Showing papers on "Surface roughness published in 2019"

Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

Abstract: Additive manufacturing (AM), also known as 3D printing, has been increasingly adopted in the aerospace, automotive, energy, and healthcare industries over the past few years. While AM has many advantages over subtractive manufacturing processes, one of the primary limitations of AM is surface integrity. To improve the surface integrity of additively manufactured parts, a data-driven predictive modeling approach to predicting surface roughness in AM is introduced. Multiple sensors of different types, including thermocouples, infrared temperature sensors, and accelerometers, are used to collect temperature and vibration data. An ensemble learning algorithm is introduced to train the predictive model of surface roughness. Features in the time and frequency domains are extracted from sensor-based condition monitoring data. A subset of these features is selected to improve computational efficiency and prediction accuracy. The predictive model is validated using the condition monitoring data collected from a set of AM tests conducted on a fused filament fabrication (FFF) machine. Experimental results have shown that the proposed predictive modeling approach is capable of predicting the surface roughness of 3D printed components with high accuracy.

3D printing of bone scaffolds with hybrid biomaterials

Abstract: In this research, a novel hybrid material bone implant manufacturing through the integration of two materials using additive manufacturing (AM) technology is proposed. Biomimetic application can manufacture high strength biomechanical implants with optimised geometry and mass. The combination of polymers allows a significant leap in the development and production of a great diversity of components and applications of biomaterials. A novel hybrid scaffold with a poly lactic acid (PLA) matrix reinforced with carbohydrate particles (cHA) is analysed using digital surface software in the mass proportions of 100/0, 95/5, 90/10 and 80/20 for application in tissue and regenerative engineering, seeking a higher proposition strength of PLA. Filaments are used to fabricate scaffolds by 3D printing, using the fused deposition method. The frameworks are submitted to bioactivity tests, surface roughness evaluation, apparent porosity and mechanical analysis. Analysis of the microstructure of the composite particle evaluates the 3D surface luminance structure and the profile structure. Cross-sectional views of the specimens are extracted and analysed, and the surface roughness, waviness profile, and Gaussian filter of the structures are observed. In summary the structures are checked and analysed by SEM and EDS where possible, to observe the bioactive behaviour of the materials. The relationship between cHA content and roughness is shown to be proportional. The mechanical properties are shown to be affected by the reduced interaction between the PLA matrix and the cHA particles.

Corrosion of metallic materials fabricated by selective laser melting

Abstract: Additive manufacturing is an emerging technology that challenges traditional manufacturing methods. However, the corrosion behaviour of additively manufactured parts must be considered if additive techniques are to find widespread application. In this paper, we review relationships between the unique microstructures and the corresponding corrosion behaviour of several metallic alloys fabricated by selective laser melting, one of the most popular powder-bed additive technologies for metals and alloys. Common issues related to corrosion in selective laser melted parts, such as pores, molten pool boundaries, surface roughness and anisotropy, are discussed. Widely printed alloys, including Ti-based, Al-based and Fe-based alloys, are selected to illustrate these relationships, and the corrosion properties of alloys produced by selective laser melting are summarised and compared to their conventionally processed counterparts.

On the role of powder flow behavior in fluid thermodynamics and laser processability of Ni-based composites by selective laser melting

Abstract: A deep understanding of the underlying relation of powder paving and subsequent melting/solidification behavior is critical in predicting and tailoring the processing quality of components fabricated by selective laser melting (SLM) additive manufacturing technology based on the mesoscopic scale. Combining discrete element method (DEM) and finite volume method (FVM), the present work proposed a newly developed three-dimensional DEM physical model coupling with mesoscopic FVM model to give a unique insight into the influence of powder particle size on the flow behavior of powder and resultant melting/solidification characteristics of SLM-processed WC/Inconel 718 composites. The contacting force among powder particles, transition of solid/liquid, and surface tension of melt were considered in the model. By characterizing the packing profiles, velocity vectors and packed state of particles, the simulation results and corresponding experimental validations generally revealed that the powder possessed an elevated flow-ability as increasing the average diameter of powder below 25 μm, due to the alleviation of the domination of adhesive force among neighboring particles. The considerably high-quality SLM-processed surface without apparent laser-induced defects was obtained with a significantly low surface roughness of 2.2 μm, attributing to the formation of a denser and more homogeneous powder-bed during SLM. However, when the average diameter of powder particles was elevated over 25 μm, the combined influence of the elevated gravity of large-scaled powder and the considerable space between neighboring powder particles worsened the flow-ability of powder. The discontinuous and loose powder-bed with a low packing density of particles had a high tendency to produce the top surface with a higher surface roughness, resulting in the formation of a number of defects in SLM-processed parts such as cave-like porosities, balling and discontinuous laser-melted tracks.

