Optimization of inconel 625 laser cladding on X65 steel via response surface methodology

Abstract

To meet the extreme corrosion resistance requirements of deep-sea API 5 L X65 carbon steel pipelines, this study utilizes the Box-Behnken response surface methodology to optimize the laser cladding of Inconel 625. Unlike previous studies focused on single-performance metrics, this research provides a novel coordinated regulation of multi-factor interactions to control interface martensite thickness. By establishing a quadratic regression model (R-squared = 0.9484), we systematically explored the effects of laser power, scanning speed, and powder feeding rate. Results indicate the powder feeding rate most significantly impacts cladding thickness (P < 0.0001), while its interaction with scanning speed (P = 0.0422) is critical for geometric regulation. The optimized parameters—1910 W laser power, 1365 mm/min scanning speed, and 0.47 r/min powder feeding rate—yielded an ideal 1 mm cladding thickness. This precise control window produces refined dendrites and a stable 50–60 μm martensite layer, offering highly reliable process parameters for pipeline remanufacturing.

Introduction

API 5 L X65 carbon steel has become a core pipe material in the fields of oil and gas transportation and marine engineering due to its excellent mechanical properties and cost advantages. However, in harsh service environments containing Cl⁻, H₂S, and CO₂, it is prone to corrosion, wear and interface failure, seriously threatening the safety and service life of pipeline operations¹. Surface strengthening technology is a key means to solve this pain point. Among them, laser cladding is recognized as the preferred solution for surface modification of X65 steel pipes due to its ability to achieve metallurgical bonding between the cladding layer and the substrate, low dilution rate, and dense structure². Its core advantage is that it can accurately control the composition and microstructure of the cladding layer, avoiding the defects of weak bonding and uneven performance in traditional surface treatment technologies³.

Selecting an appropriate cladding material is the core to ensure the strengthening effect. As a typical Ni-based super alloy, Inconel 625 relies on the synergistic effect of key alloy elements such as Cr, Mo, and Nb to form a dense passivation film to resist corrosion and optimize the microstructure through solid solution and second phase strengthening. Its comprehensive performance has been verified by multiple studies. Xu⁴ et al. studied X65-Inconel 625 composite pipe joints and found that the cladding layer can hinder Fe diffusion and significantly reduce intergranular corrosion sensitivity; Su⁵ et al. used hot wire laser cladding to prepare the Inconel 625 layer on the surface of carbon steel. The deposition rate reached 1.72 kg/h, the wear resistance was 4 times higher than that of the base material, and the average hardness was 262 HV₁. Wang & Zhang^6,7 et al. designed a WC-enhanced Inconel 625 gradient coating to alleviate thermal stress through a 0 ~ 30 wt% WC content gradient design. The wear rate was reduced by 57% compared with a single coating, and the salt spray corrosion rate was only 0.67 g/(m²·h); Iqbal⁸ et al. used double-pulse GTAW cladding on API 5 L X65 steel to control the Fe dilution rate below 5 wt%, the coating shows excellent corrosion resistance in acidic media, with a yield strength of 523 MPa; Fu⁹ et al. revealed the interface phase transformation mechanism of Inconel 625 cladding layer at a high temperature of 700 °C, maintaining the stability of the γ-Ni matrix through element diffusion control, and controlling the interface lattice mismatch between 0.029 ~ 0.059; Huang¹⁰ et al. used bypass coupling micro-plasma arc cladding technology to prepare on the surface of cast iron. The hardness of the fusion zone of the Inconel 625 layer interface reaches 630 HV, and the structure is dense and crack-free. The coating prepared by Zhang¹¹ and others through ultra-high-speed laser cladding has a cooling rate of 10⁴ K/s, which effectively suppresses element segregation. Its corrosion resistance is 30% higher than that of traditional processes, and its density exceeds 99.5%. These studies have confirmed that it is an ideal material for surface strengthening of X65 steel pipes.

Laser cladding process parameters (laser power, scanning speed, powder feeding rate, etc.) play a decisive role in the quality of the cladding layer. Improper parameter matching can easily cause defects such as lack of fusion, cracks, pores, and out-of-control martensite layer thickness.

Similar process-optimization challenges have also been reported for other Inconel series: for instance, Gupta and Mulik¹² systematically optimized welding current, torch speed, and wire feed speed in wire arc additive manufacturing (WAAM) of Inconel 825, and demonstrated that coordinated multi-parameter control is essential for achieving defect-free bead geometry, equiaxed γ-phase microstructure, and stable mechanical properties. Such studies on related Ni-based systems highlight that, regardless of the specific alloy (Inconel 625, 718, or 825) or deposition technique (laser cladding, WAAM, CMT), the common bottleneck lies in decoupling the synergistic interactions among thermal, geometric, and metallurgical parameters. Li¹³ et al. used plasma spraying-assisted laser cladding process on the surface of X65 steel pipe and found that the mismatch between power and scanning speed will lead to a decrease in interface bonding strength; Chen¹⁴ et al. have shown that when the powder feed rate fluctuates by more than 0.1 r/min, the porosity of the cladding layer will significantly increase to more than 5%. These phenomena highlight the necessity of precise optimization of multiple parameters. As an efficient multi-variable optimization tool, response surface method (RSM) establishes a quadratic regression model of parameters and performance through experimental design, which can intuitively reflect parameter interactions and accurately predict the optimal combination. Zhao¹⁵ et al. established a coupling model of laser power, scanning speed, powder feeding rate and cladding layer density and hardness based on RSM. The prediction accuracy R² reached 0.96, which provides a reliable idea for complex process optimization.

