Surface Roughness Of Parts Research Articles

An In-Depth Exploration of Numerical Simulations for Stress Fields in Multi-Directional Rolling Processes

To address issues such as large surface roughness, coarse grains, and poor mechanical properties in low-carbon steel parts produced through wire arc additive manufacturing (WAAM), this paper proposes a method combining multi-directional incremental forming with the WAAM process. The additive manufacturing and cooling processes were simulated using the finite element software Abaqus to analyze the effects of multi-directional additive manufacturing on the stress field of the fabricated parts. The results indicate that after multi-directional incremental forming, the residual stress in the fabricated parts shifts from tensile stress to compressive stress, thereby reducing the risk of defects such as cracks. Moreover, the equivalent plastic strain of the processed parts increases, and the surface microhardness improves, with the most significant impact of multi-directional incremental forming observed in the contact area of the rolling head.

Impacts of Material and Machine on the Variation of Additively Manufactured Cooling Channels

Abstract While additive manufacturing (AM) can reduce component development time and create unique internal cooling designs, the AM process also introduces several sources of variability, such as the selection of machine, material, and print parameters. Because of these sources, wide variations in a part's geometrical accuracy and surface roughness levels can occur, especially for small internal cooling features that are difficult to post-process. This study investigates how the selection of machine and material in the AM process influences variations in surface quality and deviations from the design intent. Two microscale cooling geometries were tested: wavy channels and diamond-shaped pin fins. Test coupons were fabricated with five different additive machines and four materials using process parameters recommended by the manufacturers. The as-built geometry was measured non-destructively with computed tomography scans. To evaluate surface roughness, the coupons were cut open and examined using a laser microscope. Three distinct roughness profiles on the coupon surfaces were captured including upskin, downskin, and channel walls built at 90 deg to the build plate. Results indicated that both material and machine contribute to producing different roughness levels and very different surface morphologies. The roughness levels on the downskin surfaces are significantly greater than on the upskin or sidewall surfaces. Geometric analysis revealed that while the hydraulic diameter of all coupons was well captured, the pin cross section varied considerably. Along with characterizing the coupon surfaces, cooling performance was investigated by experimentally measuring friction factor and heat transfer. The variations in surface morphology as a function of material and machine resulted in heat transfer fluctuating by up to 50% between coupons featuring wavy channels and 26% for coupons with pin fin arrays. Increased arithmetic mean surface roughness led to increased heat transfer and pressure drop; however, a secondary driver in the performance of the wavy channels was found to be the roughness morphology, which could be described using the surface skewness and kurtosis.

Enhanced electrical and mechanical properties of additively manufactured pure copper with green laser

The effort to improve the additive manufacturing (AM) of copper, which is essential in various industrial sectors, has led to the exploration of green laser powder bed fusion (GL-PBF). By using a high-energy green laser, we overcome the challenges posed by the high reflectivity of copper, which has previously hindered achieving the desired component densities and functionalities through AM. This study uncovers the key role of GL-PBF process parameters on the densification, microstructure, surface roughness, mechanical properties, and electrical conductivity of pure copper parts. Our findings demonstrate that optimizing GL-PBF parameters can achieve copper components with over 99.9 % relative density and 98 % international annealed copper standard (IACS) electrical conductivity. The combination of comprehensive experiments and finite element modeling also reveals how the critical role of defect morphology in affecting electrical conductivity. This work contributes to the broader application of AM technologies, especially for high-reflectivity metals, and also provides new insights into how these defects affect conductivity and should be controlled during the AM process.

