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In this study, experimental studies were carried out using different process parameters of the soft pneumatic gripper (SPG) fabricated by the fused deposition modeling method. In the experimental studies, the surface quality of the gripper was examined by determining four different levels and factors. The experiment was designed to estimate the surface roughness of the SPG.

The methodology consists of an experimental phase in which the SPG is fabricated and the surface roughness is measured. Thermoplastic polyurethane (TPU) flex filament material was used in the fabrication of SPG. The control factors used in the Taguchi L16 vertical array experimental design and their level values were determined. Analysis of variance (ANOVA) was performed to observe the effect of printing parameters on the surface quality. Finally, regression analysis was applied to mathematically model the surface roughness values obtained from the experimental measurements.

Based on the Taguchi signal-to-noise ratio and ANOVA, layer height is the most influential parameter for surface roughness. The best surface quality value was obtained with a surface roughness value of 18.752 µm using the combination of 100 µm layer height, 2 mm wall thickness, 200 °C nozzle temperature and 120 mm/s printing speed. The developed model predicted the surface roughness of SPG with 95% confidence intervals.

It is essential to examine the surface quality of parts fabricated in additive manufacturing using different variables. In the literature, surface roughness has been examined using different factors and levels. However, the surface roughness of a soft gripper fabricated with TPU material has not been examined previously. The surface quality of parts fabricated using flexible materials is very important.

Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps to estimate and evaluate without the complexity and time-consuming experimental procedures. The purpose of this study is to develop and select the best regression model equations for the prediction and evaluation of surface roughness of plain-woven fabrics.

In this study, a linear and quadratic regression model was developed for the prediction and evaluation of surface roughness of plain-woven fabrics, and the capability in accuracy and reliability of the two-model equation was determined by the root mean square error (RMSE). The Design-Expert AE11 software was used for developing the two model equations and analysis of variance “ANOVA.” The count and density were used for developing linear model equation one “SMD1” as well as for quadratic model equation two “SMD2.”

From results and findings, the effects of count and density and their interactions on the roughness of plain-woven fabric were found statistically significant for both linear and quadratic models at a confidence interval of 95%. The count has a positive correlation with surface roughness, while density has a negative correlation. The correlations revealed that models were strongly correlated at a confidence interval of 95% with adjusted R² of 0.8483 and R² of 0.9079, respectively. The RMSE values of the quadratic model equation and linear model equation were 0.1596 and 0.0747, respectively.

Thus, the quadratic model equation has better capability accuracy and reliability in predictions and evaluations of surface roughness than a linear model. These models can be used to select a suitable fabric for various end applications, and it was also used for tests and predicts surface roughness of plain-woven fabrics. The regression model helps to reduce the gap between the subjective and objective surface roughness measurement methods.

The purpose of this study was to develop a correlation between the properties of acrylonitrile butadiene styrene parts 3D printed by material extrusion (MEX) process.

The two MEX parameters and their values have been selected by design of experiment method. Three properties of MEX parts, i.e. strength (tensile and three-point bending), surface roughness and the dimensional accuracy, are studied at different build speeds (35 mm/s, 45 mm/s and 55 mm/s) and the layer heights (0.06 mm, 0.10 mm and 0.15 mm).

The results show that tensile strength and three-point bending strength both increase with the decrease in build speed and the layer height. The artifact selected for dimensional accuracy test shows higher accuracy of the features when 3D printed with 0.06 mm layer height at 35 mm/s build speed as compared to those of higher layer heights and build speeds. The optical images of the 3D-printed specimen reveal that lower build speed and the layer height promote higher inter-layer diffusion that has the effect of strong bonding between the layers and, as a result, higher strength of the specimen. The surface roughness values also have direct relation with the build speed and the layer height.

The whole experiments demonstrate that the part quality, surface roughness and the mechanical strength are correlated and depend on the build speed and the layer height.

