Laser Cleaning Process for Low-Pressure Turbine Blade Paint Removal with Remelting Suppression

Abstract

This study aims to develop an efficient laser cleaning process for removing paint coatings from low-pressure turbine blades while suppressing substrate remelting, focusing on elucidating the underlying paint removal mechanisms on coated aluminum alloy substrates. A pulsed fiber laser (1064 nm, 100 ns) was used to perform single-factor and orthogonal experiments, with laser power (70–100 W), scanning speed (1000–3000 mm/s), and repetition frequency (150–300 kHz) as the main variables. The energy density for each of the 16 orthogonal test samples ranged from 11.9 to 51.0 J/cm². Complete paint removal without substrate damage was achieved within an optimal energy density window of approximately 17–27 J/cm² (e.g., 23.8 J/cm²), whereas higher values above 35 J/cm² (e.g., 35.7 J/cm²) frequently caused localized remelting and pitting. The optimized parameter combination (90 W, 1500 mm/s, 300 kHz) achieved 98% paint removal efficiency in four passes with no observable substrate degradation. Mechanistic analysis indicated that low-to-moderate energy densities promoted interfacial debonding and controlled film ablation, while high energy densities led to substrate melting and reflow. This work clarifies the quantitative correlation between laser parameters, paint removal mechanisms, and remelting suppression, providing a scientific basis for turbine blade maintenance applications.

1. Introduction

Compared to extensively studied high-pressure turbine blades, low-pressure blades located at the compressor front stage operate in relatively milder service environments. However, their increased susceptibility to moisture and salt spray corrosion necessitates thick anti-corrosion paint coatings as protective layers. While high-pressure blades typically employ nickel-based superalloys or titanium alloys for superior thermal stability and ablation resistance, low-pressure blades predominantly use aluminum alloys (e.g., 2A02) which, despite their high thermal conductivity, face greater remelting risks during laser cleaning due to lower melting points.

During service, micro-debris impacts progressively induce blade corrosion—initially asymptomatic but eventually manifesting as surface erosion pits that may develop into cracks (Figure 1). As blade refurbishment constitutes a critical maintenance technology, paint removal represents the primary preparatory step. Given the exorbitant costs of blade replacement, surface cleaning and inspection are essential to avoid substantial increases in aircraft maintenance and operational expenses [1]. Although traditional chemical cleaning methods remain prevalent for low-pressure blade paint removal, their environmental pollution, operational complexity, and particularly substrate damage issues compromising subsequent service safety highlight the need for innovative alternatives like laser cleaning technology.

Current research has extensively explored laser paint removal for aircraft skins [2,3,4], with growing industrial adoption. Kim J.-E. et al. [5] demonstrated that plasma behavior monitoring enables real-time removal completeness detection. Hu et al. [6] achieved effective paint stripping on aluminum alloy skins using a 150 W CO₂ laser while preserving substrate integrity through optimized parameters. Zhao et al. [7] reported complete removal of 30 µm paint without substrate damage at 1.66 J/cm² energy density. Eskandari et al. [8] observed that lower scan speeds promoted molten layer formation on high-pressure turbine blades (HPTBs). Yang et al. [9] found that 27.79 J/cm² nanosecond fiber laser processing increased aluminum alloy surface roughness and microhardness by 17.3%.

Existing studies identify thermal ablation and mechanical stripping as the two primary removal mechanisms [10], with wavelength-dependent dominance. Long-pulse lasers predominantly induce thermal ablation, often leaving carbonized residues that compromise quality. Conversely, short-pulse lasers preferentially generate thermal stress and plasma shock effects, enabling cleaner mechanical stripping with superior surface quality.

Pulse fiber lasers have been widely investigated for paint removal due to their precise energy delivery and controllable thermal effects, with reported mechanisms including coating ablation, photothermal decomposition, interfacial debonding, and layer-by-layer delamination under high repetition rates [11,12,13,14,15]. Therefore, this study aims to develop an optimized pulsed laser cleaning process for paint removal on low-pressure turbine blades while suppressing substrate remelting. By systematically analyzing the effects of laser power, scanning speed, and repetition frequency through both single-factor and orthogonal experiments, this work establishes a quantitative relationship between laser energy density, overlap rate, and paint removal mechanisms and provides clear process guidelines and practical insights for the aerospace industry, enabling efficient and non-destructive maintenance of high-value components. The aim of this study is to systematically investigate the laser paint removal process on aluminum alloy substrates, focusing on the physical mechanisms of coating removal and strategies for remelting suppression. By establishing quantitative relationships between laser processing parameters and removal quality, this work aims to provide practical guidelines for parameter optimization in turbine blade maintenance and other high-value aerospace applications.

