Статті в журналах: "Roughness prediction"

1

Nalbant, Muammer, Hasan Gokkaya, and İhsan Toktaş. "Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning." Modelling and Simulation in Engineering 2007 (2007): 1–14. http://dx.doi.org/10.1155/2007/92717.

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Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance(R2)values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning.

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2

Lin, Wan-Ju, Shih-Hsuan Lo, Hong-Tsu Young, and Che-Lun Hung. "Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis." Applied Sciences 9, no. 7 (April 8, 2019): 1462. http://dx.doi.org/10.3390/app9071462.

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The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

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3

Saleh, A., D. W. Fryrear, and J. D. Bilbro. "AERODYNAMIC ROUGHNESS PREDICTION FROM SOIL SURFACE ROUGHNESS MEASUREMENT." Soil Science 162, no. 3 (March 1997): 205–10. http://dx.doi.org/10.1097/00010694-199703000-00006.

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4

Cai, Xiao Jiang, Z. Q. Liu, Q. C. Wang, Shu Han, Qing Long An, and Ming Chen. "Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network." Advanced Materials Research 188 (March 2011): 535–41. http://dx.doi.org/10.4028/www.scientific.net/amr.188.535.

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Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

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5

Li, Shilong, Xiaolei Yang, and Yu Lv. "Predictive capability of the logarithmic law for roughness-modeled large-eddy simulation of turbulent channel flows with rough walls." Physics of Fluids 34, no. 8 (August 2022): 085112. http://dx.doi.org/10.1063/5.0098611.

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Direct numerical simulation (DNS) and large-eddy simulation (LES) resolving roughness elements are computationally expensive. LES employing the logarithmic law as the wall model, without the need to resolve the flow at the roughness element scale, provides an efficient alternative for simulating turbulent flows over rough walls. In this work, we evaluate the predictive capability of the roughness-modeled LES by comparing its predictions with those from the roughness-resolved DNS for turbulent channel flows with rough walls. A good agreement is observed for the mean streamwise velocity. The Reynolds stresses predicted by the roughness-modeled LES also reasonably agree with the roughness-resolved predictions. Differences, on the other hand, are observed for the dispersive Reynolds stresses, integral scales, and space-time correlations. The roughness-modeled LES fails to predict the dispersive stresses as one can expect. In the outer layer, the integral length scale predicted by the roughness-modeled LES is lower than the roughness-resolved prediction, which cannot be improved by refining the grid. As for the space-time correlations, discrepancies are shown for the streamwise velocity fluctuations, with a faster decay of the correlation in the outer layer observed in the roughness-modeled predictions. Examination of the space-time correlation using the elliptic approximation model shows that the roughness-modeled LES underpredicts the convection velocity in the near wall region while overpredicting the sweeping velocity in the outer layer with no improvements observed when refining the grid.

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6

Alajmi, Mahdi S., and Abdullah M. Almeshal. "Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method." Materials 13, no. 13 (July 4, 2020): 2986. http://dx.doi.org/10.3390/ma13132986.

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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7

Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (May 22, 2023): 4969. http://dx.doi.org/10.3390/s23104969.

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Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.

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8

Ng, J. J., Z. W. Zhong, and T. I. Liu. "Prediction of Roughness Heights of Milled Surfaces for Product Quality Prediction and Tool Condition Monitoring." Journal of Materials and Applications 8, no. 2 (November 15, 2019): 97–104. http://dx.doi.org/10.32732/jma.2019.8.2.97.

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The objective of this research is to predict the roughness heights of milled surfaces, which indicates product quality and tool conditions. Two experiments are carried out to evaluate relevant factors such as vibration, force, and surface roughness. The purpose of the first experiment is to find out the limits of the machining variables compared to the constraints of the materials. The purpose of the second experiment is to identify, collect, and compare how each factor affects product quality and tool conditions. Based on this study, the vibration, force, and surface roughness are good indicators for tool conditions. When the magnitudes of the vibration and force increase, the surface roughness also increases. The increase in surface roughness with constant cutting parameters indicates the degrading of product quality and the decrease of the tool life. Thus, the variables, such as vibration and forces, are used as the inputs, and the surface roughness is used as the output of neural networks. By optimizing the network variables, it has been found that a 4,4,8,1 neural network can achieve the least absolute error, and accurately predict the actual roughness heights collected from the experiment. The minimum error of the prediction of surface roughness is 0.11%, the average error is 2.11%, and the maximum error is 6.98%. The prediction of surface roughness of milled surfaces is very important for the product quality prediction and tool condition monitoring.

