Rough Model Research Articles

Experimental study of NiTi alloy cardiovascular stent formed via SLM

Selective laser melting (SLM) has garnered significant attention in the manufacturing of cardiovascular stents due to its potential for fabricating customized stents with intricate geometries that meet clinical requirements. This study aims to optimize the SLM parameters for NiTi stents to achieve minimal porosity and surface roughness, and investigate the biocompatibility and compression tests of SLMed stents. Firstly, the single-factor experiments of laser power, scanning speed and hatch spacing are conducted, respectively. Subsequently, a Box-Benhnken experimental was employed to establish regression prediction models for porosity and surface roughness using response surface methodology (RSM). The results show that scanning speed is the most influential factor on porosity, while laser power significantly affects surface roughness. The optimized parameter combination with minimum porosity of 0.165% and surface roughness of 6.17 μm as follows: laser power of 156W, scanning speed of 712mm/s, and hatch spacing of 0.9mm. The relative errors of them are 0.19% and 0.8%, respectively, indicating that the regression model possesses good prediction ability. Additionally, the corrosion rate and nickel ion release gradually increase with the immersion time, reaching the highest corrosion rate of 2.46g/m² and nickel ion release of 1.08μg after 42 days. Besides, the SLMed stent demonstrated good recovery ability in the compression test. The findings promote the utilization of SLM in the manufacturing of NiTi cardiovascular stents for medical implant applications.

Optimization of cycloid wheel shaping based on thermal mixed lubrication and wear characteristics

The cycloid pinwheel mechanism plays a key role in the function of RV reducers, making the lubrication and wear characteristics of the cycloid tooth profile vital areas of research. This study uses the RV-20E reducer as a case study to analyze the time-varying contact force and relative velocity between the cycloid wheel and the pinwheel, factoring in the system's dynamic properties. A transient contact model for thermal mixed lubrication of the cycloid tooth profile was developed, incorporating friction and temperature rise, and was based on a rough surface model of the dynamic bonding interface. This model enabled the calculation of oil film thickness, contact stress, and wear volume at the interface between the cycloid wheel and the pinwheel. Additionally, the study assessed the impact of traditional shaping methods on wear characteristics and derived a fitted cycloid tooth profile for the profile grinding process. These findings provide valuable insights into improving the lifespan of RV reducers.

A fast prediction method of fatigue life for crane structure based on Stacking ensemble learning model

To quickly obtain the fatigue life of cranes in service, the metal structure that determines the crane life is anchored. Meanwhile, the fast prediction method of fatigue life of crane metal structures based on the Stacking ensemble learning model is proposed. Firstly, in line with the structural stress method, the global rough model of the metal structure is established by the co-simulation technology to obtain the fatigue damage regions of the structure. The local fine model is constructed by local cutting and boundary condition transplantation to determine the critical weld at the failure regions. Secondly, through weld definition, equivalent structural stress acquisition, and fatigue life calculation, the sample data set with lifting load and trolley running position as input and fatigue life cycle times as output is constructed. Then, the Stacking integrated learning model combining gradient boosting, ridge regression, Extra Trees, and linear is built. On this basis, combined with the Miner theory, the rapid prediction of crane fatigue life is realized. Finally, the proposed method is applied to the QD40t × 22.5 m × 9 m general bridge crane. The results show that the life sample set constructed by the structural stress method is more accurate and reasonable than the nominal, hot spot, and fracture mechanics methods. The life prediction results of the Stacking integration model were improved by 6.3 to 49.2% compared to the single model. The method has theoretical and practical significance in reducing accidents and ensuring the safe operation of cranes.

A general valuation framework for rough stochastic local volatility models and applications

Rough volatility models are a new class of stochastic volatility models that have been shown to provide a consistently good fit to implied volatility smiles of SPX options. They are continuous-time stochastic volatility models, whose volatility process is driven by a fractional Brownian motion with the corresponding Hurst parameter less than a half. Albeit the empirical success, the valuation of derivative securities under rough volatility models is challenging. The reason is that it is neither a semi-martingale nor a Markov process. This paper proposes a novel valuation framework for rough stochastic local volatility (RSLV) models. In particular, we introduce the perturbed stochastic local volatility (PSLV) model as the semi-martingale approximation for the RSLV model and establish its existence, uniqueness, Markovian representation and convergence. Then we propose a fast continuous-time Markov chain (CTMC) approximation algorithm to the PSLV model and establish its convergence. Numerical experiments demonstrate the convergence of our approximation method to the true prices, and also the remarkable accuracy and efficiency of the method in pricing European, barrier and American options. Comparing with existing literature, a significant reduction in the CPU time to arrive at the same level of accuracy is observed.

