Surface Roughness Prediction Model Research Articles

Experimental study on abrasive flow finishing of narrow inner cavity of alumina-dispersed copper absorber

Given the difficulties in processing and detecting the narrow and deep inner cavities of the diffusive copper absorber used by the High Energy Photon Source (HEPS), a regression equation prediction model for surface roughness and material removal depth based on abrasive flow machining was proposed through mixed-level orthogonal experiments. The influences of machining area pressure, machining cycle number, and abrasive mesh number on surface roughness and material removal were revealed. Subsequently, abrasive flow machining verification experiments were conducted on the narrow and deep inner cavities of the absorber, showing that the experimental results were within 15% error compared to the model predictions. After abrasive flow machining, the roughness decreased from about 4.4 μm to around 0.6 μm, significantly improving the surface quality of the workpiece. These research findings can provide a theoretical basis and technical support for the optical finishing of workpieces with narrow and deep variable cross-section inner cavities.

Changeable environment visual detection of grinding surface roughness based on lightweight network

ABSTRACT The texture features of the grinding surface are not obvious, when the light source environment and shooting angle change, the prediction model of grinding surface roughness based on the deep learning method is greatly affected. Moreover, when applied to the mobile end or the embedded end, the model requires smaller size and faster inference time. This paper proposes a surface roughness detection model for grinding based on lightweight network MoCoViT, which can be applied to changeable imaging environments. This model uses the advantages of Transformer and mobile convolutional networks to improve performance and efficiency, so as to complete the recognition of grinding surface roughness in a transformable image environment. For different angles and different light sources, three experiments are designed to test the recognition effect of the model under unknown angles and unknown lighting conditions. The experimental results show that the grinding surface roughness detection model based on MoCoViT network has the highest accuracy in the test set. It proves that MoCoViT model has good robustness to the grinding surface roughness detection in the image changeable environment, which lays a foundation for the industrial application of online visual detection of grinding surface roughness.

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.

Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch's method complemented by time-frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

A multi-target predictive model for predicting tool wear and surface roughness

To simultaneously predict tool wear and surface roughness accurately during machining, this paper develops a multi-target predictive model that leverages a novel two-step feature-integration approach and Multi-kernel Gaussian process autoregressive regression (MK-GPAR). Compared with the traditional Gaussian process autoregressive regression (GPAR) models, the MK-GPAR demonstrates an enhanced capability in capturing detailed structures within heterogeneous data originating from various sources or disparate data forms. The weight coefficients of the kernel functions in MK-GPAR are typically assigned a value of 1 to minimize their influence on the predictive model. Furthermore, when compared to conventional multi-target predictive models like Gradient Boosting, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), the GPAR not only delivers higher-accuracy predictions for tool wear and surface roughness but also provides corresponding confidence intervals (CI). However, the CI of predictions for tool wear and surface roughness provided by MK-GPAR are inconsistent, which will seriously affect the reliability of the evaluation of the prediction results. The proposed two-step feature-integration approach is employed to enhance signal features effectively, reduce noise, and mitigate its adverse impact, thus enhancing the consistency and smoothness of the CI. The two-step feature-integration approach integrates principal component analysis (PCA) and orthogonal neighborhood preserving projections (ONPP) to merge force and vibration signal features obtained from the fixture's monitoring signals. Following the two-step feature-integration (first PCA, then ONPP) process, the MK-GPAR is utilized to build a multi-target predictive model for tool wear and surface roughness prediction. The impact of the proposed two-step feature-integration method on the prediction performance of the MK-GPAR is demonstrated revealed milling experiments. The experimental findings indicate that the proposed two-step feature-integration method significantly enhances the accuracy of predictions and the consistency of CI of the MK-GPAR. This research lays the foundation for implementing the multi-target predictive model in real industrial environments to predict tool wear and surface roughness.

Multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

Explainable prediction of surface roughness in multi-jet polishing based on ensemble regression and differential evolution method

Surface roughness is a critical parameter for quantifying the surface quality of a workpiece. Accurate surface roughness prediction can significantly influence the overall quality of the final components by reducing costs and enhancing productivity. Although various prediction methods have been explored in previous research, less attention has been given to explainable prediction methods for surface roughness. This paper proposes a surface roughness prediction model based on the Differential Evolution (DE) algorithm with ensemble learning. It aims to elucidate the intrinsic correlation between processing parameters and surface roughness values in Multi-Jet Polishing (MJP) using explainable analysis methods. The proposed Ensemble Regression with Differential Evolution (ERDE) algorithm comprises two main modules: data processing and analytics, and ensemble model prediction. These modules investigate the surface roughness of MJP from the data and model levels, respectively. To validate the effectiveness of ERDE, MJP experiments were conducted on three-dimensional printed components of 316L stainless steel. The experimental data indicated that ERDE outperforms other existing algorithms, reducing the mean absolute percentage error by approximately 42 %.

