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Effect of multiple laser re-melting on microstructure and properties of Fe-based coating

  • Linyi Xie , Wenqing Shi EMAIL logo , Teng Wu , Meimei Gong , Jiang Huang , Yuping Xie and Kuanfang He
Published/Copyright: December 2, 2022
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High Temperature Materials and Processes
From the journal High Temperature Materials and Processes Volume 41 Issue 1

Abstract

The aim of this article is to explore the effect of re-melting times on the microstructure and properties of Fe-based coating. In this study, the Fe-based coating is prepared on 316L stainless steel by laser cladding and laser re-melting. Meanwhile, the microstructure and properties of the coating are studied by 3D laser scanner, Vickers microhardness tester, X-ray diffractometer, and scanning electron microscope. In addition, the effect of laser re-melting times on microstructure formation that is analyzed by numerical simulation. The results show that re-melting can lead to the decrease in coating height, increase in coating width, and increase in both depth and width of melting pool. The hardness of coatings is enhanced by six times compared with the substrate. However, it was found that the hardness of the coating decreased with the increase in laser re-melting times. The abnormal decrease in hardness was analyzed because of the continued growth of crystals in the coating and an increase in the coating dilution rate. The first laser re-melting results in the obvious change of coating crystal. The crystals of the multiple laser re-melting coating continue to grow. Our research results can provide reference for laser multiple re-melting in industry.

Keywords: laser cladding; laser re-melting; numerical simulation; Ansys

1 Introduction

FeCrMo alloys are potential candidates due to their superior mechanical properties. Because of its low production cost, 316L is commonly used in industry. Laser melting is just an advanced surface modification technology. By converting light energy into heat energy, the substrate and alloy powder are fused to form molten metal by high energy density. The molten metal is solidified and metallurgically bonded with the substrate to develop a high-performance coating [1,2,3]. FeCrMo can be coated on 316L by laser cladding, which has become a low cost method to prepare high-performance coatings.

In conventional thermal processing, it is difficult to form a coating that meets the requirements (dilution rate, porosity, surface roughness, etc.) [4,5,6]. Laser re-melting is a way of laser melting after the first sample preparation has been completed [7]. In recent years, researchers have used laser re-melting to produce coatings that meet these needs [8,9,10].

Fals et al. [11] sprayed the NbC coating flame onto the AISI 304L substrate and re-melted it using a fiber laser. It is found that the hardness of laser re-melting zone is higher than that of the substrate. Panziera et al. [12] conducted laser re-melting on the surface of the coating. By changing the scanning speed and laser beam power, laser re-melting can obtain a coating with high hardness, no pores, or obvious defects. Zhang et al. [13] modified the surface coating of plasma spraying by laser re-melting and Al deposition to enhance its high-temperature corrosion resistance. It was found that the minimum surface roughness of the dense laser re-melting coating was the main reason for improving the high-temperature corrosion resistance. Godec et al. [14] modified the thermal barrier coatings with different shapes by laser re-melting technology and found that they had dense columnar crystal structure and high microhardness.

Wang et al. [15] prepared Fe-based coating on the surface of H13 steel by laser cladding and re-melting. It was found that after re-melting, the defects such as cracks and pores on the coating surface were significantly reduced. After re-melting, the overall corrosion resistance of the re-melting layer is greater than that of the coating. Xi et al. [16] found that re-melting not only improved the morphology of the coating but also increased the microhardness and wear resistance of the coating. When the laser power is 650 W, the coating has not only small surface roughness and no defects but also excellent mechanical properties.

As can be viewed above, laser re-melting is an effective method to improve the minimum surface roughness, wear resistance, and corrosion resistance of coatings. This work is to study the effect of laser multiple re-melting on the microstructure and properties of coatings. Through the combination of numerical simulation and experiment, the influence of re-melting times on the coating was compared and analyzed. In this article, the effects of multiple laser re-melting on the macroscopic morphology, microstructure forming ability, hardness, and microstructure of Fe-based coatings were studied. The effect of laser re-melting cooling rate on microstructure forming ability was analyzed by finite element numerical simulation.

2 Materials and methods

2.1 Experimental

FeCrMo powder was employed as the cladding material. Table 1 lists the contents of FeCrMo powder and 316L stainless steel. The substrate was polished with sandpaper before laser treatment, cleaned by anhydrous ethanol, and dried in the drying cabinet.