Fatigue behavior of additive manufactured 316L stainless steel parts: Effects of layer orientation and surface roughness

Abstract: The effects of layer orientation and surface roughness on the mechanical properties and fatigue life of 316L stainless steel (SS) fabricated via a laser beam powder bed fusion (LB-PBF) additive manufacturing process were investigated. Quasi-static tensile and uniaxial fatigue tests were conducted on LB-PBF 316L SS specimens fabricated in vertical and diagonal directions in their as-built surface condition, as well as in horizontal, vertical, and diagonal directions where the surface had been machined to remove any effects of surface roughness. In the machined condition, horizontally built LB-PBF specimens possessed higher fatigue resistance, followed by vertically built specimens, while the lowest fatigue resistance was obtained for diagonal specimens. Similarly, in the as-built condition, vertical specimens demonstrated better fatigue resistance when compared to diagonal specimens. Furthermore, the detrimental effects of surface roughness on fatigue life of LB-PBF 316L SS specimens was not significant, which may be due to the presence of large internal defects in the specimens. Anisotropy of LB-PBF 316L SS specimens was attributed to the variation in layer orientation, affecting defects’ directionality with respect to the loading direction. These defect characteristics can significantly influence the stress concentration and, consequently, fatigue behavior of additive manufactured parts. Therefore, the elastic-plastic energy release rates, a fracture mechanics-based concept that incorporates size, location, and projected area of defects on the loading plane, were determined to correlate the fatigue data and acceptable results were achieved.

Effect of Process Parameters on the Generated Surface Roughness of Down-Facing Surfaces in Selective Laser Melting

Abstract: Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind conventional manufacturing in terms of surface quality. This proves a hindrance for many companies considering investment in AM. The aim of this work is to investigate the effect of varying process parameters on the resultant roughness of the down-facing surfaces in selective laser melting (SLM). A systematic experimental study was carried out and the effects of the interaction of the different parameters and their effect on the surface roughness (Sa) were analyzed. It was found that the interaction and interdependency between parameters were of greatest significance to the obtainable surface roughness, though their effects vary greatly depending on the applied levels. This behavior was mainly attributed to the difference in energy absorbed by the powder. Predictive process models for optimization of process parameters for minimizing the obtained Sa in 45° and 35° down-facing surface, individually, were achieved with average error percentages of 5% and 6.3%, respectively, however further investigation is still warranted.

Investigating the role of model structure and surface roughness in generating flood inundation extents using one- and two-dimensional hydraulic models

Abstract: Funding information United States National Science Foundation, Grant/ Award Number: ACI-1261727 Hydraulic models play an important role in determining flood inundation areas. When considering a wide array of one(1D) and two-dimensional (2D) hydraulic models, selecting an appropriate model and its calibration are crucial in an accurate prediction of flood inundation. This study compares the performance of four commonly used 1D and 2D hydraulic models, including HEC-RAS 1D, HECRAS 2D, LISFLOOD-FP diffusive, and LISFLOOD-FP subgrid, with respect to their model structure and their sensitivity to surface roughness characterisation. Application of these models to four study reaches with different river geometry and roughness characterisation shows that for a given set of roughness condition, the geometry, including the sinuosity, reach length and floodplain width, does not affect the performance of a 1D or 2D model. Overall, the performance of a 1D model is comparable to the 2D models used in the study, with the 2D models showing slightly better results. The performance of 2D models is affected by low channel roughness, and it improves with increasing channel roughness that enables more water to enter into the floodplain. On the contrary, the performance of 1D model is positively affected with increasing floodplain roughness. When the models are evaluated for uniform versus distributed roughness characterisation in the floodplain, the uniform surface characterisation provides the best results compared to the distributed roughness characterisation.