Modeling and Prediction of Surface Roughness in Ball End Milling of Oxygen-Free High Conductivity Copper Using Adaptive Neuro Fuzzy Inference System

Oxygen Free High Conducting Copper (OFHC) is one of the reasons materials for numerous applications due to its high thermal conductivity, great machinability, and great quality In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed as a predicting model of the machining performance of OFHC measured for surface roughness in terms of process parameters, namely, the cutting feed rate or feed per tooth, axial depth of cut, radial depth of cut, and the cutting speed.

A drug repurposing screen reveals dopamine signaling as a critical pathway underlying potential therapeutics for the rare disease DPAGT1-CDG.

DPAGT1-CDG is a Congenital Disorder of Glycosylation (CDG) that lacks effective therapies. It is caused by mutations in the gene DPAGT1 which encodes the first enzyme in N-linked glycosylation. We used a Drosophila rough eye model of DPAGT1-CDG with an improperly developed, small eye phenotype. We performed a drug repurposing screen on this model using 1,520 small molecules that are 98% FDA/EMA-approved to find drugs that improved its eye. We identified 42 candidate drugs that improved the DPAGT1-CDG model. Notably from this screen, we found that pharmacological and genetic inhibition of the dopamine D2 receptor partially rescued the DPAGT1-CDG model. Loss of both dopamine synthesis and recycling partially rescued the model, suggesting that dopaminergic flux and subsequent binding to D2 receptors is detrimental under DPAGT1 deficiency. This links dopamine signaling to N-glycosylation and represents a new potential therapeutic target for treating DPAGT1-CDG. We also genetically validate other top drug categories including acetylcholine-related drugs, COX inhibitors, and an inhibitor of NKCC1. These drugs and subsequent analyses reveal novel biology in DPAGT1 mechanisms, and they may represent new therapeutic options for DPAGT1-CDG.

A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters

Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

Intelligent prediction of surface roughness of PSZ ceramic grinding based on correlation analysis and CNN-BiLSTM neural network

Partially stabilized zirconia (PSZ) ceramics are widely used in aerospace industry and other fields due to their superior performance. Surface roughness is a key indicator for evaluating the grinding level of PSZ ceramics. In order to reduce the prediction error of grinding surface roughness, an acoustic emission prediction model for PSZ ceramic grinding surface roughness based on correlation analysis and convolution-bidirectional long short term memory neural network (CNN-BiLSTM) was proposed. By analyzing the correlation between the eigenvalues of grinding acoustic emission signals and the grinding surface roughness values, the most relevant frequency bands and feature matrices between the grinding acoustic emission signals and the grinding surface roughness were selected as the input parameters of the CNN-BiLSTM neural network to reduce the error of acoustic emission prediction of grinding surface roughness. The research results show that the average prediction error of PSZ ceramic grinding surface roughness based on correlation analysis and CNN-BiLSTM neural network is less than 3.92%.

Evaluation of railway noise calculation models using CNOSSOS-EU considering the effect of rail roughness

Railway noise assessment is an important tool in decision-making for urban planning, investment in transportation infrastructure and implementation of noise reduction strategies. Currently, the procedure is based on CNOSSOS-EU (Common Noise Assessment Methods in Europe), which was developed in accordance with European Directive 2002/49/EC. This method takes into account a variety of factors impacting rail noise levels, such as vehicle characteristics and track features. The purpose of this study was to investigate the influence of rail roughness on generated railway noise and to evaluate the accuracy of calculation models using CNOSSOS-EU. The study involved in-situ railway noise measurements at three different locations near the city of Zagreb, Croatia, and measuring rail roughness at each location. In order to eliminate the influence of other factors, the rail track structures at the measurement locations were similar, and an identical train configuration was used for the study. Finally, a comparison was made between calculation models based on default roughness values and models including values obtained from in-situ measurements. This approach enabled an assessment of the variations in CNOSSOS-EU calculation models taking into account actual in-situ rail roughness measurements.

