Startseite Naturwissenschaften A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method
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A hybrid approach for the machinability analysis of Incoloy 825 using the entropy-MOORA method

  • Saurabh Kumar Sahu EMAIL logo , Shiena Shekhar , Akhtar Khan , Dheeraj Lal Soni , Prashant Kumar Gangwar EMAIL logo und Manish Gupta
Veröffentlicht/Copyright: 20. November 2024
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High Temperature Materials and Processes
Aus der Zeitschrift High Temperature Materials and Processes Band 43 Heft 1

Abstract

With its exceptional qualities, Incoloy 825 is highly valued in a range of industries, including nuclear power plants, petrochemical plants, and chemical industries. Nevertheless, the unique combination of these properties presents a formidable challenge when it comes to machining Incoloy 825. Its low heat conductivity, rapid strain hardening, strong chemical affinity, and the presence of hard and abrasive particles in its microstructure all contribute to the difficulty. The objective of this study is to examine important factors related to the machinability of Incoloy 825. To achieve this, a hybrid tool called entropy coupled with MOORA will be used to determine the optimal cutting conditions. In order to achieve this, three specific input parameters were chosen: the spindle speed, feed rate, and depth of cut. Meanwhile, the major outcomes taken into account were the cutting force, cutting temperature, material removal rate, roughness of the machined surface, and flank wear. The experiments were conducted using Taguchi’s L27 orthogonal array, following the principles of experimental design. The findings indicate that the proposed hybrid approach is capable of accurately determining the best combination of parameters for cutting the chosen work material and can be employed in structural applications. For turning Incoloy 825, the optimal parametric setting was determined to be a spindle speed of 1,285 rpm, a feed rate of 0.0625 mm·rev−1, and a depth of cut of 0.3 mm.

Keywords: Entropy-MOORA; cutting force; spindle speed; surface roughness; cutting temperature; MRR

1 Introduction

Nickel alloy is frequently referred to as the metal of versatility and serves as a prominent illustration of human innovation in the field of materials science. Chemical facilities, aerospace industries, marine equipment, pollution control systems, and nuclear reactors extensively utilize nickel-based alloys [1]. In fact, Ni-based alloys are employed in 50% of aircraft propulsion system weight because of their thermal stability, fatigue strength, and corrosion resistance in hostile environments [2,3]. Incoloy 825 is a nickel-based super alloy that acquires a remarkable combination of properties to withstand chemical attacks, especially in environments containing sulfuric acid, phosphoric acid, and chloride, as well as oxidation and corrosion at elevated temperatures. It has a good precipitation-hardening ability [4]. However, these alloys are categorized as difficult-to-machine substances due to their low heat conductivity and strong chemical reactivity. As a result, these alloys have poor machinability [5]. To produce a component, the work material goes through various manufacturing operations like casting, forming, welding of similar and dissimilar metals, and machining [6]. The final and essential stage of the manufacturing procedure is the machining process. The turning process has been a major operation in manufacturing since the beginning of industrialization [7]. The turning operation is widely recognized as the most prevalent machining process that is utilized to produce intricate shapes and geometries with a high degree of dimensional precision and exceptional surface quality. Turning is a viable alternative to the traditional grinding technique. It is a versatile and cost-effective method for working with hardened steel [8]. The turning attributes are composed of two separate characteristics, namely quality parameters (namely, surface quality, machining temperature, and cutting force) and performance attributes, i.e. the rate of material removal [9], to ascertain the parameters of turning and conduct trials under appropriate conditions. Improper choice of machining attributes can lead to accelerated wear of the cutting tool, resulting in inadequate machining of the workpiece or degradation of its surface quality [10]. This results in economic and temporal losses in the production industry, which affect the reputation of the industry in the market [11]. With an appropriate choice of cutting tools and cutting conditions, machinability can be improved [12]. In the past, research has been mostly focused on conducting experimental studies to analyse the machinability of diverse work materials, including titanium alloys [13], steel, and nitinol alloys [14,15]. Further, most of the researchers have analysed the machinability of various materials by assessing a single output parameter of the machining process [16]. In addition, design methods such as factorial design, response surface methodology [17], and Taguchi methods [18–20] are commonly employed instead of the one-factor-at-a-time experimental approach due to their time-consuming nature and high cost [21]. Furthermore, various multi-response optimization decision-making procedures also exist, including the analytic hierarchy process (AHP), analytical network processing, and the technique for order of preference by similarity to the ideal solution (TOPSIS). Azizi et al. discussed the combination of three multi-criterion decision-making (MCDM) methods, i.e. AHP, entropy, and TOPSIS to optimize the grinding parameters of Inconel 738 [22]. The techniques employed to manage multiple responses in the presence of competing criteria, i.e. MOORA and multi-objective optimization on the basis of simple ratio analysis (MOOSRA), were used to examine non-traditional machine selection criteria. MOORA and MOOSRA are used to evaluate expert group data subjectively. Since all of the traits may be taken into account together with their relative importance, the MOORA technique offered a more accurate assessment of the provided options. Also, for material selection for the storage tank and flywheel, VIekriterijumsko KOmpromisno Rangiranje (VIKOR) gives better result compared with ELimination et Choix Traduisant la REalite (ELECTRE) and other methods mentioned in the literature [23,24]. Jhodkar et al. [25] conducted a turning process of machining on a titanium alloy using an L27 array. Then, a hybrid MOORA with the FUZZY approach is used to modify the regulating parameters. Additionally, it has been demonstrated that MOORA might be utilized to maximize several contradictory outcomes that are associated with some specific limitations. MOORA consists of two key components: reference points and ratio points. These components play a crucial role in assessing the performance of given alternatives. The COPRAS method was utilized to transform several outputs into a single index known as the multi-performance characteristics index [26].