2. Materials and Methods

The experiments were conducted using an IPG pulsed laser (SP-100P-W-EP-Z-F-Y), (IPG Photonics, Oxford, MA, USA) with a wavelength of 1064 nm. As shown in Figure 2, the system consists of a computer control unit, laser source, beam expander, and three-axis motion platform. The maximum laser power is 100 W, with an F-theta lens focal length of 225 mm, a beam spot diameter of 50 μm, and a single-pulse energy of 1 mJ [16] In the scanning parameter settings, the hatch spacing was 40 μm, corresponding to a line overlap ratio of 20%.

A vacuum cleaner (rated power 1.5 kW) was positioned with its suction inlet 50 mm above the sample surface and moved synchronously with the laser head to ensure effective coverage of the cleaning area. During processing, the sample was fixed on the work platform, and the laser head scanned in a unidirectional parallel path according to the preset trajectory. No assist gas or pretreatment was applied, simulating a single-pass paint removal scenario under industrial conditions. After the final laser scan, the sample surface was gently wiped with anhydrous ethanol to remove all loose and powdery residues. This post-wiping state represents the final, irreversible outcome of the process. All subsequent surface analyses, including the measurement of carbon content (C%) via EDS reported in this study, were performed on this wiped surface. Therefore, the measured C% specifically characterizes carbon that was strongly adherent to or integrated within the substrate surface, rather than removable debris. The laser paint removal performance was systematically characterized using multiple analytical techniques. Surface micro-morphology was examined using a field-emission scanning electron microscope (SEM, Hitachi SU8010, Hitachi High-Tech Corporation, Tokyo, Japan) equipped with an energy-dispersive X-ray spectrometer (EDS, Oxford Instruments X-MaxN 80, Oxford Instruments plc, Abingdon, UK) for elemental analysis. The elements shown in the figure were selectively selected based on their correlation with the specific microstructural features or thermal processes being discussed. Before observation, the samples were ultrasonically cleaned in anhydrous ethanol for 5 min and sputter-coated with gold (thickness: 5 nm). Metallographic analysis was performed using an MP-2A grinding and polishing machine (Shanghai Chenchi Instrument Co., Ltd., Shanghai, China). The specimens were sequentially ground with 800–2000# SiC abrasive papers, polished with 1 μm diamond paste, and etched in Keller’s reagent for 10 s. Cross-sectional remelted layers were observed with a metallographic microscope (Leica DM2000X, Leica Microsystems GmbH, Wetzlar, Germany) at magnifications of 200–500×. Surface three-dimensional morphology and roughness were measured using a 3D digital microscope (Keyence VHX-7000, Keyence Corporation, Osaka, Japan) by scanning a 5 × 5 mm² area with a step size of 1 μm. Each measurement was repeated three times, and the arithmetic mean height (Sa) was calculated. The number of cleaning passes was dynamically adjusted based on visual inspection results (2–4 passes in single-factor tests; in orthogonal tests, cleaning was terminated upon complete paint removal). In this study, carbon content was chosen as the primary analytical indicator to evaluate paint removal effectiveness because of its strong correlation with residual organic coating. This approach provides a straightforward, reproducible, and efficient metric for comparing different laser parameter combinations in the orthogonal experiment. Although advanced techniques such as Raman spectroscopy, 3D surface profilometry, or thermal modeling can yield additional detail, they are time- and resource-intensive, making them less suitable for rapid process optimization at this stage. For each factor, the average value at each level (K₁–K₄) and the range (R = max(K₁–K₄) − min(K₁–K₄)) were calculated to evaluate the significance of parameter influence. Furthermore, it can be seen that the formula for the overlap rate of the light spot is

$$U_x={\displaystyle \frac{D-d}{D}}$$

Among them, $U_x$ is the overlap rate of the light spot, $D$ is the diameter of the light spot, and $d$ is the spacing between adjacent light spots on the scanning path. The energy density $E_d$ (J/cm²) was calculated as follows:

$$E_d={\displaystyle \frac{4P}{\pi ·f·D^2}}$$

where $P$ is the average power of the laser (W), $D$ is the diameter of the laser spot (cm), and $f$ is the repetition frequency (Hz).

