Prediction Of Surface Roughness Research Articles

Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining

Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.

Investigation on surface characteristics of wall structures out of stainless steel 316L manufactured by laser powder bed fusion

Pressure equipment poses a high risk of harming people and the environment in case of failure. They are, therefore, highly regulated by the Pressure Equipment Directive. To enable laser powder bed fusion of metals (PBF-LB/M) for the manufacturing of such components, component appearance and quality need to be characterized and qualified for each specific system. In this study, the surface roughness of wall structures out of austenitic stainless steel (316L) is investigated. Wall structure specimens were produced by four manufacturing systems on different PBF-LB/M machines and with different powder materials. Surface roughness of specimens are compared in the upskin and downskin areas in relation to different slope angles and wall thicknesses. Although different process setups, parameters and powder feedstocks have been used, similarities in the dependency of the surface roughness related to the slope angle and wall thickness can be observed. This work furthermore presents a mechanism-based analytical approach to predict system-specific surface roughness. Particularly, the analytical approach on the influence of slope angle on the surface roughness of the downskin areas has not been covered in publications about PBF-LB/M before. The results of this work enable the prediction of system-specific surface roughness, which is especially important for parts with downskin areas and hidden surfaces without the possibility of additional surface treatment.

Surface roughness prediction and roughness reliability evaluation of CNC milling based on surface topography simulation

Surface roughness is influenced by various factors with uncertainty characteristics, and roughness reliability can be used for the assessment of the surface quality of CNC milling. The paper develops a method for the assessment of surface quality by considering the coupling effect and uncertainty characteristic of various factors. According to the milling kinematics theory, the milling surface topography simulation was conducted by discretizing the cutting edge, machining time, and workpiece. Considering the coupling effect of various factors, a roughness prediction model is established by the SSA-LSSVM, and its prediction accuracy reaches more than 95%. Then, the roughness reliability model was developed by applying the response surface methodology to achieve the assessment of surface quality. The proposed method is verified by the milling experiments. The maximum values of the relative errors between the simulation and experimental results of the surface roughness and roughness reliability are 9% and 1.5% respectively, indicating the correctness of the method proposed in the paper.

Development of Surface Roughness Prediction and Monitoring System in Milling Process

High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.

Prediction of undeformed chip thickness distribution and surface roughness in ultrasonic vibration grinding of inner hole of bearings

Prediction of undeformed chip thickness distribution and surface roughness in ultrasonic vibration grinding of inner hole of bearings

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.

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Surface Roughness Prediction in the Hardened Steel Ball-End Milling by Using the Artificial Neural Networks and Taguchi Method

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.

Prediction of surface roughness using deep learning and data augmentation

PurposeSurface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.Design/methodology/approachThis study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.FindingsThe adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.Originality/valueA roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Prediction of Surface Roughness in Additive Manufacturing Using Artificial Neural Networks

In this work, we applied an Artificial Neural Networks (ANN) approach for prediction of the part surface roughness for 3D printing technology. A small number of neurons was used for building ANN model with the help of MATLAB environment. The predicted values are found to be in excellent agreement with the experimental data with average error value of 8%.In addition, we compared the proposed ANN model to another regression-based approach. Results show that the proposed model has high accuracy in comparison to statistical approach. Therefore, we can use ANN model to predict the part surface roughness for 3D printing technology.

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.

Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

Prediction of surface roughness and depth of cut in abrasive waterjet milling of alumina ceramic using Machine learning algorithms

In abrasive waterjet (AWJ) milling process, the ability to accurately predict outcomes such as surface roughness (Ra) and depth of cut (DoC) holds immense significance for cost reduction, resource optimization, and minimizing material wastage. However, a major challenge lies in the limited availability of AWJ milling datasets, hindering the development of robust machine learning models. To address the predicament of outcome, the present study employs a bootstrapping data augmentation technique to expand small AWJ milling datasets. Augmented data is used to train machine learning models, and hyperparameter fine-tuning is conducted using grid search and 5-fold cross-validation.In the prediction of surface roughness (Ra) for AWJ milling of alumina ceramic, the study finds that a Multi-Layer Perceptron (MLP) Regression model with a 4–100-100–1 architecture, 'relu' activation function, 'adam' solver, alpha value of 0.0001, and 'adaptive' learning rate outperforms the decision tree and random forest regression models. In the case of prediction of AWJ milling depth of cut (DoC) in alumina ceramic, the random forest regression model surpasses MLP and decision tree regression models. It employs hyperparameters including 100 estimators, 'gini' criterion, 'log2′ for maximum features, a maximum depth of 50, a minimum sample split of 10, a minimum sample leaf of 2, bootstrap as ‘True’, and a minimum impurity decrease of 0.The methodology followed to develop the machine learning models in this study is also tested in wire electrical discharge machining (WEDM) process in the machining aspects of metal matrix composites (MMC) for the prediction of surface roughness (Ra). The experimental dataset was adopted from (Satishkumar et al., 2011). In case of prediction of surface roughness (Ra) in WEDM, the random forest regression model outperforms MLP and decision tree regression models. These developed machine learning models hold the potential to greatly assist manufacturing engineers in selecting optimal input parameters for AWJ milling and WEDM, thereby reducing the wastage of machine resources.

Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining

This study investigated the experimental work of titanium alloy in the die-sinking electrical discharge (EDM) machining process to enhance surface integrity (surface roughness) by applying regression-based modeling. Furthermore, a multiple polynomial regression (MPR) model was developed to predict surface roughness responses under optimized conditions. The effects of EDM parameters, such as pulse-on time (ON), pulse-off time (OFF), peak current (IP), and servo voltage (SV), on surface roughness were studied. The experiment was conducted using a two-level full factorial design with four center points. Roughness was measured using a surface roughness tester (Formtracer SJ-301). The significant cutting parameters for surface roughness were determined using analysis of variance (ANOVA). The results showed that increasing the servo voltage significantly reduced the surface roughness by 46.48%. The developed model also predicted surface roughness values lower than those observed in the experimental data, with an R2 value of 0.608.

Improving the Accuracy of the Surface Roughness Model in Grinding Through Square Root Transformation

Surface roughness is a crucial parameter for mechanical products. To achieve small surface roughness, the grinding method is often chosen as the final machining process. The regression model of surface roughness forms the basis for controlling the grinding process and predicting surface roughness under specific conditions. The effectiveness of process control and the accuracy of predicted surface roughness depend on the precision of the surface roughness regression model. This study aims to enhance the accuracy of the surface roughness regression model by employing square root transformation. An experimental process was conducted with a total of eighteen experiments. In each experiment, three cutting parameters, including workpiece speed, tool feed rate, and cutting depth, were varied. Surface roughness was measured in each experiment. After conducting experiments, a surface roughness regression model was established, denoted as Model (1), without using any data transformation. The square root transformation was applied to convert the surface roughness dataset into another set of data. From this dataset, another surface roughness model, referred to as Model (2), was developed. Both models were used to predict surface roughness, and the predicted results were compared with the actual surface roughness in the experiments. Four parameters were used to compare Models (1) and (2), including the coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), mean absolute error percentage (%MAE), and mean squared error (%MSE). All four parameters for Model (2) were superior to those for Model (1). The results confirmed that the square root transformation successfully improved the accuracy of the surface roughness regression model in grinding applications.

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.

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.

Investigating the Accuracy of Artificial Neural Network Models in Predicting Surface Roughness in Drilling Processes

In recent times, the application of Artificial Intelligence (AI) has become widespread across various fields, including machining operations like milling, drilling, shaping, turning, grinding, counter drilling, and counter sinking. Among the techniques used in these applications, Artificial Neural Networks (ANN) have gained popularity. This study focuses on utilizing ANN to optimize drilling hardened non-shrinking (OHNS) die steel. The methodology presented in this research involves the implementation of ANN to analyze the impact of drilling process parameters on surface roughness. MATLAB 2023 software was used to effectively train the neural network. The dataset for this study consists of input and output expressions derived from experimental analysis. The input data includes variables such as cutting speed, feedrate, drill size, and depth of cut. The objective of this study is to predict surface roughness based on input datasets, with 70% of the samples allocated as training data and the remaining 30% for testing and validation. The experimental results demonstrate that ANN provides a reliable prediction capability, with Coefficient of Determination (R2) and Mean Square Error (MSE) values of 0.997 and 0.231893, respectively. The low MSE value indicate better model performance, also the R2 value obtained indicate a strong correlation between the predicted and actual values, suggesting that the ANN model provides a satisfactory fit to the data, indicating a high level of confidence in the statistical results. Thus, it can be concluded that the results obtained from the ANN model are statistically significant, slightly superior to the classical model, and exhibit a good fit.

Surface Roughness Prediction Based on Laser Speckle Images and Convolutional Neural Network-Support Vector Regression

Surface Roughness Prediction Based on Laser Speckle Images and Convolutional Neural Network-Support Vector Regression

Parameter optimization of 3D printing process for ceramic sculptures

Abstract In this paper, for the problems of ceramic sculpture 3D printing process, such as the accuracy is difficult to predict and the process control parameters are difficult to choose, we propose to establish an accuracy prediction model using BP neural network optimized by particle swarm algorithm, and put forward the optimization algorithm of the printing process parameters based on genetic algorithm, and construct the fitness function by using the method of the weight coefficients, to achieve the search for the molding angle under the optimal surface quality and to improve the printing efficiency. In the simulation experiments of printing accuracy prediction, the average errors of dimensional error and surface roughness prediction of the PSO-BP network model proposed in this paper are 5.59% and 5.02%, respectively, and the prediction is accurate and reliable. In the 3D printing parameter optimization experiments, the average deviation of the Y and Z dimensions of the physical size printed with the optimized parameters is 0.0496mm and 0.058mm, respectively, which is smaller than the deviation before optimization. Parameter optimization has also reduced surface roughness in the molded parts of the porcelain sculpture. The extrusion head and hot bed temperature were adjusted to reach the temperature target after maintaining hollow finger precision within 3 degrees.