Rough Model Research Articles

Understanding dynamics in coarse-grained models. III. Roles of rotational motion and translation-rotation coupling in coarse-grained dynamics.

This paper series aims to establish a complete correspondence between fine-grained (FG) and coarse-grained (CG) dynamics by way of excess entropy scaling (introduced in Paper I). While Paper II successfully captured translational motions in CG systems using a hard sphere mapping, the absence of rotational motions in single-site CG models introduces differences between FG and CG dynamics. In this third paper, our objective is to faithfully recover atomistic diffusion coefficients from CG dynamics by incorporating rotational dynamics. By extracting FG rotational diffusion, we unravel, for the first time reported to our knowledge, a universality in excess entropy scaling between the rotational and translational diffusion. Once the missing rotational dynamics are integrated into the CG translational dynamics, an effective translation-rotation coupling becomes essential. We propose two different approaches for estimating this coupling parameter: the rough hard sphere theory with acentric factor (temperature-independent) or the rough Lennard-Jones model with CG attractions (temperature-dependent). Altogether, we demonstrate that FG diffusion coefficients can be recovered from CG diffusion coefficients by (1) incorporating "entropy-free" rotational diffusion with translation-rotation coupling and (2) recapturing the missing entropy. Our findings shed light on the fundamental relationship between FG and CG dynamics in molecular fluids.

Curved-Line Path-Following Control of Fixed-Wing Unmanned Aerial Vehicles Using a Robust Disturbance-Estimator-Based Predictive Control Approach

In this research, the design of a robust curved-line path-following control system for fixed-wing unmanned aerial vehicles (FWUAVs) affected by uncertainties on the latitude plane is studied. This is undertaken to enhance closed-loop system robustness under unknown uncertainties and derive the control surface deflection angle directly used to control FWUAVs, which has rarely been studied in previous works. The system is formed through the mass center position control (MCPC) and yaw angle control (YAC) subsystems. In the MCPC, the desired yaw angle, which is treated as the reference signal for the YAC subsystem, is calculated analytically using path-following errors, current flow angles, and the yaw angle. In the YAC, a disturbance estimator is designed to estimate uncertainties such as nonlinearities, couplings, time variations, model parameter perturbations, and unmodeled dynamics. Predictive functional controllers are designed to target nominal systems in the absence of uncertainties, such that the estimations of the uncertainties can be incorporated through feedback for closed-loop system robustness enhancement. The simulation results show that higher path-following precision and stronger robustness for the FWUAVs based on the proposed approach can be achieved using only rough model parameters compared with the conventional nonlinear dynamic inversion, which requires detailed model information.

Frictional Resistance Increase Due to Hull Roughness: An Analysis of the Hull Form Parameters Influence

An awareness of the drag increase brought on by biofouling's roughness on the ship hull is one technique to cut emissions aboard ship. However, predicting the increased drag on ships poses significant challenges. When predicting the rise in frictional resistance brought on by roughness, the hull is considered flat. In fact, ship hulls have a variety of shapes, and it is not certain whether this is a factor influencing the magnitude of the increase in resistance due to roughness. In this article, the effect of the hull's form parameters— (ratio of length per breadth), (coefficient block), and (Length of center buoyancy)—on the increase in frictional resistance brought on by roughness have been investigated. The method used to calculate the ship resistance is Computational Fluid Dynamics (CFD) simulation, complemented by roughness modelling using the wall function approach method. The Design of Experiment (DOE) method has been used to vary the shape of the hull model as a variation of the test specimen in this study. The verification and validation tests have been carried out on the CFD simulation results, where the results have been compared with proven empirical methods. Based on the study results, the value of frictional resistance and increased frictional resistance () of all specimens has shown no significant difference in value, evidenced by the variance values, ranging only 1.57-2.1%. Thus, these results prove that the increase in frictional resistance due to roughness is sufficient to assume the ship's hull as a flat plate. The other finding is that roughness can also increase the pressure resistance, and hull shape parameters also contribute towards changes in the value of resistance.