However, a critical analysis of the existing literature reveals several significant methodological limitations. First, previous research has predominantly focused on the unidirectional enhancement of specific performance metrics—such as merely increasing surface hardness via ceramic reinforcements or maximizing deposition rates using hot-wire methods¹⁶. These isolated approaches often overlook the complex metallurgical compatibility at the bimetallic interface, inadvertently increasing the risk of thermal stress accumulation and cracking. Second, while advanced predictive tools like machine learning and multi-objective algorithms have been introduced, they frequently treat macroscopic geometric quality (e.g., density or lack-of-fusion) and microscopic metallurgical behavior (e.g., interfacial phase transformation) as completely isolated phenomena^17–19.For the specific Inconel 625/X65 bimetallic system operating in harsh deep-sea environments, the geometric consistency of the cladding layer and the precise control of the heat-affected interfacial martensite layer are equally critical to prevent premature failure under complex bending stresses. Unfortunately, current studies lack a coordinated regulatory strategy that simultaneously addresses macroscopic dimensions, interfacial integrity, and overall environmental resistance. Furthermore, the specific thermo-physical mechanisms dictating the optimal thickness of this interface martensite layer remain poorly defined^20,21. To bridge these critical gaps, the response surface methodology (RSM) emerges as an ideal solution. RSM offers an excellent balance of experimental efficiency and optimization accuracy, effectively decoupling complex multi-parameter interactions to meet the stringent reliability demands of engineering applications.

Based on this, Inconel 625 is used to laser cladding API 5 L X65 carbon steel pipes, with the thickness of the cladding layer, microstructure and cladding spatter as the research goals, and the key process parameters are optimized by the response surface method, the parameter interaction mechanism is revealed, and the advantages and disadvantages of other optimization methods are compared, so as to provide efficient technical support for the surface strengthening of X65 steel pipes and promote the improvement of service safety and durability in harsh environments.

Experiment

Cladding preparation method

In this paper, a laser coating system is used to prepare a sample of 2205 coating layer: the coating system parameter as shown in Table 1.

Table 1.

Laser system specifications.

Component	Specification/value
Wavelength range	1075–1085 nm
Maximum output power	3000 W
Spot size and shape	3 mm × 3 mm (Rectangular)
Powder coating head	TL440-AM 60 mm
Powder feeder	PF330 intelligent monocular
Water cooler	SGS water cooler
Power supply	People’s electrical stabilized
Standoff distance	7.5 mm

Shielding and carrier gas, with a shielding flow rate of 30 L/min and a carrier flow rate of 2.6 L/min. The powder was delivered using a PF330 intelligent monocular powder feeder, and baseline powder characteristics provided by Höganäs were verified via SEM image analysis.

Before the experiment, the base tube was fixed to the three-jaw chuck, and the 90° rotating displacement machine and the coated gun head were straightened. Horizontal calibration is performed by placing the level meter on the surface of the coating head and the pipe wall at the same time. The distance between the base surface of the coating head and the workpiece is set to 7.5 mm (to keep the spot consistent with the powder delivery path).In order to ensure that the thickness of the coating layer on both sides of the substrate after laser coating is uniform, the micrometer is repeatedly used for correction to achieve the maximum parallelism between the chuck and the center of the tube.

The coating material is Inconel 625 powder produced by Höganäs, with a main particle size distribution range of 53–150 μm. The actual chemical compositions of both the Inconel 625 powder and the API 5 L X65 carbon steel substrate were measured and verified using handheld X-ray Fluorescence (XRF) alloy analyzer in accordance with standard testing procedures (ASTM A751). The morphological characteristics of the powder were observed using Scanning Electron Microscopy (SEM), as shown in Fig. 1C. The chemical composition of the powder is shown in Table 2. The base material grade is API X65 and the size is φ167WT20150mm. Before coating, the surface of the pipe is mechanically processed and pre-treated, and then the sample is ultrasonic cleaned and pre-treated to remove oil stains and oxides on the surface of the sample, thereby enhancing the metallurgical bond between the coating layer and the base material.

Fig. 1.

Cladding equipment, cladding principle, Morphological characterization of the powder via Scanning Electron Microscope (SEM). (A) Laser cladding equipment; (B) Pipeline laser powder schematic diagram; (C) Scanning electron microscopy (SEM) topography characterization of spherical S32205 powders.

Table 2.

The chemical composition of the cladding powder Inconel625 and the substrate API X65 (wt%).

