Surface Roughness Prediction Model Research Articles

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.

Surface Roughness Prediction in Hard Turning (Finishing) of 16MnCr5 Using a Model Ensemble Approach

This paper investigates the predictive quality in finishing turning processes of 16MnCr5, specifically focusing on the use of model ensemble methods to predict surface roughness. Surface roughness plays a critical role in determining the quality and functionality of machined components. The objective of this study is to develop an accurate and robust predictive model for surface roughness after finishing turning operations. The proposed approach employs a model ensemble method, which combines multiple predictive models to enhance the accuracy and reliability of the predictions. The experimental setup includes collecting data using various types of in-situ sensors along with corresponding surface roughness measurements. The collected dataset is used to train and test the ensemble model, which integrates different machine learning algorithms and statistical techniques. The results demonstrate that the model ensemble method yields superior predictive performance compared to individual models, effectively capturing the complex relationships between process signals and surface roughness.

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.

High-precision turning and ultra-smooth direct polishing of aluminum alloy mirrors.

Due to the high surface roughness requirements of aluminum alloy mirrors used in the visible light band, there are still great challenges in single point diamond turning of high-surface quality aluminum alloy mirrors. In this paper, a processing method for aluminum alloy mirrors is proposed. Based on single point diamond turning technology, the prediction model of aluminum alloy surface roughness was established. The mapping relationship between the surface roughness of the aluminum alloy mirror and each turning parameter was obtained, and the maximum possible surface quality was achieved. On the basis of the turning results, the method of small tool polishing was used to remove the turning texture generated by the copy effect of the tool arc radius, suppress errors of the medium and high-frequency, and reduce the surface roughness. The single abrasive removal efficiency model was established and mechanical removal in the polishing process was analyzed. Combined with the chemical action in the polishing process, two types of polishing liquid-acidic and neutral, were prepared and analyzed. The optimal polishing parameters were obtained through multiple single-factor experiments. On the basis of this, the surface roughness of the aluminum alloy after turning was optimized. The results show that the value was reduced from 4.811 to 1.482 nm, an increase of 69.2%. This method can effectively improve the machining accuracy of aluminum alloy mirrors and provide an important process guarantee for the application of aluminum alloy materials in visible-light systems.

Study on surface quality of CNTs/2009Al composites in grinding process with a volume fraction ratio of 3%

Composite materials made of aluminum-based carbon nanotubes (CNTs/Al) are often employed in industries like aerospace and national defense that place a high priority on material processing accuracy at the material’s surface. The most popular machining technique for enhancing the surface quality of materials is grinding. This paper built a prediction model for the surface roughness of CNTs/2009Al composites and identified the process of micro-grinding. Using the ABAQUS software, a non-uniform finite element simulation model of the single abrasive grain grinding process of CNTs/2009AL composites was created. An electroplated diamond grinding rod with a diameter of 2 mm was used in the micro-grinding experiment on the CNTs/2009Al composite, and the impact of the grinding parameters on the quality of the machined surface was examined. According to the findings, the feed speed has the least impact on the material’s surface roughness, whereas the spindle speed and grinding depth both have a significant impact. The measurement of the minimal roughness is 0.117 m. A shallower grinding depth, a lower feed rate, and a correspondingly higher spindle speed should be used in order to provide a superior surface quality. According to the prediction model of micro-grinding surface roughness and laser confocal microscope scanning, the primary phenomena of CNTs in the grinding process include pull-out, fracture, and elastic deformation.

Grinding surface roughness prediction for silicon nitride ceramics: A dynamic grinding force and frequency domain approach

The existing surface roughness theoretical predictive models exhibit high parameter sensitivity and complexity, resulting in low reliability. Moreover, machine learning-based predictive models are constrained by the training parameter range, limiting their precision and generalization. In response to these limitations and from the frequency domain perspective, this paper proposes an innovative surface roughness (Ra) predictive model for silicon nitride ceramics grinding based on the dynamic grinding force model. First, a dynamic grinding force model was established using single grain scratch test results from both ductile and brittle removal stages, with model parameter estimation assisted by Genetic Algorithm. The results demonstrated a Mean Relative Error (MRE) of 19.51% between experimental and theoretical dynamic grinding force values, and a Relative Error (RE) of 9.37% for average grinding force. Subsequently, the frequency domain characteristics of both experimental grinding force signals and surface topography texture curves were extracted, which exhibited a significant correlation based on cosine similarity and Kendall's tau. Finally, leveraging the frequency domain characteristics of grinding force, the surface roughness predictive model was developed through the Convolutional Neural Network. The predictive model achieved an MRE of 12.79%, with random grinding experiments further confirming the model's accuracy with an MRE of 13.65%. These findings indicate that the proposed surface roughness predictive model offers high generalization capability and accurate prediction across a broader parameter range.