Development of an expert system for technological support of required roughness when machining hardened steel parts on numerically controlled machines

This work addresses the problem of improving the surface quality of parts of rotation bodies, which are made of hardened steels, in the course of their machining on lathes with numerical program control. The research object was the surface roughness of parts. The methodology involved system analysis and synthesis, artificial neural networks, fuzzy logic, experiment, and processing of experimental results. A decomposition scheme for the expert system structure was developed, which can be used as the basis for formulating requirements to a future expert system for monitoring and prediction of roughness parameters. It was established that the models currently applied to describe the relationship between surface quality and the technological regimes used to ensure the technological level of roughness give a high error of over 20%. The possibility of using models that apply artificial intelligence and contain neural network blocks and decision-making devices based on fuzzy logic is substantiated. It is shown that such a combination makes it possible to customize the system for processing parts of a certain production range, as well as a more correct assessment of the onset of catastrophic wear of cutting tools. The neuro-fuzzy model was confirmed to have an error of less than 10%, which is significantly lower than when using spectral or correlation models. According to the testing results, the proposed expert system for monitoring and prediction of roughness parameters enables a 2.5-fold reduction in the scatter of roughness parameters under an increase in tool wear, compared to without its application. Thus, the proposed system makes it possible to assess the level of cutting tool wear more correctly and determine the onset of its limit state.

An experimental study on the wear performance of 3D printed polylactic acid and carbon fiber reinforced polylactic acid parts: Effect of infill rate and water absorption time

AbstractRapid prototyping, also known as additive manufacturing, is a nascent technology that is gaining traction in the context of environmental concerns and waste reduction, as well as the growing trend towards customized design. The additive manufacturing method, which has applications in diverse fields such as aviation, architecture, biomedical and automotive engineering, has also begun to be utilized in the construction of yachts and yacht hulls within the maritime industry. In this experimental study, the influences of sea water on polylactic acid (PLA) and carbon fiber reinforced polylactic acid (PLA/CF) parts manufactured at different infill rates (20%, 60% and 100%) were investigated. The parts were exposed to sea water for three different periods (1, 5, and 10 days) and subsequently subjected to wear tests. The dimensional accuracy, surface roughness, hardness, water absorption, volume loss, and friction coefficient of parts were measured and calculated. Additionally, the worn surfaces of the parts were investigated using field emission scanning electron microscope (FESEM) images. The findings indicate that PLA and PLA/CF parts can be produced with high dimensional accuracy. Furthermore, it can be reported that the water absorption of PLA/CF parts increased, particularly with an increase in the infill rate, while the volume loss decreased. Obtained results indicate the necessity of optimizing the 3D printing parameters and the relationship between the ambient conditions and the wear performance of the 3D printed parts.Highlights 3D printing is a highly promising method for the production of polymer composites. A pioneering study into the effect of infill rate and water absorption on the wear performance. Coefficient of friction values of PLA and PLA/CF parts ranged between 0.37 and 0.75. PLA/CF mostly exhibited higher volume loss than PLA with water absorption. Volume loss declines with a raise in the infill rate from 20% to 100%.

Research on the surface quality improvement of 3D-printed parts through laser surface treatment

This study investigates laser polishing as a post-processing technique to reduce surface roughness in ABS and Nylon parts produced via Fused Deposition Modeling (FDM). The research utilized CO2 and Nd:YAG lasers to polish these materials, achieving surface roughness reductions of 95.9 % and 96.0 % for CO2-treated ABS and Nylon samples, respectively, while Nd:YAG-treated samples exhibited reductions of 67.3 % and 68.6 %. Additionally, the study explores the impact of various laser parameters on surface quality through both single-factor and orthogonal experiments. It also introduces wet laser polishing as a novel method to mitigate color changes and enhance polishing effectiveness. This innovative approach demonstrates substantial improvements in performance metrics, underscoring its potential for industrial-scale production of plastic components and biomedical implants. The technique, in particular, shows promising applications in meeting the polishing requirements of 3D-printed parts.