A rough set-based model for predicting soil greenhouse gases response to biochar

Biochar application to soil is a potential climate change mitigation strategy. In addition to long-term sequestration of the carbon content of the biochar itself, its application may reduce the emissions of other greenhouse gases (GHGs) from the soil. However, the reported effects of biochar application on soil GHG fluxes exhibit inconsistent results. Prediction of such effects is an important gap that needs to be addressed in biochar research. In this study, rule-based machine learning models were developed based on rough-set theory. Data from the literature were used to generate the rules for predicting the effects of biochar application on soil GHG (CO2, N2O, and CH4) fluxes. Four rule-based models for CO2 fluxes, two rule-based models for N2O fluxes, and three rule-based models for CH4 fluxes were developed. The validity of these models was assessed based on both statistical measures and mechanistic plausibility. The final rule-based models can guide the prediction of changes in soil GHG fluxes due to biochar application, and thus serve as a decision-support tool to maximize the benefits of biochar application as a negative emission technology (NET). In particular, mechanistically plausible rules were identified that predict triggers for GHG fluxes that can offset carbon sequestration gains. This knowledge allows biochar application to be calibrated to local conditions for maximum efficacy.Graphical

Experimental investigation on in-situ laser-assisted machining of single crystal silicon based on response surface methodology

Abstract In this paper, Response Surface Methodology (RSM) was utilized as a robust and convenient predictive tool to establish the correlation between process parameters in in situ laser-assisted machining and the surface roughness of single-crystal silicon. An optimized design of the diamond tool, a novel temperature field analysis method, and Response Surface Methodology (RSM) were utilized. The contribution rate of each process parameter on surface roughness was laser power > rotation speed > cutting depth > feed rate. The optimal process parameter combination is: rotation speed as 1001 r min−1, feed rate as 4.9 μm/r, cutting depth as 7.55 μm, and laser power as 28.81 W. Experimental validation of these optimal parameters compared surface roughness values obtained experimentally with those predicted. The surface roughness model showed a maximum relative error of 5.2%, with an average error of 4.8% across three experimental sets. These errors are within acceptable limits, indicating an alignment between predicted and experimental results.

Determination of interaction forces of (sub)-micron sized particles via the capillary rise method and colloidal probe atomic force microscopy: A combined approach

HypothesisThe adhesion forces of particles in the submicron range play a decisive role in many particle processes such as agglomeration. These forces are influenced by many factors such as particle size, surface roughness, and contact area. The quantification of these influences should be possible by linking adhesion forces, measured with colloidal probe atomic force microscopy (cp-AFM), and surface energy, measured with the capillary rise method, using different adhesion force models. ExperimentsSilica-silica (SiO2-SiO2), polystyrene-polystyrene (PS-PS) and mixed adhesive force contacts of micrometer-sized particles were measured by cp-AFM. The surface energy was determined by the capillary rise method using the Owens-Wendt-Rabel-Kaelble (OWRK) method. Various adhesive force models were used to link the experimentally measured forces. In the adhesive force models, the particle size, the distance between the particles, and the roughness were varied. FindingsAdhesion forces measured with the AFM can be linked to the OWRK method via adhesion force models such as the particle–particle Van-der-Waals (VDW) model, Rumpf’s roughness model, and a proprietary model for multiple particle–particle interactions with the surface energy. The main influencing factors are the substrate and particle roughness as well as their plastic or elastic behavior, which influences the contact area of the adhesive contact.

