Computer Numerical Control Milling Research Articles

Virtual instrumentation-based data collection and analysis for CNC machining process

Using Computer Numerical Control (CNC) machine tools in manufacturing is crucial for exact processing materials. The quality of the machined product heavily relies on the machine's condition. Therefore, monitoring and assessing the machine's status is essential to ensure product quality and enhance its lifespan. However, due to cost, space, and technical limitations, only a few physical variables, such as acceleration, force, displacement, acoustic radiation, temperature, and flow conditions, can be measured. Efficient transfer and extraction of these data are critical for monitoring machine status using statistical methods or valuable signatures. Traditional measurement instruments are complex and numerous, whereas measurement and analysis systems based on virtual instrumentation technology collect necessary data from sensors and data acquisition cards, meeting the needs of test analysis. Virtual instrumentation utilizes computer hardware resources, modularization hardware, and software systems for data analysis, communication, and operation interface, providing greater versatility, flexibility, compatibility, and repeatability. Previous research has shown successful attempts at developing LabVIEW-based systems for monitoring CNC milling machines and analyzing cutting parameters and forces. This project aims to collect and process data from the CNC machining process. The main objectives include writing a LabVIEW software program, recording data from CNC machine programs using the provided hardware (myRIO) and LabVIEW program, processing the data using MATLAB software, and analyzing and discussing the obtained results. The report consists of four parts, starting with an introduction to the project's background and literature review, followed by a description of the project steps, methods, experiments, and data processing. The processed data graphs will be presented and discussed, and recommendations for future research will be provided before concluding with a study summary.

A low-cost design approach to vanadium redox flow test cell

Vanadium redox flow batteries (VRFB) will be essential in maximizing the large-scale use of renewable energy after achieving technical maturity. But as the test cells needed to conduct VRFB research are very expensive, the cost factor can prevent participation of developing countries in critical research. To address such issue, this work presents a novel low-cost design for laboratory VRFB cell yielding good performances. This design relies on sturdy ultra-high molecular weight polyethylene (UHMWPE) end plates with flow channels to provide the strength needed to seal other components while properly distributing electrolyte. This simpler design uses a reduced number of components compared to traditional cells, thus resulting in easier manufacturing and assembly while keeping costs low. This novel design approach achieved performances that are comparable to a commercial VRFB test cell in various testing conditions, but at roughly a third of the cost. Performances have been further improved by 3D printing end plates with optimized flow channels, with the added benefit of avoiding the complexity and costs of computer numerical control (CNC) milling. This novel design approach can thus be useful to many researchers in developing countries for their VRFB test.

A D-band extracted pole waveguide bandpass filter based on TE202 and TE301 resonator

This paper presents a D-band waveguide bandpass filter (BPF) with high selectivity based on extracted pole technology. Unlike a single transmission zero (TZ) in the traditional design, the basic structure of the proposed extracted pole resonator is the novel TE202 and TE301 resonator, which provides two TZs. In which, each TZ position can be independently controlled by adjusting the dimensions of the resonant cavity. As an illustrative example, a pseudo-elliptic waveguide filter with two pairs of TZs generated on both sides of the passband using the extracted pole resonator and two dual-mode cavities. The filter is fabricated using high-precision computer numerical control (CNC) milling technology. The strong agreement between measured and simulated results validates the enhanced selectivity of the filter achieved by this novel extracted pole resonator.

Comparative investigation and optimization of cutting tools performance during milling machining of titanium alloy (Ti6Al4V) using response surface methodology

The purpose of this paper is to study the optimization of the cutting performance of three different cutting inserts, during the machining operation of titanium alloy (Ti6Al4V) by making use of the response surface methodology (RSM) on a computer numerical control (CNC) milling. The cutting tools employed for the optimisation of the cutting performance during machining operation are silicon, aluminium, oxygen, nitrogen (SiAlON), cubic-boron nitride and carbide cutting inserts. Scanning electron microscope (SEM) was used for the determination of the tool wear for the cutting inserts being compared during machining of Ti6Al4V, and the cutting parameters, which are cutting speed (Vc), feed per tooth (fz) and depth-of-cut that were evaluated from the cutting tools as per the manufacturer’s design specifications. The determination of the tool wear on the cutting inserts was achieved by using the SEM, while the machining operation for the experimental trails was performed from the CNC milling machine, where face milling operation was executed. The optimization process showed that carbide cutting inserts yielded the best performing results and were considered the most significant choice of cutting insert in machining Ti6Al4V when compared to SiAlON and CBN cutting inserts. This choice was from the cutting tool life obtained where a cutting tool life of 29 min was obtained from a use of carbide cutting inserts; 28 min resulted from a use SiAlON cutting inserts and 26 min from a use of CBN cutting inserts. This work finds appropriate value in assisting the machinists in the selection of the best most performing and cost-effective cutting tool.

