. Here, Surface roughness and tool wear should be referred to as smaller is better and MRR as higher is better. The following equations will be used to normalize the experimental data: [27].

Smaller is better (SB): The normalization for SB is performed (Table 7) using the following equation:

(2) p i ( n ) = max . q i 0 ( n ) − q i 0 ( n ) max . q i 0 ( n ) − min . q i 0 ( n ) .

Table 7

Normalized experimental data along with grey relational coefficients and corresponding grey relation grades

Trial No. Nor-Ra Nor-MRR Nor-TW Gr-Ra Gr-MRR Gr-TW Overall R S/N ratio P-S/N ratio
1 0.714 0.000 0.158 0.636 0.333 0.373 0.447 −6.986 1.0289
2 0.286 0.212 0.776 0.412 0.388 0.691 0.497 −6.075
3 1.000 0.136 0.981 1.000 0.367 0.964 0.777 −2.195
4 0.000 0.120 0.423 0.333 0.362 0.464 0.387 −8.255
5 0.000 0.525 0.230 0.333 0.513 0.394 0.413 −7.673
6 0.714 0.758 0.000 0.636 0.674 0.333 0.548 −5.227
7 0.857 0.101 0.984 0.778 0.357 0.969 0.702 −3.079
8 0.429 0.406 1.000 0.467 0.457 1.000 0.641 −3.859
9 0.429 1.000 0.568 0.467 1.000 0.536 0.668 −3.509

Larger is better (LB): The normalization for LB is performed using the following equation:

(3) p i ( n ) = q i 0 ( n ) − min . q i 0 ( n ) max . q i 0 ( n ) − min . q i 0 ( n ) ,

where p i (n) represents the normalized value, min.q i 0(n) denotes the minimum response value of q i 0(n), max.q i 0(n) indicates the maximum response value of q i 0(n), and q i 0(n) represents the experimental value. The following formula determines the grey relational coefficient using the computed normalized response data:

(4) ξ i ( n ) = Δ min + ψ Δ max Δ 0 i ( n ) + ψ Δ max ,

(5) Δ 0 i ( n ) = ∣ y 0 ( n ) − y i ( n ) ∣ .

In the given context, where ξ i (n) signifies the Grey relational coefficient for the ith experiment, Ψ represents the identification coefficient (typically set at 0.5), ∆ max corresponds to the maximum value of ∆0i (n), and ∆ min denotes the minimum value of ∆ 0i (n). Overall, Grey’s relational grade (R i ) Table 7 optimal combination using Taguchi (Figure 10).

(6) R i = 1 / n ∑ k = 1 n ξ i ( k ) .

Figure 10 Main effect plot for overall R.
Figure 10

Main effect plot for overall R.

4.4 Optimal process combination employing utility analysis

The evaluation of the output feature involves the use of lower and higher values as benchmarks. In this context, two arbitrary arithmetic values, namely 0 and 9, which are commonly referred to as preferred numbers, have been designated for this purpose. The preference number (N p) can be computed using an equation designed to assess the feature on a logarithmic scale (Table 8):

(7) N p = M × log B q B q ′ .

Table 8

Utility values for O u (overall utility) with S/N ratio and predicted SN ratio and corresponding output characteristics

Sr. no. N p-Ra N p-MRR N p-TW O u S/N ratio P-S/N ratio
1 5.861 0.000 0.532 2.131 6.571081 19.91697
2 2.089 4.570 4.296 3.652 11.24946
3 9.000 3.527 8.225 6.918 16.79906
4 0.000 3.265 1.674 1.647 4.331736
5 0.000 7.048 0.808 2.619 8.361157
6 5.861 8.145 0.000 4.669 13.384
7 7.352 2.919 8.343 6.205 15.85434
8 3.248 6.305 9.000 6.184 15.82568
9 3.248 9.000 2.519 4.922 13.84321

In equation (7), the representation of the output characteristic x is denoted as B q. The lower value of the output characteristic x, labeled as B q′, is expressed. M is a constant, and its calculation is determined by equation (8), only if B q = B * (where A * is the optimal value), then N p = 9 . Hence,

(8) o = 9 log B q B q ′ .

