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Performance analysis of WEDM during the machining of Inconel 690 miniature gear using RSM and ANN modeling approaches

  • Atul Raj , Joy Prakash Misra EMAIL logo , Ravinder Pal Singh , Gurminder Singh , Shubham Sharma and Sayed M. Eldin EMAIL logo
Published/Copyright: March 29, 2023
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REVIEWS ON ADVANCED MATERIALS SCIENCE
From the journal REVIEWS ON ADVANCED MATERIALS SCIENCE Volume 62 Issue 1

Abstract

The present work aims to carry out a feasible study of wire electro-discharge machining (WEDM) during the machining of Inconel 690 superalloy gears. Processing conditions of power-on time, power-off time, current, and spark-gap voltage are varied to evaluate the process performance in terms of material removal rate (MRR), surface roughness (SR), and wire consumption. Parametric optimization has been carried out using combined approach of response surface methodology (RSM) and artificial neural network (ANN). Results revealed that ANN predicted values are 99% in agreement with the experimental results which validates its effectiveness as compared to RSM predicted values. A viability study of noise characteristics of the processed gear is also done using a noise testing setup. Additionally, FE-SEM has been used to analyze the machined surface’s topography. Greater discharge energy brought by a longer pulse length raises the values of MRR, SR, and recast layer thickness (RLT). This study explores the capability of WEDM to produce a more precise and smooth gear profile as compared to other conventional machining methods. Additionally, RLT and microhardness of the machined surface have been critically studied to comprehend the better understanding of the process mechanism.

Keywords: Inconel 690; surface roughness; optimization; gear noise characteristics; response surface methodology; artificial neural network; FE-SEM

1 Introduction

With the advance in micro-electromechanical systems and micro systems, the need for micro-components and micro-devices also increases. For the transmission of motion and power in these micro-systems, there is a need of miniature gears. Miniature gears used in micro-systems have a vast area of application which include miniature motors and pumps, actuators, miniature gearbox, robots, timing mechanism, scientific instruments, aerospace industry, military applications, medical equipment, electronic devices and instruments, etc. [1,2]. Depending on the design and the required quality level, a variety of manufacturing procedures are utilized for the machining of small gears. When machining gears, some of the traditional procedures utilized are hobbing, stamping, extrusion, forging, powder metallurgy, etc. However, in the case of these standard procedures, a secondary finishing operation is necessary to achieve the requisite surface quality of the micro gear, which is a significant disadvantage for them [3]. Electro-spark erosion-based non-conventional machining techniques are now available to help manufacturers produce high-quality small gears with improved surface quality and dimensional precision.

When it comes to machining electrically conductive materials, regardless of their toughness and hardness, wire electro-discharge machining (WEDM) has become a unique form of electro-spark erosion. It creates surfaces with little grit and has the potential to cut any intricate shape or geometry with great precision. Literature can enlighten readers about important traits, influencing variables, process capacity, etc. [4,5,6,7]. The WEDM’s material removal method is depicted in Figure 1. For the production of miniature parts for the aerospace sector, dentistry and medical equipment, gear pumps, and motors, among other things, WEDM is frequently utilized. Every machining process has a number of elements that influence how successfully it accomplishes one or more objectives. To improve the process performance, a variety of optimization approaches for single-objective, multi-objective, or both forms of process characteristic optimizations are available.

Figure 1 Process mechanism for removing material: (a) schematic and (b) pictorial view.
Figure 1

Process mechanism for removing material: (a) schematic and (b) pictorial view.

