Surface Roughness Model Research Articles

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 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.

A Closure Contact Model of Self-Affine Rough Surfaces Considering Small-, Meso-, and Large-Scale Stage Without Adhesive

Contact interface is essential for the dynamic response of the bolted structures. To accurately predict the dynamic characteristics of bolted joint structures, a fractal extension of the segmented scale model, i.e., the JK model, is proposed in this paper to comprehensively analyze the dynamic contact performance of engineering surfaces and revisit the multi-scale model based on the concept of asperities. The influence of asperity geometry, dimensionless material properties, and the elastic, elastoplastic, and full plastic mechanical models of a single asperity is established considering the asperity–substrate interaction. Then, a segmented scale contact model of rough surfaces is proposed based on the island distribution function in a strict sense. The mechanical contact process of determining rough surfaces is divided into small-scale, medium-scale, and large-scale stages. Moreover, cross-scale boundary conditions, i.e., al1′, al2′, and al3′, are provided through strict mathematical deduction. The results show that the real contact area and contact stiffness are positively correlated with fractal dimension and negatively correlated with fractal roughness. On a small scale, the contact damping decreases with an increase in load. In meso-scale and large-scale stages, the contact damping increases with the load. Finally, the reliability of the proposed model is verified by setting up three groups of modal vibration experiments.

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%.

Mesoscopic coupled plasticity-damage model of rough surface considering size-dependent plasticity

Metallic materials exhibit significant size effect at mesoscopic scale have been revealed by many experiments. Thus, it is essential to consider size effect when conducting mesoscopic scale studies in tribology. However, there is no constitutive model considering both ductile fracture and size effects of rough surface in the literature. At the same time, the simulation of friction and wear at the mesoscopic scale suffer from computational inefficiency using the conventional theory of mechanism-based strain gradient (CMSG) plasticity theory. To address the aforementioned challenges, we propose a mesoscopic coupled plasticity-damage model by combining the coupled plasticity-damage model and the simplified CMSG plasticity theory. This model can take into account not only the effect of stress state, temperature, strain rate on plasticity and fracture, but also the effect of the effective plastic strain gradient. Meanwhile, the model is able to solve the effective plastic strain gradient by a simplified method, thereby enhancing the computational efficiency. Then, model parameters for bearing bushing material of an engine are calibrated. Finally, scratch simulation and scratch tests of bearing bushing material at the mesoscopic scale are conducted to validate the effectiveness of the mesoscopic constitutive model. The residual scratch depth and width, coefficient of friction at different normal loads are investigated by experiment and simulation, respectively. Results proved the effectiveness of mesoscopic coupled plasticity-damage model, which is applicable for investigating issues related to wear, friction, and contact.

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.

Enhancing Surface Quality and Tool Longevity in EDM of D2 Steel Using Copper Composite Tools

Hard material machining and die manufacture are the main applications for electrical discharge machining (EDM). For EDM, conventional electrode materials include graphite, steel, brass, pure copper, and alloys based on copper. Unfortunately, excessive tool wear is a common problem with these materials, which raises the cost of machining. In this investigation, hardened D2 steel was machined using a composite tool made of 90% copper and 10% silicon carbide. The powder metallurgy method was used to create the composite tool. Surface roughness (SR) and electrode wear rate (EWR) were compared to a number of process variables. For experimentation, a response surface methodology based on CCD design in design of experts software was used. SR and electrode wear rate models were created using the CCD approach. Utilizing analysis of variance, the most important factors and their effects on EWR and SR were determined. The lowest tool wear rate of 0.39 gm/min is achieved with a current of 5 Amps, a pulse duration of 100 µs, and a pulse interval of 10 µs with an R2 of 0.9957, which accounts for 99.57% of the variability, the tool wear rate model fits the data very well and obtained lowest surface roughness of 2.8 µm.

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.

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.

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.