ERNiCrMo-3	C	Mo		Nin		Fe		Mn		Cr		Si		N		Al	Ti		Nb	O
wt%	0.03	10		Bal		0.9		0.35		22.50		0.35		0.14		0.10	0.10		3.15 ~ 3.85	0.07

API X65	C		Mn		P		S		Cu		Ni		Cr		Mo			V + Nb+Ti		Fe
wt%	≤ 0.28		≤ 1.40		≤ 0.03		≤ 0.03		≤ 0.5		≤ 0.5		≤ 0.5		≤ 0.50			≤ 0.15		Bal

Cladding thickness test

The thickness of the cladding layer is the key performance index of laser cladding technology. It refers to the vertical height of the metallurgical binding layer formed by the cladding material on the surface of the substrate, which directly affects the mechanical properties, corrosion resistance, wear resistance and engineering reliability of the cladding layer.It is mainly determined by process parameters such as laser power, scanning speed, powder delivery rate, spot diameter and lap rate. The appropriate thickness can ensure a good metallurgical combination of the substrate and the cladding layer, and avoid residual stress concentration, cracks, pores and other defects caused by improper thickness²². The thickness of the cladding layer is closely related to corrosion resistance. Thicker layers can extend the diffusion path of the corrosive medium to improve corrosion resistance, but too thick can easily reduce the binding strength due to the accumulation of thermal stress²³.

Thickness uniformity is also an important indicator for evaluating the stability of the cladding process. In corrosion resistance fields such as oil pipelines and marine equipment, precise thickness control is essential to ensure the long-term service performance of components²⁴.Therefore, optimizing the process parameters to obtain the ideal thickness and exploring its correlation with hardness, porosity and other properties is an important research direction in this field.In this paper, nickel-based alloy powder ERNiCrMo-3 is used for laser cladding of API X65 carbon steel pipe to improve the corrosion resistance of the pipe wall, and its chemical composition conforms to ASME II-C²⁵.

Because the X65 carbon steel substrate is strongly magnetic, while the Inconel625 cladding layer is non-magnetic, the accuracy of the traditional ultrasonic thickness measurement method is limited.In this study, the DR6000 iron-based cladding thickness gauge based on the principle of electromagnetic induction (Fig. 2) is used, which can effectively distinguish between magnetic substrates and weak magnetic cladding, with a measurement range of 0–6000 μm and an accuracy of ± 1 μm. Prior to the actual measurement, a rigorous calibration procedure was performed to ensure data reliability. Specifically, zero-point calibration was first conducted directly on the bare, uncoated API X65 carbon steel substrate after surface pretreatment to eliminate the base material’s magnetic interference. Subsequently, standard non-magnetic calibration foils with known thicknesses (e.g., 500 μm, 1000 μm, and 1500 μm) were placed tightly on the bare substrate for multi-point calibration. This standard procedure ensured that the measurement deviation was strictly controlled within the ± 1 μm accuracy range.When measuring, first use an electric wire brush to remove the surface oxide film and pretreatment (to meet the PMI test and accuracy requirements), in the pipeline 0°, 90°, 180°, 270° A measuring point is set up to measure the total thickness of the pipeline and the thickness of the substrate separately. The difference is the thickness of the cladding layer, and the final average value is taken as the result.

Fig. 2.

DR6000 iron-based cladding thickness gauge and thickness gauge schematic diagram.

Box-Behnken experimental design

A scientific and rational design of laser parameters can effectively improve the quality of the cladding interface, optimize the process efficiency and powder utilization rate of laser cladding, and enhance the reliability of experimental results. In the process optimization of corrosion-resistant overlay (CRO) prepared by laser cladding, the commonly adopted optimization methods include the orthogonal experiment method, Taguchi method, artificial neural network method and response surface methodology (RSM)²⁶.

The response plane method is an experimental design technique that determines the main influencing factors by establishing a functional relationship between the response and multiple input variables.In the response plane method, central composite design (CCD) and Box-Behnken design (BBD) are the most commonly used second-order model development techniques²⁷. To clearly justify the experimental design, the Box-Behnken Design (BBD) was specifically chosen over the Central Composite Design (CCD) and full-factorial designs because it purposefully avoids extreme parameter combinations (i.e., the corners of the cubic design space). From a metallurgical perspective in laser cladding, extreme parameter matching—such as simultaneously applying maximum laser power, minimum scanning speed, and maximum powder feed rate—can lead to catastrophic heat input, resulting in severe defects like substrate over-melting, keyhole formation, or severe powder vaporization. By excluding these extreme corner points, BBD ensures that all test runs remain within a physically viable and safe operating window. Furthermore, the 16 experimental runs generated in this study precisely consist of 12 mid-edge points to systematically evaluate the primary and interactive effects of the variables, alongside 4 replicated center points to estimate the pure experimental error and assess the model’s curvature. This structured configuration provides robust statistical power and sufficient fitting accuracy for the second-order model while significantly reducing the experimental cost compared to the 27 runs required for a 3³ full-factorial design²⁸.

This design method is both economical and can provide sufficient fitting accuracy to meet the needs of practical applications.The thickness of the cladding layer is a function of laser power (P, Laser Power), scanning speed (V, Scan Velocity), and powder feed rate (R, powder feed Rate), which can be expressed as: =Laser Power, =Scan Velocity, =Powder feed Rate.