Selective Laser Melting: A Novel Method for Surface Roughness Analysis

The present study introduces a novel approach to analyse the surface roughness of metal parts made by 3D selective laser melting (SLM). This technology, known for its ability to efficiently produce functional prototypes and limited-run series, is particularly effective when surface conditions directly meet usage requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing the importance of new methods for predicting their quality. Here fractal geometry and network theory are integrated to delve into the complexities of SLM-produced surfaces, while machine learning and pattern recognition concepts are employed to evaluate the surface roughness. Specifically, genetic programming, artificial neural networks, support vector machine, random forest, k-nearest neighbors are compared in terms of accuracy demonstrating that only the first method provided valid estimation due to the presence of very little training data. Experimental work with EOS Maraging Steel MS1 and an EOS M 290 3D printer validates the method’s practicality and effectiveness. Then, the research offers a fresh perspective in surface analysis and has significant implications for quality control in additive manufacturing, potentially enhancing the precision and efficiency of 3D metal printing.

Critical state-of-the-art literature review of surface roughness in incremental sheet forming: A comparative analysis

Incremental sheet forming (ISF) is a controlled and die-less forming process that can produce customized products from the sheets layer by layer using the same setup, i.e., forming tools and machine. This novel and emerging forming technique can potentially be useful as a green aspect of manufacturing by reducing the required power and by saving the resources for producing the components of lightweight materials because material is deformed locally during ISF. The surface quality of the formed parts from sheet material affects the aesthetics aspects, stress concentration, fatigue life and their applicability. This review article aims to provide a state-of-the-art review for various aspects and parameters of ISF process that affect the surface roughness significantly by conducting literature survey quantitatively. Furthermore, the techniques of ISF and roles of various parameters responsible for affecting the surface quality of parts have also been explored for providing the critics for existing literature and setting the further guidelines for the researchers. Various instruments used for measuring the surface roughness of parts formed by ISF has also been discussed. Comparative analysis of exiting literature available in the context of various process parameters, ISF hardware, and surface roughness makes this study comprehensive and exhaustive for the researchers to find the gaps and challenges in this field. The relationship of wall angle with other process parameters can be explored in the future to obtain the desired surface quality of a particular material. Significant parameters responsible for the surface quality have also been discussed critically. Further, the new tool paths can be developed for high surface quality and low forming time. A study on the selection of feed rate for various applications and relation with other parameter is also recommended for the future work. The lubricants with additives can further be tested for high surface quality of ISF. Further, significant study on surface roughness is not conducted on hybrid sheet forming and other advanced variants of ISF (like hot forming, friction stir assisted incremental forming, waterjet forming). It is required to develop the comparisons between the different techniques of ISF. Results also showed that majority of researchers used SPIF technique followed by DSIF and hybrid ISF.

Modeling and Prediction of Surface Roughness in Hybrid Manufacturing–Milling after FDM Using Artificial Neural Networks

Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method.

Effects of Particle Size on Mechanical Properties and Forming Accuracy of Prosopis chilensis Powder/Polyethersulfone Composites Produced via Selective Laser Sintering.

Wood-plastic composites are becoming increasingly recognized for their sustainability and their potential for use in various production processes. Nevertheless, enhancing their mechanical strength continues to be a difficult challenge. The objective of this research was to improve the mechanical strength of wood-plastic composite components manufactured through selective laser sintering (SLS). This was achieved by integrating a sustainable composite material, Prosopis chilensis (PCP), with polyethersulfone (PES) to form a composite referred to as PCPC. This study showcased the effect of various PCP particle sizes on mechanical strengths, dimensional accuracies (DAs), and surface roughness of PCPC parts manufactured using AFS-360 SLS. Single-layer sintering was employed to assess PCPC powder's formability with varying PCP particle sizes, and various tests were conducted to understand the materials' thermal properties and analyze particle dispersion and microstructure. The results demonstrated that PCP particle sizes ≤ 0.125 mm significantly enhanced the mechanical strength, forming quality, and DA compared to other particle sizes and pure PES. Key findings for PCPC parts with PCP ≤ 0.125 mm included a bending strength of 10.78 MPa, a tensile strength of 4.94 MPa, an impact strength of 0.91 kJ/m2, and a density of 1.003 g/cm3. Post-processing further improved these parameters, confirming that optimizing PCP particle size is crucial for enhancing the mechanical properties and overall quality of PCPC parts produced via SLS.

