Showing papers on "Surface roughness published in 2021"

Thermal radiation and surface roughness effects on the thermo-magneto-hydrodynamic stability of alumina–copper oxide hybrid nanofluids utilizing the generalized Buongiorno’s nanofluid model

Abstract: Sequel to the fact that hybrid nanofluidic systems (e.g. scalable micro-/nanofluidic device) exhibit greater thermal resistance with increasing nanoparticle concentration, little is known on the significance of thermal radiation, surface roughness and linear stability of water conveying alumina and copper oxide nanoparticles. This study presents the effects of thermal radiation and surface roughness on the complex dynamics of water conveying alumina and copper oxide nanoparticles, in the case where the thermophysical properties of the resulting mixture vary meaningfully with the volume fraction of solid nanomaterials, as well as with the Brownian motion and thermophoresis microscopic phenomena. Based on the linear stability theory and normal mode analysis method, the basic partial differential equations governing the transport phenomenon were non-dimensionalized to obtain the simplified stability equations. The optimum values of the critical thermal Rayleigh number depicting the onset of thermo-magneto-hydrodynamic instabilities were obtained using the power series method and the Chock–Schechter numerical integration. The increase in the strength of Lorentz forces, thermal radiation and surface roughness has a stronger stabilizing impact on the appearance of convection cells. On the contrary, the stability diminishes with the increasing values of the volumetric fraction and diameter of nanomaterials. The partial substitution of the alumina nanoparticles by the copper oxide nanomaterials in the mixture stabilizes importantly the hybrid nanofluidic medium.

Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities

Abstract: Metal additive manufacturing is a rapidly expanding area owing to its capacity to fabricate parts of intricate geometries with customized features for a wide range of applications. However, these parts generally exhibit inadequate and poor surface quality in the as-built configuration. The surface imperfections and defects ranging from staircase effect due to the layer by layer nature of the deposition techniques, partially fused feedstock material, balling effects, spatters, or inadequate fusion lead to a notably irregular surface morphology. This high surface roughness can significantly deteriorate the performance of the additive manufactured parts imposing a substantial limit on their prospective applications; for instance, fatigue performance, wear and scratch resistance, dimensional accuracy, and aesthetical aspects can be highly affected by these surface defects. A great effort has been lately dedicated to developing post-treatments for improving the surface quality of additively manufactured metallic parts. In this paper, various treatments applied to as-built samples fabricated using different additive manufacturing technologies are introduced and discussed. The advances in this area are highlighted, and the results obtained from different categories of post-treatments are compared and reviewed. Challenges and opportunities to gain more control on the surface roughness of additively manufactured metallic parts through the application of these post-treatments are addressed.

Grindability of carbon fiber reinforced polymer using CNT biological lubricant.

Abstract: Carbon fiber-reinforced polymer (CFRP) easily realizes the integrated manufacturing of components with high specific strength and stiffness, and it has become the preferred material in the aerospace field. Grinding is the key approach to realize precision parts and matching the positioning surface for assembly and precision. Hygroscopicity limits the application of flood lubrication in CFRP grinding, and dry grinding leads to large force, surface deterioration, and wheel clogging. To solve the above technical bottleneck, this study explored the grindability and frictional behavior of CNT biological lubricant MQL through grinding experiments and friction-wear tests. Results showed that the CNT biological lubricant reduced the friction coefficient by 53.47% compared with dry condition, showing optimal and durable antifriction characteristics. The new lubrication was beneficial to suppressing the removal of multifiber block debris, tensile fracture, and tensile-shear fracture, with the advantages of tribological properties and material removal behavior, the tangential and normal grinding force, and the specific grinding energy were reduced by 40.41%, 31.46%, and 55.78%, respectively, compared with dry grinding. The proposed method reduced surface roughness and obtained the optimal surface morphology by preventing burrs, fiber pull-out, and resin smearing, and wheel clogging was prevented by temperature reduction and lubricating oil film formation. Sa and Sq of the CNT biological lubricant were reduced by 8.4% and 7.9%, respectively, compared with dry grinding. This study provides a practical basis for further application of CNT biological lubricant in CFRP grinding.