This study aims to reveal the influences of three-dimensional (3D) printing parameters such as layer heights (0.1 mm, 0.2 mm and 0.4 mm), infill rates (40, 70 and 100%) and geometrical property as tapered angle (0, 0.25 and 0.50) on vibrational behavior of 3D-printed polyethylene terephthalate glycol (PET-G) tapered beams with fused filament fabrication (FFF) method.

In this performance, all test specimens were modeled in AutoCAD 2020 software and then 3D-printed by FFF. The effects of printing parameters on the natural frequencies of 3D-printed PET-G beams with different tapered angles were also analyzed experimentally, and numerically (finite element analysis) via Ansys APDL 16 program. In addition to vibrational properties, tensile strength, elasticity modulus, hardness, and surface roughness of the 3D-printed PET-G parts were examined.

It can be stated that average surface roughness values ranged between 1.63 and 6.91 µm. In addition, the highest and lowest hardness values were found as 68.6 and 58.4 Shore D. Tensile strength and elasticity modulus increased with the increase of infill rate and decrease of the layer height. In conclusion, natural frequency of the 3D-printed PET-G beams went up with higher infill rate values though no critical change was observed for layer height and a change in tapered angle fluctuated the natural frequency values significantly.

The influence of printing parameters on the vibrational properties of 3D-printed PET-G beams with different tapered angles was carried out and the determination of these effects is quite important. On the other hand, with the addition of glass or carbon fiber reinforcements to the PET-G filaments, the material and vibrational properties of the parts can be examined in future works.

As a result of this study, it was shown that natural frequencies of the 3D-printed tapered beams from PET-G material can be predicted via finite element analysis after obtaining material data with the help of mechanical/physical tests. In addition, the outcome of this study can be used as a reference during the design of parts that are subjected to vibration such as turbine blades, drone arms, propellers, orthopedic implants, scaffolds and gears.

It is believed that determination of the effect of the most used 3D printing parameters (layer height and infill rate) and geometrical property of tapered angle on natural frequencies of the 3D-printed parts will be very useful for researchers and engineers; especially when the importance of resonance is known well.

When the literature efforts are scanned in depth, it can be seen that there are many studies about mechanical or wear properties of the 3D-printed parts. However, this is the first study which focuses on the influences of the both 3D printing parameters and tapered angles on the vibrational behaviors of the tapered PET-G beams produced with material extrusion based FFF method. In addition, obtained experimental results were also supported with the performed finite element analysis.

Since the biomedical implants with an improved compressive strength, near bone elastic modulus, controlled porosity, and sufficient surface roughness, can assist in long term implantation. Therefore, the fine process tuning plays its crucial role to develop optimal settings to achieve these desired properties. This paper aims to find applications for fine process tuning in laser powder bed fusion of biomedical Ti alloys for load-bearing implants.

In this work, the parametric porosity simulations were initially performed to simulate the process-induced porosity for selective laser-melted Ti₆Al₄V as per full factorial design. Continually, the experiments were performed to validate the simulation results and perform multiresponse optimization to fine-tune the processing parameters. Three levels of each control variable, namely, laser power – Pl (180, 190, 200) W, scanning speed – Vs (1500, 1600, 1700) mm/s and scan orientation – ϴ{1(0,0), 2(0,67°), 3(0,90°)} were used to investigate the processing performance. The measured properties from this study include compressive yield strength, elastic modulus, process-induced porosity and surface roughness. Finally, confirmatory experiments and comparisons with the already published works were also performed to validate the research results.

The results of porosity parametric simulation and experiments in selective laser melting of Ti₆Al₄V were found close to each other with overall porosity (less than 10%). The fine process tuning was resulted in optimal settings [Pl (200 W), Vs (1500 mm/s), ϴ (0,90°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,67°)], [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] and [Pl (200 W), Vs (1500 mm/s), ϴ (0,0)] with higher compressive strength (672.78 MPa), near cortical bone elastic modulus (12.932 GPa), process-induced porosity (0.751%) and minimum surface roughness (2.72 µm). The morphology of the selective laser melted (SLMed) surface indicated that the lack of fusion pores was prominent because of low laser energy density among the laser and powder bed. Confirmatory experimentation revealed that an overall percent improvement of around 15% was found between predicted and the experimental values.