Extreme learning machine oriented surface roughness prediction at continuous cutting positions based on monitored acceleration

Surface roughness of mechanical parts plays an essential role in the practical application. Due to the complexity of the machining process, establishing a comprehensive theoretical model that can accurately simulate the real machined surface is challenging. Therefore, data-driven prediction methods are both practically useful and scientifically sound. This paper proposes an approach of using extreme learning machine (ELM) to predict surface roughness based on the monitored vibration signals, which can achieve the prediction of the surface roughness at any position of the machined surface, rather than using a single value to evaluate the processing quality under specific situations. To ensure the accuracy of the vibration intensity representation, the amplitude of monitored signals is compensated, accounting for varying positions and timings. The linear relationship between the spindle speed and acceleration is investigated to mitigate the impact of machining parameters on the monitored acceleration. Combined with grey relational analysis and mutual information coefficient, 18 features substantially linked to the surface roughness are selected from all extracted features and then used as the inputs for an ELM-based predictive decision-making system. The results indicate that the average and minimum errors of the developed method are 5.09% and 1.03%, respectively, demonstrating its accuracy and feasibility. In contrast to other machine learning models of SVR and ANN, the proposed method in this paper exhibits optimal predictive precision and efficiency, suggesting its potential application in the fast response of quality control.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

A surface quality optimization strategy based on a dedicated melt-pool control via the dashed-scan contouring technique in electron beam powder bed fusion

Electron beam powder bed fusion (EB-PBF) is a key metal additive manufacturing (AM) technology known for its high energy absorption and low heat stress. However, the high surface roughness of EB-PBF parts has limited its broader application. To address this issue, this paper proposes a dashed-scan contouring strategy to obtain lower surface roughness in EB-PBF. This strategy involves melting the contour in the form of segmented staggered scans and employing high-frequency jumping of the electron beam (EB) to melt multiple positions synchronously. A comprehensive analysis, combining experimental investigations and multi-physical simulations, elucidates the intrinsic connection between control processes and melt pool stability. Results show that by controlling the length-to-width ratio of the melt pool, regulating overlap distances between melt pools, and fine-tuning cooling times, balling phenomena and Plateau-Rayleigh instabilities can be suppressed. Additionally, these measures effectively mitigate irregularities in track formation when compared to the traditional process. Experimental validation demonstrates the efficacy of the approach, achieving reduced surface roughness (Ra) of thin-walled Ti-6Al-4 V parts from over 25 μm to below 12.6 μm, while enhancing dimensional accuracy and reducing melt anomalies at corners. The proposed dashed-scan contouring strategy opens new avenues for AM of highly reliable and intricate structures, such as lattice sandwich structures, thin walls, and internal flow passages.

Chemical treatment in 3D dental model production for clear aligners via additive manufacturing: a comprehensive evaluation

PurposeStereolithography (SLA) additive manufacturing (AM) technique has enabled the production of inconspicuous and aesthetically pleasing orthodontics that are also hygienic. However, the staircase effect poses a challenge to the application of invisible orthodontics in the dental industry. The purpose of this study is to implement chemical postprocessing technique by using isopropyl alcohol as a solvent to overcome this challenge.Design/methodology/approachFifteen experiments were conducted using a D-optimal design to investigate the effect of different concentrations and postprocessing times on the surface roughness, material removal rate (MRR), hardness and cost of SLA dental parts required for creating a clear customized aligner, and a container was constructed for chemical treatment of these parts made from photocurable resin.FindingsThe study revealed that the chemical postprocessing technique can significantly improve the surface roughness of dental SLA parts, but improper selection of concentration and time can lead to poor surface roughness. The optimal surface roughness was achieved with a concentration of 90 and a time of 37.5. Moreover, the dental part with the lowest concentration and time (60% and 15 min, respectively) had the lowest MRR and the highest hardness. The part with the highest concentration and time required the greatest budget allocation. Finally, the results of the multiobjective optimization analysis aligned with the experimental data.Originality/valueThis paper sheds light on a previously underestimated aspect, which is the pivotal role of chemical postprocessing in mitigating the adverse impact of stair case effect. This nuanced perspective contributes to the broader discourse on AM methodologies, establishing a novel pathway for advancing the capabilities of SLA in dental application.