As shown in Figure 3, to minimize the influence of geometric variations on laser focusing and paint removal results, this study selected the middle section of the blade for sample preparation, obtaining 16 well-controlled 20 mm × 20 mm specimens for experimental research.

The blades undergo wet grit blasting before painting, resulting in a smooth surface with a roughness of approximately Ra 1.4. Under metallographic microscope examination, the painted samples show an average paint thickness of about 70 μm, as shown in Figure 4.

The data of the 2A02 aluminum alloy substrate and H04-586 paint were measured using an X-ray energy spectrometer, and the average values were taken from five different areas on the surface, as shown in

Table 1. Element analysis table of 2A02 aluminum alloy substrate (%).

Element	C	O	Al	Cu	Mg	Mn	Ca
2A02	10.04	11.29	73.94	2.38	1.54	0.44	0.08

and

Table 2. H04-586 paint element analysis table (%).

Element	C	Al	O	Ti	Si
paint	85.46	0.23	13.75	0.37	0.19

.

As can be seen from the above table, there are significant differences in the chemical composition values of Al and C elements between the paint and substrate materials; therefore, these elements can be used for elemental analysis to characterize paint removal results.

3. Laser Paint Removal Process Experiment

Currently, there is no unified standard for evaluating the effectiveness of laser paint removal. Relying solely on a single evaluation criterion often fails to accurately reflect the actual cleaning performance. In addition to assessing whether laser cleaning effectively removes paint, practical experiments are necessary to determine whether the thermal removal process affects the substrate. Substrate damage primarily manifests as a remelted layer induced by laser cleaning. Since the remelted layer can lead to reduced mechanical properties, deteriorated corrosion resistance, and weakened coating adhesion, it significantly shortens the service life of blades and compromises compressor performance and safety. Based on practical requirements, the experimental objectives are to remove the yellow surface paint using laser cleaning technology, enabling visual inspection of the blade’s service condition and thus prioritizing effective paint removal, and to ensure no visible laser cleaning marks remain on the surface.

For a more systematic evaluation of how laser parameters affect paint removal, this study adopts a combined “single-factor + orthogonal experiment” approach. In preliminary work, single-factor testing was used to roughly determine the effective ranges of control parameters. Building upon this foundation, orthogonal experiments were then introduced to screen for major influencing factors and their interactive trends, thereby improving experimental efficiency.

3.1. Single-Factor Paint Removal Experiment

To determine the appropriate scanning speed range, single-factor experiments were conducted to observe the cleaning effects under different parameters. Based on extensive preliminary testing, single-factor experiments were performed at scanning speeds ranging from 1000 to 3000 mm/s, with fixed parameters of 80 W power, 100 kHz repetition frequency, 100 ns pulse width, and 0.04 mm line spacing, covering a cleaning area of 20 × 20 mm. Figure 5 shows the laser paint removal results after 2, 3, 3, and 4 cleaning passes.

From Figure 5, it can be observed that as the number of cleaning passes increases, the paint color gradually lightens. This is because the thermal ablation mechanism dominates during the initial paint removal process, where the thermal stress does not exceed the adhesion between the paint and substrate, preventing complete paint peeling. As cleaning passes increase, the thermal ablation mechanism gradually weakens while the thermal vibration peeling mechanism becomes more prominent.

When the scanning speed is only 1000 mm/s, the high overlap ratio results in significant thermal ablation effects, leaving more black carbonized residues on the substrate surface. These difficult-to-remove black areas are likely caused by molten pool formation on the metal surface during cleaning, where reaction products become mechanically embedded or metallurgically bonded within the molten pool.

At 1500 mm/s scanning speed, three distinct surface regions appear: bare substrate, carbonized layer, and whitened surface. With increasing scanning speed, the thermal vibration mechanism begins to dominate, resulting in complete paint peeling in some substrate areas. This phenomenon indicates that the residual paint did not undergo combustion or other chemical reactions, with most molecular bonds remaining intact. However, the adhesion between paint and substrate has been compromised, allowing physical peeling. Intermediate areas still show carbonized ablation traces, while edge regions, where cohesion is lower than central areas, experience preferential warping and peeling. Peeled paint that receives additional laser energy undergoes complete ablation, leaving removable carbonized particles. Since the substrate surface is not directly exposed to laser energy in these areas, no obvious damage marks appear.