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

Terahertz couplers based on branch lines and waveguide resonators

AbstractThis paper presents two waveguide quadrature hybrids in the WR‐3 band, which are designed to be fabricated by computer numerical control milling process with relaxed branch width requirements. An E‐plane four‐branch directional coupler gives an amplitude imbalance less than 0.8 dB and a phase difference between 88° and 94° from 240 to 324 GHz. Another design is an H‐plane bandpass coupler composed of cavity resonators, showing a bandwidth of 2% near 300 GHz.

Effects of Cutting Parameters and Grain Direction on Surface Quality of Three Wood Species Obtained by CNC Milling

Computer Numerical Control (CNC) machines are increasingly popular in the production of furniture and wood products, because they combine high processing quality with short production time. The effective use of CNC machines depends on the processing parameters, which also affects the quality of the processed surface. The aim of this study was to determine the effect of feed rate, cutting direction, and grain direction on the surface roughness of various types of wood. Three European wood species (oak, beech, fir) were cut with a spindle speed of 16,000 rpm and two different feed rates (5,000 and 10,000 mm/min) using end mill tools on the CNC machine. The milling was performed in two cutting directions (radial and tangential) and two grain orientations (0° and 90°). An analysis of variance (ANOVA) was performed to evaluate the impact of the cutting parameters. The surface roughness measurements were taken, and two surface roughness parameters (Ra and Rz) were measured to determine the surface quality of the wood. According to the results of this study, the lowest surface roughness values, milling with the same processing parameters, occurred for oak wood, while the highest values occurred for fir.

Optimization-based non-equidistant toolpath planning for robotic additive manufacturing with non-underfill orientation

Additive manufacturing (AM) technology has achieved universal application in a great number of fields, such as aerospace, medicine, and military industry. As a significant factor causing weak mechanical properties and part flaws, underfill is inevitable in AM based on the conventional equidistant toolpaths with a limited extra cost. To eliminate underfill caused by sharp corners and voids, this paper develops an optimization-based non-equidistant toolpath, i.e., isoperimetric-quotient-optimal toolpath (IQOP). Firstly, an optimization problem minimizing the isoperimetric quotient of toolpaths is designed to generate smooth toolpaths and is convexified. Then, a unilateral rolling circle method is proposed to guarantee the well-defined condition of the optimization-based toolpath planning process. Finally, the application of the depth tree makes the IQOP method adopt slices with complex boundaries and topological structures. The experimental results show that the proposed IQOP achieves an average 88.5% lower underfill rate than the contour-parallel toolpath (CP). IQOP significantly outperforms the dense CP (DCP) on toolpath smoothness and printing efficiency, with better performance on underfill. With obvious advantages on toolpath smoothness and underfill rate, IQOP achieves higher printing quality than CP in the real world. The proposed approach also provides a general framework of non-equidistant toolpath planning for complex slices in AM and computer numerical control (CNC) milling.

Investigation of computer numerical control wood milling parameters for occupationally safer by the minimization of produced wood dust

ABSTRACT Computer numerical control (CNC) wood machining produces wood dust, harming the operator's health. In this research, the effective parameters of the amount of produced wood dust (PM) in CNC milling are investigated. The design of the experiment was done based on the response surface method (RSM) and subsequently, 27 experimental tests were performed. The results of the analysis of variance show that the parameters of the depth of cut (ap ), step over (ae ), feed rate (vw ) and cutting speed (vc ) have a great impact on PM, respectively. Changing these machining parameters leads to severe changes in the PM (0.043–11.200 mg/m3). Also in 78% of experimental tests, the amount of PM is higher than the National Institute for Occupational Safety and Health (NIOSH) standard value (1 mg/m3). In the next step, for the reduction and minimization of PM, a quadratic regression equation was derived. After model validation, the model was minimized by the distance-based optimality method at Minitab software. Optimization result shows that by choosing the optimum parameters ap = 3 mm, vw = 50 mm/s, vc = 15,393 rpm and ae = 6.270 mm, PM is reduced to 0.011 mg/m3, which is much lower (about 89%) than the NIOSH standard value. This prediction was tested and validated by the experimental test.