1

The second-order polynomial (regression) equation used to represent the response surface of the cladding thickness is shown in Eq. (2):

2

Where: Equipment control: γ is the response variable of the cladding thickness; ε is the various errors; β is the coefficient estimate, and x is the factor coding. The schematic diagram of the response surface model is shown in Fig. 3.

Fig. 3.

Schematic diagram of the BBD model.

In order to effectively reduce the number of experiments and obtain better process parameters without affecting the prediction accuracy of the model, a model between the thickness (T) of the 625 cladding layer and the laser power (P), scanning speed (V) and powder delivery rate (R) during the cladding process is established according to the Box-Behnken response surface method.Table 3 lists the factors and levels of the 625 cladding layer in the laser test (three factors and three levels).Compared with the 27 test points required for the full-factor experiment, the response plane method only requires 16 test points to analyze the corresponding changes, thus avoiding repeated experiments.Note: The speed of the powder feeder turntable is 0.3 r/min = 12.75 g/min, 0.4 r/min = 17 g/min, and 0.5 r/min = 21.25 g/min.

Table 3.

Test factors and level table.

Factor	Encoded values and levels
	-1	0	1
Laser power (W)	1600	1800	2000
Scanning velocity(mm·min− 1)	1100	1250	1400
Powder feeding (r·min− 1)	0.3	0.4	0.5

Results and discussion

Establishment and significance analysis of response equations

The Design-Expert software was used to conduct the central combination experiment and generate 15 sets of response surface test results (as shown in Table 4).Within the selected laser parameter range, the thickness of the cladding layer exhibits obvious fluctuation characteristics: the cladding layer is too thin, which will directly weaken its corrosion resistance and reduce the overall service performance of the additive manufacturing layer; the cladding layer is too thick, it is easy to cause the accumulation of unfused raw powder on the cladding surface, inducing large pores, cracks and other defects.This shows that the reasonable configuration of laser process parameters is of great significance to improve the quality of the cladding layer.

Table 4.

Response surface test scheme and results.

No.	Code			Laser power(W)	Scanning velocity(mm·min− 1)	Powder feeding speed(r·min− 1)	Cladding thickness(mm)	Heat input(J·mm− 1)
1	-1	-1	0	1600	1100	0.4	1.05	87.27
2	1	-1	0	2000	1400	0.4	1.2725	85.71
3	-1	1	0	1600	1400	0.4	0.75	68.57
4	1	1	0	2000	1000	0.4	0.91	120
5	-1	0	-1	1600	1250	0.3	0.7465	76.8
6	1	0	-1	2000	1250	0.3	0.894	85.7
7	-1	0	1	1600	1250	0.5	1.23	76.8
8	1	0	1	2000	1250	0.5	1.2	85.7
9	0	-1	-1	1800	1100	0.3	0.8725	86.4
10	0	1	-1	1800	1400	0.3	0.818	77.14
11	0	-1	1	1800	1000	0.5	1.486	86.4
12	0	1	1	1800	1400	0.5	1.1	77.14
13	0	0	0	1800	1250	0.4	0.85	86.40
14	0	0	0	1800	1250	0.4	0.96	86.40
15	0	0	0	1800	1250	0.4	0.829	86.40
16	0	0	0	1800	1250	0.4	0.977	86.40

From a physical perspective, the regression coefficients in the Quadratic model clearly reflect the thermodynamics and mass transfer dynamics of the laser cladding process. The powder feeding rate (C) exhibits the highest positive coefficient (+ 0.2106), indicating that the deposition height is strictly mass-transfer-dominated. Conversely, the scanning speed (B) shows a negative coefficient (-0.1378). Physically, an increased velocity not only reduces the effective powder catchment time per unit area but also decreases the linear heat input HI, leading to a shallower melt pool. The laser power (A) has a relatively minor positive coefficient (+ 0.0625), as its primary physical role is to provide sufficient enthalpy to overcome the latent heat of fusion of the Inconel 625 powder, rather than directly contributing to the mass volume of the cladding layer.

The analysis of various models of the test results shows that the linear model has the smallest fitting coefficient R² and the Cubic model has the largest fitting coefficient, but the difference between it and the Quadratic model is small.This shows that the higher the order of the equation, the greater the fitting coefficient R² of the model, and the more accurate the fitting degree.However, considering that the P-value of the Cubic model is large and the degree of lack-of-fit is high at this time, it is difficult to form a suitable response surface; the F-value and probability P(> F) of the Quadratic model meet the requirements of small lack-of-fit, and the P-value is also small, so the Quadratic model is selected for fitting.The final fitted equation is: A: laser power; B: scanning speed; C: powder delivery rate.