In the past, Hori and Murtaza used micro- WEDM for the processing of micro spur gear. It was concluded that the micro spur gear has a uniform profile and there was no undercutting error at root area of the gear profile [8]. Benavides et al. used micro-WEDM for the machining of 304 SS. Results show that machined surface has micro-level surface finish with good profile characteristics. The thickness of recast layer formed during the machining was also less [9]. Uhlmann et al. [10] concluded that micro-WEDM is highly capable of manufacturing miniaturized components with high surface finish and low profile error. Ali et al. [11] discussed the comparative study of conventional and WEDM for the fabrication of meso spur gear of copper. It is concluded that gears formed by WEDM process have an average surface roughness (SR) of 1 µm along with the dimensional variation of less than 2%. Talon et al. [12] used EDM to manufacture the Ti-6AL-4V spur gear. It was concluded that use of process parameters in a controlled way gives highly precise results. To forecast the value of CS during the WEDM of SiCp/6061 Al metal matrix composite, Shandilya et al. employed a combined method of response surface methodology (RSM) and artificial neural network (ANN) [13]. During the WEDM, the wire tool breaking process was investigated by Liao et al. During the processing, a computer-aided pulse-discrimination system was cast-off to acquire a large amount of sparking frequency data. The main causes of wire tool breakage were determined to be an excessive spark arc and a sudden increase in the sparking frequency [14]. Paul et al. [15] conducted an experimental study on manufacturing of miniature gear of Inconel 718 using WEDM process. Results show that at high discharge energy levels there is a high presence of coral reef on the machined surface, while in case of low discharge energy levels there are random micro-cracks present on the machined surface. Chaubey and Jain [16] studied the microstructure of meso gears machined by WEDM process on a rectangular plate of stainless steel. It was concluded that the gears have uniform tooth profile and are free from the presence of sharp edges at both the end faces. Chaudhary et al. [17] conducted a multi-response optimization for miniature gear of Nimonic alloy fabricated by WEDM process using different dielectric fluids. Results reveals that ethylene glycol mixed de-mineralized water used as a dielectric fluid is the most significant parameter among all the input process parameters. Singh and Misra [18] used a combined RSM and ANN technique to examine the surface finish of WEDM-machined combustor material. The predicted and experimental values were found to be in good agreement, proving the validity of the experimental study. Abhilash and Chakradhar have employed the Naive Bayes classifier technique to analyze and categorize the wire rupture during Superalloy 718 machining utilizing WEDM. Short-circuit sparks were found to be the most important factor in wire tool failure [19]. Depending upon the characteristics and its application, various materials like tool-steel, carbon-steel, copper and its alloys, aluminum and its alloys, titanium and its alloys, nickel and its alloys, plastics, nylon, Teflon etc., are used for the manufacturing of miniature gears. Miniature gear made of superalloys has been widely used in aerospace industry, medical implants, electronic industry, gas turbine industry, automobile sector, etc. [20].

Published articles lack any work on formation of miniature gears from nickel-based superalloys and studying the effects of operating conditions on noise characteristics of the gears. Very few authors discuss about the effects of process variables on the tribological behavior of the processed component. So, the present investigation aimed at:

  1. Formation of miniature gears from nickel-based superalloy Inconel 690 using WEDM and studying the surface integrity of the tooth profile.

  2. Studying the postprocess changes in the surface topography of the processed component by employing scanning electron microscopy (SEM).

  3. Analysis of recast layer formation on the gear tooth surface and variation in recast layer thickness (RLT) along the machined surface using FE-SEM.

  4. RSM and ANN-based multi-objective process variable optimization

  5. Studying the effects of operating conditions on the gear noise characteristics.

2 Materials and methodology

2.1 Materials

In the present study a nickel-based superalloy Inconel 690 is used as the workpiece material. Inconel 690 has high chromium content in it which offers high strength and high corrosion resistance property in aqueous medium and high temperature. It has immense demand in industrial area and is very much needed in aerospace and gas turbine industry. A rectangular plate with dimensions 150 mm × 150 mm × 15 mm is used for the manufacturing of miniature gears. Miniature spur gears with outer diameter of 10 mm are cut from the rectangular plate. EDS analysis of the workpiece material and wire tool is demonstrated in Figure 2, respectively.

Figure 2 EDS analysis of (a) Inconel 690 and (b) fresh wire.
Figure 2

EDS analysis of (a) Inconel 690 and (b) fresh wire.

2.2 Methodology

In the present study, WEDM machine (Model: Sprint cut- ELECTRONICA) is used for the cutting of miniature gears from the workpiece surface. Superalloy 690 is a very hard material to cut because of its high strength and hardness. A pictorial view of the process followed during the present study is shown in Figure 3. Initially, grinding and buffing of workpiece plate is done to remove the layer of impurities present on its surface in order to avoid the wire breakage and proper spark generation during WEDM. In the present analysis, power-on time (T on), power-off time (T off), peak current (I p), and spark gap voltage (S v) are used as input process variables. Process performance is evaluated in respect of material removal rate (MRR), SR, and wire consumption (WC). Ranges and level of input process variables are tabulated in Table 1.