A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Mapping

The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size. Five-axis computer numerical control (CNC) milling is the main parts machining method, while dynamics analysis has always been a research hotspot. The cutting conditions determined by the cutter axis, tool path, and workpiece geometry are complex and changeable, which has made dynamics research a major challenge. For this reason, this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces, and proposes an efficient dynamics analysis model. To simplify the research object, the cutter position points along the tool path were discretized into inclined plane five-axis machining. The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining. These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object. Based on the in-cut cutting edge solved by the space limitation method, the dynamics of the inclined plane five-axis machining unit were studied, and the results were uniformly stored in the abstract space to produce a database. Finally, the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency. Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model. This study has great potential for the online synchronization of intelligent machining of large surfaces.

Selection of Material and Manufacturing Technology for Batik Canting Stamps Based on Multi-Criteria Decision-Making Methods

This study aimed to develop alternative materials and technologies for making canting stamps used in producing batik canting (stamped batik) to transfer hot wax from the pan to the fabric. Previous researchers have studied materials such as wood, aluminum, multiplex, acrylic, and acrylonitrile butadiene styrene (ABS). Manufacturing technologies have also been analyzed, including manual manufacturing, computer numerical control (CNC) milling, laser cutting, and additive manufacturing. However, none of these materials and technologies were considered suitable alternatives for copper canting stamps. This paper proposes Conductive ABS-Electroformed By Copper (CABS-EBC) through additive manufacturing and electroforming processes as alternative material for canting stamps. A multi-criteria decision-making (MCDM) approach was used to assess alternative materials and technologies. The alternatives and criteria were calculated using the Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) techniques. Besides this, assessment was also carried out based on expert opinions. The results showed that copper was the most suitable material, with Closeness = 1.000, Yi = 0.995, and Phi = +1.00. Meanwhile, CABS-EBC ranked second, with Closeness = 0.627, Yi = 0.864, and Phi = +0.50. The selected technology was additive manufacturing combined with electroforming, with Closeness = 0.700, Yi = 0.895, and Phi = +0.39. By using MCDM on the material-technology development candidates it was found that CABS-EBC processed with additive manufacturing is capable of substituting copper as a canting stamp material. It is expected that the production capacity of the traditional manufacturing process can be enhanced by adopting these new materials and technologies.

High Gain Slot Array Antenna at 110 GHz Based on Computer Numerical Control.

This paper presents a waveguide-slot antenna to generate a radiation beam with high gain fed by a low-loss feeding network at 110 GHz. The proposed antenna consists of a compact eight-way power divider and a waveguide-slot array. The eight-way power divider provides equal-amplitude and alternative-phase excitation for the slot array, and each of them supports two waveguides. The integral structure is implemented by two layers with a channeled substratum and a slotted superstratum. To verify the proposed slot array, the designed array is fabricated with computer numerical control (CNC) milling and measured. The measured peak gain of the designed antenna is 32 dBi at 110 GHz. The proposed antenna with a simple structure provides a promising solution to develop high gain antenna in upper millimeter-wave and sub-terahertz (THz) applications.

Precision-milled simulated curved root canals in bovine dentine for the assessment of chemo-mechanical root canal preparation.

The aim was to develop a standardized curved root canal model in bovine dentine and to assess whether that natural substrate would behave differently from the resin in standard plastic training blocks when prepared chemo-mechanically. The impact of substrate microhardness on simulated canal transportation was considered. High-precision computer numerical control (CNC) milling was used to recreate a simulated root canal from a resin training block (Endo Training Bloc J-Shape, size 15) in longitudinally sectioned, dis- and re-assembled bovine incisor roots. Optical overlays obtained from 10 resin blocks were used to identify an average canal and program the CNC milling apparatus accordingly. Resin and dentine microhardness were measured. Simulated root canals in resin training blocks and their bovine counterparts were then instrumented at 37°C using Reciproc R25 instruments (VDW) with water or 17% EDTA (n = 10). Open-access image processing software was used to superimpose and analyse pre- and postoperative images obtained with a digital microscope. Centering ratios were averaged to indicate canal transportation. The effects of substrate and irrigant on canal transportation were assessed by two-way anova. Superimposed images showed that resin blocks under investigation varied considerably in terms of simulated canal length and curvature, whilst the milled canals were highly similar. The microhardness of dentine was more than three times higher than that of the resin. Conversely, canal transportation was considerably greater in dentine compared to resin, and in dentine had a tendency to be increased by EDTA. There was a strong effect of substrate on canal transportation (p < .001), no overall effect of irrigant, and a marginally significant interaction between irrigant and substrate (p = .077). CNC milling allows to create standardized simulated curved root canals in bovine dentine. These models may be useful to test and compare materials and concepts of chemo-mechanical root canal instrumentation. Microhardness is a bulk feature that does not predict the response to chemo-mechanical instrumentation of a composite material such as dentine.