The overall utility (equation 9) is specified as in Table 8 and optimal combination using Taguchi (Figure 11)

(9) O u = ∑ x = 1 k W q ( N p ) .

Figure 11 Main effect plot for O u.
Figure 11

Main effect plot for O u.

In accordance with the conditions, (equation 10)

(10) ∑ x = 1 k W x = 1 .

4.5 Optimal process combination employing TOPSIS

To initiate the process, the mentioned evaluation characteristics undergo normalization using equation 11, and the resultant normalized values are presented in Table 9.

(11) r i j = x i j ∑ i = 1 m x i j 2 ,

where, for attribute X j . , r i j depicts the normalized performance of A i .

Table 9

Weighted and normalized values corresponding to the output values

Trial no. r-Ra r-MRR r-TW V-Ra V-MRR V-TW
1 0.287 0.044762 0.49018 0.095667 0.014921 0.163393
2 0.365273 0.175802 0.158886 0.121758 0.058601 0.052962
3 0.234818 0.128669 0.049018 0.078273 0.04289 0.016339
4 0.417454 0.118972 0.348196 0.139151 0.039657 0.116065
5 0.417454 0.369141 0.451303 0.139151 0.123047 0.150434
6 0.287 0.512665 0.574693 0.095667 0.170888 0.191564
7 0.260909 0.10726 0.047328 0.08697 0.035753 0.015776
8 0.339182 0.2955 0.038876 0.113061 0.0985 0.012959
9 0.339182 0.662166 0.270444 0.113061 0.220722 0.090148

In the existing work, all the responses are supposed to have equivalent importance; therefore, an equal weight (0.25) has been assigned to each characteristic, and weighted values are calculated using equation (11) and tabulated in Table 9.

(12) V = [ v i j ] V = w j r i j .

It is crucial to determine negative and positive ideal solutions, recognizing the inherent incompatibility of the evaluation qualities mentioned earlier. In this context, the optimal values are identified as the lowest for qualities like TW and Ra, where lower values are preferable and conversely for others. Similarly, the positive ideal solution is characterized by the highest MRR, while the negative ideal solution exhibits the lowest MRR. Both negative and positive optimal solutions are detailed in Table 10. Subsequently, the separation distance from the negative and positive ideal solutions was computed using equations (13) and (14), respectively, and the results are documented in Table 11. Preference is accorded to proximity coefficient values that are closest, as determined by equation (15). Finally, the application of the Taguchi methodology to the proximity coefficient aids in identifying the optimal combination (Figure 12).

(13) D i + = ∑ j = 1 n ( y i j − y j + ) 2 , i = 1 , 2 … … … . m ,

(14) D i − = ∑ j = 1 n ( y i j − y j − ) 2 , i = 1 , 2 … … … . m ,

(15) C c + = D i − D i + + D i − , i = 1 , 2 … … … … … m , 0 ≤ C c + ≤ 1 .

Table 10

Negative (I−) and positive (I+) ideal values

Ra MRR TW
I− 0.139151 0.035753 0.191564
I+ 0.078273 0.220722 0.012959
Table 11

Closeness coefficient corresponding to S/N ratios, predicted S/N ratio for optimal setting, and positive and negative separation distances

Sr. no. D+ D− C c + S/N ratio P-S/N ratio
1 0.255554 0.055843 0.179331 −14.9269 −1.43018
2 0.1726 0.141545 0.450573 −6.92469
3 0.177915 0.185636 0.51062 −5.83805
4 0.217117 0.075599 0.258269 −11.7586
5 0.17932 0.096498 0.349861 −9.12209
6 0.186255 0.141959 0.432521 −7.27986
7 0.185244 0.183369 0.497457 −6.06489
8 0.127124 0.191096 0.600515 −4.42953
9 0.084666 0.212555 0.715141 −2.91216
Figure 12 Main effect plot for Cc.
Figure 12

Main effect plot for Cc.

4.6 Confirmatory test

In a later stage, the confirmatory test was carried out for favorable machining conditions (i.e., at S = 835 RPM, f = 0.133 mm·rev−1, and d = 0.06) obtained from the intended optimization model in order to validate the optimal conditions, and results are shown in Table 12. It was observed that overall output characteristics improved.