Mechanical behaviors and damage characteristics of excavated jointed rocks within 3D printed structures

The presence of structural planes in rock masses reduces their mechanical properties and greatly affects their failure mode, resulting in physical and mechanical anisotropy. This study investigated the mechanical behaviors and damage characteristics of excavated jointed rocks in three-dimensional (3D) printed structures. A digital fracture network model based on the fractal principle was also developed to simulate a complex and rough network of fractures. Using 3D printing technology, rock specimens within a single rough structure surface, different dip angles, and rough fracture network models were established, considering different excavation sizes. Uniaxial compression was performed experimentally to investigate the mechanical response of rock masses. The failure processes of the jointed specimens were analyzed based on the digital image correlation (DIC) technique. The uniaxial compressive strength and elastic modulus were compared considering different excavation sizes. The results show that the geometric characteristics of the joints significantly influence the strength and failure mode of the rock mass. The strength of the layered rock mass shows a significant linear correlation with increasing excavation sizes. This research could provide a reference for studying the mechanical behavior of jointed rock masses using 3D printing technology.

Relationship between machining accuracy and optical properties of convex blazed grating in ultra-precision cutting.

For ultra-precision machining of convex blazed grating elements there are inevitable machining errors, surface defects, and surface roughness, all of which can have an impact on their diffraction efficiency. In this paper, we use PCGrate software based on the integration method to establish the machining error model, surface defect model, and surface roughness model of convex spherical blazed grating with a curvature radius of 41.104 mm, a substrate diameter of 14 mm, a grating density of 53.97 line/mm and a blazed angle of 3.86° as the basic specification. To investigate the effect of base curvature radius error, grating period error, blazed angle error, grating ridge and valley passivation radius, Poisson burr height, and blaze surface roughness on their -1 order diffraction efficiency in the 0.95-2.5 µm spectral range. The results show that when the curvature radius error of the spherical base is less than ±80µm, the influence on diffraction efficiency can be ignored. Among the three groups of grating microstructure parameters, the influence of blazed angle on grating diffraction efficiency is the largest, followed by a grating period, and the influence of grating apex angle is the smallest, among which when the error of blazed angle is less than ±0.1° and the error of grating period is less than ±0.1µm, the influence on diffraction efficiency can be ignored. The effect of the passivation radius of the grating valley on the diffraction efficiency is smaller than that of the passivation radius of the grating ridge, and the maximum reduction of diffraction efficiency is 0.096 and 0.144 when the grating ridge and valley passivation radius are 50nm∼650 nm, respectively. The diffraction efficiency decreases significantly in the wavelength range of 1.9-2.5 µm with the increase of Poisson burr height and blaze surface roughness, and its effect on the diffraction efficiency can be neglected when the Poisson burr height is less than 0.5 µm and the blaze surface roughness value is less than RMS 1 nm. The machining error, surface defect, and surface roughness models of the convex blazed grating are optimized to provide a comprehensive machining accuracy basis for ultra-precision cutting of convex grating components.

Surface Roughness in Metal Material Extrusion 3D Printing: The Influence of Printing Orientation and the Development of a Predictive Model

This study investigates the influence of printing orientation on the surface roughness in metal material extrusion 3D printing of 17-4 PH stainless steel. Experimental tests were conducted on the Markforged Metal X commercial 3D printer at Vinh Long University of Technology Education, Vietnam. The samples were printed in three different orientations: flat, on-edge, and upright. Surface roughness measurements were performed using a handheld Mitutoyo SJ-210 roughness tester. Quantitative analysis of the surface roughness measurements revealed significant variations among the different printing orientations. The upright orientation exhibited the smoothest surface, with an average Ra value of 7.42 μm and Rz value of 40.49 μm. In contrast, the flat orientation showed the highest roughness, with an average Ra value of 82.83 μm and Rz value of 109.32 μm. The on-edge orientation had intermediate roughness values, with an average Ra value of 69.42 μm and Rz value of 92.17 μm. The study also introduces a novel predictive model for surface roughness based on the printing parameters. The model demonstrated accurate estimations for surface roughness values in specific cases, enabling optimization of the printing process for desired surface quality.

Validation of a full scale CFD simulation of a self-propelled ship with measured hull roughness and effect of welding seams on hull resistance

This study presents the verification and validation of a full scale self-propulsion computational fluid dynamics (CFD) model including measured hull roughness and welding seams. The reference for validation is comprised of speed trial data from five sister ships. To ensure numerical accuracy, resistance and propeller open-water CFD simulations are verified and validated against model tests from two different towing tanks and discrepancies are found to be within the experimental uncertainty of model scale testing. The self-propulsion CFD results emphasise the necessity of measuring hull roughness for accurate performance calculations with discrepancies less than 3.4% on the delivered power relative to the fitted speed trial results. It is found that the empirical Townsin/Mosaad roughness formulation with measured average hull roughness, as recommended by ITTC, provides similar results, as modifying the wall functions using the Colebrook/Grigson roughness function. Homogeneous and heterogeneous roughness modelling methods are found to provide similar results for the studied ship. Modelling of the transverse welding seams on the hull surface is found to increase the total resistance by approximately 0.1% per seam, resulting in a total estimated increase in resistance of 1.5–1.8%. Finally, adding a fairing is found to significantly reduce the resistance penalty from welding seams.