Surface roughness and fracture cracks of Al2O3/TiO2 composite coating by wet chemical mechanical grinding with structured abrasives pad

In the fields of wind power, and other industries, the Al2O3/TiO2 coating is prepared and ground on the bearing ring, in order to obtain the insulation resisting the electrical erosion for the motors at high voltage. A novel wet chemical mechanical grinding (WCMG) method is proposed to finish this hard-brittle composite material, utilizing the structured diamond abrasives pad and the NaOH solution. The softened workpiece's surface under the chemical reaction was demonstrated by means of Raman spectrum, indentation and scratching tests. The analytical model of surface roughness and fracture cracks is established based on the synthesis of geometry and kinematics, contact mechanics, material removal and indentation fracture mechanics. The surface roughness of coating was reduced from initial Sa 3.293 μm down to final Sa 0.049 μm in the WCMG process with grain size 3 μm and pH 12.01, mostly attributed to the ductile removal of chemically softened coating surface.

Simulation modeling of wafer grinding surface roughness considering grinding vibration

When using the workpiece rotation method to grind wafers, the grinding end vibration will deteriorate the surface roughness of the wafers. To study the impact law of vibration on the surface roughness of wafers during the grinding procedure, this paper presents a new approach to simulate and model the surface roughness of wafer grinding considering the grinding vibration. Firstly, the dynamics model under the consideration of grinding force was established for the grinding end of the grinding wheel and workpiece turntable. Secondly, using the iterative method to solve the dynamic equations that have been established, the vibration equation is obtained by fitting the displacement vibration curve of the end. Then, by reconstructing the surface grain of the gear teeth, a simulation model of wafer grinding surface roughness was established considering material removal, grain motion and grinding vibration. And then the grinding comparison test was conducted to compare the simulation and test surface roughness measurement results. The maximum deviation of the surface roughness Sz and Sa was 7.7 % and 5.4 %, respectively. The results indicate the accuracy of the modeling. Finally, based on the established wafer roughness model, explore the impact of vibration on wafer roughness during the grinding procedure. This model provides a reference for the research of wafer precision grinding technology.

Research on the mechanism of tool-workpiece coupling contact and theoretical modeling of surface roughness in turning brittle materials

Research on the mechanism of tool-workpiece coupling contact and theoretical modeling of surface roughness in turning brittle materials

Influence of different machining methods on the surface roughness of TC4: dry milling, water-based fluid wet milling, and foam spray milling

The surface quality of workpieces is influenced by various factors, including processing parameters, processing techniques, cutting force, and cutting vibration. Many researchers have studied wet cutting to minimize milling forces and vibrations. We explore a high-density water-based foam milling method. By this method the foam is sprayed onto the surface area of tool and workpiece to absorb vibration energy. Firstly, a milling test system was established to conduct tests on TC4 titanium alloy under three different machining conditions (dry milling, water-based fluid wet milling, and foam spray milling), and the three-dimensional (3D) milling forces and milling vibrations were simultaneously recorded during the milling process, and the workpiece’s surface roughness was measured using a contact roughness measuring instrument. Then principal component analysis method was performed on the 3D milling forces and vibrations to obtain dimensionality-reduced feature values. Subsequently, multi-feature combination prediction models of surface roughness corresponding to the three machining conditions were developed using particle swarm optimization and a generalized regression neural network. Finally, the developed prediction models were compared and analyzed. The research results indicate that high-density water-based foam spray milling can effectively reduce the milling force by 70% and the milling vibration by 85% at most. Foam spray milling reduces the average roughness by up to 49%. The roughness prediction accuracy reaches 95.43%, with an error of less than 0.054 µm.