An ensemble neural network for optimising a CNC milling process

Computer numerical control (CNC) milling is a common method for the efficient mass production of products. Process efficiency and product quality have a strong dependency on the cutting process conditions. Furthermore, optimising a process for material removal rate (MRR) and surface roughness (SR), which are measures of process efficiency and product quality, respectively, is a complex optimisation task due to their contrasting relationships with process parameters. In this work, CNC end milling is performed on aluminium 6061 to investigate the influence of key process input variables (feed per tooth, cutting speed, and depth of cut) on the machined part’s SR. Firstly, a full factorial parametric study is conducted and analysed using Analysis of Variance (ANOVA) before an Ensemble Neural Network (ENN) is trained on the experimental data. To capture the complex nonlinear relationships accurately, each base model of the ENN is a combined genetic algorithm-artificial neural network, whose hyperparameters are optimised using a Bayesian optimisation framework. Once trained, the ENN predictive model is exploited to identify optimal input parameter permutations to achieve a predefined SR value while maximising MRR. Analysing the experimental data demonstrates that the SR performance envelope is nonlinear with respect to the input variables. Furthermore, the ANOVA results indicate that feed per tooth is the dominant input parameter with a contribution of 40.2% and that there are strong interactions between the input parameters investigated. The ENN performance was subsequently validated through a further set of experiments producing a mean absolute percentage error in predicted SR of just 2.56%.

Exergy Loss Assessment Method for CNC Milling System Considering the Energy Consumption of the Operator

Modeling and assessing the sustainability of machining systems has been considered to be a crucial approach to improving the environmental performance of machining processes. As the most common machining system, the computer numerical control (CNC) milling system is a typical man–machine cooperative system where the activities of the machine tool and operator generate material and energy consumption. However, the energy consumption of the operator in the CNC milling system has often been ignored in most existing research. Therefore, existing methods fail to provide a comprehensive understanding of the sustainability of the CNC milling system. To fill this gap, an exergy loss assessment method is proposed to investigate the sustainability of the CNC milling system, where the energy consumption of the operator, the energy consumption of the machine tool, and material consumption are taken into consideration. The key performance indexes of the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss, and the specific exergy loss (SEL) are analyzed and modeled. To demonstrate the feasibility of the proposed method, a case study was carried out on a three-axis machining center (XH714D), in which the energy consumption of the operator, the energy consumption of the machine tool, the exergy loss of energy consumption, the exergy loss of chips, the exergy loss of compressed air, the exergy loss of cutting tool wear, the exergy loss of cooling liquid dissipation, and the SEL were found to be 169,750 J, 758,211 J, 603,131 J, 2,031,404 J, 22,023 J, 301,868 J, 2673 J, and 88.04 J/mm3, respectively. The proposed method is effective to assess the sustainability of the CNC milling system, and the established exergy loss models build a good basis for exergy efficiency optimization.

An efficient analytical model for the swept volume generation of a flat-end mill in 5-axis CNC milling

5-axis CNC (Computer Numerical Control) milling is widely used to create complex part geometries in the industrial area. The cutting tool creates the swept volume as it moves along the defined path. The swept volume is subtracted from the initial stock for the machining simulation. Although a swept volume consists of three parts such as ingress, egress, and swept envelope, the number of faces of the swept volume is higher than three. In this work, parametric equations of the faces of the swept volume were obtained for a flat-end mill in four steps. The model is based on the decomposition of the tool into circles along the tool orientation vector since a flat-end mill can be modeled as a cylinder. The fundamental principle is that each circle is tangent to the swept envelope of the cylinder. Locations of the grazing points with respect to a local coordinate system were determined by applying the Envelope Theory to the parameterized circle of the cylinder. Then, each face of the swept volume was represented based on the equation of the parameterized circle. The model was verified by using an alternative analytical model. As a result, boundaries of the swept volume were represented fully analytically for a flat-end mill in 5-axis CNC milling in an efficient way.