Ensemble learning with support vector machines algorithm for surface roughness prediction in longitudinal vibratory ultrasound-assisted grinding

It is critical to have an accurate prediction of surface roughness (Sa) in order to improve grinding productivity, reduce costs, and minimize the period of time required for trials and testing. Although many prediction methods have been developed, fewer studies have been conducted on the prediction of surface roughness in longitudinal ultrasonic vibration-assisted grinding (LUVAG). In this paper, a surface roughness prediction model algorithm based on ensemble learning of support vector machines (ELSVM) is proposed that can be used for surface roughness prediction of LUVAG alumina ceramics. This paper first details the development of the ELSVM surface roughness prediction model, which consists of four modules: the prepossessing module, the multi-algorithm regression module, the support vector machine algorithm (SVM) module, and the ensemble module. In addition, ELSVM was compared with four other machine learning methods based on experimental results for surface roughness prediction modeling. The error of ELSVM model was reduced by 6.3%, 7.9%, 8.9%, and 7.5%, respectively, compared to the individual prediction models such as I-AISPSO, I-AIS, SPSO, and KBaNN. Comparison results show that the ELSVM model has the lowest average ratio of Mean Absolute Error (MAE) for surface roughness prediction in LUVAG.

An integrated framework for a multi-material surface roughness prediction model in CNC turning using theoretical and machine learning methods

An integrated framework for a multi-material surface roughness prediction model in CNC turning using theoretical and machine learning methods

Research on tool wear and surface quality in laser-assisted ultrasonic elliptical vibration cutting of cemented carbide

The Laser-ultrasonic elliptical vibration cutting (LA-UEVC) method was introduced for machining challenging cemented carbide. The machining parameters were estimated through numerical simulation, and the cutting forces, the tool wear and the workpiece surface quality were analyzed and investigated. Compared to precision cutting, during LA-UEVC of cemented carbide, the main cutting force and the cutting radial thrust force were reduced by 44.3% and 31.2%, respectively. Moreover, the tool wear decreased, which led to an increase in tool life by 118.8%, and a maximum reduction of 89.7% of Workpiece surface roughness was received. The impact of laser power and cutting parameters on surface roughness was analyzed through orthogonal experiments, and a surface roughness prediction model was established using a quadratic regression model.

Investigation on evaluation and prediction of surface roughness and terahertz reflectivity analysis of the SiCp/Al composites component by diamond turning

SiCp/Al composites are suitable for satisfying the lightweight requirements of terahertz wave-reflecting components. In this paper, diamond-turned surfaces of 60 % high volume fraction SiCp/Al composites are characterized in terms of surface roughness and terahertz reflectivity. The finished surfaces of SiCp/Al composites are obtained using single-point diamond turning with different process parameters, and surface roughness measurements as well as SEM and EDS analyses are conducted on the surfaces. The theoretical relationship between surface roughness and terahertz reflectivity is analyzed, and terahertz reflectivity testing experiments are conducted and validated. The surface roughness evaluation system and prediction model for SiCp/Al composites with a volume fraction of 60 % are established, and the errors between the experimental results and the predicted results are verified within 10 % by random experiments. Finally, the turning process parameters are optimized using a genetic algorithm, and the optimized turning process parameters are obtained as the spindle speed of 750 r/min, the feeding speed of 1 mm/min and the turning depth of 6 μm.

Prediction of surface roughness and optimization of process parameters for efficient cutting of aluminum alloy

At present, there are obvious deficiencies in the research on the high-speed machining mechanism of aluminum alloy materials, especially how the high-speed cutting parameters of the new 7 series aluminum alloy materials affect the machining quality of the workpiece, and how to optimize the high-efficiency cutting process parameters under the premise of ensuring the machining quality. This paper summarizes the research results of the optimization methods, means and paths of 7075 aluminum alloy cutting process parameters, and proposes a multi-objective and multi-constraint high-efficiency cutting process parameter optimization technical scheme. In addition, using the response surface method to establish a surface roughness prediction model for plane milling, and analyze the influence of cutting speed, feed, and amount of cutting tool on the surface roughness, thereby establishing a multi-constrained objective equation for efficient cutting. Then use the cuckoo search algorithm to obtain a set of optimized cutting parameters. Finally, using the optimized set of cutting parameters to process verification of a cover plate. The result shows that: the processing efficiency of the workpiece can be improved by about 15.6% man-hours, and the surface processing quality of the workpiece can be effectively guaranteed.