Table 1

Contents of FeCrMo powder and 316L stainless steel (wt%)

Type Fe S (%) Si (%) B (%) C (%) Mo (%) Cr (%) Ni (%) Mn (%)
FeCrMo Balance 0.0226 0.35 2.08 2.44 16.8 25.8 — —
316L Balance — ≤1 — — 2–3 16–18 12–15 ≤2

The laser source is provided by 2,000 W fiber laser (XL-F2000T). Prefabrication method was used to coat powder on the substrate. The processing schematic diagram is shown in Figure 1. The laser cladding process is divided into two stages: laser cladding and laser re-melting. According to the relevant literature [15,17], low power and low scanning speed are selected by melting, and high power and high scanning speed are selected by laser re-melting. Laser cladding power is 300 W, scanning speed is 300 mm·min−1, laser re-melting power is 1,600 W, scanning speed is 2,400 mm·min−1, defocusing is 0 mm, and the experimental scheme is shown in Table 2. Re-melting is carried out in inert argon at 5 L·min−1. Use corrosion solution (aqua regia) for 10 s after processing. The macro morphology of the coating was measured by Scan Tech 3D laser scanner (PRINCE335). The microhardness of the coating was measured by Wilson Vickers microhardness tester (BUEHLER VH1202). The crystalline phase composition of the coating was analyzed by X-ray diffraction (XRD). The microstructure of the coating was observed by JSM 6460 scanning electron microscope (SEM). Figure 2 shows the coating schematic.

Figure 1 Processing schematic.
Figure 1

Processing schematic.

Table 2

Experimental scheme

Sample name Sample 1 Sample 2 Sample 3
Re-melting passes 0 1 2
Figure 2 Schematic diagram of coating. (a) The coating distribution diagram. (b) The schematic of coating geometry. (c) The position distribution of SEM observation. CH: coating height, CW: coating width, DMP: depth of molten pool, WMP: width of molten pool.
Figure 2

Schematic diagram of coating. (a) The coating distribution diagram. (b) The schematic of coating geometry. (c) The position distribution of SEM observation. CH: coating height, CW: coating width, DMP: depth of molten pool, WMP: width of molten pool.

2.2 Finite element analysis

For crystalline alloys, cooling rate is the most important factor to restrain nucleation and growth of competitive crystalline phase. The cooling rate of the coating under multiple laser re-melting also affects its microstructure forming ability. Therefore, the finite element method is used to solve the heat conduction equation, and the temperature change process of the coating during laser re-melting is obtained, which can provide prediction for the thermal cycle of microstructure formation and change.

In the machining process, with the effect of laser heat source, the temperature of the workpiece changes, and the thermal physical parameters of the material also change. Therefore, the analysis of temperature field is an obvious nonlinear transient heat conduction problem. The three-dimensional temperature field equation is shown as [18]

(1) ρ c ∂ T ∂ t − ∂ ∂ x k x ∂ T ∂ x − ∂ ∂ y k y ∂ T ∂ y − ∂ ∂ z k z ∂ T ∂ z − Q = 0 ∀ ( x , y , z ) ∈ Ω ,

where ρ and c are material density and specific heat capacity, respectively; k x , k y , and k z are the thermal conductivity in three directions in turn; T is the temperature field distribution function; and Q is the thermal power per unit volume. Convection and radiation heat dissipation Q1 exists on the coating surface (interface between substrate material and air) and around the substrate surface, which satisfies [19,20]

(2) Q 1 = h c ( T − T 0 ) + ε σ ( T 4 − T 0 4 ) ,

where T 0 is the ambient temperature; h c is the convection coefficient, W·m−2·K−1; surface emissivity of ε matrix; and σ is the Stefan–Boltzmann constant of 5.67 × 10−8 W·m−2·K−4.

At present, the moving heat source models for numerical simulation of laser machining mainly include surface heat source model, body heat source model, and combined heat source model [21,22,23]. The heat source expression of conical heat source model is as follows [24]:

(3) E = α ( 1 − β ) 2 η P e π r 2 d e − ( x 2 + y 2 ) r 2 ⋅ ( z + a ) d ,

where E is the heat generation rate, α is the substrate absorption efficiency, β is the powder loss efficiency, η is the laser power efficiency, P is the total laser power, r is the laser spot radius, d is the total heat source depth of the cone, and a is the XOY plane offset coefficient.