Influence of surface roughness in turning process — an analysis using artificial neural network

Abstract: This paper presents methodology to identify the surface roughness value in CNC machining process using a soft computing approach. The aim of this paper is to achieve a roughness accuracy value abov...

The Functional Influence of Breast Implant Outer Shell Morphology on Bacterial Attachment and Growth

Abstract: Background The introduction of texture to the outer shell of breast implants was aimed at increasing tissue incorporation and reducing capsular contracture. It has also been shown that textured surfaces promote a higher growth of bacteria and are linked to the development of breast implant-associated anaplastic large cell lymphoma. Methods The authors aimed to measure the surface area and surface roughness of 11 available implants. In addition, the authors aimed to subject these implant shells to an in vitro bacterial attachment assay with four bacterial pathogens (Staphylococcus epidermidis, S. aureus, Pseudomonas aeruginosa, and Ralstonia pickettii) and study the relationship among surface area, surface roughness, and bacterial growth. Results Surface area measurement showed grouping of implants into high, intermediate, low, and minimal. Surface roughness showed a correlation with surface area. The in vitro assay showed a significant linear relationship between surface area and bacterial attachment/growth. The high surface area/roughness implant texture grew significantly more bacteria at 24 hours, whereas the minimal surface area/roughness implant textures grew significantly fewer bacteria of all types at 24 hours. For implants with intermediate and low surface areas, some species differences were observed, indicating possible affinity of specific bacterial species to surface morphology. Conclusions Implant shells should be reclassified using surface area/roughness into four categories (high, intermediate, low, and minimal). This classification is superior to the use of descriptive terms such as macrotexture, microtexture, and nanotexture, which are not well correlated with objective measurement and/or functional outcomes.

Hybrid cooling-lubrication strategies to improve surface topography and tool wear in sustainable turning of Al 7075-T6 alloy

Abstract: In machining of soft alloys, the sticky nature of localized material instigated by tool-work interaction exacerbates the tribological attitude and ultimately demeans it machinability. Moreover, the endured severe plastic deformation and originated thermal state alter the metallurgical structure of machined surface and chips. Also, the used tool edges are worn/damaged. Implementation of cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability. That is why, this paper deliberately discussed the influence of pure C/L methods, i.e., such as dry cutting (DC) and nitrogen cooling (N2), as well as hybrid C/L strategies, i.e., nitrogen minimum quantity lubrication (N2MQL) and Ranque–Hilsch vortex tube (RHVT) N2MQL conditions in turning of Al 7075-T6 alloy, respectively. With respect to the variation of cutting speed and feed rate, at different C/Ls, the surface roughness, tool wear, and chips are studied by using SEM and 3D topographic analysis. The mechanism of heat transfer by the cooling methods has been discussed too. Furthermore, the new chip management model (CMM) was developed under all C/L conditions by considering the waste management aspects. It was found that the R-N2MQL has the potential to reduce the surface roughness up to 77% and the tool wear up to 118%. This significant improvement promotes sustainability in machining industry by saving resources. Moreover, the CMM showed that R-N2MQL is more attractive for cleaner manufacturing system due to a higher recyclability, remanufacturing, and lower disposal of chips.

The effect of post-processing operations on surface characteristics of 316L stainless steel produced by selective laser melting

Abstract: Metal additive manufacturing is an emerging method to fabricate components used in the aerospace and biomedical industries. However, one of the significant challenges in this approach is the surface quality of the fabricated components. After metal additive manufacturing operations, post-processing is essential to meet the expected surface quality. This study presents the surface characteristics of as-built specimens manufactured by selective laser melting (SLM), where improvement of the surface can be achieved by post-processing operations. The post-processing operations in focus are finish machining (FM), vibratory surface finishing (VSF) and drag finishing (DF) operations. Surface topography, average surface roughness, microhardness, microstructure and XRD analysis have been carried out to examine the surface characteristics resulting from the post-processing operations. This study demonstrates that the drag finishing operation can be used for post-processing to meet the surface quality requirement of SLM manufactured parts.