Figure 3 Pictorial view of process flow chart: (a) WEDM machine setup, (b) processing of superalloy 690 by WEDM, (c and d) miniature spur gears and their cavity in the plate after WEDM, (e) SR measurement, and (f) microhardness analysis.
Figure 3

Pictorial view of process flow chart: (a) WEDM machine setup, (b) processing of superalloy 690 by WEDM, (c and d) miniature spur gears and their cavity in the plate after WEDM, (e) SR measurement, and (f) microhardness analysis.

Table 1

Ranges and levels of input variables

Variables Input process parameters
Source Range Unit Level
1 2 3
Power-on time (T on) A 105–131 µs 105 118 131
Power-off time (T off) B 30–60 µs 30 45 60
Peak current (I p) C 40–220 A 40 130 220
Spark gap voltage (S v) D 20–80 V 20 50 80

In the next step, an experimental plan is set for accomplishment of the experiments using WEDM with the help of Box–Behnken design (BBD) of RSM [21,22,23,24]. Although the central composite design (CCD) has been chosen in various past research [25,26,27,28,29], BBD was chosen over CCD as it provides lesser number of experiments for same number of parameters. As per Box behnken design, a total of 29 runs are done with 5 replications of center shown in Table 2. The manufacturing of miniature gears from the super alloy 690 square plate using WEDM is shown in Figure 3(c). Following WEDM processing, the SR of the miniature gears is measured using a Telesurf surfcom (Make: Accretech) roughness tester. The treated surface’s roughness is measured according to ISO 1997 standards. The calculations used a cut-off value of 2.5 m and a sampling length of 4 mm. Five tests were run on each machined surface, and the average result was used to determine the roughness value. FE-SEM (Make: Joel, JSM-6390LV, 20.0 keV) was used for microstructural investigation, and Vickers’ hardness tester was used for microhardness measurement [30,31,32].

Table 2

Experimental runs

No. of runs Input process variables Experimental results RSM predicted ANN predicted
T on T off I p S v MRR SR WC MRR SR WC MRR SR WC
1 A1 B1 C2 D2 3.83 1.64 42.98 9.17 1.67 51.46 3.83 1.67 42.97
2 A3 B1 C2 D2 6.67 3.82 19.56 14.6 3.69 30.00 9.03 3.55 17.90
3 A1 B3 C2 D2 1.27 0.73 144.92 4.85 0.81 137.26 1.28 0.81 144.66
4 A3 B3 C2 D2 5.55 3.18 31 11.7 3.10 25.30 5.54 3.12 31.08
5 A2 B2 C1 D1 4.2 2.46 40.09 6.34 2.25 47.91 9.09 1.48 18.50
6 A2 B2 C3 D1 6.11 3.11 27.17 15.7 3.06 25.91 6.10 3.06 27.32
7 A2 B2 C1 D3 2.34 1.54 71.75 4.23 1.54 75.79 2.31 1.59 71.47
8 A2 B2 C3 D3 6.42 2.85 25.79 15.8 3.01 20.75 9.04 2.84 18.06
9 A1 B2 C2 D1 2.23 1.63 78.11 8.30 1.43 77.47 3.34 0.94 73.32
10 A3 B2 C2 D1 6.67 3.02 28.22 13.8 3.09 22.13 6.71 3.25 28.22
11 A1 B2 C2 D3 1.68 0.72 94.8 6.60 0.55 100.2 1.49 0.86 94.56
12 A3 B2 C2 D3 7.47 3.11 22.15 13.4 3.21 22.11 7.47 3.18 22.02
13 A2 B1 C1 D2 4.64 2.39 36.42 6.65 2.36 33.31 7.96 2.39 20.64
14 A2 B3 C1 D2 1.6 1.82 105.14 3.04 1.92 101.44 1.76 1.69 104.01
15 A2 B1 C3 D2 4.84 3.98 19.34 17.1 3.79 22.37 4.82 3.84 19.39
16 A2 B3 C3 D2 5.18 2.83 32.91 13.55 2.77 35.34 5.18 2.78 32.93
17 A1 B2 C2 D2 1.36 0.47 130.65 2.49 0.58 126.39 1.33 0.81 116.33
18 A3 B2 C1 D2 3.98 2.51 43.1 6.30 2.51 42.33 4.07 2.44 42.63
19 A1 B2 C3 D2 2.41 1.39 71.85 10.62 1.50 70.53 2.44 1.38 71.87
20 A3 B2 C3 D2 6.86 3.88 18.98 19.19 3.88 21.15 6.83 3.81 18.97
21 A2 B1 C2 D1 5.14 2.81 32.5 12.09 3.07 24.22 5.16 2.74 32.65
22 A2 B3 C2 D1 7.28 2.54 32.95 10.92 2.63 41.40 7.27 2.55 33.13
23 A2 B1 C2 D3 5.14 2.96 22.75 13.50 2.98 12.21 5.15 2.84 22.76
24 A2 B3 C2 D3 2.42 2.11 69.94 7.47 1.96 76.13 2.42 2.15 70.11
25 A2 B2 C2 D2 9.38 2.83 17.96 14.48 2.31 18.13 8.65 2.39 18.22
26 A2 B2 C2 D2 8.34 2.08 18.46 14.48 2.31 18.13 8.65 2.39 18.22
27 A2 B2 C2 D2 8.26 2.17 17.63 14.48 2.31 18.13 8.65 2.39 18.22
28 A2 B2 C2 D2 8.37 2.29 18.35 14.48 2.31 18.13 8.65 2.39 18.22
29 A2 B2 C2 D2 8.38 2.22 18.22 14.48 2.31 18.13 8.65 2.39 18.22