Transfer Learning Enhanced Gaussian Process Model for Surface Roughness Prediction with Small Data

Surface roughness is critical for the functionality (e.g., wear and fatigue) of a machine component. Nevertheless, the accuracy and efficiency are limited for analytical and simulation methods to predict surface roughness. On the other hand, data-driven methods (e.g., machine learning) provide an alternative for this purpose. However, the power of machine learning models is often limited by the availability of machining data. Therefore, this paper proposed a new model by integrating Transfer Learning (TL) and Gaussian Process (GP) for roughness prediction. The datasets from the literature are leveraged with additional measured data to improve the model accuracy. The model performance is benchmarked with the non-augmented Gaussian Process Model (GPM) and the results show that the TL-enhanced model performs better than the non-enhanced model.

Surface roughness prediction of SiCp/Al composites in ultrasonic vibration-assisted grinding

SiCp/Al composites are important materials in the automotive and aerospace fields. In order to improve surface quality of SiCp/Al composites, ultrasonic vibration assisted grinding was used to grind SiCp/Al composites. This paper observed and analyzed the microscopic morphology of the ground surface. Combined with the influence coefficient of axial ultrasonic vibration, a prediction model for the surface roughness of ultrasonic vibration-assisted grinding of SiCp/Al composites was established. Compared with the experimental results, its accuracy was verified. The results show that compared with conventional grinding, ultrasonic vibration-assisted grinding can improve surface roughness to a certain extent, and the maximum error of the surface roughness prediction model for ultrasonic-assisted grinding of SiCp/Al composites is 3.64 %. Finally, the grinding process was optimized using NSGA-II multi-objective optimization algorithm, the optimized grinding process parameters were vs = 35 m/s, vw = 0.7 m/min, ap = 9 μm and A = 5 μm.

Research on the Analysis and Prediction Model of Machining Parameters of Titanium Alloy by Abrasive Belt

As a high-performance and difficult-to-machine material for the manufacture of blades, titanium alloys are increasingly being used in high-end manufacturing industries such as aerospace and aircraft. As engineering applications become more demanding, so do the requirements for precision. However, to date, the choice of blade grinding parameters is still mainly dependent on the traditional “trial cut” and “experience” method, making the processing efficiency low and the quality of processing difficult to be guaranteed. In order to achieve the requirements of high precision and low surface roughness of the workpiece, to get rid of the status quo of relying on manual decision-making, and to achieve reasonable prediction and control of surface quality, this paper proposes to establish a theoretical prediction model for surface roughness of titanium alloy by abrasive belt grinding, and to analyze the influence of the main process parameters on surface roughness during the grinding process through experiments. A theoretical prediction model for surface roughness was developed. The experimental results show that the model has certain accuracy and reliability, and can provide guidance for the high-precision prediction of the surface roughness of ground titanium alloy blades, which has strong practical significance in engineering.

Estimation of surface roughness of direct metal laser sintered AlSi10Mg using artificial neural networks and response surface methodology

ABSTRACT Direct metal laser sintering (DMLS) is a metal-specific additive manufacturing (AM) technique that has grown in efficiency and precision due to compelling advancements in high-power lasers and fiber optics. This study examines the surface roughness of AlSi10Mg specimens manufactured additively using the DMLS technique. First, DMLS experiments were conducted with a range of control variables, including laser power, laser speed, orientation, and post-heat treatment temperatures. Later, surface roughness prediction models were developed using machine learning techniques and statistical methods such as artificial neural networks (ANN) and response surface methodology (RSM). The ANN model with an architecture of 4-9-9-1 is identified as the optimal network. The predictions of the ANN models were compared to those of the RSM models, and performance was quantified using the correlation coefficient (R-value) between predictions and the experimental data. The R-value of 0.96218 with experimental data and the least mean absolute percentage error (MAPE) of 0.9804% indicated that ANN predictions were more accurate than the RSM model estimates. Conclusive results prove that the developed ANN model accurately estimated the relationship between DMLS process parameters and surface roughness.