The heat source model of the conical heat source model is shown in Figure 3. By Ansys Parametric Design Language (APDL), some heat generating units (changing d 1 ) can be selected to adjust the heat source model of the conical heat source model.

Figure 3 Heat source model of cone heat source model.
Figure 3

Heat source model of cone heat source model.

According to the relevant literature [25,26,27], the nonlinear physical parameters of FeCrMo and 316L were added in Ansys material library. Table 3 shows the physical parameters of FeCrMo and 316L: T-temperature, ρ-material density, c-specific heat capacity, and k-thermal conductivity.

Table 3

Physical parameters

FeCrMo 316L
T (K) ρ (kg·m−3) c (J·kg−1·K−1) k (W·m−1·K−1) ρ (kg·m−3) c (J·kg−1·K−1) k (W·m−1·K−1)
300 7,763 826.77 16.30 7,954 498.73 13.44
500 7,840 507.23 18.50 7,864 525.51 16.8
700 7,732 543.67 20.70 7,771 551.87 19.87
900 7,626 576.06 22.90 7,674 578.65 22.79
1,100 7,524 607.13 25.09 7,574 605.01 25.46
1,300 7,423 637.87 27.29 7,471 631.78 28.02

To simplify the model, only half of the workpiece model is simulated by symmetrical modeling. In the finite element numerical simulation, the coating size is 1.8 mm × 0.5 mm × 20 mm, and the 316L stainless steel substrate size is 50 mm × 50 mm × 2 mm. In numerical simulation, the software environment is set as follows:

  1. The molten pool flow is not considered for the simplified model.

  2. Ansys software is used for thermal indirect coupling, and APDL code is used to load the moving heat source and control the birth and death of the coating unit.

  3. The ambient temperature is set to 300 K.

  4. In order to improve the convergence of the calculation results, the empirical formula for heat transfer coefficient is used as follows:

    (4) h ε = 2.41 × 10 − 3 ε ⋅ T 1.61 ,

    where T is the temperature (K), hε is the heat transfer coefficient (W·m−2·K−1), and surface emissivity of ε matrix.

  5. Adiabatic boundary condition is applied on the symmetric surface of the symmetric model, and heat transfer coefficient (4) is applied on the other outer surfaces. Considering the latent heat of phase transformation of materials, it can be expressed as

    (5) Δ H = L ⋅ f 1 ,

    where f 1 is the liquid mass fraction and L is the solidification latent heat value. According to ref. [27], the solid phase line of 316L was 1520.5 K, the liquid phase line was 1,609 K, and the latent heat value was 2.8 × 105 K·kg−1. The gasification temperature is 3,133 K and the latent heat is 6.1 × 106 K·kg−1. Due to the lack of relevant heat values of FeCrMo, 316L latent heat data were used.

    (6) f 1 = 0 , T < T s , T − T s T l − T s , T s ≤ T ≤ T l , 1 , T ≥ T l ,

    where T l is the liquid line, T s is the solid line, and T is the unit temperature.

  6. For the setting of the solution, the time step is set to 20 ms, the open time integral, and the workpiece is cooled to 20 s.

The geometric model is drawn by external three-dimensional software and imported into Ansys. The solid186 advanced unit is used to mesh the model, as shown in Figure 4. The nonlinear hexahedral mesh is adopted, with the minimum mesh side length of 0.1 mm and the maximum mesh side length of 0.8 mm. The total number of units is 43,200, and the number of nodes is 206,236.

Figure 4 Meshing of finite element model.
Figure 4

Meshing of finite element model.

3 Experimental results

3.1 Macroscopic morphology

Figure 5 shows the three-dimensional macro morphology of the coatings of each sample under different re-melting times. The surface of sample 1 coating (Figure 5a) has a large fluctuation, and the maximum height difference is 0.16 mm. The surface of sample 2 coating (Figure 5b) has some fluctuation, and the maximum height difference is 0.11 mm. The surface of sample 3 coating (Figure 5c) has a small fluctuation, and the maximum height difference is 0.09 mm. The morphology of the first re-melting coating changed obviously. The coating height (CH) decreases along the X axis. In other words, the CH decreases slightly along the scanning path. The surface of sample 1 coating appeared bump phenomenon. The high-power heat input is performed again by the re-melting process, and the unevenness of the sample 2 is alleviated, while the CH is reduced and the coating width (CW) is increased. Increasing the re-melting times, the above phenomenon of sample 3 is further obvious. The increase in re-melting times reduces the fluctuation of coating surface.