Table 12

Confirmatory test

Ra (μm) MRR (mm3·s−1) TW
Confirmatory test 1.233 18.734 0.0026

5 Conclusions

This study observes the machinability aspects of Nitinol 56 with a focus on TW, surface roughness, and MRR. The following is a summary of the main findings:

  1. A number of optimization techniques, including utility, grey, and TOPSIS, have been presented and combined to identify the proper machine parameters for Nitinol 56 cutting.

  2. ANOVA was conducted to evaluate the impact of process variables on the mentioned output performance. The analysis revealed that the feed significantly influences the MRR and surface roughness, contributing 50.65 and 33.62%, respectively. Meanwhile, the spindle speed plays a significant role in TW, accounting for a major contribution of 51.9%.

  3. The optimal output process responses for machining Nitinol 56 were observed at f = 0.133 mm·rev−1, d = 0.6, and S = 835 RPM.

  4. Significantly, all the previously mentioned methods result in the same optimal input settings.

Acknowledgments

The authors express their gratitude to IITRAM and MIT, MAHE Bengaluru Campus for providing them with facilities and financial assistance. The authors further express their gratitude for the valuable input and suggestions provided by the anonymous reviewers, guest editor, and editor-in-chief, which greatly contributed to enhancing the quality of the article.

  1. Funding information: The authors state no funding is involved.

  2. Author contributions: Conceptualization: Kumar Abhishek, Rakesh Chaudhari, and Venkatachalam Revathi; methodology: Dev Sureja, Soni Kumari, and Basireddy Bhavani; investigation: Dev Sureja; data curation: Dev Sureja and Mahendra Singh; formal analysis: Dev Sureja, Rakesh Chaudhari, and Soumyashree M Panchal; resources: Soni Kumari, Kumar Abhishek, and Mahendra Singh; software: Dev Sureja and Basireddy Bhavani; supervision: Kumar Abhishek and Venkatachalam Revathi; validation: Soni Kumari and Rakesh Chaudhari; writing – original draft: Dev Sureja and Soni Kumari; writing – review and editing: Kumar Abhishek and Soumyashree M Panchal.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Data availability statement: All data generated or analyzed during this study are included in this published article.