At 2000 mm/s scanning speed, after three cleaning passes, large areas become manually peelable. Under these parameters, partial paint peeling is achieved, while residual paint may remain due to uneven thickness or plasma shielding preventing sufficient thermal stress for complete peeling. The whitened substrate areas appear where paint had already peeled before the final laser pass, allowing for direct observation of clear laser spot marks on the exposed substrate surface.

At 3000 mm/s scanning speed, complete paint peeling is achieved, revealing a substrate-colored surface without laser spot marks, representing optimal paint removal results. Theoretical calculations based on experimental parameters show that at 3000 mm/s scanning speed and 100 kHz repetition frequency, the single-pulse spacing is approximately 30 μm, slightly smaller than the spot diameter (50 μm), indicating that each point receives about 1.67 laser pulses. The overlap rate, calculated as 1 − pulse spacing/pot diameter, is therefore 40%, which in this study is considered a moderate overlap. In contrast, “high overlap rate” in this work is defined as ≥90%, corresponding to conditions where ≥10 pulses consecutively irradiate the same point, producing strong thermal accumulation.

These experiments demonstrate that at 1000 mm/s scanning speed, the high spot overlap ratio produces obvious thermal ablation effects, while at 3000 mm/s scanning speed, near-perfect thermal vibration mechanism performance is achieved.

3.2. Orthogonal Experimental Design

Based on preliminary experiments, higher power parameters are required to improve paint removal efficiency for thicker paint layers. Therefore, an orthogonal experimental design method was adopted to systematically optimize the process parameters. According to the design principles, power, scanning speed, and repetition frequency were selected as key influencing factors. The pulse duration was fixed at 100 ns for all orthogonal experiments, identical to the setting used in the single-factor experiments described in Section 3.1, ensuring that variations in paint removal efficiency were solely attributed to changes in these three parameters. The number of cleaning times was determined by visual inspection after each cleaning. Cleaning was stopped only when no visible yellow paint residue was observed under uniform bright field illumination. This inspection is always performed by the same operator to minimize subjectivity. This process continues until each parameter set meets this criterion. The specific experimental parameters are shown in

Table 3. Orthogonal test table for laser paint removal.

Number	Scanning Speed /mm·s−1	Power/W	Frequency/kHz	Cleaning Times	C Content/%	Roughness Sa/μm	Energy Density J/cm2
1	1000	100	300	2	9.17	10.83	17.0
2	1000	90	200	2	15.21	2.63	22.9
3	1000	80	150	2	23.23	3.61	27.2
4	1000	70	100	3	11.6	2.46	35.7
5	1500	100	200	3	24.26	1.85	25.5
6	1500	90	300	3	14.43	2.3	15.3
7	1500	80	100	3	18.35	2.63	40.8
8	1500	70	150	4	14.55	3.5	23.8
9	2000	100	150	3	16.12	2.3	34.0
10	2000	90	100	4	14.24	7.8	45.9
11	2000	80	300	5	19.17	1.4	13.6
12	2000	70	200	5	18.1	5.11	17.8
13	3000	100	100	5	13.77	1.56	51.0
14	3000	90	150	4	17.6	2	30.6
15	3000	80	200	6	14.24	1.47	20.4
16	3000	70	300	7	15.75	3.36	11.9

.

4. Test Results and Analysis

4.1. Macroscopic Cleaning Results and Phenomenological Analysis

Figure 6 shows the condition of the samples after cleaning. None of the samples achieved complete paint removal in a single operation. Due to the short pulse width of nanosecond pulsed lasers, the thermal penetration depth is relatively small. As laser cleaning progresses, the paint surface color becomes lighter but the paint does not detach, indicating that thermal ablation occurs preferentially on the paint surface at this stage, but the ablation depth and thermal expansion force generated are insufficient to remove the paint. The cleaning results show that different process windows have varying effects on the cleaning outcome.