The mechanism of proppant transport dynamic propagation in rough fracture for supercritical CO2 fracturing

Supercritical CO2 fracturing technology has shown great potential for enhancing production in unconventional reservoirs. It is essential to clarify the transport mechanism of proppant under the dynamic propagation conditions within rough fractures. A realistic rough fracture model is reconstructed, and Computational Fluid Dynamics simulations are conducted to track proppant movement during fracture propagation. The typical transport characteristics of proppant within rough fractures are revealed, and the effects of fracture propagation rate, proppant density, mass flow rate, particle size, sand ratio, and temperature on the support effect are discussed. The results show that the flow channels formed by sand carrying fluid in rough fractures are complex, with fracture propagation changing some flow channels. The proppant forms an irregular sand bed interspersed with unfilled areas, and complex flow characteristics are generated. The increase in fractal dimension increases the resistance in the fluid flow process and affects the movement of the proppant, which tends to create unfilled areas. Low density and size of proppant can improve the proppant placement length. In a certain temperature range, high temperature injection of sand carrying fluid can improve the proppant placement effect. In addition, the low sand ratio and high mass flow rate pumping can be used to form the dominant channel, followed by pumping with a high sand ratio and low mass flow rate for effective support.

Role of wall roughness on the energy performance for a mixed flow pump with tip clearance

Abstract This study delves into the intricate influence of wall roughness on the mixed flow pump energy performance, particularly focusing on the simultaneous presence of tip clearance. Utilizing a well-performing mixed flow pump with a tip clearance of 0.8 mm as the subject of investigation, the research employs numerical simulation at different roughness to comprehensively analyse the effects of wall roughness on pump head, efficiency, and cavitation performance. The numerical simulation employs the SST k-ω model and the equivalent sand-gain roughness model to simulate the turbulent flow account for wall roughness. The findings discerned that both head and efficiency are inversely related to wall roughness, with decrements amplified at elevated flow rates. Notably, the ‘hump’ in head performance near 60% of the design flow rate is significantly influenced by roughness beyond 31.6μm. Additionally, efficiency exhibits a minor uptick at 0.56μm roughness before a subsequent decline. The study also highlights the multifaceted impact of roughness on the internal flow structures, including vortex and cavitation patterns. These insights emphasize the importance of balancing roughness levels to enhance energy efficiency and cavitation resistance, crucial for the practical application and long-term reliability of such machinery.

Influence of tool wear on geometric surface modeling for TC4 titanium alloy milling

Addressing the insufficient accuracy of milling surface roughness prediction model, the formation causes of different roughness were analyzed, and a surface roughness prediction model which takes tool wear into account was established based on an improved Z-MAP algorithm. Combining tool geometry and tool wear morphology, a mathematical model of surface profile was built; And the motion trajectory equations of the milling cutter teeth were established via machining kinematics. The servo rectangular encirclement and Newton iterative method were adopted to improve the Z-MAP algorithm. Combining the Z-MAP algorithm with the insert motion trajectory equations, the values at different height coordinates of the workpiece grid nodes were obtained, allowing numerical simulation of workpiece surface geometry and analysis of the effect of cutting trajectory overlap on the machined surface morphology. TC4 titanium alloy milling experiment was conducted to validate the simulation model. The simulated surface roughness Sa exhibited significant deviation from experimental values before tool wear, with a maximum error of 53.08 %. Nevertheless, the simulated values of Sa were closer to experimental values after tool wear, with a maximum error of 15.45 % and a minimum of only 3.07 %. Additionally, the predicted results for the bearing length ratio Rmr(c) were consistent with experimental values. The mathematical model for surface roughness taking tool wear into account effectively predicted the shape profile and surface roughness of the machined surface, providing theoretical guidance and technical support for practical production.

Detecting rough volatility: a filtering approach

In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index H < 1 2 . Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock.

Study on the wear characteristics of cylindrical needle bearings on the crankshaft of RV decelerator