In order to mitigate the possibility of adopting unsuitable alternatives in unconventional manufacturing processes, Chakraborty presented a novel methodology called the MOORA method. The MOORA approach described in this study proved to be more productive, time-saving, and accessible compared to the existing multiobjective decision-making strategies [27]. In a different study, Shakeel Ahmed adopted TOPSIS method for enhancing drilling performance. The TOPSIS combination method proved more efficient in resolving multiresponse issues in the drilling process [28,29]. Assignment of an appropriate weightage to the selected criteria was marked as the major challenge in the above-discussed research. Therefore, in the later investigations, to ensure a comprehensive and adaptable analysis, the alternatives were assigned weights using one of the mathematically proven tools, such as the weighted entropy method. These tools helped the researchers to evaluate the weightage of the individual criterion more accurately. These weights were then utilized as input in various MCDM techniques: TOPSIS, VIKOR, and Multi MOORA. TOPSIS is a reliable method for rating alternatives, multi-MOORA is highly proficient in optimizing numerous objectives, and VIKOR provides a solution that involves making compromises [30]. The selection of the most suitable grinding conditions involved the utilization of an amalgam of three techniques: AHP, entropy, and TOPSIS [31,32]. In the machining business, it is common for the objectives of tool consumption and surface roughness (R a) to be minimized while the material removal rate (MRR) should be maximized. To determine the objective and constant weights of the mentioned criteria, the AHP approach was employed. The TOPSIS and WASPAS algorithms were utilized to select the optimal alternative based on predetermined criteria weights [33]. Among the methods that are described in the above literature, the MOORA approach is characterized by its user-friendly nature, simplicity, and ease of use. This method offers a comparable or nearly equivalent alternative answer with a high level of accuracy, as observed by multiple researchers who have employed various tough MODM strategies [34].

The primary goal of this research is to propose reduced machining time along with the higher surface quality and rate of material removal, low temperature and force during machining of the selected Incoloy 825 work part. To attain this objective, the present study focuses on turning Incoloy 825 and analysing five significant quality attributes: surface R a, machining force (F c), temperature (T m), MRR, and flank width (VBc) by using Taguchi’s L27 orthogonal array. An integrated MCDM tool, i.e. entropy-MOORA, is exploited with the aim of acquiring the desired goal of the investigation. The results demonstrated that the proposed methodology is able to effectively choose the most suitable combination of machining parameters for turning operations.

2 Materials and methodology

2.1 Design of experiment

The identification of the optimum combination of parameters has been recognized as a complicated challenge in production processes due to some effects of interactions among different cutting variables. The optimization strategy is crucial in ensuring both the quality of the final item and the efficiency of production in this situation. The orthogonal array design (OAD) proposed by Taguchi has been actively adopted and advocated by several researchers worldwide. It aids in enhancing the quality attributes by providing optimal combinations of input parameters. The Taguchi orthogonal array is a valuable tool for obtaining solutions to full factorial designs while limiting the trial experiments required [35]. To achieve the optimal set of input parameters, Taguchi’s OAD helps to find the impact of these parameters on the specified performance indicators. Furthermore, these arrays offer an optimal solution by minimizing the number of experimental trials while maintaining the quality.

For this experiment, three elements that can be controlled, viz., the spindle speed, feed rate, and depth of cut, were chosen. These factors were examined at three different levels, and an orthogonal array was created according to the specifications provided in Table 1. As per Taguchi L27 OAD, the layout of cutting parameters at different levels with their numeric values is depicted in Table 2.

Table 1

Cutting parameters (factors) and their levels

S. no. Parameters Unit Symbol Level
Low Medium High
1 Spindle speed rpm SS 645 1,000 1,285
2 Feed rate mm·rev−1 FR 0.0625 0.125 0.25
3 Depth of cut mm DOC 0.1 0.2 0.3
Table 2

Taguchi’s L27 OAD

Run Cutting variables
Spindle speed (rpm) Feed rate (mm·rev−1) Depth of cut (mm)
1 645 0.0625 0.1
2 645 0.0625 0.2
3 645 0.0625 0.3
4 645 0.125 0.1
5 645 0.125 0.2
6 645 0.125 0.3
7 645 0.25 0.1
8 645 0.25 0.2
9 645 0.25 0.3
10 1,000 0.0625 0.1
11 1,000 0.0625 0.2
12 1,000 0.0625 0.3
13 1,000 0.125 0.1
14 1,000 0.125 0.2
15 1,000 0.125 0.3
16 1,000 0.25 0.1
17 1,000 0.25 0.2
18 1,000 0.25 0.3
19 1,285 0.0625 0.1
20 1,285 0.0625 0.2
21 1,285 0.0625 0.3
22 1,285 0.125 0.1
23 1,285 0.125 0.2
24 1,285 0.125 0.3
25 1,285 0.25 0.1
26 1,285 0.25 0.2
27 1,285 0.25 0.3

2.2 Workpiece and cutting tool material

The present study employed Taguchi’s L27 OAD for the execution of the experimentation. A round bar of Incoloy 825 with a diameter of 40 mm and a length of 250 mm was selected as an experimental work material. The chemical composition of this material is 38.51% Ni, 20.14% Cr, 2.98% Mo, 1.54% Cu, and 0.65% Ti. Incoloy 825 workpiece was machined (turned) on conventional lathe (Manufacturer: National Small Industries Limited) using a diamond-shaped tool made of tungsten carbide having ISO designation CNGM120408MG (Manufacturer industry: Kennametal, Grade: KC5010). For each trial run, a fresh insert cutting edge was employed. The cutting inserts were securely held in place using an ISO-designated tool holder, PCLNL2020K12.