The regression coefficients in the quadratic equation are not merely mathematical fitting parameters; they inherently reflect the physical thermodynamics and mass transfer dynamics of the laser cladding process. The linear coefficient for the powder feeding rate (C) exhibits the highest positive value (+ 0.2106), physically indicating that the cladding layer thickness is a strictly mass-transfer-dominated response. An increase in the powder supply directly increases the deposition mass entering the melt pool per unit time. Conversely, the scanning speed (B) shows a significant negative coefficient (-0.1378). From a dynamic physical standpoint, a higher scanning velocity shortens the interaction time between the laser beam, the powder stream, and the substrate. This effectively decreases both the powder catchment efficiency and the linear heat input, resulting in a shallower melt pool and a thinner cladding layer. Meanwhile, the laser power (A) demonstrates a relatively minor positive coefficient (+ 0.0625). Physically, while the laser power provides the crucial enthalpy required to overcome the latent heat of fusion for the Inconel 625 particles, it does not directly contribute to the physical mass of the deposit. Its positive effect on thickness is primarily due to the slight geometric expansion of the liquid melt pool, which allows it to capture slightly more powder during the process.

The thickness value of the 625 cladding varies widely over the selected laser parameters, ranging from 0.7425 mm to 1.486 mm. The amount of heat input is obtained by the formula HI = P/V * 60. Cladding chemistry compliance is verified according to the ASME BPVC IX 2025 standard (as shown in Table 5).

Table 5.

ASME BPVC IX (Welding Qualifications,2025) ERNiCrMo-3 composition requirements.

ERNiCrMo-3	C	Mn	Fe	P	S	Si	Cu	Niⁿ	Al	Ti	Cr	Nb ༆Ta	Mo	Others
wt%	0.1	0.5	5.0	0.02	0.015	0.5	0.5	58.0 min.	0.4	0.4	20.0~23.0	3.15~4.15	8.0~10.0	0.50

Generally, analysis of variance (ANOVA) is commonly used to check the adequacy of established models. The Analysis of Variance (ANOVA) was utilized to evaluate the statistical adequacy and predictive capability of the established quadratic model, with the detailed results presented in Table 6. The Model F-value of 14.30, coupled with a remarkably low p-value of 0.0010 (less than 0.05), indicates that the regression model is highly significant. This suggests there is only a 0.10% probability that an F-value this large could occur due to random experimental noise. Furthermore, the ‘Lack-of-Fit’ F-value of 1.15 and its corresponding p-value of 0.4307 imply that the lack-of-fit is not significant relative to the pure error. A non-significant lack-of-fit is highly desirable, confirming that the quadratic model effectively captures the actual data correlation without overfitting. Additionally, the ‘Adeq Precision’ measures the signal-to-noise ratio; the calculated value of 14.702 is well above the acceptable threshold of 4.0, indicating an adequate signal. Consequently, this statistical evidence demonstrates that the model is highly reliable and can be confidently used to navigate the design space and predict the cladding thickness.This shows that the model established by the experiment can more accurately analyze and predict the thickness of the cladding layer of 625 powder on the X65 carbon steel pipe.

Table 6.

Analysis of variance of inconel 625 cladding thickness.

Items	Sum of squares	Degree of freedom	Root mean square	F-value	p-value
Model	0.6486	9	0.0721	14.30	0.0010
A-laser power	0.0313	1	0.0313	6.20	0.0416
B-volocity	0.1521	1	0.1521	30.18	0.0009
C-powder	0.3549	1	0.3549	70.44	< 0.0001
AB	0.0010	1	0.0010	0.1938	0.6730
AC	0.0079	1	0.0079	1.56	0.2514
BC	0.0275	1	0.0275	5.45	0.0422
A²	0.0026	1	0.0026	0.5207	0.4939
B²	0.0246	1	0.0246	4.89	0.0628
C²	0.0408	1	0.0408	8.10	0.0248
Residual	0.0353	7	0.0050
Lack of fit	0.0163	3	0.0054	1.15	0.4307
Pure error	0.0189	4	0.0047
R2	/	/	0.9484	/	/
Adeq precision	/	/	14.702	/	/

In the secondary model (Table 7), the p-values of laser power, scanning speed, and powder delivery rate for the thickness of the cladding layer are 0.0416, 0.0009, and < 0.0001, respectively.This shows that the powder feed rate has a very significant impact on the thickness of the cladding layer, followed by the scanning speed. When the heat input is low, the influence of laser power on the thickness of the cladding layer is usually more obvious.For the interaction, the p-value of the interaction factor coefficient of the scanning speed and powder feeding rate is 0.0422, which is less than 0.05, and the F-value is large, indicating that the impact is the most significant.The A-B and A-C are both greater than 0.05, indicating that only B-C needs to be considered in the law of interaction on the thickness of the cladding.The normal probability graph of the residual of the response and the graph of the relationship between the residual and the predicted value are shown in Fig. 4. It can be seen from Fig. 4 that the residual basically falls in a straight line, which shows that the error is approximately normally distributed. At the same time, there is no obvious law in the distribution of the relationship between the residual and the predicted value, which further confirms the accuracy of the model.

Table 7.

Fitting results of linear, 2FI, 2FI, and 3rd power models.