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Received: 2023-12-21
Revised: 2024-01-31
Accepted: 2024-02-06
Published Online: 2024-04-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/htmp-2022-0324
Keywords for this article
MADM; grey; utility; Nitinol 56; Taguchi; TOPSIS
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. De-chlorination of poly(vinyl) chloride using Fe2O3 and the improvement of chlorine fixing ratio in FeCl2 by SiO2 addition
  3. Reductive behavior of nickel and iron metallization in magnesian siliceous nickel laterite ores under the action of sulfur-bearing natural gas
  4. Study on properties of CaF2–CaO–Al2O3–MgO–B2O3 electroslag remelting slag for rack plate steel
  5. The origin of {113}<361> grains and their impact on secondary recrystallization in producing ultra-thin grain-oriented electrical steel
  6. Channel parameter optimization of one-strand slab induction heating tundish with double channels
  7. Effect of rare-earth Ce on the texture of non-oriented silicon steels
  8. Performance optimization of PERC solar cells based on laser ablation forming local contact on the rear
  9. Effect of ladle-lining materials on inclusion evolution in Al-killed steel during LF refining
  10. Analysis of metallurgical defects in enamel steel castings
  11. Effect of cooling rate and Nb synergistic strengthening on microstructure and mechanical properties of high-strength rebar
  12. Effect of grain size on fatigue strength of 304 stainless steel
  13. Analysis and control of surface cracks in a B-bearing continuous casting blooms
  14. Application of laser surface detection technology in blast furnace gas flow control and optimization
  15. Preparation of MoO3 powder by hydrothermal method
  16. The comparative study of Ti-bearing oxides introduced by different methods
  17. Application of MgO/ZrO2 coating on 309 stainless steel to increase resistance to corrosion at high temperatures and oxidation by an electrochemical method
  18. Effect of applying a full oxygen blast furnace on carbon emissions based on a carbon metabolism calculation model
  19. Characterization of low-damage cutting of alfalfa stalks by self-sharpening cutters made of gradient materials
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  22. Effect of calcium treatment on inclusions in 38CrMoAl high aluminum steel
  23. Effect of isothermal transformation temperature on the microstructure, precipitation behavior, and mechanical properties of anti-seismic rebar
  24. Evolution of residual stress and microstructure of 2205 duplex stainless steel welded joints during different post-weld heat treatment
  25. Effect of heating process on the corrosion resistance of zinc iron alloy coatings
  26. BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
  27. Innovative approaches to enhancing crack repair: Performance optimization of biopolymer-infused CXT
  28. Structural and electrochromic property control of WO3 films through fine-tuning of film-forming parameters
  29. Influence of non-linear thermal radiation on the dynamics of homogeneous and heterogeneous chemical reactions between the cone and the disk
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  37. Review Articles
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  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
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  44. Topical Issue on Focus of Hot Deformation of Metaland High Entropy Alloys - Part II
  45. Synthesis of aluminium (Al) and alumina (Al2O3)-based graded material by gravity casting
  46. Experimental investigation into machining performance of magnesium alloy AZ91D under dry, minimum quantity lubrication, and nano minimum quantity lubrication environments
  47. Numerical simulation of temperature distribution and residual stress in TIG welding of stainless-steel single-pass flange butt joint using finite element analysis
  48. Special Issue on A Deep Dive into Machining and Welding Advancements - Part I
  49. Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle
  50. Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach
  51. Experimental and numerical analysis of temperature distributions in SA 387 pressure vessel steel during submerged arc welding
  52. Optimization of process parameters in plasma arc cutting of commercial-grade aluminium plate
  53. Multi-response optimization of friction stir welding using fuzzy-grey system
  54. Mechanical and micro-structural studies of pulsed and constant current TIG weldments of super duplex stainless steels and Austenitic stainless steels
  55. Stretch-forming characteristics of austenitic material stainless steel 304 at hot working temperatures
  56. Work hardening and X-ray diffraction studies on ASS 304 at high temperatures
  57. Study of phase equilibrium of refractory high-entropy alloys using the atomic size difference concept for turbine blade applications
  58. A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network
  59. A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method
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  61. Innovations for sustainable chemical manufacturing and waste minimization through green production practices
  62. Topical Issue on Conference on Materials, Manufacturing Processes and Devices - Part I
  63. Characterization of Co–Ni–TiO2 coatings prepared by combined sol-enhanced and pulse current electrodeposition methods
  64. Hot deformation behaviors and microstructure characteristics of Cr–Mo–Ni–V steel with a banded structure
  65. Effects of normalizing and tempering temperature on the bainite microstructure and properties of low alloy fire-resistant steel bars
  66. Dynamic evolution of residual stress upon manufacturing Al-based diesel engine diaphragm
  67. Study on impact resistance of steel fiber reinforced concrete after exposure to fire
  68. Bonding behaviour between steel fibre and concrete matrix after experiencing elevated temperature at various loading rates
  69. Diffusion law of sulfate ions in coral aggregate seawater concrete in the marine environment
  70. Microstructure evolution and grain refinement mechanism of 316LN steel
  71. Investigation of the interface and physical properties of a Kovar alloy/Cu composite wire processed by multi-pass drawing
  72. The investigation of peritectic solidification of high nitrogen stainless steels by in-situ observation
  73. Microstructure and mechanical properties of submerged arc welded medium-thickness Q690qE high-strength steel plate joints
  74. Experimental study on the effect of the riveting process on the bending resistance of beams composed of galvanized Q235 steel
  75. Density functional theory study of Mg–Ho intermetallic phases
  76. Investigation of electrical properties and PTCR effect in double-donor doping BaTiO3 lead-free ceramics
  77. Special Issue on Thermal Management and Heat Transfer
  78. On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach
  79. Time dependent model to analyze the magnetic refrigeration performance of gadolinium near the room temperature
  80. Heat transfer characteristics in a non-Newtonian (Williamson) hybrid nanofluid with Hall and convective boundary effects
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  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
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