3 Results and discussion

T on, T off, I p, and S v are selected as input factors for the current study, while MRR, SR, and WC are regarded as response variables. The effects of various input factors on process performance are depicted in Figure 4. Among all the input factors, T on is discovered to have the greatest influencing power. The output data demonstrate that as T on increases, MRR and SR also rise and vice versa. The number of electrons striking the work surface in a single discharge increases as T on and I p grow. More materials are removed from the work surface for each discharge. As a result, MRR rises as T on and I p rises. The metal is removed from the work surface in the form of craters, which is a well-known phenomenon of WEDM. Increased material removal results in a poor surface finish and a higher SR value. On the other hand, WC has a different response as compared to MRR and SR for T on [33,34,35]. High discharge energy is associated with higher T on and I p values. As a result, the wire tool length required to eradicate the material from the work piece is reduced. As the wire feed remains constant throughout the process, T off increases the pulse duration, requiring more wire tool length to eradicate the material from the work piece. A larger S v widens the space between the work piece and the wire tool, resulting in reduced spark concentration. As a result, the amount of wire used increases. It is obvious from the results that I p also has similar possessions like T on on MRR, SR, and WC. T off and S v also have some severe effects on MRR, SR, and WC [36,37,38]. As the T off and S v value drops, some rise in the value of MRR and SR is noticed.

Figure 4 Various input factors’ effect on: (a) MRR, (b) SR, and (c) WC.
Figure 4

Various input factors’ effect on: (a) MRR, (b) SR, and (c) WC.

The time period of sparks creation in the WEDM process increases with the increase in T on value, which inexorably influences the MRR. The spark gap narrows as the S v decreases, increasing the number of sparks produced per unit of time. The machining rate is indirectly accelerated, increasing the MRR [39,40,41]. The findings show that higher T on and I p values result in increased MRR and SR. According to interaction plots, the increase in T on and I p values is predominantly associated with an increase in MRR; however, after reaching a certain point, MRR begins to decline. Though, as seen in Figure 4a, MRR for T off and S v declines as their values increase. According to the findings for SR, which are depicted in Figure 4b, SR rises as T on and I p values increase.

3.1 Multi-objective optimization

ANN is a MATLAB toolkit. Backpropagation neural network (BPNN) models are commonly created with it. WEDM has a large sum of input process factors which have a substantial impact on the process output. Because of the intricacy and stochastic nature of the data, statistical analysis is possible, but precise guess of values using a scientific equation is not valid. As a training vector, four input process variables are included in the current study.

Various limitations, such as the number of iterations, hidden layer nodes, network type, and so on, affect the performance of a BPNN model. As a result, during BPNN training, selecting the most optimal values for input process variables is crucial [30].