Since no significant works are available on the collaborative optimization and fine process tuning in laser powder bed fusion of biomedical Ti alloys for different load bearing implants. Therefore, this work involves the comprehensive investigation and multi-objective optimization to determine optimal parametric settings for better mechanical and physical properties. Another novel aspect is the parametric porosity simulation using Ansys Additive to assist in process parameters and their levels selection. As a result, selective laser melted Ti alloys at optimal settings may help in examining the possibility for manufacturing metallic implants for load-bearing applications.

This study aims to present the optimization of parameters and effects of annealing and vapor smoothing post-processing treatments on the surface roughness and tensile mechanical properties of fused deposition modeling (FDM) printed acrylonitrile butadiene styrene (ABS).

Full-factorial test matrices were designed to determine the most effective treatment parameters for post-processing. The parameters for annealing were temperature and time, whereas the parameters for the vapor smoothing were volume of acetone and time. Analysis of surface roughness and tensile test results determined influences of the levels of parameters to find an ideal balance between mechanical properties and roughness.

Optimal parameters for vapor smoothing and annealing were determined. Vapor smoothing resulted in significantly higher improvements to surface roughness than annealing. Both treatments generally resulted in decreased mechanical properties. Of all treatments tested, annealing at 100 °C for 60 min provided the greatest benefit to tensile properties and vapor smoothing with 20 mL of acetone for 15 min provided the greatest benefit to surface roughness while balancing effects on properties.

Vapor smoothing and annealing of FDM ABS have typically been studied independently for their effects on surface roughness and material properties, respectively, with varying materials and manufacturing methods. This study objectively compares the effects of each treatment on both characteristics simultaneously to recommend ideal treatments for maximizing the balance between the final quality and performance of FDM components. The significance of the input variables for each treatment have also been analyzed. These findings should provide value to end-users of 3D printed components seeking to balance these critical aspects of manufacturing.

This paper aims to investigate the dimensional accuracy and surface roughness of printed masked stereolithography (m-SLA) parts. The fabricated specimens of photosensitive polymer resin have complex shapes and various features. The influence of four process parameters of m-SLA, including layer height, exposure time, light-off delay and print orientation, is studied on response characteristics.

The Box–Behnken design of response surface methodology is used to examine the effect of process parameters on the shrinkage of various geometrical dimensions like diameter, length, width, and height of different features in a complex shape. Additionally, a multi-response optimization has been carried out using the desirability function to minimize the surface roughness and printing time and maximize the dimensional accuracy.

The layer height and print orientation influence the surface roughness of parts. An increase in layer height results in increased surface roughness, and the orientation parallel to the z-axis of the machine gives the highest surface roughness. The dimensional accuracy of m-SLA parts is influenced by layer height, exposure time, and print orientation. Although not significant in dimensional accuracy and surface roughness, the light-off delay can affect printing time apart from other parameters like layer height and print orientation.

The effect of layer height and print orientation on dimensional accuracy, printing time, and surface roughness is investigated by researchers using simple shapes in other vat photopolymerization techniques. The present work is focused on studying the effect of these parameters and additional parameters like light-off delay in complicated geometrical parts in m-SLA.

Material extrusion is one of the most commonly used approaches within the additive manufacturing processes available. Despite its popularity and related technical advancements, process reliability and quality assurance remain only partially solved. In particular, the surface roughness caused by this process is a key concern. To solve this constraint, experimental plans have been exploited to optimize surface roughness in recent years. However, the latter empirical trial and error process is extremely time- and resource consuming. Thus, this study aims to avoid using large experimental programs to optimize surface roughness in material extrusion.

This research provides an in-depth analysis of the effect of several printing parameters: layer height, printing temperature, printing speed and wall thickness. The proposed data-driven predictive modeling approach takes advantage of Machine Learning (ML) models to automatically predict surface roughness based on the data gathered from the literature and the experimental data generated for testing.