Model	F-Value	P-value	Fit coefficient	Analysis of unfitting degree
				Sum of squares	DOF	Root mean square	F-Value	P-Value
Linear	16.01	0.0001	0.7870	0.1457	9	0.0141	2.98	0.1529
2FI	8.76	0.0016	0.8401	0.1093	6	0.0151	3.18	0.1410
Quadratic	14.30	0.0010	0.9484	0.0353	3	0.0054	1.15	0.4307
Cubic	11.71	0.0147	0.9723	0	0	—	—	—

Fig. 4.

(a) Residual normal probability plot and (b) residual and predicted value of ERNiCrMo-3 cladding.

Response surface analysis

From the Quadratic fitting equation and the analysis of variance of the model, it can be seen that A has the greatest impact on the thickness of the cladding. Among the interaction terms, the thickness of the cladding is more sensitive to changes in B-C.Therefore, the influence of B-C on the cladding thickness at laser power of 1600, 1800, and 2000 W was studied separately. The response surface diagram of the cladding thickness T and its contour plot are shown in Fig. 5.

Fig. 5.

Response surfaces and contour plots showing the interaction effect of scanning speed (V) and powder feeding rate (R) on cladding thickness (T) at three fixed laser power (P) levels: (a) 1600 W-3D; (b) 1800 W-2D; (c) 2000 W-3D; (d) 1600 W-3D; (e) 1800 W-2D; (f) 2000 W-3D; V varied over 1100–1400 mm·min⁻¹; R varied over 0.3–0.5 r·min⁻¹.

As illustrated by the single-factor perturbation plots (Fig. 6), the powder feeding rate (C) exhibits the steepest positive gradient. Given sufficient heat input, an increased powder delivery directly escalates the particle volume entering the melt pool per unit time, thereby promoting a linear growth in cladding thickness. Conversely, scanning speed (B) shows a distinct negative correlation. A higher scanning velocity curtails the laser-substrate interaction time and reduces the specific linear heat input, leading to incomplete powder melting and a shallower deposition. Meanwhile, laser power (A) demonstrates a moderate positive trend, reflecting its dual regulatory mechanism. While adequate power ensures complete particle melting and robust metallurgical bonding, excessive power induces melt pool instability and elevates the dilution rate, which ultimately restricts further thickness growth.

Fig. 6.

Disturbance diagram of cladding thickness.

Furthermore, the multi-factor response surfaces elucidate the complex thermodynamics of the melt pool under varying thermal conditions. At a lower laser power (1600 W), the limited heat input makes the cladding thickness predominantly dependent on the powder feed rate, amplifying the inhibitory effect of the scanning speed. As the power increases to the optimal 1800 W, the expanded thermal processing window significantly enhances the crucial interaction between scanning speed and powder feeding rate (B-C). When high powder flow is coupled with a rapid scanning speed, the specific energy (energy absorbed per unit mass of powder) drops drastically. This suppresses Marangoni convection, increases the liquid metal’s viscosity, and traps un-melted particles and gas bubbles before rapid solidification, inducing porosity. Conversely, the optimal matching of these two parameters provides a sufficient liquid lifetime, allowing surface tension-driven fluid flow to homogenize the temperature field and form a defect-free, continuous metallurgical bond.

Finally, at a high laser power (2000 W), the abundant heat input minimizes the inhibitory effect of the scanning speed and maximizes the deposition sensitivity to the powder feed rate. However, this excessive heat input significantly increases the risk of expanding the heat-affected zone (HAZ) and inducing thermal cracking. Ultimately, this comprehensive parametric analysis intrinsically bridges macroscopic process variables with underlying energy transmission and deposition dynamics, providing a rigorous theoretical foundation for precise microstructural regulation.

Performance analysis of cladding layer

The microstructure is the core factor that determines the mechanical properties and corrosion resistance of the cladding layer. The microscopic morphology of the Inconel 625 cladding layer prepared under optimized process parameters is observed by scanning electron microscope (SEM), and its microstructural characteristics, interface bonding state, and process parameters are analyzed. The regulation law of the organization and the results are shown in Figs. 7 and 8. It is important to acknowledge that the microstructural characterization in this current study is primarily based on these SEM morphological observations. While advanced characterizations such as Energy Dispersive Spectroscopy (EDS) mapping or X-ray Diffraction (XRD) are highly valuable for precise elemental segregation and crystallographic phase identification, the primary scope of this engineering-oriented research is centered on macroscopic process optimization (via RSM) and the geometric regulation of the interface martensite layer. Nevertheless, the microstructural features observed via SEM—specifically the distinct transition from planar growth at the fusion line to fine cellular dendrites in the cladding zone—are highly consistent with the classical solidification mechanisms and phase distributions of the Inconel 625 alloy reported in widely established literature. Future in-depth fundamental studies will incorporate EDS and TEM to further reveal the nanoscale precipitation behavior under these optimized parameters.

Fig. 7.

Microstructure of cladding interface 100 times microstructure.

Fig. 8.

500-fold microstructure near the fusion line.