PurposeThe purpose of this study is to systematically analyze the wear of cylindrical needle bearings in rotary vector reducers under temperature rise and identify the influencing factors.Design/methodology/approachBased on the dynamic characteristics of the RV-20E reducer, the time-varying contact force of the cylindrical needle bearing and the entrainment speed of the inner and outer raceways were calculated. A mixed elastohydrodynamic lubrication model of the needle bearing, considering friction and temperature rise, was established using a dynamic rough tooth surface model. The model solved for the oil film thickness, contact stress and wear conditions of the bearing raceway contact area. The effects of the number of rolling needles, the diameter of rolling needles and surface strength on the wear characteristics were analyzed.FindingsThe results of this study show that the oil film thickness, oil film pressure and surface scratches of cylindrical needle bearings exhibit an uneven, patchy distribution under the combined effects of friction and temperature rise. When the radius of the rolling needle is less than 1.44 mm, inner ring wear is less than outer ring wear. Conversely, when the radius exceeds 1.44 mm, inner ring wear is greater. The optimal rolling needle radius is 1.6 mm. Increasing the number of rolling needles and enhancing the yield strength of the contact surface significantly extend bearing life.Originality/valueThis study provides valuable recommendations for optimizing bearing structural parameters and material characteristics in the design of rotary vector reducers.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0242/

Experimental study of wet and dry milling effect on surface roughness of TC4 titanium alloy

Titanium alloys are important engineering materials used in various industry areas, such as aviation, aerospace and medical devices. The surface roughness is a significant parameter for assessing the machining quality of titanium alloys. In this study, in order to compare the effects of dry and wet milling conditions on the surface roughness of titanium alloys, we established a test system to obtain the surface roughness, three-dimensional milling vibration and three-dimensional milling force signals. The gray correlation theory was applied to investigate the correlation between surface roughness and milling vibration or milling force under dry and wet milling conditions. Using the least squares method, the prediction models of surface roughness corresponding to dry and wet milling conditions were developed according to the features of vibration or force signals. Furthermore, the combined prediction models of surface roughness that integrate multiple signal features and milling parameters were established. The comparison results revealed that there was a stronger correlation between roughness and milling force, milling vibration in wet milling. Wet milling exhibited significantly lower roughness compared to dry milling under the same machining parameters, with up to a 59% reduction. The correlation coefficient of the wet milling model is as high as 0.95.

Strongly Different Adhesion Reduction for 1D or 2D Random Fractal Roughness, and an Extension of the BAM Model to Anisotropic Surfaces

The influence of roughness on adhesion has been studied since the time of Fuller and Tabor, but recently there has been debate about how roughness exactly seems to kill (but sometimes enhance!) adhesion, particularly with reference to the accepted model of fractal roughness. We show that the Persson–Tosatti criterion does not depend on anisotropy of the surface for a typical power law PSD if written in terms of rms roughness and magnification. Instead, a very simple extension of the Bearing Area Model (BAM) of Ciavarella to anisotropic fractal surface shows a weak but clear dependence on the anisotropy, with higher adhesion in the 1D case, showing better agreement than the Persson–Tosatti criterion to actual numerical results of Afferrante Violano and Dini. However, neither of the two models permit to capture the strong hysteresis found in experiments between loading and unloading, which is very likely to enhance adhesion more as we move from the isotropic to the full 1D case. This suggests the mechanism of load amplification along contact lines and the associated elastic instabilities, is not captured by either the Persson–Tosatti or the BAM model applied to anisotropic surfaces.

Sustainable EDM production of micro-textured die-surfaces: Modeling and optimizing the process using machine learning techniques

The present research explores the role of die-sinking EDM parameters, such as peak current, pulse duration, and gap voltage, and their proper selection for the cost-effective fabrication of negative micro-pillar-textured surfaces using a die-material of H13 steel alloy. The developed artificial neural networks (ANN) models of surface roughness and overcut outperform the response surface methodology models in predicting responses as higher ‘R2 values’ and lower ‘mean squared error’ with ANN models. A lower peak current of 2 A, lower pulse duration of 55.46 µs, and intermediate gap voltage of 37.83 V is selected from metaheuristic optimization approaches such as teaching–learning-based optimization and particle swarm optimization that are efficient and comparable with the desirability function-based approach while finding optimum parameter values. Further, sustainable fabrication of micro-textured H13 die surfaces is carried out on large areas using selected optimized parameters with a form tool electrode. The negative micro-pillar pattern surface demonstrates an average overcut of ∼ 40 ± 15 µm in comparison to the dimensions of the form tool. This research emphasized the sustainability of the EDM process by utilizing reusable dielectric fluid, prioritizing optimal parameters for high-quality fabrication using low electrical-energy utilization, and enabling large-scale production using a form tool.