Experimental evaluation of the lubrication performances of different nanofluids for minimum quantity lubrication (MQL) in milling Ti-6Al-4V

Abstract: The objective of this research is to experimentally evaluate the lubrication performances of different nanofluids in milling titanium alloy Ti-6Al-4V. Six types of nanofluids, namely, Al2O3, SiO2, MoS2, CNTs, SiC, and graphite, were selected. Cottonseed oil was used as the base oil. The lubrication performance was investigated in terms of milling force, surface roughness, and morphology of workpiece surface. Experimental results demonstrated that the Al2O3 nanoparticle obtained the minimal milling force (Fx = 277.5 N, Fy = 88.3 N), followed by the SiO2 nanoparticle (Fx = 283.6 N, Fy = 86.5 N). The surface roughness obtained by the Al2O3 nanofluid was the minimum (Ra = 0.594 μm), whereas it was the maximum by using minimum quantity lubrication (Ra = 1.772 μm). The surface roughness of the six nanofluids was described by the following order: Al2O3 < SiO2 < MoS2 < CNTs < graphite < SiC. The workpiece surface morphology was the best for Al2O3 and SiO2. The viscosity of the nanofluids was also analyzed. Spherical Al2O3 and SiO2 nanoparticles improved the lubrication effect of base oil mostly and were more suitable as environment-friendly additives for the base oil compared with the others.

Reducing the roughness of internal surface of an additive manufacturing produced 316 steel component by chempolishing and electropolishing

Abstract: Surface roughness of an as produced AM component is very high, which prohibits the direct utilization of additively manufactured (AM) components for the intended applications. Reducing surface roughness is exponentially more challenging for the internal surfaces of an AM component. This paper reports our research in the area of postprocessing of interior surfaces of an AM component. We have investigated electropolishing and chemical polishing (chempolishing) methods to reduce the surface roughness of the internal surface. We found that chempolishing was effective in simultaneously reducing the internal and external surface roughness of 316 steel AM components. Chempolishing is found suitable for any complicated AM shape and geometry. Our electropolishing methodology was effective in reducing the surface roughness of the internal or external surfaces provided that a counter electrode could be positioned in the proximity of the surface to be polished. We have performed optical profilometry, scanning electron microscopy, and contact angle measurement study to investigate the difference between electropolishing and chemical polishing methods.

Surface Quality of 3D-Printed Models as a Function of Various Printing Parameters.

Abstract: Although 3D-printing is common in dentistry, the technique does not produce the required quality for all target applications. Resin type, printing resolution, positioning, alignment, target structure, and the type and number of support structures may influence the surface roughness of printed objects, and this study investigates the effects of these variables. A stereolithographic data record was generated from a master model. Twelve printing processes were executed with a stereolithography Desktop 3D Printer, including models aligned across and parallel to the printer front as well as solid and hollow models. Three layer thicknesses were used, and in half of all processes, the models were inclined at 15°. For comparison, eight gypsum models and milled polyurethane models were manufactured. The mean roughness index of each model was determined with a perthometer. Surface roughness values were approximately 0.65 µm (master), 0.87–4.44 µm (printed), 2.32–2.57 µm (milled), 1.72–1.86 µm (cast plaster/alginate casting), and 0.98–1.03 µm (cast plaster/polyether casting). The layer height and type and number of support structures influenced the surface roughness of printed models (p ≤ 0.05), but positioning, structure, and alignment did not.

Experimental study and analysis of machinability characteristics of metal matrix composites during drilling

Abstract: In this study, the metal matrix composite materials were produced by hot press with various production parameters. The drilling experiments were performed on computer numerical control vertical machining centre without cutting fluid. Analysis of variance (ANOVA) was carried out in order to determine the effects of the production parameters on thrust force and surface roughness of metal matrix composites drilled with different feed rate. The effect of production parameters such as temperature, pressure and reinforcement ratio were investigated, and their effects were presented. The optimal level for each production parameters was determined by ‘Maximize the S/N ratio approach with a Taguchi design’. The test results revealed that the reinforcement ratio was the main factor affecting the surface roughness of the metal matrix composites for both feed rate. However, same singularity was not matter on thrust force due to close contribution rates of production parameters and high error rates of analysis. In literature, an increase on the thrust force and the surface roughness values was reported as the feed rate increased during machining. Nevertheless, in our MMCs system, the thrust force and the surface roughness values were in tendency of declination as the feed rate increased which makes this study more novel research.