Figure 5 Three-dimensional macroscopic morphology of each sample: (a) Sample 1, (b) sample 2, and (c) sample 3.
Figure 5

Three-dimensional macroscopic morphology of each sample: (a) Sample 1, (b) sample 2, and (c) sample 3.

Table 4 shows the cross sections of coating and molten pool of each sample. The dilution rate formula is calculated according to equation (7) [17,28]. Figure 2b shows the schematic of coating geometry. The CH of sample 1 is 0.76 mm. The CH of sample 2 is 0.60 mm. The CH of sample 3 is 0.54 mm. The CH of sample 2 was significantly lower than that of sample 1, and the CH of sample 3 was relatively lower than that of sample 2. The CW of each sample and the depth of molten pool (DMP) and width of molten pool (WMP) have similar CH. The above phenomena indicate that re-melting will lead to the decrease in CH and increase in CW, and the size of the first re-melting coating changes significantly. It is found that re-melting will increase the DMP and WMP, and the size of the first re-melting molten pool changes significantly.

(7) Dilution rate μ = S 2 S 1 + S 2 × 100 %,

where S 1 is the area of cladding layer and S 2 represents the area of molten substrate on the cross-section of a sample.

Table 4

Cross-sectional dimensions of coatings and molten pool for each sample

Sample CH (mm) CW (mm) DMP (mm) WMP (mm) Dilution rate (%)
1 0.76 1.35 0.03 0.42 1.21
2 0.60 1.70 0.25 1.01 19.84
3 0.54 1.78 0.31 1.10 26.19

3.2 Hardness

The microhardness of the samples along the cross section was measured under 0.2 kg loading for 10 s. Each sample has 30 indentation points, 3 along the Y axis direction and 10 along the Z axis direction. As shown in Figure 6, the hardness of sample 1 reaches 1,400 HV0.2, which is six times higher than that of matrix (about 200 HV0.2). The hardness of samples 1, 2, and 3 coatings decreased in turn. It shows that re-melting will lead to a decrease in hardness.

Figure 6 Distribution of microhardness of coatings.
Figure 6

Distribution of microhardness of coatings.

3.3 XRD and microstructure

The XRD patterns of the three sample coatings are shown in Figure 7. It can be seen that the diffraction peaks of the three samples have no obvious change. It can be said that the increase in the number of re-melted elements will not continue to generate in the form of compounds.

Figure 7 XRD diffraction patterns of each sample coating.
Figure 7

XRD diffraction patterns of each sample coating.

Figure 8 shows SEM photographs of different parts of three samples. Figure 2c shows the position distribution of SEM observation. Some crystal structures can be seen, and the microstructure of the coating changes obviously after re-melting. No crystals appear in Figure 8a. A large number of fine flower crystals (I) appear in Figure 8b. Figure 8c shows coarse snowflake crystals (II). It was found that at the top of the coating, the crystal was formed in sample 2 compared with sample 1, while the crystal continued to grow in sample 3. In the middle of the coating, there was no crystal in Figure 8d, and a small amount of coarse snowflake crystals were formed after laser re-melting (Figure 8e III). A large number of coarse snowflake crystals were formed after laser re-melting (Figure 8f IV).

Figure 8 SEM photographs of microstructure in different parts of different samples. (a–c) Top, (d–f) middle, and (g–i) bottom. (a, d, and g) Sample 1, (b, e, and h) sample 2, and (c, f, and i) sample 3.
Figure 8

SEM photographs of microstructure in different parts of different samples. (a–c) Top, (d–f) middle, and (g–i) bottom. (a, d, and g) Sample 1, (b, e, and h) sample 2, and (c, f, and i) sample 3.

