Prediction Of Roughness Research Articles

Research on surface roughness detection and prediction of ti-6Al-4v titanium alloy based on multi-feature fusion

Considering that titanium alloy is a critical aviation resource, ensuring its machining quality is of significant importance. Surface roughness remains a key parameter for surface quality inspection. This article introduces a multi-sensor titanium alloy milling monitoring system aimed at accurately monitoring surface quality during titanium alloy processing. Principal component analysis is conducted on three-dimensional milling force and vibration. We propose a multi-objective cutting parameter optimization procedure to simultaneously optimize multiple cutting parameters to improve surface roughness and tool life. Considering the high dependence of visual measurement on the light source, we strive to mitigate this limitation and improve prediction accuracy by considering one-dimensional and two-dimensional feature values. We establish a multidimensional signal feature surface roughness prediction system based on milling parameters, milling vibration, milling force, and texture image features. Using particle swarm algorithm and machine learning models, the undetermined parameters in the prediction system are obtained. The results show that the prediction accuracy of the multi-signal feature fusion surface roughness prediction system is 99.12%, with a mean square error of less than 0.01. The research can provide some theoretical guidance for the accurate monitoring of the surface quality of titanium alloy processing.

CONTRIBUTION TO PRACTICAL ANALYSIS OF SURFACE ROUGHNESS MODELING DURING DRY HARD TURNING OF COLD WORK TOOL STEEL AISI D2

Quality of products is a major concern for hard machining industry aspects. However, the prediction of surface roughness in the function of the machining parameters is a targeted objective by the majority of researchers due to important industrial interest. This work is a contribution for developing comprehensive analyses of surface roughness models during dry hard turning of cold work steel AISI D2. It is focused on the treatment of predicted surface roughness values in correlation with cutting conditions such as cutting parameters (cutting speed, feed rate, and depth of cut), tools material, tool geometry, workpieces hardness, and machining times in order to establish of orientations for use which applied successfully in the industry from optimal selection of process variables. The approach consists in first conducting a literature survey through collecting and validating surface roughness models together with the corresponding material and also the cutting conditions. Then, results have been tabulated as to permit data mining treatment under data processing software which was developed. As a significant result, a strategic dashboard generating roughness data mining was made available to researchers and industrials. The optimums of parameters which condition the achievement of best machined surface qualities have been selected.

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 %.

Design and Study of Machine Tools for the Fly-Cutting of Ceramic-Copper Substrates.

Ceramic-copper substrates, as high-power, load-bearing components, are widely used in new energy vehicles, electric locomotives, high-energy lasers, integrated circuits, and other fields. The service length will depend on the substrate's copper-coated surface quality, which frequently achieved by utilising an abrasive strip polishing procedure on the substrate's copper-coated surface. Precision diamond fly-cutting processing machine tools were made because of the low processing accuracy and inability to match the production line's efficiency. An analysis of the fly-cutting machining principle and the structural makeup of the ceramic-copper substrate is the first step in creating a roughness prediction model based on a tool tip trajectory. This model demonstrates that a shift in the tool tip trajectory due to spindle runout error directly impacts the machined surface's roughness. The device's structural optimisation design is derived from the above analyses and implemented using finite element software. Modal and harmonic response analysis validated the machine's gantry symmetrical structural layout, a parametric variable optimisation design optimised the machine tool's overall dimensions, and simulation validated the fly-cutterring's constituent parts. Enhancing the machine tool's stability and motion accuracy requires using the LK-G5000 laser sensor to measure the guideway's straightness. The result verified the machine tool's design index, with the Z- and Y-axes' straightness being better than 2.42 μm/800 mm and 2.32 μm/200 mm, respectively. Ultimately, the device's machining accuracy was confirmed. Experiments with flying-cut machining on a 190 × 140 mm ceramic-copper substrate yielded a roughness of Sa9.058 nm. According to the experimental results, the developed machine tool can fulfil the design specifications.

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

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.

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

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.

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.

Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method

Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 μm, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.

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.

Roughness prediction of end milling surface for behavior mapping of digital twined machine tools

Background The quality of machined parts is considered as a relevant factor to evaluate the production performance of machine tools. For mapping the production performance into a digital twin machine tool, a virtual metrology model for surface roughness prediction, which affects products' mechanical capacity and aesthetic performance, is proposed in this paper. Methods The proposed model applies a three-layer backpropagation neural network by using real-time vibration, force, and current sensor data collected during the end milling machining process. A grid search plan is used to settle down the number of neurons in the middle layer of the backpropagation neural network. Results The experimental results indicate that the model with multiple signals as input performs better than it with a single signal. In detail, when the model input is the combination of force, vibration, and current sensor data, the prediction accuracy reaches the optimum with the mean absolute percentage error of 1.01%. Conclusions Compared with the state-of-the-art convolutional neural network method with automatic feature extraction ability and other commonly used traditional machine learning methods, the proposed data preprocessing procedure integrated with a three-layer backpropagation neural network has a minimum prediction error.

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.

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.