Under visual inspection, Samples 1, 2, 5, and 6 showed rough substrate surfaces with no paint adhesion, which could be preliminarily judged as successful paint removal. However, since cleaning marks were visible, the substrate may have been damaged. Samples 4, 8, 10, and 13 had black residues on the surface, which were soft in texture and easy to wipe off, generally being carbon-rich residues from heated paint. The main reason for paint peeling in these samples was the large temperature difference of the metal, as there were thermal decomposition products at the interface with carbonization traces. Samples 3, 7, 9, 11, 12, 14, 15, and 16 all had paint adhesion. It is worth noting that except for Sample 3, the paint on the other samples could be easily brushed off or peeled off by hand. At this time, the paint removal should be mainly due to the laser shock effect, showing large, brittle fractures with clean interfaces and no melting or thermal degradation marks. As shown in Figure 7, it can be seen that a small portion of the paint has been removed, while most of the paint appears flaky and can be easily peeled off from the substrate.

The samples with black residues and paint mentioned above were wiped with alcohol or attempts were made to peel off the paint, and the results are shown in Figure 8, where the red-marked areas indicate the original paint residue locations. After wiping off the soft black residues, the samples (4, 8, 10, and 13) showed color differences between black and white areas on the surface. The reason is that the products left after incomplete combustion of the paint did not completely cover the substrate surface, so part of the laser energy acted directly on the substrate surface. After wiping, the substrate or original oxide film would be exposed, and obvious laser spot marks could be seen in the areas where these products were originally present.

4.2. SEM and EDS Analysis of Surface Morphology

The preliminary results from the samples indicate that thermal ablation mechanism and thermal vibration mechanism are the main mechanisms for paint removal by nanosecond pulsed lasers. Due to uneven paint thickness and possible plasma shielding effects, the paint removal results often cannot be completely consistent within the same sample. To further analyze the residues after laser paint removal and the surface morphology of the samples, the surface characteristics of samples with different cleaning results were observed, respectively, using electron microscopy and energy spectrum analysis.

First, Samples 1, 2, 5, and 6, which showed no obvious paint residue under visual inspection, were analyzed. The SEM observation results are shown in Figure 9, and the surface elemental analysis is presented in

Table 4. Elemental analysis table of the samples.

	Mass Fraction %
Sample	C	O	Al	Mn	Mg	Ti	Si	Cu
1	9.17	12.12	73.72	0.66	1.61	0.26	0.14	/
2	15.21	14.85	64.88	0.52	1.35	0.62	0.17	/
5	24.26	34.15	38.82	0.1	1.06	0.47	0.24	/
6	14.43	17.37	63.37	0.1	1.36	0.6	0.28	0.48

.

Sample 1 showed distinct laser spot marks on the substrate surface after two cleaning passes. EDS analysis indicated the surface contained 73.72% Al with minimal C content, while other element ratios matched the base material, confirming substrate remelting. This surface microstructure results from rapid cooling—nanosecond laser irradiation instantly melts a thin surface layer that solidifies before complete spreading due to extreme cooling rates. Dark image areas represent non-irradiated zones, typically caused by laser-induced craters from paint impact, or plasma-impacted partial melting where molten substrate accumulates peripherally, with rapid thermal cycling forming these craters.

Sample 2 exhibited relatively flat morphology (Sa = 2.63 μm). Binarization processing revealed paint splatter products (Figure 10). Clustered residues originate from ablation/ionization, where irradiation produces particle mixtures (μm scale to hundreds of μm) along with vapors and nanoscale clusters that deposit on the substrate. Van der Waals forces adhere to these particles. EDS point analysis (

Table 5. Analysis of surface element points at different positions of Sample 2.

	Mass Fraction %
Point X	C	O	Al	N	Mg	Ti	Si	Ca	S
2	30.28	46.71	19.57	/	0.8	/	0.26	1.79	0.59
3	39.93	33.69	18.63	5.4	0.43	/	0.32	1.22	0.38
4	24.67	48.04	24.88	/	0.68	0.22	0.37	0.95	0.2
5	15.54	19.66	60.15	/	1.09	1.11	0.26	/	0.06
6	10.16	10.75	74.24	/	1.56	0.27	/	/	0.12

) confirmed points 2–4 as paint residues (elevated C/O) and points 5–6 as cleaned substrate.