The ideal configuration of the ANN model is determined for current study by altering the sum of neurons in the hidden layer. For the current ANN model, the dataset is split into three parts: 60% for training, 20% for validation, and 20% for testing. The current model is an FF-BPNN with a Levenberg–Marquardt training procedure. In the existing model, 1 buried layer contains 20 neurons. The linear regression graphs of actual vs predicted values for ANN are shown in Figure 5. The R-values achieved for the training and validation datasets in this model are 0.99148 and 0.99459, respectively, which are extremely near to 1. The current model’s results reveal that actual and anticipated values are in good agreement. In addition, Figure 6 shows a comparison graph between the actual values and the expected outcomes. In comparison to the quadratic RSM model, the constructed ANN model provides more exact and generalized predicted values, as seen in the comparison graph.

Figure 5 Comparative graph of actual and ANN projected values, using linear regression analysis.
Figure 5

Comparative graph of actual and ANN projected values, using linear regression analysis.

Figure 6 Relative graph of actual vs predicted values for (a) MRR, (b) SR, and (c) WC.
Figure 6

Relative graph of actual vs predicted values for (a) MRR, (b) SR, and (c) WC.

4 Surface topography and noise characteristics analysis

4.1 Surface integrity

Investigation of profile characteristics on the WEDM small gear profile reveals the existence of micro-cracks and bowl-shaped cavities called craters on the gear tooth surface, as well as some spherical droplets [42,43,44]. Some molten metal and re-solidified material can also be seen on the gear tooth surface in the form of a lump of debris. Figure 7 shows a burr-free uniform tooth profile of WEDMed gears machined under various operating circumstances, as well as their microstructures. Small craters, spherical modules with a lump of debris, and re-solidified material are portrayed on the surface of the machined work piece, according to Figure 7a and b.

Figure 7 SEM micrographs of gear profile and gear tooth surface machined at (a) T on, 118; T off, 30; I p, 220; S v, 50 and (b) T on, 105; T off, 45; I p, 130; S v, 50.
Figure 7

SEM micrographs of gear profile and gear tooth surface machined at (a) T on, 118; T off, 30; I p, 220; S v, 50 and (b) T on, 105; T off, 45; I p, 130; S v, 50.

4.2 Microhardness analysis

The hardness of the work piece is affected by the formation of recast layers on its top surface. Testing for microhardness is an easy technique to confirm this. In this work, a microhardness testing machine is used to examine the variation in the microhardness values of the core material and the machined region [45,46]. The variance in microhardness along the subsurface of the recast layer is seen in Figure 8. In comparison to the core materials, microhardness rises close to the subsurface of the recast layer, as depicted in the graphs. The formation of recast layers on the face of the processed work surface enhances the hardness of the work piece. Because the recast layer is formed exclusively on the machined area of the work piece, the hardness of the core material is not affected. A total of six indentations have been made on each work surface, starting from one end to the other end, as shown in Figure 8. From the results, it can be seen that the value of hardness decreases as the testing point moves from machine area to the core area of the processed work piece.

Figure 8 Microhardness analysis.
Figure 8

Microhardness analysis.

4.3 Noise characteristics

These types of problems significantly reduce the gear’s service life. The setup used for noise measurement, as shown in Figure 9, comprises of a pair of spur gear in which one is the master gear and the other one is the testing gear. The noise generated during the meshing of the gears was measured at different time intervals. A DC motor, which is used to supply power, is connected to the master gear. Master gear can rotate from 1,500 to 2,800 RPM. The speed of the master gear can be varied with the help of the RPM controller. Two Perspex boxes are used, one is to cover the power supply chamber, i.e., motor and the other one for the gear meshing chamber to avoid the sound of the surroundings.

Figure 9 Noise testing setup: (a) schematic view and (b) photographic view.
Figure 9

Noise testing setup: (a) schematic view and (b) photographic view.

A non-contact RPM meter, i.e., testo-465 with a measuring range of +1 to +99,999 RPM and accuracy of ±0.02% of the measured value is used for monitoring the speed of rotation. For noise measurement, a sound level meter, i.e., testo 816-1 with a measuring range of 30–130 dB is used. A comparative study between gears manufactured by two different conventional processes, i.e., milling and shaping is done to analyze the effects of machining process on the noise characteristics of the gear. Both the gears are meshed with the master gear individually for different run time, and during the meshing period, their noise levels are recorded using sound level meter [47,48]. Noise level of gear manufactured through milling process is higher as compared to that of gear manufactured through shaping process. Initially, the sound level for both the processes was approximately 99 dB for a run time of 60 s. As the run time increases, the difference between the noise levels of both the processes also increases. In case of WEDM machined gears, noise was measured at three different levels of time, i.e., 60, 120 and 180 s respectively. In case of 60 s, maximum level of noise generated by the meshing gears is 98 dB, while in case of 120 s meshing time, the maximum noise level is 99.7 dB. For 180 s meshing time, the noise level reaches up to 102 dB.