Evaluation of surface roughness of differently manufactured denture base materials

The popularity of computer-aided design and computer-aided manufacturing (CAD-CAM) dentures has led to the introduction of new denture materials and resins. However, studies on the surface characteristics of these materials and how they compare to dentures fabricated by more traditional methods are lacking. The purpose of this cross-sectional study was to determine whether the surface roughness (Ra) of denture base materials differed based on manufacturing technique. Disks of Ø10×2-mm (n=10) were fabricated using 6 different manufacturing techniques, including compression molding (Lucitone 199), injection molding (Ivocap High Impact), Computer Numerical Control (CNC) milling (Ivotion Base), and additive manufacturing on the Carbon M2 (Lucitone Digital Print), the SprintRay Pro55 S (Dentca Denture Base II), and the Envision One (Flexcera Base) systems. An automatic, noncontact laser confocal microscope (VK-X1000 Series; KEYENCE) was used to analyze the Ra surface roughness of each specimen at ×5 magnification. The images were imported into a multifile analyzer, horizontal and vertical roughness profiles were inserted into each scan, and Ra values were calculated and averaged by following the International Organization for Standardization (ISO) 4287 standard. A 1-way analysis of variance (ANOVA) was performed to compare the effect of manufacturing technique on surface roughness, followed by the Tukey multiple comparisons test (α=.05). The additively manufactured Dentca Denture Base II (AM-DB) exhibited a statistically significantly higher Ra when compared with the other test groups (P<.001). The additively manufactured Flexcera Base (AM-FB) showed a higher Ra mean value when compared with injection molding (IM) (P=.036). No statistically significant difference in surface roughness was found among the other tested materials representing the different processing methods (P>.05). The manufacturing method influences the Ra of denture base materials with varying results. The injection molding method resulted in the smoothest surface compared with additively manufactured and CNC-milled denture base materials.

Compact Wideband Groove Gap Waveguide Bandpass Filters Manufactured with 3D Printing and CNC Milling Techniques.

This paper presents for the first time a compact wideband bandpass filter in groove gap waveguide (GGW) technology. The structure is obtained by including metallic pins along the central part of the GGW bottom plate according to an n-order Chebyshev stepped impedance synthesis method. The bandpass response is achieved by combining the high-pass characteristic of the GGW and the low-pass behavior of the metallic pins, which act as impedance inverters. This simple structure together with the rigorous design technique allows for a reduction in the manufacturing complexity for the realization of high-performance filters. These capabilities are verified by designing a fifth-order GGW Chebyshev bandpass filter with a bandwidth BW = 3.7 GHz and return loss RL = 20 dB in the frequency range of the WR-75 standard, and by implementing it using computer numerical control (CNC) machining and three-dimensional (3D) printing techniques. Three prototypes have been manufactured: one using a computer numerical control (CNC) milling machine and two others by means of a stereolithography-based 3D printer and a photopolymer resin. One of the two resin-based prototypes has been metallized from a silver vacuum thermal evaporation deposition technique, while for the other a spray coating system has been used. The three prototypes have shown a good agreement between the measured and simulated S-parameters, with insertion losses better than IL = 1.2 dB. Reduced size and high-performance frequency responses with respect to other GGW bandpass filters were obtained. These wideband GGW filter prototypes could have a great potential for future emerging satellite communications systems.

Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling

Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).

Monitoring and classification of polymeric surface features for enabling the adoption of polypropylene powder bed fusion as a standard tool for bioprocessing equipment production

Polypropylene (PP) powder bed fusion (PBF) with subsequent mechanical postprocessing can enable the development of more efficient and effective surfaces for bioprocessing equipment. However, there is still a lack of knowledge regarding the most relevant surface differences when compared to standardized surfaces. Herein, we present a new systematic methodology, including the evaluation of relevant roughness parameters and surface wettability, which has been implemented to assess the potential of AM to produce biopharmaceutical components. Surfaces of PP components produced by PBF and mechanically postprocessed by tumble surface finishing at 5, 10 and 15 h were compared to reference surfaces produced by computer numerical control (CNC) milling manufacturing. Thirteen roughness parameters were found to be relevant, of which the arithmetic mean peak curvature (Spc) and the density of peaks (Spd) were the most significant. The results demonstrate that the reference surfaces had Spc and Spd values of 1029 ± 36 mm−1 and 67739 ± 5440 mm−2, respectively, and moderate wettability with advancing (ACA) and receding (RCA) contact angles of 89 ± 3° and 68 ± 2°, respectively. The as-printed PBF surfaces were hydrophobic, with ACA of 119 ± 2° and RCA of 107 ± 4°, and with Spc and Spd values of 1089 ± 110 mm−1 and 30078 ± 4325 mm−2, respectively. PBF surfaces switched to a hydrophilic surface of 67 ± 3° and 44 ± 4° when the tumble surface finishing time was increased to 15 h. By taking advantage of this knowledge, we could produce surfaces using PBF and 5 h of mechanical postprocessing, which mimicked surface wetting and had Spc of 1100 ± 68 mm−1 and Spd of 55702 ± 9477 mm−2, similar to that of the CNC milling manufacturing. The developed comparative method and the results are important to better understand AM surfaces and can be used to transfer manufacturing from subtractive to additive technologies.