Research on quality evaluation of online teaching platform based on RS-BP Neural Network

Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms.

Study on the Sliding Tribological Behavior of Oleic Acid-Modified MoS2 under Boundary Lubrication.

The effects of MoS2 and MoS2 modified by adding oleic acid (OA) on the friction properties of lithium-based grease under boundary lubrication conditions are studied by molecular dynamics (MD) simulation and experiment. A rough wall boundary lubrication MD model with peaks and grooves is established to simulate the mechanical properties and lubrication effects of three lubrication systems on rough walls for the relative shear velocity between the two solid walls of 5 m/s at 500 MPa. The stress, wear amount, friction force, normal pressure, and friction heat of the friction surface are quantitively calculated. Simultaneously, a Retc friction and wear testing machine is used to measure the friction coefficient under different concentrations of additives and different pressures. The results show that the grease added with MoS2 can reduce friction, wear, and the temperature between friction pairs. However, under high pressure and shear, MoS2 can easily agglomerate and accumulate in the pits, reducing the lubricating effect. At the same time, since OA-modified MoS2 can reduce agglomeration, the modified MoS2 is adsorbed on the metal wall surface, forming a stable lubricant film. The main contributions of this article can be found in combining MD simulation and experimentation, establishing the connection between micronano structures and macroscopic properties, exploring the mechanism of the influence of wall roughness and particle size on the friction performance of lubricating oil, and providing a theory for predicting and developing high-performance new lubricating grease.

Development of mathematical model for surface roughness estimation in material jetting 3D printed parts

This research investigated the modeling of three relatively different surface profiles, based on outer layer contour, internal raster, and a combination of these two, to estimate the surface roughness of material jetting 3D printed parts. Theoretical and empirical modeling was used to determine the relationship between build orientation and layer thickness with surface roughness. Results indicated that the developed surface roughness model can be utilized to estimate the surface roughness value of other surface profiles built through the same machine using material jetting technology or even other additive manufacturing systems like fused filament fabrication. Also, the validation using existing models showed a significant reduction in the prediction error (1.008%) and provides a more accurate surface roughness model for predicting surface roughness values. To minimize the required surface roughness value in the material jetting 3D printed parts, a surface roughness optimization methodology based on changing the build orientation is suggested in this article. An expression of the direction of build orientation is presented and the relation between the surface roughness and build orientation is investigated taking color coding environment into consideration. The validity and effectiveness of the proposed optimization methodology are tested by simulation and experimental results.

Surface Roughness in Ultra-High Precision Diamond Turning of Silicone Hydrogel Contact Lenses

The research work presented in this paper highlights the use of the process of ultra-high precision diamond turning when machining advanced contact lens polymers. The study is focused on investigating the effect of diamond cutting conditions on the surface quality of the polymers machined. A silicone hydrogel (Definitive 74) was used in this study. The surface roughness measurements were taken using a surface 3D profilometer. Response surface methodology techniques were employed to create predictive models for surface roughness with respect to the cutting speed, feed rate and depth of cut. The predictive surface roughness models were successfully generated for the polymer and the cutting speed and feed rate were identified as the most influential parameters on surface roughness.

Modelling of kerf width and surface roughness using vibration signals in laser beam machining of stainless steel using design of experiments