2.3 Methodology

This research aims to investigate the suitability of a hybrid technique during the machining of hard-to-machined materials, viz. nickel-based superalloys. The methodology employed in the present investigation is outlined below.

2.3.1 MOORA method

A multi-objective or multi-attribute optimization is a method in which two or more competing features (objectives) are optimized simultaneously within a set of restrictions. A multi-objective problem is a common occurrence in various organizations, industries, corporate offices, manufacturing units, and other settings when there is a requirement to make optimal choices while considering two or more than two conflicting attributes. MODM approaches are appropriate tools for selecting one or more alternatives from a given set of options. The MOORA approach, initially proposed by Brauers (2004), is a versatile multi-objective optimization strategy that may effectively address a wide range of complicated decision-making situations. The study utilized the MOORA approach [36,37], which involves the following steps:

  1. Create a decision matrix that represents performance characteristics in relation to certain attributes or objectives using equation (1):

    (1) A = a 11 a 12 … a 1 n a 21 a 22 … a 2 n … … a ij … a m 1 a m 2 … a mn ,

    where a ij is the performance measure of the ith alternative on jth attribute. Rows represent alternatives (i = 1, 2,…, m), and the columns represent attributes (j = 1, 2,…, n).

  2. The data in the decision matrix are normalized to create a ratio system. The normalized value can be computed using equation (2):

    (2) a ij * = a ij ∑ i = 1 m a ij 2 ,

    where a ij * is the normalized value of ith alternative on jth criteria, and a ij * is a unitless scalar that falls inside the range of 0 and 1.

  3. Calculate the criteria weight: for calculating the weight of the criteria, several methods are available such as AHP, simple additive weighting, swing weighting, entropy method, and fuzzy logic. However, the entropy method provides impartiality by utilizing the variability of data instead of subjective judgement, thereby maintaining impartial criteria weights. In this study, the entropy method was used for calculating the criteria weight, and a complete description is provided in Section 2.3.2. Further, the weighted normalization matrix can be determined using equation (3):

    (3) W ij * = w j a ij * .

  4. The normalized performance measurements are aggregated by adding them for beneficial criteria (larger-is-better) and subtracting them for non-beneficial criteria (smaller-is-better). Equation (4) represents the overall assessment value of the performance measures:

    (4) y i = ∑ j = 1 c a ij * − ∑ j = c + 1 n a ij * ,

    where c is the number of criteria that belong to the larger-is-better type, ( n − c ) is the number of criteria that belong to the smaller-is-better criterion, and y i is the overall assessment value of the ith alternative with respect to all alternatives.

  5. Occasionally, certain criteria are considered more significant than others. Hence, to assign greater significance to specific criteria, it can be multiplied by its corresponding weight as shown in equation (5):

    (5) y i = ∑ j = 1 c w j a ij * − ∑ j = c + 1 n w j a ij * ,

    where the weight of the jth criteria is denoted as w j , which is calculated using the entropy method.

  6. After assessment, rank the overall values of y i in descending order. y i with the highest value reflects the optimal alternative, while y i with the lowest value denotes the least favourable or worst alternative.

2.3.2 Entropy method

The concept of entropy in information theory can be considered as a measure of the level of uncertainty transmitted by a discrete probability distribution. This tool can be efficiently applied in the decision-making process as it quantifies the existing discrepancies between the sets of data and elucidates the average fundamental information supplied to the decision-maker.

The procedure for determining objective/criteria weights using the entropy approach is as follows.

Step 1: An array of performance indices (decision matrix) is normalized to obtain the experimental outcomes, i.e. H ij , as depicted in equation (6):

(6) H ij = a ij * ∑ i = 1 m a ij * ( 1 ≤ i ≤ m , 1 ≤ j ≤ n ) .

Here, a ij * is the normalized value, which is calculated with the help of equation (2) and H ij represents the adjusted proportion of the ith alternative with respect to the jth criteria. Calculating H ij is a vital step in establishing the entropy value for each criterion, which helps in understanding the dispersion or diversity of the data.

Step 2: Calculate the entropy of experimental results using equation (7). Entropy measures the uncertainty associated with each criterion. Entropy represents the quantity of information offered by each criterion. Unlike subjective approaches, which give weights based on expert judgments, entropy delivers an objective mechanism for determining weights based on actual data:

(7) E j = − k ∑ i = 1 m ( H ij ln H ij ) ,

where E j is the entropy of the jth criteria and k = −1/[ln(of trials)].

Step 3: Obtain the objective weight by utilizing the theory of entropy as shown in equation (8):

(8) w j = ( 1 − E j ) ∑ i = 1 m ( 1 − E j ) ( 1 ≤ j ≤ n ) ,

where (1−E j ) represents the degree of diversification.

Sample calculation for determining the criteria weight for criteria C 1:

Here, for criteria C 1, the calculation of entropy is as follows:

Step 1: From Table 5, the value of a 11 ⁎ = 0.038, a 21 ⁎ = 0.134, …

Therefore, H 11 = 0.038/(0.038 + 0.134 + 0.22 + … + 0.217) = 0.008.

Similarly, for criteria C 1, H 21, H 31, H 41, and H 271 were calculated.

Step 2: Using equation (7) for calculating E 1, we calculated the value of k.

k = − 1 / [ ln ( 27 ) ] = − 0.303 .

This value of k is constant for all the criteria.

To calculate the entropy of criteria C 1, the value of (H ij ln H ij ) was calculated for each trial, i.e. (H 11 ln H 11) = (0.008 ln 0.008) = −0.040. Similarly, (H 21 ln H 21), (H 31 ln H 31), … were calculated, and the values are displayed in Table 6 (criteria C 1 column).

Now, the above-calculated values are substituted into equation (7) to obtain the value of entropy E 1,

E 1 = − ( 0.303 ) ( − 0.040 − 0.105 − 0.147 … − 0.146 )

E 1 = 0.945 .