From the SEM image in Figure 11, it can be observed that the surface exhibits a molten appearance. Since the elemental analysis shows Al and Mg elements corresponding to the laser spot marks, this indicates that while the surface paint has been completely cleaned off, substrate remelting has occurred.

Sample 5 contained numerous clustered residual particles primarily composed of aluminum oxide and residual paint. The elemental analysis in Figure 12 shows that the clustered particle aggregation areas were mainly composed of O element with less Al element, while C element was uniformly distributed, indicating the residues were combustion products of the paint. Prior to the final cleaning pass, the residual paint was removed through ablation, while some oxides developed cracks under stress but were not eliminated. It can be observed that the remaining surface shows no significant remelting phenomenon, classifying Sample 5 as an oxide-residue specimen.

Similarly, based on the elemental distribution, Sample 6’s surface shows no significant C element residues, confirming successful paint removal. However, the overlapping laser spot craters on its surface suggest potential remelting phenomena requiring further analysis. The elemental distribution in Figure 13 further reveals that thermal decomposition of the paint generated inorganic oxides such as CaO and MgO.

Samples 4, 8, 10, and 13 with black residues after laser cleaning were analyzed; the SEM results are shown in Figure 14, and the elemental composition is summarized in

Table 6. Elemental analysis of the surfaces of samples 4, 8, 10, and 13.

	Mass Fraction %
Sample	C	O	Al	Mn	Mg	Ti	Si	Cu	S
4	11.6	10.01	72.9	0.53	1.62	0.25	0.12	2.89	/
8	14.55	27.07	54.06	/	1.11	0.42	0.14	2.22	0.45
10	14.24	14.54	66.12	0.49	1.5	0.21	0.17	2.51	/
13	13.77	15.17	67.67	/	/	0.59	0.2	2.38	/

.

Samples 4 and 13 may have undergone remelting; therefore, different elements were selected for analysis. The specific surface morphology and elemental analysis are shown in Figure 15 and Figure 16, respectively. The paint removal results should be similar to the conclusions for Sample 1, with no apparent residual paint on the surface. However, Sample 4 exhibits laser spot cleaning marks, while Sample 13 shows regular craters similar to plasma impact phenomena. Therefore, both samples may have experienced substrate remelting.

Sample 8’s surface shows faint laser spot marks in white areas, while black areas exhibit protrusions or craters. Figure 17 reveals overlapping mounds formed by laser spot arcs and residues, which combined with elemental analysis, indicate that either residual paint or incompletely combusted oxidation products remain on the substrate surface. No distinct molten pool traces are observed.

Sample 10’s elemental distribution resembles that of Sample 2, as shown in Figure 18. The laser spot paths on its surface suggest possible slight substrate remelting. The craters present along the paths indicate that the paint accumulated after undergoing phase transformation due to heating, resulting in a higher surface roughness (7.8 μm) compared to Sample 2.

Samples 3, 7, 9, 11, 12, 14, 15, and 16 with detached paint after laser cleaning were subjected to SEM analysis. The surface morphology results are shown in Figure 19, and the surface elemental analysis is presented in

Table 7. Elemental analysis of the surfaces of samples (3, 7, 9–16).

	Mass Fraction %
Sample	C	O	Al	Mn	Mg	Ti	Si	Cu	S
3	23.23	33.8	40.22	/	0.84	0.3	0.22	1.04	/
7	18.35	32.81	45.51	/	1.02	0.42	0.3	1.35	/
9	16.12	21.22	58.3	/	1.2	0.52	0.26	2.13
11	19.17	24.88	51.76	/	1.05	0.54	0.25	1.81	0.28
12	18.1	20.23	57.45	0.34	1.16	0.47	0.24	1.76	/
14	17.6	30.67	48.09	/	1.01	0.22	0.22	1.29	/
15	14.24	19.85	61.39	0.45	1.2	0.15	/	2.6	/
16	15.75	35.48	46.24	/	/	0.21	0.23	1.44	/

.

From the SEM images, it can be seen that samples with bulk paint detachment show no obvious laser spot marks, indicating no significant remelting phenomenon. This is because the removal mechanism is primarily the thermal vibration mechanism, where the laser does not directly act on the surface. Most sample surfaces have splattered paint products in clusters, but due to the small particle size, their surface roughness Sa is relatively low. The samples also contain ablation products from incomplete paint combustion (clustered paint) and plasma impact (craters).