Manufacturing industries are increasingly interested in laser beam machining as an efficient material removal process. Accordingly, there is great motivation in the modelling of the machining process to understand the physics behind the machining process in order to improve the machining efficiency. The present study made an attempt to predict surface roughness, kerf width and metal removal rate by utilizing the vibration signals measured during the laser beam machining of AISI 304 stainless steel. As per Taguchi’s L9 design of experiments, nine experiments were conducted at three levels of laser power (2.0, 1.5 and 1.0 kW), laser frequency (10, 8 and 6 kHz), cutting speed (3.0, 2.5 and 2.0 m/min) and nozzle tip distance (1.2, 1 and 0.8 mm) on 6 mm thickness of sheet metal and the vibration of sheet metal was measured using an accelerometer. It was observed that, the sheet vibration along the cutting direction increased the metal removal rate and sheet vibration perpendicular to the cutting direction caused surface roughness. Mathematical models were developed with the vibration data and predicted the surface roughness, kerf width and metal removal rate. The root mean square error of the responses was calculated as 0.615 µm, 5.44 µm and 0.259 m3/min for surface roughness, kerf width and metal removal rate respectively, demonstrating a significant correlation with experimental results. Furthermore, effect of laser power, laser frequency, cutting speed and nozzle tip distance on the surface roughness, kerf width, heat affected zone and metal removal rate was studied. The laser power and cutting speed have significant effect on these responses. Finite element modelling based simulation was carried out and studied effect of molten metal temperature on the heat affected zone. Temperature in the molten pool reached to maximum of 1773 K and the heat affected zone increased, when the laser power increased to 2.0 kW. The root mean square error between the measured and simulated heat affected zone was computed as 4.68 µm, indicating a good correlation between both.

3D Object Tracking for Rough Models

AbstractVisual monocular 6D pose tracking methods for textureless or weakly‐textured objects heavily rely on contour constraints established by the precise 3D model. However, precise models are not always available in reality, and rough models can potentially degrade tracking performance and impede the widespread usage of 3D object tracking. To address this new problem, we propose a novel tracking method that handles rough models. We reshape the rough contour through the probability map, which can avoid explicitly processing the 3D rough model itself. We further emphasize the inner region information of the object, where the points are sampled to provide color constrains. To sufficiently satisfy the assumption of small displacement between frames, the 2D translation of the object is pre‐searched for a better initial pose. Finally, we combine constraints from both the contour and inner region to optimize the object pose. Experimental results demonstrate that the proposed method achieves state‐of‐the‐art performance on both roughly and precisely modeled objects. Particularly for the highly rough model, the accuracy is significantly improved (40.4% v.s. 16.9%).

Utilising tourist pictures to generate 3D models and automatic multi-view stereo network design

The design of image networks plays a pivotal role in image-based 3D shape reconstruction and data processing, particularly when applying combined SfM/MVS methods. This paper presents an approach to designing and planning a multi-view images network for cultural heritage objects and sites based on a rough 3D model generated from public datasets of tourist images. Images for three famous cultural heritage buildings in India, namely Taj Mahal, Qutub Minar and Hawa Mahal, were used in this study to achieve the targeted goal. The results obtained confirmed that the Agisoft Metashape software made it possible to acceptably reconstruct the entire body of the studied structures. However, it the results showed when generating point clouds and 3D models of slender objects, i.e., the Qutub Minar, it is not possible to use only images acquired from the ground since most tourists wishing to correctly represent the shape take tilted images. For this reason, it is necessary to use videos or photos from UAVs. Generally, it can be concluded that the adopted approach in this study is a promising alternative for the conventional methods that are currently used for the shape reconstruction of cultural heritage objects and sites.

Prediction of Isotropic Rough Surface Directional Spectral Emissivity with Surface-Morphology-Dependent Modelling

The surface directional spectral emissivity of rough metal surfaces in industry is of concern in infrared temperature measurement. In this research, the height and slope possibility density functions are introduced as variables, and the directional spectral emissivity of isotropic rough surface is modelled accordingly. The model is designed to derive the directional spectral emissivity of rough metal surfaces from the surface morphology (possibility distribution of the height and slope) and the material property parameters (refractivity). Then, a sandblasted surface is taken as a case study. The sandblasted surface morphology is measured. A Polynomial surface is proposed to describe the sandblasted surface morphology and is compared with a Gaussian surface and a Cox–Munk surface. Finally, the directional spectral emissivity measurement and infrared temperature measurement are conducted. It is shown that the predicted directional spectral emissivity and measured temperature with the surface-morphology-dependent isotropic rough surface directional spectral emissivity model have high precision. In this work, the possibility distribution of the height and slope of the surface is introduced as independent variables to provide better accuracy compared to the reported models. In some cases, the error of the infrared temperature measurement could be reduced to 20% (80 degrees, compared to Gaussian surface). This work contributes to improving the accuracy of IR temperature measurement of rough surfaces.

Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting.