From the experimental results, it was concluded that with the increase in the meshing time, the noise and vibration also increases, but when compared to the conventional machining processes, the gear manufactured by WEDM process generates less noise [49,50]. A comparative graph of both the conventional methods with WEDM process is shown in Figure 10. Average noise levels for milling, shaping, and WEDM processed gears at different run time are shown in Table 3.

Figure 10 Comparative graph of noise level for milling, shaping, and WEDM processed gears.
Figure 10

Comparative graph of noise level for milling, shaping, and WEDM processed gears.

Table 3

Noise level results at different intervals of time

S. No Gears Average noise levels
60 s 120 s 180 s
1 Milling gear 99.40 dB 108.12 dB 109.38 dB
2 Shaper gear 99.42 dB 102.37 dB 107.60 dB
3 WEDM gear 98.13 dB 99.75 dB 101.91 dB

5 Conclusion

The present study accomplishes that the gear tooth profile, which was machined at different parametrical settings (maximum MRR and SR conditions), has small craters and spherical modules with a lump of debris and re-solidified materials on its surface, according to surface integrity analysis. As there are fewer craters and less amount of debris on the surface of the machined surface under minimum MRR and SR parametrical settings, this surface is found to be smoother. From experimental runs, it was concluded that, among all the input process variables, pulse-on time (T on) is the most significant variable which affects the process performance mostly. When BPNN models are compared to quadratic RSM models, it is found that the generated BPNN models have more precision and accuracy. Noise characteristic analysis shows that with the increase in the meshing time, the gear noise level also increases. From the comparative analysis, it is clear that the gears machined by WEDM process have good micro-geometry and high surface quality as compared to that manufactured by conventional gear manufacturing processes.

Acknowledgments

The researchers would like to thank Research Facility Cell, National Institute of Technology Kurukshetra for their support and guidance.

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

  2. Author contributions: Conceptualization: Atul Raj, Joy Prakash Misra, Ravinder Pal Singh, Gurminder Singh, and Shubham Sharma; formal analysis: Atul Raj, Joy Prakash Misra, Ravinder Pal Singh, Gurminder Singh, and Shubham Sharma; investigation: Atul Raj, Joy Prakash Misra, Ravinder Pal Singh, Gurminder Singh, and Shubham Sharma; writing – original draft preparation: Atul Raj, Joy Prakash Misra, Ravinder Pal Singh, Gurminder Singh, and Shubham Sharma; writing – review and editing: Shubham Sharma and Sayed M. Eldin; supervision: Shubham Sharma and Sayed M. Eldin; project administration: Shubham Sharma and Sayed M. Eldin; and funding acquisition: Shubham Sharma and Sayed M. Eldin. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

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

  4. Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-10-04
Revised: 2022-11-17
Accepted: 2022-11-28
Published Online: 2023-03-29

© 2023 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/rams-2022-0288
Keywords for this article
Inconel 690; surface roughness; optimization; gear noise characteristics; response surface methodology; artificial neural network; FE-SEM
Creative Commons
BY 4.0