At the bottom of the coating, the matrix below the interface appears martensite structure. However, different crystals appear above the interface. Figure 8g shows that no crystal appears in the fused layer. In Figure 8h, some vertical columnar dendrites (V) form arc bands. A large number of columnar dendrites (VI) parallel to the heat flow direction form larger arc bands in Figure 8i. It shows that the first laser re-melting leads to the crystallization of the coating, forming an arc crystal band as shown in the figure, and the laser re-melting continues to form a larger arc crystal band. According to the rapid solidification theory [29], higher cooling rate and undercooling rate can be obtained by laser scanning speed, and the grains grow in columnar dendrites parallel to the direction of heat flow (V, VI).

Figure 9 shows grain size of different parts of three samples. At the top, the grain sizes of samples 1, 2, and 3 are 0.339, 0.806, and 3.04 μm, respectively. In the middle, the grain sizes of samples 1, 2, and 3 are 0.534, 1.44, and 6.24 μm, respectively. At the bottom, the grain sizes of samples 1, 2, and 3 are 0.311, 1.66, and 6.52 μm, respectively. The grain size of samples 1, 2, and 3 in each part increases in turn.

Figure 9 Grain size of different parts of each sample.
Figure 9

Grain size of different parts of each sample.

3.4 Numerical simulation

Figure 10a shows the temperature distribution of sample 1 laser melting process. It is clear that the moving heat source is comet-like. Figure 10b–d shows the comparison between the numerical simulation of each sample and the actual molten pool. The red region is higher than the melting point of 1,609 K (as the coating is assumed rectangular size, it is not shown in the picture). It indicates that the red region is melted during laser melting. The comparison between the experimental results and the temperature field shows that the temperature predicted by the established model is in good agreement with the simulation results, which can be used for the thermal cycle prediction of laser re-melting process.

Figure 10 Finite element numerical simulation group diagram (a) sample 1 temperature distribution diagram. (b–d) Sample 1, sample 2, and sample 3 molten pool contrast diagram.
Figure 10

Finite element numerical simulation group diagram (a) sample 1 temperature distribution diagram. (b–d) Sample 1, sample 2, and sample 3 molten pool contrast diagram.

Figure 11 shows the temperature change calculation results of each sample during laser re-melting. It can be seen that the highest temperature of sample 1 is 1,946 K, sample 2 is 2,616 K, and sample 3 is 2,656 K at the top of the surface. We calculated the cooling rate from the highest temperature to 700 K. The cooling rates of samples 1–3 are 1523.4, 6922.2, and 5609.7 K·s−1, respectively. The interfacial cooling rates of samples 1–3 were 1332.3, 3207.4, and 2547.1 K·s−1, respectively. It was found that the cooling rates of the surface vertices and interfaces of the three samples had similar trends. Sample 2 has the highest cooling rate and sample 1 has the lowest cooling rate.

Figure 11 Curve graph of surface vertex temperature change.
Figure 11

Curve graph of surface vertex temperature change.

3.5 Discussion

In Figure 5, the increase in laser re-melting times leads to a decrease in the surface undulation of the coating. With the increase in the number of laser re-melting, the accumulated heat input makes the temperature of the coating exceed the melting point (as can be found in Figure 11), and the coating is repeatedly re-melted and solidified, resulting in the decrease in the fluctuation of the upper surface of the coating. The results are consistent with previous studies by Zhang et al. [13]. Laser re-melting can reduce the hardness of the coating (as can be seen in Figure 6). The heat input of the laser re-melting will cause the coating to continue to be heated at high temperatures, causing the amorphous structure to crystallize and the grain to continue to grow (as can be seen in Figure 8). We have measured the grain size in Figure 8, and the results are shown in Figure 9. The grain size is increased by the re-melting process. At the same time, laser re-melting increases the dilution rate (Table 4), which can result in a decrease in hardness [12,30]. Sousa et al. [31] also concluded in his study. In XRD analysis (Figure 7), it can be found that no new compound was formed in the coating after laser re-melting.

Combined with the analysis of Figure 8, the tissue of sample 2 changed significantly compared with sample 1. According to the rapid solidification theory [29], from the substrate to the coating, the microstructure will show a typical structural transformation order of planar crystal, cellular crystal, dendritic crystal, and equiaxed crystal. But only dendritic and snowflake crystals were found in the three samples. In the cold zone rate, sample 1 is the smallest, and there is no obvious crystal formation. The cooling rates of sample 2 and sample 3 are similar. After the first laser re-melting, sample 2 crystal was formed. After laser re-melting again with the same process parameters, sample 3 crystal grew. However, the tissue changes of sample 3 were not obvious compared with sample 2. This is because the behavior of laser melting before laser re-melting has preheating effect on the substrate. The substrate temperature increases during laser re-melting, resulting in a decrease in temperature gradient. But the crystal continues to grow as the laser re-melting continues to heat input again.

