The poor surface roughness associated with additively manufactured parts can influence the surface integrity and geometric tolerances of produced components. In response to this issue, laser polishing (LP) has emerged as a potential technique for improving the surface finish and producing parts with enhanced properties. Many studies have been conducted to investigate the effect of LP on parts produced using additive manufacturing. The results showed that applying such a unique treatment can significantly enhance the overall performance of the part. In LP processes, the surface of the part is re-melted by the laser, resulting in smaller peaks and shallower valleys, which enable the development of smoother surfaces with the help of gravity and surface tension. Precise selection of laser parameters is essential to achieve optimal enhancement in the surface finish, microstructure, and mechanical properties of the treated parts.
The worldwide industrial sectors such as aerospace, automotive, chemical, and food processing are increasingly interested in additive manufacturing (AM). However, due to the poor surface roughness made by AM, it may not be sufficient for some applications because of the micro surface creaks and porosities that occur during AM fabrication which affect the AM parts, especially in high- or low-temperature environments. Generally, AM components require post-processing operations such as polishing, using either mechanical or electrochemical polishing. Despite the limitations of both processes 
, it is not recommended to treat the surface roughness of AM lattice parts via electropolishing 
. In addition, electropolishing requires the use of specialized equipment and chemicals, which can increase the total cost. Various parameters influence material removal during this process, making it difficult to maintain precise dimensional tolerances consistently 
. Mechanical polishing is based on the operator’s expertise and the tools’ quality. The results may differ, which can cause inconsistent results. Creating functional products that require minimal post-processing is one of the fundamental principles of AM. Using ex situ conventional post-processing techniques can diminish the benefits of this innovative approach. The advantage of employing LP is that it can be applied in situ after the part is fabricated 
Post-processing methods such as laser polishing (LP) can be a possible and feasible approach with minimal constraints. Due to its non-contact and chip-free nature, LP has recently become an attractive alternative polishing method compared to traditional methods 
, especially for the AM processes that use a laser for sintering and fusion. In the case of laser-based AM, LP is used to achieve a smooth surface in situ during fabrication 
. Compared to other manual, mechanical, and chemical polishing methods, LP is a more effective and efficient technique because it modifies surface morphology via re-melting without altering bulk qualities. In addition, in LP, a laser beam scans the surface and re-melts a thin layer on the solid surface, forming a melting pool. Surface tension and gravity will cause the melting material to be redistributed into the melting pool, resulting in leveled peaks and valleys. The surface roughness is reduced as a result of the rapid solidification of the melting pool 
. The surface of additively manufactured material is subjected to re-melting, in which the laser beam affects the topography of the layer surface while maintaining the precision of the manufactured part. Meanwhile, the irregularities and high asperities are flattened during the liquifying process 
The LP process reduces the surface roughness of the AM metal parts over depths of 10–80 μm using a continuous-wave laser in the case of macro-polishing 
or 0.5–5 μm using a pulsed laser in the case of micro-polishing 
. Laser re-melting can occur in two phases, either after building each layer or only at the final layer. As a result of the unmolten powder particles, the final surface of AM materials is rough, which may also influence the mechanical properties. Figure 1
presents the parameters of the LP process, such as laser type, laser profile, laser diameter, laser power, laser speed, scanning type, hatch space, and overlap. The Gaussian beam is the most common laser profile because of the intensity of the laser beam.
Figure 1. Schematic of the laser polishing process and its parameters.