Using ten-fold cross-validation of data gathered from the literature, the proposed ML solution attains a 0.93 correlation with a mean absolute percentage error of 13%. When testing with our own data, the correlation diminishes to 0.79 and the mean absolute percentage error reduces to 8%. Thus, the solution for predicting surface roughness in extrusion-based printing offers competitive results regarding the variability of the analyzed factors.

There are limitations in obtaining large volumes of reliable data, and the variability of the material extrusion process is relatively high.

Although ML is not a novel methodology in additive manufacturing, the use of published data from multiple sources has barely been exploited to train predictive models. As available manufacturing data continue to increase on a daily basis, the ability to learn from these large volumes of data is critical in future manufacturing and science. Specifically, the power of ML helps model surface roughness with limited experimental tests.

First, the effect of magnetic field intensity and nano-ferrofluid concentrations on surface roughness was evaluated in magnetic minimum quantity lubrication (MMQL). Then, the effect of lubricant flow rate and nozzle position on surface roughness was investigated in MQL, MMQL, electrostatic MQL (EMQL) and electromagnetic MQL (EMMQL).

This study examined the performance of MQL under magnetic and electric fields in turning AISI 304 stainless steel in terms of surface roughness and compared the results with those obtained from wet cutting and MQL turning operations. To prepare the nano-ferrofluid used in different states of MQL, Fe₃O₄ nanoparticles were added to the base fluid.

The results showed that the surface roughness under the EMMQL technique decreased by 36% and 49.4% on average compared with wet and MQL techniques, respectively. The lubrication technique affected the surface roughness by 90.2%, whereas it was 8.3% for the lubricant flow rate. EMQL and EMMQL techniques had no significant difference in their effects on surface roughness. In the innovative MMQL technique, the nano-ferrofluid concentration of 6% and magnetic field intensity of 93 G resulted in lower surface roughness of the workpiece relative to other counterparts.

Examining previously published studies showed that using nano-ferrofluids under a magnetic field for cooling purposes in machining processes have less considered by researchers. This study applies an innovative method of lubrication under the concurrent effect of magnetic and electric fields, called EMMQL, to improve the efficiency of MQL in machining hard-to-cut materials. For comprehensively inspecting the newly presented method, the effects of several parameters, including the nano-ferrofluid concentration, magnetic field intensity, lubricant flow rate and position of lubricant spray nozzle, on the surface roughness of workpiece in turning of AISI 304 stainless steel are investigated.

This study aims to comparatively analyze the cut parts obtained as a result of cutting the Ni-based Inconel 625 alloy, which is widely used in the aerospace industry, with the wire electro-discharge machining (WEDM) and abrasive water jet machining (AWJM) methods in terms of macro- and microanalyses.

In this study, calipers, Mitutoyo SJ-210, Nikon SMZ 745 T, scanning electron microscope and energy dispersive X-ray were used to determine kerf, surface roughness and macro- and microanalyses.

Considering the applications in the turbine industry, it has been determined that the WEDM method is suitable to meet the standards for the machinability of Inconel 625 alloy. In contrast, the AWJM method does not meet the standards. Namely, while the kerf angle was formed because the hole entrance diameters of the holes obtained with AWJM were larger than the hole exit diameters, the equalization of the hole entry and exit dimensions, thanks to the perpendicularity and tension sensitivity of the wire electrode used in the holes drilled with WEDM ensured that the kerf angle was not formed.

It is known that the surface roughness of the parts used in the turbine industry is accepted at Ra = 0.8 µm. In this study, the average roughness value obtained from the successful drilling of Inconel 625 alloy with the WEDM method was 0.799 µm, and the kerf angle was obtained as zero. In the cuts made with the AWJM method, thermal effects such as debris, microcracks and melted materials were not observed; an average surface roughness of 2.293 µm and a kerf of 0.976° were obtained.