A review on turbulent flow over rough surfaces: Fundamentals and theories

Abstract: Surface roughness can significantly influence the fluid dynamics and heat transfer in convective flows by inducing perturbations in the velocity profile which affect surface drag, turbulent mixing and heat transfer. While surface roughness can often negatively affect the performance of systems, it can also lead to performance improvements, such as in convective flows where roughness elements have been shown to enhance heat transfer. Turbulent flows over rough surfaces have been studied for about a century leading to significant developments in this field. Direct Numerical Simulation (DNS) has made significant contributions to the knowledge of turbulent flows over rough surfaces as well as evaluation of the developed theories. Moreover, the turbulent closures model has seen wide use for simulation of rough-wall turbulent flows in practical applications where DNS is hindered by its complexity and computational resources. Despite a significant number of experimental and CFD studies and the latest advances in this field, a recent review was not available. Therefore, this review surveys the past and recent experimental and numerical studies to address the fundamentals and theories related to the structure of turbulent flows over rough walls. This study chiefly investigates the structure of mean flow profile over rough surfaces, and its correlation with smooth wall mean flow profile. This review study can contribute to prospective experimental and CFD work, and for characterising rough-wall turbulent flows and heat transfer in different academic and engineering applications such as aerodynamics, hydraulics, meteorology, and manufacturing. The review concludes that despite significant progress, the structure of turbulent flow is still not fully understood. This is mainly due to a lack of systematic studies on the structure of turbulent flow and also due to the variety of roughness which influence the dynamics of the flow in the roughness sublayers. The current roughness scale (sand-grain roughness height) fails to completely characterise roughness in many cases. Therefore, there is a need for a universal roughness scale that can describe every type of roughness and be used in any rough-flow regimes, including fully rough and transitionally rough regimes.

Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding

Abstract: Ground surface roughness is regarded as one of the most crucial indicators of machining quality and is hard to be predicted due to the random distribution of abrasive grits and sophisticated grinding mechanism. In order to estimate surface roughness accurately in grinding process and provide feasible monitoring scheme for practical manufacturing application, a novel prediction system of surface roughness is presented in this article, including the processing of grinding signals, selection of feature combination, and development of prediction model. Grinding force, vibration, and acoustic emission signals are collected during the grinding of C-250 maraging steel. Numerous features in time domain and frequency domain are extracted from original and decomposed signals. A hybrid feature selection approach is proposed to select features based on their relevance to surface roughness as well as hardware and time costs. A sequential deep learning framework, long short-term memory (LSTM) network, is employed to predict ground surface roughness. The results have shown that the LSTM model achieves excellent prediction performance with a feature combination of grinding force and acoustic emission. After considering the hardware and time costs, features in acceleration signal replace those in grinding force and acoustic emission signals with slight loss of prediction performance and significant reduction of costs, which proves the practicability and feasibility of proposed prediction system.

A state-of-the-art review on tool wear and surface integrity characteristics in machining of superalloys

Abstract: Today, superalloys (also known as hard-to-cut materials) such as nickel, titanium and cobalt based cover a wide range of areas in engineering applications. At the same time, challenging material properties namely high strength and low thermal conductivity cause low quality in terms of cutting tool life and surface integrity of the machined part. It is important to improve the machinability of this type of materials by applying various methods in the perspective of sustainability. Therefore, current study presents surface integrity, tool wear characteristics and initiatives to improve them during the machining of superalloys. In this manner, it is outlined the surface integrity characteristics containing surface defects, surface roughness, microstructure alterations and mechanical properties. Also, tool wear mechanisms for example abrasive, adhesive, oxidation, diffusion and plastic deformation are investigated in the light of literature review. Finally, possible improvement options for tool wear and surface integrity depend on machining parameters, tool modifications, cooling methods and trade-off strategies are highlighted. The paper can be a guide for the researchers and manufacturers in the area of sustainable machining of hard-to-cut materials as explaining the latest trends and requirements.