Surface roughness prediction model and surface topography analysis of 2.5D-Cf/SiC in two-dimensional ultrasonic assisted grinding based on GA-BP neural network

Because of the anisotropy and non-uniformity, the grinding surface quality of Cf/SiC composites is difficult to be accurately predicted. The quality of the surface directly determines the assembly accuracy. To better predict the surface quality of 2.5D-Cf/SiC composites, the BP neural network was optimized by genetic algorithm (GA), and the surface roughness prediction model of fiber orientation, ultrasonic amplitude, and machining parameters was established. The empirical formula prediction model of surface roughness was established by the method of multiple linear regression analysis. The results show that the prediction accuracy of GA optimized BP neural network is the best, followed by the empirical formula, and the prediction effect of the BP neural network is the worst. The average absolute percentage error of these three models is 10.045%, 16.912% and 26.6245% respectively. The kinematic models of conventional and two-dimensional ultrasonic-assisted grinding of single particles were established respectively. Based on the kinematic model, the reasons for the reduction of surface roughness by 2D ultrasonic-assisted grinding are explained. According to the orthogonal test results, the surface roughness decreases the most when vs is 0.66m/s, vw is 100mm/min, ap is 150μm along the fiber orientation of 0°, and the maximum percentage of reduction is 53.18%. Increasing the linear speed, reducing the feed speed, reducing the grinding depth, and applying ultrasound can reduce the extrusion pressure along the axis of 0° fiber, and then reduce the length and depth of cracks and thus reduce the surface defects. The axial shear force of the 90° oriented abrasive particles on the fiber is reduced, thus reducing the surface damage caused by torsional deformation. Reduce 3D surface profile and improve surface quality. The maximum percentage reduction of each parameter index is as follows: root mean square height decreased by 28.8%, skew decreased by 88.61%, kurtosis decreased by 48.97%, maximum crest height decreased by 48.55%, maximum sag height decreased by 40.09%, maximum height decreased by 43.93%, and arithmetic mean height decreased by 26.06%.

Surface roughness model of ultrasonic vibration-assisted grinding GCr15SiMn bearing steel and surface topography evaluation

Surface roughness model of ultrasonic vibration-assisted grinding GCr15SiMn bearing steel and surface topography evaluation

Modeling of wear performance and surface roughness of AA6061-T6/B4C composite under dry sliding conditions by RSM

Modeling of wear performance and surface roughness of AA6061-T6/B₄C composite under dry sliding conditions by RSM

Effect of machining parameters on average surface roughness during computer numerical controlled dry milling of high strength AISI 420 martensitic stainless steel

This study focuses on developing an empirical model for average surface roughness during computer numerical controlled (CNC) dry milling of AISI 420 martensitic stainless steel, utilizing response surface methodology (RSM). Experiments were designed with three levels of axial depth of cut, feed rate, and spindle speed to quantify their impact on surface roughness. The RSM-Box-Behnken design was employed to construct the empirical model. Model adequacy was validated through residual analysis and analysis of variance (ANOVA). Analysis of the main effects and interaction effects revealed that the primary influences on average surface roughness were the feed rate, spindle speed, and axial depth of cut, while interaction effects were less significant. Optimal cutting conditions were determined to be a spindle speed of 1500 rpm, a feed rate of 30 mm min−1, and an axial depth of cut of 0.3 mm. The model’s validity was further confirmed through additional validation tests.

Enhancing face-milling efficiency of wood–plastic composites through the application of genetic algorithm–back propagation neural network

ABSTRACT Wood–plastic composites (WPCs) have advanced physicochemical properties and are widely applied in various fields. How to achieve high-efficiency, high-quality machining of WPC is a pressing issue that WPC manufacturing enterprises need to address. To this end, this research focused primarily on improving the machinability of WPC with end milling experiments. In this work, a single-factor method was used to analyse the impact of axial milling depth, spindle speed, and tool rake angle on resultant forces and surface roughness. Main effects analysis was applied to explore the degree of influence of axial milling depth, spindle speed, and tool rake angle on cutting forces and surface roughness. Furthermore, three-dimensional characterisation techniques were utilised to analyse the surface morphology characteristics of WPC during end milling. Finally, a genetic algorithm–back propagation neural network was applied to develop prediction models for resultant force and surface roughness, and optimal milling conditions were identified as axial milling depth of 0.50 mm, spindle speed of 7841 r/min, and rake angle of 15°; these produced the lowest resultant force and surface roughness. The findings of this work are proposed as a guide for better cutting performance in the industrial production of WPC.