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  74. Effect of alkaline treatment time of fibers and microcrystalline cellulose addition on mechanical properties of unsaturated polyester composites reinforced by cantala fibers
  75. Optimization of eggshell particles to produce eco-friendly green fillers with bamboo reinforcement in organic friction materials
  76. An effective approach to improve microstructure and tribological properties of cold sprayed Al alloys
  77. Luminescence and temperature-sensing properties of Li+, Na+, or K+, Tm3+, and Yb3+ co-doped Bi2WO6 phosphors
  78. Effect of molybdenum tailings aggregate on mechanical properties of engineered cementitious composites and stirrup-confined ECC stub columns
  79. Experimental study on the seismic performance of short shear walls comprising cold-formed steel and high-strength reinforced concrete with concealed bracing
  80. Failure criteria and microstructure evolution mechanism of the alkali–silica reaction of concrete
  81. Mechanical, fracture-deformation, and tribology behavior of fillers-reinforced sisal fiber composites for lightweight automotive applications
  82. UV aging behavior evolution characterization of HALS-modified asphalt based on micro-morphological features
  83. Preparation of VO2/graphene/SiC film by water vapor oxidation
  84. A semi-empirical model for predicting carbonation depth of RAC under two-dimensional conditions
  85. Comparison of the physical properties of different polyimide nanocomposite films containing organoclays varying in alkyl chain lengths
  86. Effects of freeze–thaw cycles on micro and meso-structural characteristics and mechanical properties of porous asphalt mixtures
  87. Flexural performance of a new type of slightly curved arc HRB400 steel bars reinforced one-way concrete slabs
  88. Alkali-activated binder based on red mud with class F fly ash and ground granulated blast-furnace slag under ambient temperature
  89. Facile synthesis of g-C3N4 nanosheets for effective degradation of organic pollutants via ball milling
  90. DEM study on the loading rate effect of marble under different confining pressures
  91. Conductive and self-cleaning composite membranes from corn husk nanofiber embedded with inorganic fillers (TiO2, CaO, and eggshell) by sol–gel and casting processes for smart membrane applications
  92. Laser re-melting of modified multimodal Cr3C2–NiCr coatings by HVOF: Effect on the microstructure and anticorrosion properties
  93. Damage constitutive model of jointed rock mass considering structural features and load effect
  94. Thermosetting polymer composites: Manufacturing and properties study
  95. CSG compressive strength prediction based on LSTM and interpretable machine learning
  96. Axial compression behavior and stress–strain relationship of slurry-wrapping treatment recycled aggregate concrete-filled steel tube short columns
  97. Space-time evolution characteristics of loaded gas-bearing coal fractures based on industrial μCT
  98. Dual-biprism-based single-camera high-speed 3D-digital image correlation for deformation measurement on sandwich structures under low velocity impact
  99. Effects of cold deformation modes on microstructure uniformity and mechanical properties of large 2219 Al–Cu alloy rings
  100. Basalt fiber as natural reinforcement to improve the performance of ecological grouting slurry for the conservation of earthen sites
  101. Interaction of micro-fluid structure in a pressure-driven duct flow with a nearby placed current-carrying wire: A numerical investigation
  102. A simulation modeling methodology considering random multiple shots for shot peening process
  103. Optimization and characterization of composite modified asphalt with pyrolytic carbon black and chicken feather fiber
  104. Synthesis, characterization, and application of the novel nanomagnet adsorbent for the removal of Cr(vi) ions
  105. Multi-perspective structural integrity-based computational investigations on airframe of Gyrodyne-configured multi-rotor UAV through coupled CFD and FEA approaches for various lightweight sandwich composites and alloys
  106. Influence of PVA fibers on the durability of cementitious composites under the wet–heat–salt coupling environment
  107. Compressive behavior of BFRP-confined ceramsite concrete: An experimental study and stress–strain model
  108. Interval models for uncertainty analysis and degradation prediction of the mechanical properties of rubber
  109. Preparation of PVDF-HFP/CB/Ni nanocomposite films for piezoelectric energy harvesting
  110. Frost resistance and life prediction of recycled brick aggregate concrete with waste polypropylene fiber
  111. Synthetic leathers as a possible source of chemicals and odorous substances in indoor environment
  112. Mechanical properties of seawater volcanic scoria aggregate concrete-filled circular GFRP and stainless steel tubes under axial compression
  113. Effect of curved anchor impellers on power consumption and hydrodynamic parameters of yield stress fluids (Bingham–Papanastasiou model) in stirred tanks
  114. All-dielectric tunable zero-refractive index metamaterials based on phase change materials
  115. Influence of ultrasonication time on the various properties of alkaline-treated mango seed waste filler reinforced PVA biocomposite
  116. Research on key casting process of high-grade CNC machine tool bed nodular cast iron
  117. Latest research progress of SiCp/Al composite for electronic packaging
  118. Special Issue on 3D and 4D Printing of Advanced Functional Materials - Part I
  119. Molecular dynamics simulation on electrohydrodynamic atomization: Stable dripping mode by pre-load voltage
  120. Research progress of metal-based additive manufacturing in medical implants
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