Advancements in material removal mechanism and surface integrity of high speed metal cutting: A review

Abstract: The research and application of high speed metal cutting (HSMC) is aimed at achieving higher productivity and improved surface quality. This paper reviews the advancements in HSMC with a focus on the material removal mechanism and machined surface integrity without considering the effect of cutting dynamics on the machining process. In addition, the variation of cutting force and cutting temperature as well as the tool wear behavior during HSMC are summarized. Through comparing with conventional machining (or called as normal speed machining), the advantages of HSMC are elaborated from the aspects of high material removal rate, good finished surface quality (except surface residual stress), low cutting force, and low cutting temperature. Meanwhile, the shortcomings of HSMC are presented from the aspects of high tool wear rate and tensile residual stress on finished surface. The variation of material dynamic properties at high cutting speeds is the underlying mechanism responsible for the transition of chip morphology and material removal mechanism. Less surface defects and lower surface roughness can be obtained at a specific range of high cutting speeds, which depends on the workpiece material and cutting conditions. The thorough review on pros and cons of HSMC can help to effectively utilize its advantages and circumvent its shortcomings. Furthermore, the challenges for advancing and future research directions of HSMC are highlighted. Particularly, to reveal the relationships among inherent attributes of workpiece materials, processing parameters during HSMC, and evolution of machined surface properties will be a potential breakthrough direction. Although the influence of cutting speed on the material removal mechanism and surface integrity has been studied extensively, it still requires more detailed investigations in the future with continuous increase in cutting speed and emergence of new engineering materials in industries.

Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning

Abstract: WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different machine learning models, including ANFIS, ELM and SVR, were developed to predict the surface roughness. Furthermore, the ANFIS model was optimized by GA and PSO algorithms. Full factorial experiments were conducted to obtain the training data, and the K-fold Cross-validation strategy was applied to train and validate machine learning models. The comparison results indicate that GA–ANFIS has superiority in predicting surface roughness. The RMSE, $$ R^{2} $$ , MAE and MAPE for GA–ANFIS were 0.0694, 0.93516, 0.0574, 14.15% respectively. This study could also provide inspiration and guidance for surface roughness modelling in multipass arc welding and cladding.

A review on the state-of-the-art of surface finishing processes and related ISO/ASTM standards for metal additive manufactured components

Abstract: The current state-of-the-art metal additive manufacturing (AM) process still cannot meet the high industry requirements in terms of surface roughness. In addition, there are limited ISO/ASTM standa...

Experimental analysis and optimization of EDM parameters on HcHcr steel in context with different electrodes and dielectric fluids using hybrid Taguchi-based PCA-Utility and CRITIC-Utility approaches

Abstract: Industries demand stringent requirements towards economical machining without hindering the surface quality while cutting high carbon high chromium (HcHcr) steel. Electrical discharge machining (EDM) of HcHcr steel aims at reducing machining cost (i.e., maximize material removal rate (MRR) and minimize tool wear rate (TWR)) with good surface quality (i.e., minimize surface roughness (SR)). A comparative study was carried out on EDM of HcHcr D2 steel (DIN EN ISO 4957) by applying Taguchi L18 experimental design considering different electrode materials (copper, graphite, and brass), dielectric fluids (distilled water and kerosene), peak current, and pulse-on-time. The process performances were analyzed with respect to material removal rate, surface roughness, and tool wear rate. Pareto analysis of variance was employed to estimate the significance of the process variables and their optimal levels for achieving lower SR and TWR and higher MRR. Hybrid Taguchi-CRITIC-Utility and Taguchi-PCA-Utility methods were implemented to determine the optimal EDM parameters. Higher MRR of 0.0632 g/min and lower SR of 1.68 µm and TWR of 0.012 g/min was attained by graphite electrode in presence of distilled water as dielectric fluid compared to the brass and copper. Additionally, a metallographic analysis was carried out to study the surface integrity on the machined surfaces. Micrographic analysis of the optimal conditions showed lower surface roughness and fewer imperfections (lesser impression, waviness surface, and micro-cracks) compared to worst conditions.