4 Conclusions

Fe-based composite coating was synthesized by laser cladding and laser re-melting. The microstructure and mechanical properties of the samples were studied. Laser re-melting was simulated by finite element method. According to the experimental and numerical simulation results, the effects of multiple laser re-melting on microstructure forming ability and coating performance were analyzed.

  1. The morphology and size of the first re-melting coating changed obviously. The fluctuation of the coating surface decreases after multiple re-melting.

  2. Multiple laser re-melting results in the decrease of coating hardness. The reason is that the crystal phase continues to grow and the coating dilution rate increases.

  3. After the first laser re-melting, the multiple re-melting had no effect on the coating compounds.

Finally, considering the influence of multiple laser re-melting on the macroscopic morphology, microstructure forming ability, hardness and microstructure of the coating, the appropriate number of laser re-melting can be selected.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 62073089), the Special Fund for Key Projects of Colleges and Universitiesin Guangdong Province (Grant No. 2020ZDZX2061),and the Postgraduate Education Innovation Project of Guangdong Ocean University (Grant No. 040502112104), which is acknowledged.

  1. Funding information: This study was funded by National Natural Science Foundation of China (Grant No. 62073089), the Special Fund for Key Projects of Colleges and Universities in Guangdong Province (Grant No. 2020ZDZX2061), and the Postgraduate Education Innovation Project of Guangdong Ocean University (Grant No. 040502112104).

  2. Author contributions: Linyi Xie: conceptualization, methodology, formal analysis, writing – original draft, and writing – review and editing. Wenqing Shi: conceptualization, formal analysis, super-vision, and project administration. Teng Wu, Meimei Gong Jiang Huang, and Yuping Xie: discussions. Kuanfang He: fund. All authors have read and agreed to the published version of the manuscript.

  3. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

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Received: 2022-06-28
Revised: 2022-10-11
Accepted: 2022-10-12
Published Online: 2022-12-02

© 2022 Linyi Xie et al., published by De Gruyter

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

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https://doi.org/10.1515/htmp-2022-0248
Keywords for this article
laser cladding; laser re-melting; numerical simulation; Ansys
Creative Commons
BY 4.0