Samples 3, 14, and 16 have clustered residues on their surfaces. Elemental analysis shows that the distributions of C and O elements are identical, indicating the presence of a carbonized layer from paint ablation products on the surface. Figure 20 shows the surface morphology and elemental distribution of Sample 3.

Samples 7, 11, and 12 exhibit uniform C element distribution but varying O element distribution, indicating the presence of other oxide layers on the surface. Figure 21 shows the surface morphology and elemental distribution of Sample 12.

Samples 9 and 15 exhibit numerous craters within the detection area. According to the elemental distribution in Figure 22, the craters show higher Al concentration and lower O content, confirming the white craters correspond to the base material. In contrast, the black areas demonstrate uniform C distribution and O aggregation, verifying the formation of an oxide layer on the surface.

4.3. Metallographic Examination of Samples

To further verify whether remelting occurred on the substrate surface, the samples underwent coarse grinding–fine grinding–polishing procedures. The cross-sectional characteristics were observed under a metallographic microscope, with the results shown in Figure 23.

As shown in the Figure 23, Samples 1, 2, and 4 exhibit remelting phenomena. Samples 6 and 10 display arc-shaped laser spot marks but no remelted layer, indicating the substrate remained undamaged as the laser energy did not reach the melting temperature of the aluminum alloy. The craters observed in Sample 9 correspond with the SEM images, showing an oxide layer on its surface. The cross-sections of samples with detached paint remain intact with no surface damage.

Figure 24 presents the elemental distribution of samples. When visual inspection confirms the absence of yellow paint, most samples show higher carbon content than the original substrate. This suggests that after thermal decomposition or vaporization of the paint, some carbon forms a soft, easily removable layer while a smaller portion transforms into strongly adherent carbides due to incomplete oxidation. With increasing laser energy density and overlap rate, the oxygen content rises as the paint and substrate chemically react with atmospheric oxygen, forming surface oxide layers. However, oxide formation does not necessarily indicate substrate remelting, as it may result from reactions between paint elements and oxygen. To confirm whether remelting occurred, one should examine the surface for molten pool traces or conduct a metallographic cross-section analysis to detect variations in grain size or abrupt microstructural changes.

Figure 25 shows the process parameter diagram of overlap rate, cleaning passes, and energy density. The horizontal axis (X-axis) represents the spot overlap rate (%), and the right side of the vertical axis represents the calculated energy density (J/cm ²). The shape of each data point represents the main surface features observed after cleaning: (★) matrix remelting, (▲) carbides, (■) pitting corrosion, and (●) oxides. The figure indicates that samples with residual carbonized layers (e.g., Sample 8: 23.78 J/cm²; Sample 12: 17.83 J/cm²) correspond to parameter sets that are close to the paint-cleaning threshold but remain below the level that causes significant substrate damage. As the overlap rate decreases, additional cleaning passes are required to achieve complete paint removal. When laser energy density is at its maximum (e.g., Sample 4: 35.7 J/cm²), the energy impact exceeds the strength of either the substrate or adhesive layer, creating surface pits. If the scanning speed is simultaneously increased (reducing overlap rate), this decreases the coating’s energy absorption. This reduction weakens (1) coating melting/vaporization, (2) combustion effects, and (3) vapor density above the coating, while enhancing the laser’s impact on the coating and residue formation, potentially leaving surface pits. Conversely, higher overlap rates typically cause remelting damage due to excessive heat accumulation.

A comparative analysis of carbon content across all test samples offers a clear and quantifiable measure of coating removal quality and substrate exposure. While more sophisticated evaluation systems could provide deeper insight, emphasizing carbon quantification ensures efficient parameter screening and reproducibility across multiple experiments, which is essential for identifying an optimal energy density window and minimizing remelting effects, with results shown in

Table 8. Range analysis table.

	Number	Scanning Speed (mm/s)	Power/W	Frequency/kHz
C content	K1	14.8	15.3	13.88
	K2	17.9	15.92	17.45
	K3	16.91	18.19	14.88
	K4	15.34	15.53	18.69
	R	3.1	2.89	4.81
Rank			Power > scanning speed > frequency

.