Experimental Optimization of High-precision Turning Parameters of AL6061 Materials for Automotive Industry Based on Grey Relational Analysis

This research article aims to explore the relationship between the machining parameters of a Slant Bed Turning Centre Computer Numerical Control (SB/C/CNC) precision lathe and surface microhardness, dimensional error and surface roughness of AL6061. A technique called the central composite design (CCD) method with 13 experiments was used to evaluate the surface microhardness, dimensional error, and surface roughness after a turning operation using a micro-grooved texture tool. Separate prediction models were developed for each of these characteristics using the response surface method (RSM) in order to find the optimal process parameters for each characteristic. The analysis of variance revealed that the prediction models for surface microhardness, dimensional error, and surface roughness were highly significant, with p-values less than 0.0001. The process parameters that resulted in the highest surface microhardness were a cutting speed (Vc) of 154.363 m/min and a feed rate (fz) of 0.231 mm/rev. On the other hand, the process parameters that led to the lowest dimensional error and surface roughness were Vc = 154.363 m/min, fz = 0.1389 mm/rev, and Vc = 152.081 m/min, fz = 0.1025 mm/rev, respectively. The multi-objective prediction model based on gray relational analysis showed an error range of 1.5% to 3.1% and a minimum gray relational degree value of 0.3503 within the feasible process parameter range. The accuracy of this multi-objective prediction model was higher, with a stronger response to the cutting speed Vc compared to the feed rate fz. The determined feasible process parameter range serves as a useful reference for engineers working with AL6061 materials in turning operations.

Influence factors and prediction model of surface roughness in single-point diamond turning of polycrystalline soft metal

Ultrasmooth surface of soft metals is required in the field of advanced technology. However, the strong plasticity of soft metals makes it hard to achieve high surface finishing in ultraprecision machining. In this paper, the ultraprecision machining experiments of polycrystalline tin are carried out, and the factors including tool nose radius, cutting parameters, and material properties affecting the surface roughness in single-point diamond turning are investigated. Side flow, anisotropy, and grain boundary step are identified as the main causes of increasing of surface roughness, with grain boundary step demonstrating the largest impact. A surface roughness prediction model of tin in single-point diamond turning is established, which takes the minimum undeformed chip thickness, side flow, anisotropy and grain boundary step into consideration. The experiments show that the maximum prediction error of this prediction model is 6.51%.

Process Optimization of Polishing Titanium Alloy Material with a Pulsating Air Jet.

Titanium alloy is a widely used metal material, which can be applied in fields such as healthcare, petroleum exploration, aerospace, etc. In this paper, a new method for polishing the titanium alloy by a pulsating air jet is proposed. Compared with traditional abrasive jet polishing, this method has the advantages of simple structure, low nozzle wear, and high polishing flexibility. The working principle and material removal mechanism of the pulsating air jet polishing (PAJP) are introduced. Combined with orthogonal experiments, range analysis and variance analysis were used to find out the influence degree of each process parameter on the PAJP of titanium alloy, and the optimal level of each parameter was found. Through the experiments, a prediction model of surface roughness was established by regression analysis, and the predicted value was compared with the measured value. The maximum relative error of the prediction model was 10.3%, and the minimum relative error was 1.1%. The average relative error was 6.2%.

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.

Wear-induced variation of surface roughness in grinding 2.5D Cf/SiC composites

As the ideal high-temperature component materials, Cf/SiC composites require high grinding surface quality for their final preparation. It is a challenging issue to achieve the desired surface quality of such hard brittle material with serious rapid tool wear. A surface roughness predictive model involving the grinding wheel cumulative wear was developed for grinding process of 2.5D needled Cf/SiC composites. Compared with the experimental data, the predicted ground surface roughness errors of 2.5D needled Cf/SiC composites are less than 12 %. It is found that due to the grinding wheel cumulative wear effects, the resin-bonded diamond wheel presented self-sharpening phenomenon as increasing specific material removal volume (SMRV). The grinding wheel cumulative wear effects are conducive to Cf/SiC ground surface improvement. When the SMRV reached 720 mm3/mm, the surface roughness decreased by 21.2 % with a 180# grinding wheel, and 8.6 % with a 280# grinding wheel. This work provides some insights for preparing the tool condition during grinding 2.5D needled Cf/SiC composites.

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.

Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20

The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%.

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 of diamond cutting mechanism for aluminum alloy 6061 with AlFeSi particle effect: Modeling and simulation

Aluminum alloy 6061 (Al6061) contains two main intermetallic compounds: Mg2Si phase and AlFeSi particles. The hard-brittle particles generate protrude, voids and scratch marks on the machined surface which can not be ignored for the nanometric surface quality in ultra-precision machining. This paper makes a further study on the formation mechanisms of three surface defects induced by AlFeSi particle which is bigger in size and more in amount than Mg2Si on Al6061. The cutting mechanism, including stress distribution, deformations, surface morphology and chip formation is compared between conventional Al6061 with micro-particles and RSA6061 with submicro-particles by conducting diamond orthogonal cutting experiments and developing finite models. It is found that the type of surface defects is related to the particle size and locations. Better machined surface quality with fewer defects can be obtained from RSA6061 with submicron particle compared with Al6061, and better agreement is found between the simulation results from FE model with AlFeSi particles effect and experiment. Based on the cutting mechanism study, surface roughness prediction model is developed for two Al6061 alloys in SPDT by considering the AlFeSi particle effects on minimum chip thickness (MCT) and the tool-workpiece contact conditions as well as particle-induced roughness components. Diamond turning experiments are conducted to verify the proposed prediction model and shows that, the new model makes a more precise prediction for conventional Al6061.