Experimental investigation of the cooling performance of an additively manufactured prototype for nuclear fusion energy application

Metal Additive Manufacturing (MAM) is a non-traditional technology recently introduced to manufacture multifunctional mechanical components. In fact, recent developments in Laser Powder Bed Fusion (LPBF) process have enabled the production of materials characterized by high density and high thermal conductivity properties, such as copper alloys and pure copper, making the technology attractive for thermal science. In nuclear fusion energy applications, mechanical components often encounter extremely high heat fluxes. An innovative solution using unconventional integrated cooling channels is therefore required to safely manage the components. However, the high surface roughness in 3D-printed parts represents an intrinsic limitation of the LPBF technology: the cooling channels show high-pressure drops due to the high viscous dissipation generated by the rough surface. To address this challenge, a lab-scale prototype of an e-beam extraction grid for a fusion experiment with an original integrated cooling system was designed and manufactured. Additionally, a novel heating mask was designed and manufactured to reproduce the realistic heat load distribution on the grid during the experimental tests. The prototype was built using additive manufacturing with a CuCrZr copper alloy. The grid underwent heat treatment via solution annealing and age hardening, to increase thermal conductivity from about 100 W m−1 K−1 to almost 300 W m−1 K−1. The prototype was tested at three different constant heat fluxes by varying the water flow rate while measuring the pressure and the maximum temperatures of the grid. A CFD numerical model was also calibrated to estimate the thermo-hydraulic performance of the prototype under test conditions. The experimental and numerical results are presented in terms of overall thermal performance, maximum temperature, and pressure drop.

Stress concentrations and design for additive manufacturing: a design artefact approach to investigation

AbstractThe accelerated rate of product development and design complexities offered by Additive Manufacturing (AM) has allowed for innovation in the space industry. However, the surface roughness of parts poses a challenge, as it impacts performance and is tied to design choices. Design tools for traditional manufacturing methods fall short in AM contexts, prompting the need for alternative design processes. This work proposes an experimental approach to design for AM investigation using design artefacts to explore a process-structure-property-performance relationship.

Experimental investigation and optimization of the effect garnet vibratory tumbling as a post-process on the surface quality of 3D printed PLA parts

The method known as additive manufacturing causes high surface roughness between layers depending on the technique used at the end of the product development process. This can be an important problem in three-dimensional (3D) manufacturing depending on the usage area. To solve this problem, in this experimental study, the effect of vibratory tumbling (VT) on surface roughness in 3D printing was investigated using garnet abrasive particles. Optimization with the best parameters was also performed and the results were analyzed. This experimental study investigated the effect of vibratory tumbling on surface roughness in 3D printing produced from Polylactic acid (PLA) material using garnet abrasive particles. The surface roughness (Ra) values were measured at different vibration durations for each mesh size. The results provide insights into the impact of vibratory tumbling on surface roughness in 3D-printed parts. The study involved subjecting the printed parts to vibratory tumbling using garnet abrasive particles of various mesh sizes (80, 90, 100, 120, 150, 180, and 220 mesh). Surface roughness measurements were taken at different vibration durations (2, 4, 6, 8, 10, and 12 hours) for each mesh size. A surface roughness measuring device was used to obtain the roughness values. The findings reveal that vibratory tumbling with garnet abrasive particles effectively reduces surface roughness in 3D printed parts. As the vibration duration increased, smoother surfaces were achieved. The data collected for each mesh size and vibration duration offer valuable insights into the relationship between vibratory tumbling and surface roughness in 3D printing. The surface roughness of the printed samples was reduced by 60% on average by using the optimum values after post-process. This research highlights the potential of vibratory tumbling as a viable method for improving surface roughness in 3D printing applications. Emphasis is placed on optimizing the vibration duration and selecting the appropriate mesh size to achieve the desired surface quality. Overall, this study contributes to our understanding of the effect of vibratory tumbling on surface roughness in 3D printing and provides considerable insights for enhancing surface quality in additive manufacturing processes.