Articles in the same Issue

  1. Research Articles
  2. Numerical and experimental research on solidification of T2 copper alloy during the twin-roll casting
  3. Discrete probability model-based method for recognition of multicomponent combustible gas explosion hazard sources
  4. Dephosphorization kinetics of high-P-containing reduced iron produced from oolitic hematite ore
  5. In-phase thermomechanical fatigue studies on P92 steel with different hold time
  6. Effect of the weld parameter strategy on mechanical properties of double-sided laser-welded 2195 Al–Li alloy joints with filler wire
  7. The precipitation behavior of second phase in high titanium microalloyed steels and its effect on microstructure and properties of steel
  8. Development of a huge hybrid 3D-printer based on fused deposition modeling (FDM) incorporated with computer numerical control (CNC) machining for industrial applications
  9. Effect of different welding procedures on microstructure and mechanical property of TA15 titanium alloy joint
  10. Single-source-precursor synthesis and characterization of SiAlC(O) ceramics from a hyperbranched polyaluminocarbosilane
  11. Carbothermal reduction of red mud for iron extraction and sodium removal
  12. Reduction swelling mechanism of hematite fluxed briquettes
  13. Effect of in situ observation of cooling rates on acicular ferrite nucleation
  14. Corrosion behavior of WC–Co coating by plasma transferred arc on EH40 steel in low-temperature
  15. Study on the thermodynamic stability and evolution of inclusions in Al–Ti deoxidized steel
  16. Application on oxidation behavior of metallic copper in fire investigation
  17. Microstructural study of concrete performance after exposure to elevated temperatures via considering C–S–H nanostructure changes
  18. Prediction model of interfacial heat transfer coefficient changing with time and ingot diameter
  19. Design, fabrication, and testing of CVI-SiC/SiC turbine blisk under different load spectrums at elevated temperature
  20. Promoting of metallurgical bonding by ultrasonic insert process in steel–aluminum bimetallic castings
  21. Pre-reduction of carbon-containing pellets of high chromium vanadium–titanium magnetite at different temperatures
  22. Optimization of alkali metals discharge performance of blast furnace slag and its extreme value model
  23. Smelting high purity 55SiCr automobile suspension spring steel with different refractories
  24. Investigation into the thermal stability of a novel hot-work die steel 5CrNiMoVNb
  25. Residual stress relaxation considering microstructure evolution in heat treatment of metallic thin-walled part
  26. Experiments of Ti6Al4V manufactured by low-speed wire cut electrical discharge machining and electrical parameters optimization
  27. Effect of chloride ion concentration on stress corrosion cracking and electrochemical corrosion of high manganese steel
  28. Prediction of oxygen-blowing volume in BOF steelmaking process based on BP neural network and incremental learning
  29. Effect of annealing temperature on the structure and properties of FeCoCrNiMo high-entropy alloy
  30. Study on physical properties of Al2O3-based slags used for the self-propagating high-temperature synthesis (SHS) – metallurgy method
  31. Low-temperature corrosion behavior of laser cladding metal-based alloy coatings on EH40 high-strength steel for icebreaker
  32. Study on thermodynamics and dynamics of top slag modification in O5 automobile sheets
  33. Structure optimization of continuous casting tundish with channel-type induction heating using mathematical modeling
  34. Microstructure and mechanical properties of NbC–Ni cermets prepared by microwave sintering
  35. Spider-based FOPID controller design for temperature control in aluminium extrusion process
  36. Prediction model of BOF end-point P and O contents based on PCA–GA–BP neural network
  37. Study on hydrogen-induced stress corrosion of 7N01-T4 aluminum alloy for railway vehicles
  38. Study on the effect of micro-shrinkage porosity on the ultra-low temperature toughness of ferritic ductile iron
  39. Characterization of surface decarburization and oxidation behavior of Cr–Mo cold heading steel
  40. Effect of post-weld heat treatment on the microstructure and mechanical properties of laser-welded joints of SLM-316 L/rolled-316 L
  41. An investigation on as-cast microstructure and homogenization of nickel base superalloy René 65
  42. Effect of multiple laser re-melting on microstructure and properties of Fe-based coating
  43. Experimental study on the preparation of ferrophosphorus alloy using dephosphorization furnace slag by carbothermic reduction
  44. Research on aging behavior and safe storage life prediction of modified double base propellant
  45. Evaluation of the calorific value of exothermic sleeve material by the adiabatic calorimeter
  46. Thermodynamic calculation of phase equilibria in the Al–Fe–Zn–O system
  47. Effect of rare earth Y on microstructure and texture of oriented silicon steel during hot rolling and cold rolling processes
  48. Effect of ambient temperature on the jet characteristics of a swirl oxygen lance with mixed injection of CO2 + O2
  49. Research on the optimisation of the temperature field distribution of a multi microwave source agent system based on group consistency
  50. The dynamic softening identification and constitutive equation establishment of Ti–6.5Al–2Sn–4Zr–4Mo–1W–0.2Si alloy with initial lamellar microstructure
  51. Experimental investigation on microstructural characterization and mechanical properties of plasma arc welded Inconel 617 plates
  52. Numerical simulation and experimental research on cracking mechanism of twin-roll strip casting
  53. A novel method to control stress distribution and machining-induced deformation for thin-walled metallic parts
  54. Review Article
  55. A study on deep reinforcement learning-based crane scheduling model for uncertainty tasks
  56. Topical Issue on Science and Technology of Solar Energy
  57. Synthesis of alkaline-earth Zintl phosphides MZn2P2 (M = Ca, Sr, Ba) from Sn solutions
  58. Dynamics at crystal/melt interface during solidification of multicrystalline silicon
  59. Boron removal from silicon melt by gas blowing technique
  60. Removal of SiC and Si3N4 inclusions in solar cell Si scraps through slag refining
  61. Electrochemical production of silicon
  62. Electrical properties of zinc nitride and zinc tin nitride semiconductor thin films toward photovoltaic applications
  63. Special Issue on The 4th International Conference on Graphene and Novel Nanomaterials (GNN 2022)
  64. Effect of microstructure on tribocorrosion of FH36 low-temperature steels
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