According to the range analysis table, the order of influence of process parameters on paint removal effectiveness is power > scanning speed > repetition frequency, which aligns with previous studies [17]. To achieve optimal paint removal—where the substrate surface has no residual paint or remelted layer—a specific energy density must be maintained while ensuring the laser energy does not damage the substrate before paint detachment. According to the experimental results and orthogonal experimental table, the presence of remelting phenomenon is often the reason for the high overlap rate, such as Sample 1 (96.7%). If the overlap rate is low, even if the energy density is high, there may not be a remelting phenomenon, but rather impact pits on the surface of the substrate, as in Sample 13, which had a 70% overlap rate and an energy density of 51.0 J/cm².

4.4. Analysis of Laser Paint Removal Mechanism

In multi-pulse laser cleaning, thermal accumulation is the key mechanism. Under high repetition rates and moderate energy conditions, laser pulses create localized heat superposition, promoting coating softening, cracking, and decomposition, thereby reducing adhesion to the substrate and improving removal efficiency. Our parameters (100 kHz repetition rate, 3000 mm/s scan speed, ~30 μm point spacing) exhibit weak multi-pulse accumulation characteristics, consistent with thermal accumulation dominance. In contrast, plasma shielding typically occurs under high-energy dense superposition, manifesting as cleaning interruptions or molten residue—neither observed here, indicating negligible influence.

In conclusion, the optimal parameters should completely detach paint without substrate damage or remelting. However, as real-world conditions often cause uneven removal, surfaces with minimal carbonized/oxide layers are still considered effectively cleaned. Samples 9, 11, 12, and 14 demonstrated the best results. Figure 26 summarizes the general principles of nanosecond pulsed fiber laser paint removal with representative SEM images.

Laser paint removal is a complex process that can yield various outcomes. Since nanosecond pulsed lasers have relatively low total power, it is difficult to completely remove the entire paint film in a single pass through ablation mechanisms. Therefore, multiple laser cleaning passes mainly rely on the ablation effect to gradually thin the coating. When the paint layer becomes sufficiently thin, the thermal expansion generated by the laser energy exceeds its adhesion force, enabling removal through the thermal vibration peeling mechanism. According to the thickness of the paint, different mechanisms dominate. For thicker pollutants or to prevent potential damage to the substrate from higher laser energy densities (e.g., Sample 4: 35.7 J/cm²; Sample 3: 27.2 J/cm²), multiple cleaning cycles may yield better results. When the paint layer becomes thin and lacks adhesion, mechanical removal can ensure that the underlying layer is not damaged. If laser cleaning continues at this stage, since the remaining paint is very thin, most of the laser energy will act on the substrate after ablating the residual paint, potentially exceeding its damage threshold and causing melting and ablation phenomena.

As the number of cleaning passes increases and the paint thickness continuously decreases, the process sequentially produces splatter products, carbides, inorganic oxides, and pits. This progression occurs because with each cleaning pass, the paint layer becomes thinner, changing how the laser energy interacts with the material—initially removing paint, then increasingly affecting the substrate surface. The transition between different removal mechanisms (ablation vs. vibration peeling) depends largely on the instantaneous thickness of the remaining paint layer during the cleaning process.

5. Conclusions

This study systematically investigated the pulsed laser cleaning process for removing paint coatings from low-pressure turbine blades, with a focus on suppressing substrate remelting. By fixing the pulse duration at 100 ns and varying laser power, scanning speed, and repetition frequency, the quantitative relationship between process parameters, energy density, and paint removal mechanisms was established. Different process windows result in different surface residues on the substrate, which can be categorized as exposed substrate, black residues, and paint detachment. The 16 orthogonal test conditions produced energy densities ranging from 11.9 to 51.0 J/cm². An optimal energy density window of approximately 17–27 J/cm² enabled complete paint removal without visible substrate damage, primarily through interfacial debonding and controlled film ablation. In contrast, energy densities exceeding 35 J/cm² frequently caused localized remelting and pitting due to excessive thermal accumulation. The optimal parameter combination—90 W laser power, 1500 mm/s scanning speed, and 300 kHz repetition frequency—achieved 98% paint removal efficiency in four passes with no detectable substrate degradation. The findings demonstrate that controlling the laser energy density within the identified window is critical for balancing removal efficiency and substrate integrity. These results lay a foundation for developing an efficient, repeatable, and damage-free laser cleaning process suitable for aerospace applications.