Neosanding postprocessing for improving surface roughness of extrusion-based 3D printing of PLA parts: a comparative analysis of stylus profilometer and confocal profilometry methods

Extrusion-based 3D printing (E3DP) is a popular additive manufacturing technique known for its versatility in creating prototypes and functional parts. However, achieving high surface quality has posed challenges regarding accuracy and finish. To address this issue, this study aims to enhance the surface quality of E3DP components fabricated by the fused filament fabrication (FFF) method and polylactic acid (PLA) material by applying neosanding postprocessing. The research investigates the impact of key neosanding process factors on surface roughness, namely neosanding spacing, neosanding speed, and flow rate. To ensure a comprehensive evaluation, each factor is examined at four levels, covering a wide range of values relevant to the neosanding process. Surface roughness is quantified using the average roughness parameter (Ra) and measured using both stylus profilometer and confocal profilometry methods. The results highlight a substantial decrease in surface roughness achieved through the neosanding method. At default factor levels of the neosanding method, the stylus profilometer method achieves an impressive 83% reduction in surface roughness, while the confocal profilometry method achieves an 80% reduction. Among the neosanding process factors, neosanding spacing significantly influences surface roughness values. Understanding and optimizing this factor is crucial for achieving desired surface quality in FFF-produced PLA parts. This study makes a valuable contribution to the field by optimizing surface roughness in FFF-produced PLA parts through neosanding postprocessing. By exploring the influence of neosanding tool factors and comparing measurement methods, manufacturers can enhance the surface quality of FFF-manufactured parts, paving the way for broader applications across various industries.

Modeling of wear process of a sealing washer in a flanged joint during fretting

The influence of steel surface roughness parameters on the coefficient of sliding friction and wear of sealing material in flanged joints is investigated. The dependence of the friction coefficient on the parameter Ra of the surface roughness of the steel part and the pressing force is established. The critical shear stresses for the flange-washer interface are determined, causing failure of the surface layer of the washer and a violation of the tightness of the flanged joint. Keywords:fretting, flanged joint, surface roughness, vibration, friction coefficient, tangential stresses, cyclic deformation elaguina.o@gubkin.ru, dubinova.o@gubkin.ru

Study on Laser Polishing of Ti6Al4V Fabricated by Selective Laser Melting.

Laser-based additive manufacturing has garnered significant attention in recent years as a promising 3D-printing method for fabricating metallic components. However, the surface roughness of additive manufactured components has been considered a challenge to achieving high performance. At present, the average surface roughness (Sa) of AM parts can reach high levels, greater than 50 μm, and a maximum distance between the high peaks and the low valleys of more than 300 μm, which requires post machining. Therefore, laser polishing is increasingly being utilized as a method of surface treatment for metal alloys, wherein the rapid remelting and resolidification during the process significantly alter both the surface quality and subsurface material properties. In this paper, the surface roughness, microstructures, microhardness, and wear resistance of the as-received, continuous wave laser polishing (CWLP), and pulsed laser polishing (PLP) processed samples were investigated systematically. The results revealed that the surface roughness (Sa) of the as-received sample was 6.29 μm, which was reduced to 0.94 μm and 0.84 μm by CWLP and PLP processing, respectively. It was also found that a hardened layer, about 200 μm, was produced on the Ti6Al4V alloy surface after laser polishing, which can improve the mechanical properties of the component. The microhardness of the laser-polished samples was increased to about 482 HV with an improvement of about 25.2% compared with the as-received Ti6Al4V alloy. Moreover, the coefficient of friction (COF) was slightly reduced by both CWLP and LPL processing, and the wear rate of the surface layer was improved to 0.790 mm3/(N∙m) and 0.714 mm3/(N∙m), respectively, under dry fraction conditions.