This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, vfi; and pressure, pi), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was developed using the obtained maximum acceleration values and surface roughness parameters (Ra, Rz, and RSm). In this study, five different models were adopted. Due to the characteristics of the data, five regression methods were selected: Random Forest Regressor, Linear Regression, Gradient Boosting Regressor, LGBM Regressor, and XGBRF Regressor. The maximum vibration amplitude reached the lowest acceleration value for aluminum alloy (not exceeding 5 m/s2), indicating its susceptibility to cutting while maintaining a high surface quality. However, significantly higher acceleration amplitudes (up to 60 m/s2) were registered for steel and titanium alloy in all process zones. The predicted roughness parameters were determined from the developed models using second-degree regression equations. The prediction of vibration parameters and surface quality estimators after waterjet cutting can be a useful tool that for allows for the selection of the optimal abrasive waterjet machining (AWJM) technological parameters.

Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool

High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, machine learning models and algorithms were developed with support vector machine (SVM), Gaussian process relation (GPR), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that all machine learning models gave excellent predictive accuracy with an average MAPE value of 7.38%. The validation tests were also statistically significant, with ANFIS and ANN having MAPE values of 9.98% and 3.43%, respectively. Additional validation tests for the models with new experimental data indicate average R, RMSE, and MAPE values of 0.78, 0.19, and 36.17%, respectively, which are satisfactory. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Rɑ, among the process parameters, with 92% influence, and optimal cutting conditions were found to be cutting speed = 100 m/min, feed = 0.025 mm/rev, and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.

Influence of wall roughness on pressure distribution and performance of centrifugal dredge pumps

Wall roughness is an important factor that affects pump head and efficiency. Based on the open source program OpenFOAM, the wall function method and the equivalent sand roughness model were used to study the influence of wall roughness on the performance of centrifugal dredge pumps. Then, the steady state of the dredge pump under 10 different types of wall roughness was calculated using the k-omega SST turbulence model. The pressure distribution under different flow rates and rotation speeds was also analyzed. The results reveal that the relationship between the increase of wall roughness and the performance of the dredge pump can be roughly divided into three stages, which may be related to the link between roughness and the viscous layer. In particular, the pump performance decreases significantly, and the shaft power increases linearly when it is in the third stage (complete rough region). Finally, different degrees of roughness have minimal effects on the pressure distribution of the impeller.

Experimental study of plastic cutting in laser-assisted machining of SiC ceramics

In this paper, the cutting process of laser-assisted machining (LAM) of SiC ceramics is studied. Three processing states of brittleness, plasticity and thermal damage are determined based on the characterization analysis of surface integrity, tool wear and chip morphology, and the surface roughness and tool wear regression model established under the plastic processing state are verified. Firstly, the surface integrity, tool wear and chip morphology are investigated by the single-factor experiment of laser power to explain the material removal mechanism according to the material fracture theory and to determine the laser power range of plastic cutting (185 W–225 W). Then the Box-Benhnken experimental scheme is designed to establish a multivariate prediction model based on the response surface methodology (RSM) to obtain the regression equation of surface roughness and tool wear and further analyze the interaction between process parameters. Finally, the experimental values of surface roughness and tool wear are compared with the predicted values through optimization analysis. The results show that the maximum error is 5 % and 7.57 % respectively, which proves that the model has good prediction ability. The surface roughness and tool wear regression prediction models provide guidance for the selection of plastic cutting process parameters for SiC ceramics.

A Study on the Influence of Process Parameters on the Workpiece Surface Quality in the Cutting of Hard and Brittle Materials with Trepanning Drill.

Hard and brittle materials have excellent physical and mechanical properties and are widely used in the fields of microelectronics and optoelectronics. However, due to their high hardness and brittleness, the machining quality of a workpiece struggles to meet the requirements of practical applications. In order to improve the surface quality of deep-hole machining of hard and brittle materials, this article analyzes the formation mechanism of surface roughness and the exit-chipping width during the drilling machining of hard and brittle materials and establishes a mathematical prediction model for the surface roughness and the exit-chipping width of hard and brittle materials using a trepanning cutter. The experimental study on K9 optical glass machining shows that the surface roughness of the workpiece and the exit-chipping width increase with the increase in feed rate, and decrease with the increase in rotational speed. Through comparison and verification between theoretical and experimental values, the average errors of workpiece surface roughness and the exit-chipping width are 13.15% and 6.73%, respectively. This article analyzes the reasons for the errors. The results indicate that the theoretical model proposed in this article can be used to predict the surface roughness and the exit-chipping width of hard and brittle materials processed under the same conditions, providing a theoretical basis to optimize process parameters to improve the surface quality of the workpiece.