Similarly, entropies E 2 , E 3 , E 4 , and E 5 were calculated and are displayed in Table 6.

Step 3: Using equation (8), the weight for criteria C 1 was calculated as

w 1 = ( 1 − E 1 ) ( 1 − E 1 ) +   ( 1 − E 2 ) +   ( 1 − E 3 ) +   ( 1 − E 4 ) +   ( 1 − E 5 )

w 1 = ( 1 − 0.945 ) ( 1 − 0.945 ) +   ( 1 − 0.997 ) +   ( 1 − 0.869 ) +   ( 1 − 0.919 ) +   ( 1 − 0.981 )

w 1 = 0.190 .

Similarly, w 2 , w 3 , w 4 , and w 5 were calculated and are displayed in Table 6.

3 Experimentation and data collection

In the current experiment, a standard Lathe was employed to cut the specified work material, following the configuration displayed in Table 1. Taguchi L27 orthogonal array was applied to examine 27 numerous combinations of the spindle speed, feed rate, and depth of cut in the machining process. Figure 1 depicts the experimental configuration. The most critical machinability attributes that were studied in this experiment are the cutting force (F c), surface R a, cutting temperature (T m), MRR, and VBc. A three-dimensional lathe tool dynamometer was used to measure the cutting force, and its value was recorded after each run. The temperature of the machining zone was monitored using a non-contact type pyrometer. The average R a of the machined surface was measured using a surface R a tester (Surfcom touch). The value of R a was recorded at four distinct positions of the machined part (90° apart), and the average was taken to be the final R a of the relevant experiment. Flank wear and chip profile were evaluated using a digital metallurgical microscope (Make: Olympus, Model: DSX1000). The MRR was calculated using equation (9):

(9) MRR = π d N f D avg mm 3 · min − 1 ,

where D avg is the average diameter of the work material (mm) calculated by using the following equation:

(10) D avg = D i + D f 2 ,

where D i is the initial diameter of the workpiece, D f is the final diameter of the workpiece, d is the depth of the cut (mm), f is the feed rate (mm·rev−1), and N is the spindle speed (rpm). The results of the experiment are presented in Table 3.

Figure 1 Experimental setup.
Figure 1

Experimental setup.

Table 3

Experimental results

Run/alternatives Cutting parameters Experimental responses: Criteria (C 1–C 5)
SS (rpm) FR (mm·rev−1) DOC (mm) F c (N) T m (°C) R a (µm) MRR (mm3·min−1) VBc (µm)
1 645 0.0625 0.1 34.330 584.200 0.038 473.414 187.832
2 645 0.0625 0.2 122.620 668.600 0.092 978.473 216.841
3 645 0.0625 0.3 201.100 693.800 0.169 1463.912 232.315
4 645 0.125 0.1 98.100 539.500 0.079 981.005 97.501
5 645 0.125 0.2 191.290 598.530 0.229 1888.592 178.797
6 645 0.125 0.3 284.490 619.500 0.730 2927.824 164.708
7 645 0.25 0.1 127.530 564.300 0.092 1962.009 105.420
8 645 0.25 0.2 189.330 632.000 0.524 3913.892 153.227
9 645 0.25 0.3 224.650 665.900 1.003 5650.587 185.637
10 1,000 0.0625 0.1 49.050 582.700 0.085 760.469 63.089
11 1,000 0.0625 0.2 177.560 717.500 0.122 1464.025 116.472
12 1,000 0.0625 0.3 312.940 826.800 0.317 2269.631 76.215
13 1,000 0.125 0.1 24.520 539.200 0.238 1520.938 75.222
14 1,000 0.125 0.2 106.930 647.100 0.233 3034.025 174.718
15 1,000 0.125 0.3 271.740 746.400 0.545 4380.300 100.700
16 1,000 0.25 0.1 29.430 565.100 0.088 2935.900 100.553
17 1,000 0.25 0.2 137.340 682.600 0.348 6068.050 154.590
18 1,000 0.25 0.3 237.400 800.000 1.080 9078.525 146.803
19 1,285 0.0625 0.1 48.070 503.800 0.091 977.202 96.729
20 1,285 0.0625 0.2 161.860 580.900 0.509 1949.361 104.981
21 1,285 0.0625 0.3 273.700 763.900 1.515 2814.343 162.576
22 1,285 0.125 0.1 33.354 609.600 0.141 1886.316 91.947
23 1,285 0.125 0.2 133.420 657.300 0.083 3898.722 121.854
24 1,285 0.125 0.3 262.910 837.700 0.332 5832.952 149.803
25 1,285 0.25 0.1 48.070 603.800 0.084 3908.809 60.942
26 1,285 0.25 0.2 142.240 669.000 0.157 7525.089 76.663
27 1,285 0.25 0.3 198.160 857.800 0.866 11665.905 148.045

4 Results and discussion

4.1 Multi-objective optimization using the entropy-MOORA method

In the current investigation, the hybrid entropy-MOORA method is used. MOORA works by normalizing the decision matrix, calculating criterion ratios for each alternative, and aggregating these ratios to determine the optimum alternative. In the first step, the decision matrix was formed using equation (1) and is shown in Table 4.