Optimization on Operation Parameters in Reinforced Metal Matrix of AA6066 Composite with HSS and Cu

Abstract: This optimization investigation focused on the reinforced metal matrix composite of aluminium alloy. Novel of this work is to fabricate the AA6066 composite with HSS and Cu, continually conduct machining tests, and evaluate the tool wear, surface roughness, and thrust force of the stir-casted specimens. The aluminium composite has 90 percentage of AA6066 alloy reinforcement with six percentage of high-speed steel and four percentage of copper alloy made by the casting method. The fabricated composites’ turning parameters were optimized through the Taguchi method. The turning operation can be done with the help of the normal lathe with the CBN insert tool. The operation parameters such as feed, depth of cut, and steam pressure of the cutting fluid were considered with three different equal intervals in each parameter. In this investigation, the L9 orthogonal array method is used to identify the optimum values of the turning parameters among the considered machining parameters concerning the response such as wear on the turning tool and thrust forces created on machining. The outcome based on the parameters was identified and mentioned as the rank order for individual and combination of all responses with different conditions. Then, the separate and combined optimized input parameters were provided as the conclusion.

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Abstract: Machining of shape memory alloys (SMAs) without losing the shape memory effect could immensely extend their applications. Herein, the wire electric discharge machining process was used to machine NiTi—a shape memory alloy. The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology. The effects of input variables including pulse on time, pulse off time, and current were investigated on the material removal rate, surface roughness, and microhardness. ANOVA tests were performed to check the robustness of the generated empirical models. Optimization of the process parameters was performed using a newly formulated, highly efficient heat transfer search algorithm. Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry. In addition, 2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm. The optimization route yielded encouraging results. Single objective optimization yielded a maximum material removal rate of 1.49 mm3/s, maximum microhardness 462.52 HVN, and minimum surface roughness 0.11 µm. The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions. The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

Optimization of surface roughness and dimensional accuracy in LPBF additive manufacturing

Abstract: Laser powder bed fusion (LPBF) is one of the most promising additive manufacturing technologies. It has been utilized in the high level and stringent requirements fields such as aerospace and biomedicine industries. However, compared to subtractive manufacturing, the relatively poor surface finish and dimensional accuracy of the LPBF part hamper its widespread applications. In this work, a data-driven framework is proposed to obtain optimal process parameters of LPBF to get satisfactory surface roughness and dimensional accuracy. The effects of key process parameters on the surface roughness and dimensional accuracy are analyzed. Specifically, a machine learning technique is defined to reflect the dimensional accuracy and the surface roughness of the as-built products under different combinations of process parameters. Considering the limited experimental data, a machine learning model is introduced to predict the surface roughness and dimensional accuracy in the whole process parameters space. Then the predicted value is considered as an objective value when using the whale optimization algorithm (WOA) to search the global optimal process parameters. In the verification experiments, LPBF parts with better surface finish and dimensional accuracy were obtained with optimized process parameters which indicates that the optimized results are consistent with the experimental results.

A numerical solution to the effects of surface roughness on water-coal contact angle

Abstract: Coal dust is a great threat to coal mine workers' health and safety in coal mine production. Wet dust removal is one of the effective dust removal methods. As a solid, coal has different rough surfaces, which have a certain effect on the wetting effect of coal. In this paper, three coal samples with different surface wettability are used as the research objects. Phase-field interface tracking method is used to simulate the wetting of droplets on rough surfaces. From the simulation results, it can be concluded that the influence of the rough interface on the contact angle of the droplets is in accordance with the change rule described in the Wenzel model. As the roughness increases, the contact angle of the hydrophilic lignite surface gradually decreases. As the roughness increases, the contact angle of hydrophobic coking coal gradually increases. The change trend of the contact on the surface of weakly hydrophilic anthracite coal is the same as that of lignite. Due to the local and global differences, the contact angles obtained from the numerical model are slightly different from the values calculated from the Wenzel model.