LP has successfully post-processed a variety of metallic alloys, including titanium alloys 
, stainless steel 
, cobalt–chromium (CoCr) alloys 
, and Inconel alloys 
. However, LP is not always capable of producing a precisely leveled surface. Several studies found that melting, evaporation, and solidification phases caused some effects of characteristic texture, such as surface pores, voids, and micro-cracking 
2. Laser Polishing Mechanisms
In LP, a thin layer of the metal surface is re-melted by directing the laser beam onto the surface. The scan velocity determines the movement of the laser beam, whereas energy density is a crucial element that significantly impacts the melt pool flow. Energy density is a combination of laser power and scanning speed, which influences surface melting via surface over-melting (SOM) and surface shallow melting (SSM). SOM occurs when the surface is overheated and goes beyond the valleys, whereas SSM occurs when the surface peaks melt and fill the valleys. Insufficient peak melting of the surface is due to the low laser energy and fast scanning speed. The re-melting process should be in the peak–valley range 
. Figure 2
illustrates the mechanism of laser re-melting 
Figure 2. Laser re-melting mechanism.
On the other hand, high laser energy and slow scanning speed melt beyond the surface valleys. The molten metal flows back toward the solidified metal region in SOM, producing a surface peak higher than SSM. To achieve the best LP results, the LP must combine SSM in the form of fine polishing, then SOM for the coarse polishing mechanism 
3. LP of Titanium Alloys
Titanium alloy is an α + β type dual-phase alloy with high strength, hardness, and corrosion resistance. Due to its excellent properties, it is used in many different applications, such as aerospace, military, and biomedical implants 
. Titanium alloys are good candidates for AM 
; the AM represents one cost-effective approach to the fabrication of titanium components. This alloy category has contributed to the creation of alternative organs, tissues, biomedical implants, and pharmaceutical delivery systems in the biomedical sector 
. The AM approach has been utilized to enhance the performance and effectiveness of Ti in medical operations and minimize the need for further treatment 
3.1. Mechanical Properties
The hardness of the polished area of the titanium alloy is significantly higher than the hardness of the unpolished site. However, in most studies, the heat-affected zone (HAZ) decreased. The cross-section from the top surface to the inside of the material is divided into three zones: the remolten zone (top surface), the heat-affected zone (HAZ), and the substrate layer (base material). The increased hardness of the polished area is due to the formation of the martensite phase, and the bulk modulus is greater than the base material. The density of dislocations in the martensite phase is high, and the number of phase boundaries in a double needle is large 
In terms of fatigue, the unbalanced thermal process of LP affects the polished surface by making it susceptible to residual stress due to re-melting and rapid solidification. Internal defects, microstructures, and residual stress are the primary causes of fatigue performance after LP. Residual stress reduces fatigue life by widening fatigue cracks. The fatigue life of the as-built Ti alloy specimen resulted in 107
cycles, which can be reduced to 104
cycles after LP. The fatigue testing method was used for the high cycle fatigue (HCF) experiment at two stress levels of 500 and 600 Mpa, with the chosen mean stress condition for the stress ratio being R = 0.1 and the test frequency 10 Hz 
Fabricating Ti6Al4V using electron beam melting (EBM) will cause changes in both the orientation of the melted surface and the direction of beam scanning. As a result, significant residual tensile stresses can form near the surface. The grain structure and texture can differ at 200 μm depth due to the remolten surface layer, and the grains regrow vertically to the molten surface 
. During LP, HAZ was found to be ultimately ß annealed, and it may become martensitic upon cooling.
3.2. Microstructure and Surface Quality of Titanium Alloy
The effect of the LP process parameters on the resultant surface roughness strongly depends on the laser power (P). Increasing the laser power with constant scanning velocity (v) equal to 40 mm/s and scanning pitch (S) of 0.1 mm resulted in an increase in surface roughness of Ti alloy from 1.127 μm at 150 W to 3.25 µm at 300 W, as shown in Figure 3. Due to the SSM process that enables the removal of surface defects and the creation of a smoother surface, SSM may result in better surface quality and reduced surface roughness. However, the technique’s efficacy depends on several variables, and the optimal laser parameters must be chosen depending on the material and the desired surface finish.