Table 4

Decision matrix

Run Criteria
C 1 C 2 C 3 C 4 C 5
1 34.330 584.200 0.038 473.414 187.832
2 122.620 668.600 0.092 978.473 216.841
3 201.100 693.800 0.169 1463.912 232.315
4 98.100 539.500 0.079 981.005 97.501
5 191.290 598.530 0.229 1888.592 178.797
6 284.490 619.500 0.730 2927.824 164.708
7 127.530 564.300 0.092 1962.009 105.420
8 189.330 632.000 0.524 3913.892 153.227
9 224.650 665.900 1.003 5650.587 185.637
10 49.050 582.700 0.085 760.469 63.089
11 177.560 717.500 0.122 1464.025 116.472
12 312.940 826.800 0.317 2269.631 76.215
13 24.520 539.200 0.238 1520.938 75.222
14 106.930 647.100 0.233 3034.025 174.718
15 271.740 746.400 0.545 4380.300 100.700
16 29.430 565.100 0.088 2935.900 100.553
17 137.340 682.600 0.348 6068.050 154.590
18 237.400 800.000 1.080 9078.525 146.803
19 48.070 503.800 0.091 977.202 96.729
20 161.860 580.900 0.509 1949.361 104.981
21 273.700 763.900 1.515 2814.343 162.576
22 33.354 609.600 0.141 1886.316 91.947
23 133.420 657.300 0.083 3898.722 121.854
24 262.910 837.700 0.332 5832.952 149.803
25 48.070 603.800 0.084 3908.809 60.942
26 142.240 669.000 0.157 7525.089 76.663
27 198.160 857.800 0.866 11665.905 148.045

In the next step, the decision matrix was normalized using equation (2) and is shown in Table 5. The objective of normalization is to standardize each element of the matrix in order to ensure that all elements have a comparable value [38].

Table 5

Normalized decision matrix

Run Criteria
C 1 C 2 C 3 C 4 C 5
1 0.038 0.169 0.014 0.021 0.260
2 0.134 0.194 0.034 0.043 0.300
3 0.220 0.201 0.063 0.065 0.321
4 0.107 0.156 0.029 0.044 0.135
5 0.210 0.173 0.085 0.084 0.247
6 0.312 0.179 0.272 0.130 0.228
7 0.140 0.163 0.034 0.087 0.146
8 0.207 0.183 0.195 0.174 0.212
9 0.246 0.193 0.373 0.251 0.257
10 0.054 0.169 0.032 0.034 0.087
11 0.194 0.208 0.045 0.065 0.161
12 0.343 0.239 0.118 0.101 0.105
13 0.027 0.156 0.089 0.068 0.104
14 0.117 0.187 0.087 0.135 0.242
15 0.298 0.216 0.203 0.194 0.139
16 0.032 0.164 0.033 0.130 0.139
17 0.150 0.198 0.129 0.269 0.214
18 0.260 0.232 0.402 0.403 0.203
19 0.053 0.146 0.034 0.043 0.134
20 0.177 0.168 0.189 0.087 0.145
21 0.300 0.221 0.564 0.125 0.225
22 0.037 0.177 0.052 0.084 0.127
23 0.146 0.190 0.031 0.173 0.168
24 0.288 0.243 0.124 0.259 0.207
25 0.053 0.175 0.031 0.174 0.084
26 0.156 0.194 0.058 0.334 0.106
27 0.217 0.248 0.322 0.518 0.205

These normalized values were used to calculate the criterion weight by using the entropy method. Equation (6) and (7) were used to calculate the value of entropy of each criterion. Then, by using the entropy values of each criterion, the weight of the respective criterion was computed by using equation (8). The entropy and criterion weights are listed in Table 6.

Table 6

Evaluation of entropy and criterion weights

Run Criteria
C 1 C 2 C 3 C 4 C 5
1 −0.040 −0.112 −0.022 −0.027 −0.156
2 −0.105 −0.123 −0.044 −0.048 −0.171
3 −0.147 −0.127 −0.070 −0.066 −0.179
4 −0.089 −0.106 −0.039 −0.048 −0.099
5 −0.142 −0.114 −0.088 −0.080 −0.151
6 −0.185 −0.117 −0.194 −0.110 −0.143
7 −0.108 −0.110 −0.044 −0.082 −0.105
8 −0.141 −0.119 −0.157 −0.134 −0.136
9 −0.159 −0.123 −0.233 −0.171 −0.154
10 −0.053 −0.112 −0.041 −0.040 −0.072
11 −0.135 −0.130 −0.055 −0.066 −0.112
12 −0.196 −0.143 −0.111 −0.091 −0.083
13 −0.030 −0.106 −0.090 −0.068 −0.082
14 −0.095 −0.121 −0.089 −0.112 −0.148
15 −0.179 −0.133 −0.161 −0.145 −0.101
16 −0.035 −0.110 −0.042 −0.110 −0.101
17 −0.113 −0.125 −0.119 −0.179 −0.137
18 −0.164 −0.140 −0.243 −0.228 −0.132
19 −0.052 −0.101 −0.043 −0.048 −0.098
20 −0.127 −0.112 −0.154 −0.082 −0.104
21 −0.180 −0.135 −0.289 −0.106 −0.141
22 −0.039 −0.116 −0.061 −0.080 −0.095
23 −0.111 −0.122 −0.040 −0.134 −0.116
24 −0.176 −0.144 −0.115 −0.175 −0.134
25 −0.052 −0.115 −0.041 −0.134 −0.070
26 −0.116 −0.124 −0.066 −0.204 −0.083
27 −0.146 −0.146 −0.215 −0.262 −0.133
Entropy 0.945 0.997 0.869 0.919 0.981
Criteria weight 0.190 0.011 0.453 0.281 0.066

Entropy and Criteria weight are calculated by using the above mentioned data in the table and these are further used in calculations, therefore, they are indicated as bold.

Furthermore, these criterion weights were utilized to form a weighted normalized matrix, as listed in Table 7. The inclusion of criterion weights in MCDM is crucial to ensure that the decision-making process effectively captures the preferences of the decision-makers, resulting in well-informed and fair decisions.