The thickness of the martensitic layer is directly related to the interfacial stress state of the laser cladding and the transition efficiency of the composition. Too thick can easily lead to stress concentration due to mismatch between the brittleness of the martensitic and the coefficient of thermal expansion (similar to the interfacial hidden dangers when alloy steel is coated with high-hardness martensitic steel), and too thin weakens the metallurgical binding strength, and precise regulation of the thickness can construct a stable “cladding layer-martensitic layer-substrate” composition gradient, which is highly compatible with the technical logic of “controlling the thickness of the transition zone according to service requirements” in the design of the interfacial buffer layer.In terms of single-factor influence, the powder delivery rate is the factor that has the most significant impact on the quality of the cladding layer (P < 0.0001). Its changes directly dominate the tissue refinement of the cladding layer and the thickness regulation of the interfacial martensitic layer: When the powder delivery rate increases from 0.3r/min to 0.5r/min, the amount of powder input per unit time increases from 12.75 g/min to 21.25 g/min, and a large amount of powder absorbs heat. The cooling rate of the bath is significantly increased, and the columnar crystal dendrite spacing of the cladding layer is reduced. At the same time, the powder input is increased from 12.75 g/min to 21.25 g/min. The cooling rate of the bath is significantly increased, and the columnar crystal dendrite spacing of the cladding layer is reduced. The increase in volume reduces the thermal influence range of the bath on the X65 substrate, inhibits the full progress of the martensitic phase transition, and reduces the thickness of the interfacial martensitic layer from 80 μm to 50 μm; the scanning speed regulates tissue evolution by changing the bath residence time, from 1000 mm/min to 1400 mm/min, the bath residence time = t spot length (3 mm)÷ welding speed (mm/min)×60 = shortened from 0.18s to 0.129s, and the cooling rate is increased by 40%; grain fineness 1. The thickness of the martensitic layer is reduced from 75 μm to 55 μm, but when the scanning speed is too fast (such as 1400 mm/min), insufficient heat input can easily lead to non-fused defects (such as samples 3 and 10); the laser power mainly regulates the energy density of the bath, and the heat input is insufficient at 1600 W (such as samples 3 and 5, the heat input is 68.57 ~ 76.8 J/mm), the fused layer appears unfused raw powder aggregates, and the thickness of the martensitic layer increases to 70 μm due to the increase in the thermal conductivity of the substrate. At 2000 W, the heat input is too high (such as samples 4 and 6, the heat input is 85.7 ~ 120 J/mm), the grains of the cladding layer are thick, and although the thickness of the martensitic layer is reduced to 45 μm, it is easy to cause cracks; at 1800 W, the heat input is stable at 86.4 J/mm, which can obtain the ideal tissue of “refined dendrites + moderate martensitic layer (50 ~ 60µm)” without obvious defects.

Martensite thickness (50–60 μm) was determined by averaging five perpendicular measurements along the fusion line—specifically at the center, ±¼ width, and near the edges. This approach accounts for fusion-line curvature and HAZ fluctuations, ensuring a statistically representative value for the interfacial layer. In terms of interaction, the synergy between the scanning speed and the powder delivery rate is the most critical (P = 0.0422). The degree of matching between the two directly determines the tissue integrity of the cladding layer and the rationality of the thickness of the martensitic layer: When the scanning speed of 1250 mm/min matches the powder delivery rate of 0.4r/min (17 g/min) (sample 13–16), the bath is in a state of “full melting-rapid cooling” equilibrium, the heat input is maintained at 86.4 J/mm, the cladding layer has no defects such as unfused raw powder and cracks, and the martensitic The thickness of the layer is stable at about 55 μm, which forms a good mechanical match with the cladding layer; when the scanning speed is low (1000 mm/min) and the powder delivery rate is high (0.5r/min) (sample 11), the volume of the bath is too large, resulting in part of the powder is not fully melted, and the unfused raw powder gathers, and the accumulation of HI increases the thickness of the martensitic layer to 65 μm; when the scanning speed is high (1400 mm/min) and the powder delivery rate is low (0.3 r/min) (sample 10), insufficient heat input causes the thickness of the martensitic layer to increase to 65 μm. The interface is not fused, and the thickness of the martensitic layer fluctuates to 75 μm due to insufficient interface binding.The interaction between laser power and powder delivery rate is weak (P = 0.2514), but when low power (1600 W) is matched with high powder delivery rate (0.5r/min) (sample 7), insufficient heat input is superimposed with excessive powder, and the defect of unfused raw powder coexists with the thickening of the martensitic layer (68 μm); when high power (2000 W) is matched with low powder delivery rate (0.3r/min) (sample 6), the heat input is too high and the powder is insufficient, which is prone to cracks and the martensitic layer is too thin ( 40 μm) problems affect the service reliability of the cladding layer.

Fundamentally, the correlation between macroscopic process parameters, interfacial microstructure, and overall cladding quality is intrinsically linked by the thermal history, specifically the heat input HI and the subsequent cooling rate. When the process parameters are optimally matched (e.g., 1800 W, 1250 mm/min, and 0.4 r/min), a moderate HI of 86.4 J/mm establishes an ideal thermal gradient and solidification rate. This precise thermal control restricts excessive carbon diffusion from the X65 substrate, resulting in a structurally sound martensite transition layer of 50–60 μm and a refined cellular dendritic network within the cladding zone. This balanced microstructure directly ensures excellent cladding quality by suppressing crack susceptibility and ensuring robust metallurgical bonding without porosity.