Effect of different polishing process parameters on sample surface roughness (a
) laser power P = 150 W, scanning pitch S = 0.1 mm, (b
) scanning speed V = 40 mm/s, scanning pitch S = 0.1 mm for Ti, (c
) laser power P = 150 W, velocity V = 40 mm/s 
As temperatures rise, the surface tension of molten Ti6Al4V decreases because the temperature coefficient of surface tension is negative. When the molten pool begins, the temperature gradient is greatest in the center and diminishes to the lowest at the molten pool’s edge. Furthermore, the surface tension gradient formation spreads gradually from the surface’s center (high temperature) to the molten pool’s edge. As a result of the action of the surface tension gradient and gravity, the molten liquid flows back to the solidification area and the edge of the molten pool. Gravity causes a different distribution of liquid height (ripple) in the molten pool, which increases surface roughness 
Ti6Al4V alloy powder has more flowability because its particles are rounder than AlSi10Mg, 316L, and IN718 alloy powders 
. As a result, the layer’s homogeneity and the final components’ surface roughness are influenced to be smooth. After LP, the initial peak–valley value of additively manufactured Ti6Al4V alloy is lowered from 70 μm to around 10 μm 
The effect of rescanning cycles (number of laser re-melting passes) on the characteristics of the Ti6Al4V SLM sample was investigated. The residual stress increased with a single rescanning cycle and decreased with multiple rescanning cycles. The ultimate tensile strength (UTS), yield strength, micro-hardness, and micro-strain of the samples all increase as the laser re-melting cycles increase from 0 to 3 but decrease after the fourth time of rescanning 
Medical implants using Ti-6Al-4V Grade 23 ELI with superior surface qualities via the LPBF method, followed by LP using a CO2 laser source, were investigated. The surface qualities of the products mentioned above are compared to those manufactured by AM techniques. LPBF paired with LP yields a surface roughness reduction of almost 80% and a peak-to-valley reduction of 90%. In addition, a significantly reduced processing time is reported, and the procedure is more cost-effective than other methods. The uses of a CO2 laser to decrease surface roughness and enhance surface physical properties were examined, and cylindrical and flat samples were manufactured.
The effects of various scanning strategies, laser parameters, and the initial surface were examined 
. Four different scanning strategies with varying angles were used. The best scanning pattern on better initial surface quality obtained an 85% reduction in surface roughness after 12 scans with angles (18°, 71°, 0°, and 45°) of halftone. The effect of three different scanning strategies on the relative density of a Ti6Al4V AM part was investigated. The fiber laser beam quality and the process environment are crucial for attaining greater relative densities.
4. Laser Polishing of Inconel Alloys
4.1. Microstructure and Surface Quality of Inconel Alloys
IN718, manufactured via laser metal deposition (LMD), was investigated to determine the relationship between laser power, scanning speed, and laser diameter. The melting of the outer layers has been assisted by raising the laser power to some level. After that, the increased laser energy will not be able to polish the surface layer any longer effectively, and it may have a negative effect. This is because high laser energy may damage the surface and extend beyond the surface valley that marks the transition between the SSM and SOM regimes. LP should not increase the peak–valley distance 
The melt pool geometry (single track) of SLM IN625 was investigated. Laser power significantly impacts the melt pool more than scanning speed. It was observed that increasing laser power and decreasing scan speed significantly increase track width and melt depth. Furthermore, increasing laser power reduced the contact angle of the melt pool; however, the scan speed had less of an effect on the height and surface roughness of the melt pool 
4.2. Mechanical Properties
The effect of hatching and contour on the initiation of fatigue crack in the IN625 SLM part was investigated. Seven as-built and laser-polished samples were subjected to a fatigue test at a 20 Hz frequency, using a load R-ratio of −1 and increasing the stress by 25 MPa increments to determine the number of cycles to failure. It was found that the optimum parameters resulted in excellent surface roughness and porosity reduction. Fatigue testing on as-built and polished samples revealed three mechanisms that cause fatigue damage, which are unmolten particles during the SLM process, porosity that occurs during SLM, and local plasticity that appears in the microstructure of the material.