Table 7

Weighted normalized matrix

Run Criteria
C 1 C 2 C 3 C 4 C 5
1 0.0071 0.0018 0.0064 0.0059 0.0171
2 0.0255 0.0021 0.0155 0.0122 0.0197
3 0.0418 0.0022 0.0285 0.0183 0.0211
4 0.0204 0.0017 0.0133 0.0122 0.0089
5 0.0397 0.0019 0.0386 0.0236 0.0162
6 0.0591 0.0019 0.1230 0.0365 0.0150
7 0.0265 0.0018 0.0154 0.0245 0.0096
8 0.0393 0.0020 0.0883 0.0488 0.0139
9 0.0467 0.0021 0.1690 0.0705 0.0169
10 0.0102 0.0018 0.0143 0.0095 0.0057
11 0.0369 0.0022 0.0206 0.0183 0.0106
12 0.0650 0.0026 0.0534 0.0283 0.0069
13 0.0051 0.0017 0.0402 0.0190 0.0068
14 0.0222 0.0020 0.0393 0.0379 0.0159
15 0.0564 0.0023 0.0918 0.0547 0.0091
16 0.0061 0.0018 0.0148 0.0366 0.0091
17 0.0285 0.0021 0.0586 0.0757 0.0140
18 0.0493 0.0025 0.1819 0.1133 0.0133
19 0.0100 0.0016 0.0153 0.0122 0.0088
20 0.0336 0.0018 0.0858 0.0243 0.0095
21 0.0568 0.0024 0.2552 0.0351 0.0148
22 0.0069 0.0019 0.0238 0.0235 0.0084
23 0.0277 0.0020 0.0140 0.0487 0.0111
24 0.0546 0.0026 0.0559 0.0728 0.0136
25 0.0100 0.0019 0.0142 0.0488 0.0055
26 0.0295 0.0021 0.0264 0.0939 0.0070
27 0.0412 0.0027 0.1459 0.1456 0.0134

To calculate the assessment values (Y i ), the weighted normalized values of the non-beneficial criteria, i.e. F c, T m, R a, and VBc, are subtracted from beneficial criteria, i.e. MRR, by using equation (7), and are listed in Table 8. On the basis of assessment values, the rank was given to each alternative. The option with the highest Y i value is identified as being near the optimal solution.

Table 8

Overall assessment values and ranks of the alternative experiments

Run Criteria Y i Rank
C 1 C 2 C 3 C 4 C 5
1 0.0071 0.0018 0.0064 0.0059 0.0171 0.0265 20
2 0.0255 0.0021 0.0155 0.0122 0.0197 0.0505 14
3 0.0418 0.0022 0.0285 0.0183 0.0211 0.0752 9
4 0.0204 0.0017 0.0133 0.0122 0.0089 0.0320 17
5 0.0397 0.0019 0.0386 0.0236 0.0162 0.0729 10
6 0.0591 0.0019 0.1230 0.0365 0.0150 0.1624 3
7 0.0265 0.0018 0.0154 0.0245 0.0096 0.0288 18
8 0.0393 0.0020 0.0883 0.0488 0.0139 0.0946 8
9 0.0467 0.0021 0.1690 0.0705 0.0169 0.1640 2
10 0.0102 0.0018 0.0143 0.0095 0.0057 0.0226 22
11 0.0369 0.0022 0.0206 0.0183 0.0106 0.0520 13
12 0.0650 0.0026 0.0534 0.0283 0.0069 0.0996 7
13 0.0051 0.0017 0.0402 0.0190 0.0068 0.0348 16
14 0.0222 0.0020 0.0393 0.0379 0.0159 0.0415 15
15 0.0564 0.0023 0.0918 0.0547 0.0091 0.1051 6
16 0.0061 0.0018 0.0148 0.0366 0.0091 -0.0048 25
17 0.0285 0.0021 0.0586 0.0757 0.0140 0.0276 19
18 0.0493 0.0025 0.1819 0.1133 0.0133 0.1338 4
19 0.0100 0.0016 0.0153 0.0122 0.0088 0.0234 21
20 0.0336 0.0018 0.0858 0.0243 0.0095 0.1064 5
21 0.0568 0.0024 0.2552 0.0351 0.0148 0.2941 1
22 0.0069 0.0019 0.0238 0.0235 0.0084 0.0174 23
23 0.0277 0.0020 0.0140 0.0487 0.0111 0.0061 24
24 0.0546 0.0026 0.0559 0.0728 0.0136 0.0539 12
25 0.0100 0.0019 0.0142 0.0488 0.0055 −0.0172 26
26 0.0295 0.0021 0.0264 0.0939 0.0070 −0.0289 27
27 0.0412 0.0027 0.1459 0.1456 0.0134 0.0576 11

Bold value indicates to show the best result in optimization as per ranking.

According to Table 8, experimental run number 21 has the highest assessment (Y i ) value, indicating the ideal setting of the process parameter. It denotes the ultimate combined score for every alternative. The assessment value offers a clear and concise overview of how effectively each alternative fulfils the numerous objectives taken into account during the decision-making process.

Therefore, it is concluded that the maximum assessment value can be obtained when the spindle speed is at a high level (1,285 rpm), the feed rate is at low level (0.0625 mm·rev−1), and the depth of cut is at a high level (0.3 mm).

4.2 Macro-morphological study of chip formation

The chip samples were collected during the experiment with the aim to examine their morphology and, hence, their impact on the machinability of the selected work material. The macroscopic images of the chips were captured using a smartphone and a digital metallurgical microscope (Olympus, Model: DSX1000), respectively. A few of the selected images were chosen to understand the pattern of the chip formed during machining and are shown in Figures 2–4. These combinations are essentially determined by maintaining two parameters at a fixed value while altering the third cutting parameter [39].