From the analysis of the number of defects and microstructure, 0.89 mm ~ 1.08 mm is a more reliable range.Therefore, in this paper, the cladding thickness of 1 mm is set as the target cladding thickness. According to the optimal cladding process parameters obtained by the optimization module of the response surface method, the laser power is set to 1910 W, the scanning speed is 1365 mm·min⁻1 and the powder delivery rate is 0.47 r·min⁻1 for spraying test.Cladding thickness T = 1.00 mm (measured mean 0.98–1.02 mm, deviation ≤ ± 2% from the 1 mm target), corresponding to a heat input HI = 83.96 J·mm⁻¹. This input-output correspondence was verified by independent validation experiments, confirming both the high predictive accuracy of the quadratic regression model (R² = 0.9484) and the direct transferability of this parameter set to industrial remanufacturing of API 5 L X65 pipelines where a 1 mm Inconel 625 cladding layer is specified. Figure 9 shows the cross-section and interfacial combined morphology of the cladding layer of the prediction group. It can be seen from the 100 times and 500 times magnification of Figs. 9a and b: The fusion transition layer between the cladding layer and the X65 carbon steel base material presents a continuous and smooth arc morphology, the interface transition is natural, no mutations, no metallurgical defects such as non-fusion, cracks or holes have been observed, indicating that the two have achieved a uniform and reliable metallurgical combination, reflecting the precise matching of heat input and heat dissipation in the laser cladding process.

Fig. 9.

Microstructure of the prediction group: (a) 100 times cladding interface; (b) 500x fused interface.

Conversely, when parameter combinations result in an excessive HI (> 100 J/mm), the drastically lowered cooling rate drives aggressive grain coarsening and severely thickens the brittle martensite layer, which directly degrades the cladding quality by promoting cracking. On the other hand, an insufficient HI (< 70 J/mm) generated by mismatched parameters causes an ultra-fast quenching effect; this produces a thin but incomplete microstructural bond characterized by un-melted powder aggregates and lack-of-fusion defects, destroying the overall quality of the coating. Therefore, the coordinated regulation of process parameters is essentially the precise control of the melt pool’s thermal dynamics to achieve the optimal microstructural balance and flawless cladding quality.

Conclusion

In this study, the Box-Behnken response surface methodology (RSM) was successfully employed to systematically optimize the Inconel 625 laser cladding process on API 5 L X65 carbon steel. Rather than merely reporting optimal statistical parameters, this research reveals the underlying mechanisms dictating cladding quality. The key findings are highlighted as follows:

• The cladding thickness is fundamentally a mass-transfer-dominated response, governed primarily by the powder feeding rate. However, macroscopic defect suppression and geometric consistency are strictly dictated by the synergistic interaction between scanning speed and powder feed rate, which intrinsically controls the melt pool’s specific heat input and fluid dynamics.

• Precise multi-parameter coordination perfectly regulates the thermal history and restricts excessive carbon diffusion from the X65 substrate. This precise thermal control limits solid-state phase transformation, yielding an optimal, structurally sound martensite transition layer of 50–60 μm alongside a refined cellular dendritic network.

• The optimal parameters derived from the BBD model (1910 W, 1365 mm/min, 0.47 r/min) were experimentally corroborated, reliably yielding an ideal 1 mm defect-free cladding (measured mean 0.98–1.02 mm, deviation ≤ ± 2% from the 1 mm target) and demonstrating the high predictive accuracy of the established process window.

• The established process window (1800–2000 W, 1200–1300 mm/min, 0.35–0.45 r/min) yields highly consistent cladding, offering viable parameters for transition into automated large-scale production. Because this optimization strictly controls interfacial martensite thickness (50–60 μm) and suppresses macroscopic defects, it is expected to significantly mitigate the risk of coating delamination. Ultimately, this offers a promising surface remanufacturing strategy to help extend the service life of API 5 L X65 marine pipelines facing complex bending and corrosive environments (H₂S, CO₂, Cl-).

Author contributions

Qinwei Yu*: Conceptualized and designed the research project; guided the experimental design and data analysis strategy; reviewed and revised the manuscript critically; responsible for correspondence and overall academic supervision.Zheng Zhang: Participated in refining the experimental protocol; conducted laser cladding sample preparation, process parameter adjustment, and cladding thickness testing; collected and organized primary experimental data.Yong Pan: Assisted in setting up the experimental platform (laser cladding system and thickness measurement equipment); participated in Box-Behnken experimental design and partial experimental operations; verified the reliability of experimental data.Zhenmin Zhang: Responsible for cladding layer microstructure characterization , defect analysis, and the correlation study between microstructure and performance; participated in data analysis and model validation.Feng Cao: Engaged in response surface methodology optimization analysis; conducted verification experiments for optimal process parameters; assisted in writing the results and discussion section of the manuscript.

Funding

This work was supported by the Qatar Energy Strategy NFPS COMP 3 (Z2024SFLXBWO1002).

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.