5. Cobalt Chromium—CoCr
5.1. Microstructure and Surface Quality of Cobalt Chromium
The effect of LP on the surface microstructure and corrosion resistance of AM CoCr was studied. It was found that laser-polished specimens have a higher corrosion resistance of about 30% compared to other polishing methods, such as thermos mechanical treatment. The important parameters to obtain optimized results are the laser power and the distance of the object 
. This result matches Yung et al.’s 
study; it was found that argon gas significantly smooths the surface roughness. The optimal parameter for laser polishing the AM CoCr part. The flow concentrations of air, nitrogen, and argon were 2.0 L/min, 6.0 L/min, and 10.0 L/min. They mentioned that the CoCr sample performed best with argon gas at a flow rate of 6.0 L/min 
5.2. Mechanical Properties
A slight enhancement in hardness (about 8%) of a complex surface geometry was achieved by adjusting the distance of the laser along with the surface shape compared to the as-built surface. A hardness test was conducted each 100 µm from the surface to the in-depth. There was a reduction in hardness from the surface to the depth of the material. The highest value on the surface is 413 HV and remained at 150 μm of the depth due to the heat-affected zone. At the final depth, 350 μm, the hardness value was changed to 384 HV. This demonstrated that the LP could slightly improve the surface compared to the as-received hardness 
6. Laser Polishing of Steel
6.1. Microstructure and Surface Quality of Steel
The PW and CW laser irradiation outcomes were examined and compared in many different studies 
. The material they investigated was 18Ni Maraging steel. Laser irradiation was applied to the top and the side of the sample. One of the process parameters that was introduced is a pitch distance of 25–50 μm. It was found that laser power significantly impacts the surface roughness of the part, whereas the combination of low power with high speed or low speed was not significant. However, combining high power with either low speed or high speed resulted in melt pool disruptions. Other studies indicated that the diameter of the spot is perhaps the most difficult to monitor. The beam spot diameter of a focused laser beam on a working surface is identified, and the beam type and optics determine it 
Furthermore, corrosion-resistant austenitic X2CrNiMo17-12-2 steel was investigated 
. For PW, the optimized result was achieved when the laser diameter was measured at 12 mm and intensity at 1.74 kW/mm2
. Any change in the spot diameter increasing or decreasing resulted in higher surface roughness as in EV. While any decrease in intensity caused a low melting bath, any increase caused a high melting bath. When the power is at the minimum, there is no effect on the surface of CW. Increasing laser power at 1400 W with a feed rate ranging from 200 to 350 mm/min decreased the surface roughness to less than 0.25 μm.
6.2. Mechanical Properties
The hardness improved slightly by 14% after applying high-power CW LP compared to the hardness of the steel of the AM tool received; on the other hand, it increased by 9% after applying low-power PW 
. Two different scanning strategies were used to study their influence on microhardness. Variation of flow strategies was found to cause microhardness variations in AM parts.
7. Machine Learning in Laser Polishing
Laser polishing and machine learning have shown an improvement in surface quality and surface microstructure for LPBF Ti-6Al-4 V alloy. An artificial neural network (ANN) algorithm was used in this study 
and included three input nodes as three process parameters (laser power, scanning velocity, and track offset) with different levels of each parameter. The output was the surface roughness of the laser-polished area.
Another study used machine-learning-based image processing for LP on AM PH-steel parts using convolutional neural networks (CNN) to identify optimal laser-polished surface quality and integrity. CNN is a machine-learning method that can classify images and consists of a series of hierarchically arranged convolutional layers that gradually assemble low-level elements into high-level elements to improve and characterize the input image. CNN was trained with 432 images as a data set that was taken from pre-processed images with segments into the size of 333 px × 150 px. A total of 80% (344) of the images were used for CNN training, and 20% (88) were used for CNN validation. The CNN identified LP process conditions based on hatch spacing and an overlapping ratio with an accuracy of up to 97%