Figure 2 Macroscopic images of chips under a constant speed and depth of cut. (a) Image of chip under 1 × zoom at constant speed and depth of cut and (b) Macroscopic image of chip at constant speed and depth of cut.
Figure 2

Macroscopic images of chips under a constant speed and depth of cut. (a) Image of chip under 1 × zoom at constant speed and depth of cut and (b) Macroscopic image of chip at constant speed and depth of cut.

Figure 3 Macroscopic images of chips under a constant speed and feed rate. (a) Image of chip under 1 × zoom at constant speed and feed rate and (b) Macroscopic image of chip at constant speed and feed rate.
Figure 3

Macroscopic images of chips under a constant speed and feed rate. (a) Image of chip under 1 × zoom at constant speed and feed rate and (b) Macroscopic image of chip at constant speed and feed rate.

Figure 4 Macroscopic images of chips under a constant depth of cut and feed rate. (a) Image of chip under 1 × zoom at constant depth of cut and feed rate and (b) Macroscopic image of chip at constant depth of cut and feed rate.
Figure 4

Macroscopic images of chips under a constant depth of cut and feed rate. (a) Image of chip under 1 × zoom at constant depth of cut and feed rate and (b) Macroscopic image of chip at constant depth of cut and feed rate.

As shown in Figure 2, a lower feed rate resulted in the formation of thin chips and increased the cutting temperature. Furthermore, a lower feed rate typically resulted in an improved surface quality (as seen from the experimental results) and a more seamless flow of chips. A lower feed rate also contributed to a lower amount of tool wear rate as it decreased the mechanical stress and accumulation of heat experienced by the cutting tool. On the contrary, an increase in the feed rate resulted in a substantial increase in the chip thickness. On the other hand, the chips exhibit a tightly coiled, long washer-like structure when subjected to a high feed rate [40]. The changes in chip shape are more prominent during the variation in the feed rate as compared to the speed and depth of the cut. The surface R a of the work material was observed to be high with increasing feed rate.

It is evident from Figure 3 that a low depth of cut generated thinner chips compared to its counterparts, which in turn helped in curtailing the total load on the tool and work material. Thus, it contributed to a reasonable surface quality, tool wear, and cutting force, as shown in Table 3. From Figure 3, it is also evidenced that continuous ribbon-type chips were formed at a medium depth of cut, i.e. 0.2 mm, whereas when the depth of cut increased, the pattern of chips was also changed to hard and twisted. In addition, continuous ribbon chips containing nearly uniform thickness were formed at a medium depth of cut. Conversely, for a high depth of cut, chips were more likely to be serrated and sharp-toothed in structures with higher thickness values. This might be due to the high chemical affinity of the selected work part.

Finally, chips were examined at varied spindle speeds and a fixed depth of cut and feed rate to understand their impact on the machinability of the work material. Discontinuous and round-toothed elemental chips were perceived at lower speeds. These chips are separated from the workpiece in fragments because of the brittleness and high degree of hardness of the work material at the initial stage of cutting. This might be due to the significant lack of thermal softening at the beginning of the machining process. On the other hand, at a lower cutting speed, the flank wear of the tool was more apparent since the contact time and stress were high. Further, an increase in the cutting speed to a moderate level decreased the chances of formation of the built-up edge (BUE) due to elevated temperatures (the temperature at Run 12 is comparatively higher), which in turn reduced the adherence of the material to the tool. Therefore, it became feasible to attain a state of equilibrium between the reduction in hardness due to heat and the structural strength, which could potentially result in a longer lifespan for the tool (it is clearly visible from Figure 3 and Table 3 that the flank wear is minimum at a moderate speed, i.e. 1,000 rpm). Besides, at a higher spindle speed, serrated chips were formed and the chip’s serrated texture was the consequence of thermal softening and intermittent shearing in the cutting region [41].

4.3 Study of flank wear

Tool wear is characterized by flank and crater wear. In this study, flank wear during dry turning of Incoloy 825 by using a coated carbide tool was investigated using a metallurgical microscope. Figure 5 depicts the microscopic images of the flank face of the tool insert. Since coated carbide tools were used for turning the work material, BUE formation was very rare because of the anti-sticking properties of the coated material. The results indicate that the BUE formation was more prominent in experiment numbers 1, 11, 22, and 23. This also indicated that when the depth of cut and feed rate were low, BUE was observed considerably. However, with increasing cutting speed and the depth of cut, flank wear was more visible as abrasive marks on the face of the inserts, as depicted in Figure 5. In addition, abrasion wear was also observed on the flank faces, exhibiting distinct sharp ridges and grooves aligned with the flow of chip and workpiece material travel directions. The abrasion wear mechanism in the Incoloy 825 alloy during machining is mostly produced by the mechanical wear resulting from the cutting action of hard carbide particles. Because of inadequate thermal softening, nickel-based superalloys commonly create discontinuous, segmented, or serrated chips at low cutting speeds. Chip hammering is more common with these kinds of chips, as shown in Figure 5, and is also supported by researchers previously [42].

Figure 5 Images of flank wear.
Figure 5

Images of flank wear.

5 Conclusion

This research aims to determine the practicality of employing the entropy-embedded MOORA approach to achieve an optimal combination of parameters when machining Incoloy 825 with coated carbide inserts during the dry environment of machining. The primary focus was on five specific qualitative parameters of machinability: cutting temperature, surface R a, MRR, cutting force, and flank width. The following results are concluded based on the experimental study.

  • A set of ranges was decided as per the available resources and literature review. The objective of this research was to achieve optimum values of surface R a, cutting temperature, cutting force, MRR, and flank wear. The most efficient and optimum machining parameters were observed at a spindle speed of 1,285 rpm, feed rate of 0.0625 mm·rev−1, and depth of cut of 0.3 mm; these parameters occurred during trial number 21, as shown in Table 2.

  • The proposed methodology, which combines entropy with the MOORA approach, was found to be efficient, simple, and easily understandable for resolving problems that involve multiple parameters.

  • The utilization of entropy-MOORA proved to be an effective and suitable approach in attaining an optimal configuration of parameters under specific cutting conditions. This could be valuable in addressing additional MCDM problems in other academic and industrial fields.

  • It is obvious that a knowledge of chip formation and a study of chip morphology are quite necessary to understand the machinability of materials, especially for turning superalloys and particularly for machining of Ni-alloys, including Incoloy 825.

  • Flank wear of carbide tools in turning Incoloy 825 is greatly influenced by high cutting temperatures and abrasive wear, demanding improved coatings and adjusted cutting conditions to enhance tool life and performance. Reducing operating costs and increasing machining efficiency require careful management of these variables.

  • The findings of the current investigation are restricted to the selected range of cutting parameters, cutting environment (i.e. dry), and cutting tools.

  • The proposed methodology may help the researchers to identify the best treatment combinations in a similar scenario.

Acknowledgements

I would like to thank AICTE for providing the financial support during the Ph.D. tenure. I would like to thank the Department of Mechanical Engineering of NIT, Raipur and IIITDM, Kurnool for allowing me to perform the experiment in their lab facilities.

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

  2. Author contributions: Conceptualization: Shiena Shekhar, Akhtar Khan, Dheeraj Lal Soni; methodology: Saurabh Kumar Sahu, Prashant Kumar Gangwar, Manish Gupta; investigation: Saurabh Kumar Sahu, Shiena Shekhar, Akhtar Khan; data curation: Dheeraj Lal Soni, Prashant Kumar Gangwar, Manish Gupta; formal analysis: Saurabh Kumar Sahu, Akhtar Khan, and Prashant Kumar Gangwar; resources: Akhtar Khan, Dheeraj Lal Soni, and Manish Gupta; supervision: Akhtar Khan and Dheeraj Lal Soni; validation: Saurabh Kumar Sahu and Shiena Shekhar; writing – original draft: Saurabh Kumar Sahu, Shiena Shekhar and Akhtar Khan; writing – review and editing: Dheeraj Lal Soni, Prashant Kumar Gangwar, and Manish Gupta.

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

  4. Author statement: The authors are aware of its content and approve its submission. This article has not been submitted anywhere else for parallel publication.

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

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

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Received: 2024-08-06
Revised: 2024-09-23
Accepted: 2024-09-30
Published Online: 2024-11-20

© 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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  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
  20. Thermo-mechanical effects and microstructural evolution-coupled numerical simulation on the hot forming processes of superalloy turbine disk
  21. Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms
  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
  30. Thermodynamic modeling of stacking fault energy in Fe–Mn–C austenitic steels
  31. Research on the influence of cemented carbide micro-textured structure on tribological properties
  32. Performance evaluation of fly ash-lime-gypsum-quarry dust (FALGQ) bricks for sustainable construction
  33. First-principles study on the interfacial interactions between h-BN and Si3N4
  34. Analysis of carbon emission reduction capacity of hydrogen-rich oxygen blast furnace based on renewable energy hydrogen production
  35. Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features
  36. Effect of tempering temperature on the microstructure and mechanical properties of Q125 shale gas casing steel
  37. Review Articles
  38. A review of emerging trends in Laves phase research: Bibliometric analysis and visualization
  39. Effect of bottom stirring on bath mixing and transfer behavior during scrap melting in BOF steelmaking: A review
  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
  42. Experimental investigation on the deterioration of the physical and mechanical properties of autoclaved aerated concrete at elevated temperatures
  43. Damage evaluation of the austenitic heat-resistance steel subjected to creep by using Kikuchi pattern parameters
  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
  60. Special Issue on Recent Developments in 3D Printed Carbon Materials - Part II
  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
  81. Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
https://doi.org/10.1515/htmp-2024-0058
Schlagwörter für diesen Artikel
Entropy-MOORA; cutting force; spindle speed; surface roughness; cutting temperature; MRR
Creative Commons
BY 4.0

Artikel in diesem Heft

  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
  20. Thermo-mechanical effects and microstructural evolution-coupled numerical simulation on the hot forming processes of superalloy turbine disk
  21. Endpoint prediction of BOF steelmaking based on state-of-the-art machine learning and deep learning algorithms
  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
  30. Thermodynamic modeling of stacking fault energy in Fe–Mn–C austenitic steels
  31. Research on the influence of cemented carbide micro-textured structure on tribological properties
  32. Performance evaluation of fly ash-lime-gypsum-quarry dust (FALGQ) bricks for sustainable construction
  33. First-principles study on the interfacial interactions between h-BN and Si3N4
  34. Analysis of carbon emission reduction capacity of hydrogen-rich oxygen blast furnace based on renewable energy hydrogen production
  35. Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features
  36. Effect of tempering temperature on the microstructure and mechanical properties of Q125 shale gas casing steel
  37. Review Articles
  38. A review of emerging trends in Laves phase research: Bibliometric analysis and visualization
  39. Effect of bottom stirring on bath mixing and transfer behavior during scrap melting in BOF steelmaking: A review
  40. High-temperature antioxidant silicate coating of low-density Nb–Ti–Al alloy: A review
  41. Communications
  42. Experimental investigation on the deterioration of the physical and mechanical properties of autoclaved aerated concrete at elevated temperatures
  43. Damage evaluation of the austenitic heat-resistance steel subjected to creep by using Kikuchi pattern parameters
  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
  60. Special Issue on Recent Developments in 3D Printed Carbon Materials - Part II
  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
  81. Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
  82. Thermal conductivity evaluation of magnetized non-Newtonian nanofluid and dusty particles with thermal radiation
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