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9

Zhang, Qi, Yuechao Pei, Yixin Shen, Xiaojun Wang, Jingqi Lai, and Maohui Wang. "A New Perspective on Predicting Roughness of Discontinuity from Fractal Dimension D of Outcrops." Fractal and Fractional 7, no. 7 (June 22, 2023): 496. http://dx.doi.org/10.3390/fractalfract7070496.

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In tunnel construction, predicting the roughness of discontinuity is significant for preventing the collapse of the excavation face. However, currently, we are unable to use a parameter with invariant properties to quantify and predict the roughness of discontinuity. Fractal dimension D is one such parameter that be used to characterize the roughness of discontinuity. The study proposes a new method to predict the roughness of discontinuity from the fractal dimension D of outcrops. The measurement method of the coordinates of outcrops is firstly summarized, and the most suitable method of calculating fractal dimension D is then provided. For characterizing the spatial variability of fractal dimension D, the random field of fractal dimension D is discretized, and the prediction model is then established based on Bayesian theory. The proposed method is applied to one tunnel for predicting the roughness of discontinuity, and the results indicate that the relative errors of prediction are less than 1.5%. The sensitivities of correlation function and discontinuity size are analyzed. It is found that the different correlation functions have no obvious effect on the prediction results, and the proposed method is well applied to relatively large sizes of discontinuity.

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10

Gu, Jiali, and Pingxiang Cao. "Prediction of straight tooth milling of Scots pine wood by shank cutter based on neural net computations and regression analysis." BioResources 17, no. 2 (February 4, 2022): 2003–19. http://dx.doi.org/10.15376/biores.17.2.2003-2019.

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Regression models and a neural net approach were used to predict the cutting performance during milling of Scots pine (Pinus sylvestris L.) by shank cutter. The influence of rake angle, spindle speed, and milling depth on surface roughness of the workpiece, as well as the connection between the milling force and the surface roughness, were thoroughly considered. Four approaches were used to predict the workpiece’s surface roughness based on the experimental data: Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machines (SVM), and multiple linear regression. The comparative analysis of the predictive models showed that Neural Network (NN) had preferable performance for prediction of machined surface roughness, with an R2 of 0.98. The SVM had certain fluctuations and the R2 of the multiple linear regression was just 0.87, indicating that they did not fit well for prediction machined surface roughness. In summary, the effective trend of milling parameters on the machined surface roughness of Scots pine was similar to multiple nonlinear regression, and the accurate prediction by BPNN model can provide technical support for the surface roughness of the Scots Pine and enhance shank cutter performance.

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11

Alam, S., A. K. M. Nurul Amin, Anayet Ullah Patwari, and Mohamed Konneh. "Prediction and Investigation of Surface Response in High Speed End Milling of Ti-6Al-4V and Optimization by Genetic Algorithm." Advanced Materials Research 83-86 (December 2009): 1009–15. http://dx.doi.org/10.4028/www.scientific.net/amr.83-86.1009.

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In this study, statistical models were developed using the capabilities of Response Surface Methodology (RSM) to predict the surface roughness in high-speed flat end milling of Ti-6Al-4V under dry cutting conditions. Machining was performed on a five-axis NC milling machine with a high speed attachment, using spindle speed, feed rate, and depth of cut as machining variables. The adequacy of the model was tested at 95% confidence interval. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting little deviations in surface roughness prediction. A very good performance of the RSM model, in terms of agreement with experimental data, was achieved. It is observed that cutting speed has the most significant influence on surface roughness followed by feed and depth of cut. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the surface roughness in flat end milling of Ti-6Al-4V materials. The developed quadratic prediction model on surface roughness was coupled with the genetic algorithm to optimize the cutting parameters for the minimum surface roughness.

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12

Lin, Yung-Chih, Kung-Da Wu, Wei-Cheng Shih, Pao-Kai Hsu, and Jui-Pin Hung. "Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network." Applied Sciences 10, no. 11 (June 5, 2020): 3941. http://dx.doi.org/10.3390/app10113941.

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This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

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13

Mirifar, Siamak, Mohammadali Kadivar, and Bahman Azarhoushang. "First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors." Journal of Manufacturing and Materials Processing 4, no. 2 (April 25, 2020): 35. http://dx.doi.org/10.3390/jmmp4020035.

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The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.

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14

Sun, Hao, Chaochao Zhang, Yikai Li, Tingting Yin, Hanming Zhang, and Jin Pu. "Study on prediction model of surface roughness of SiCp/Al composites based on Neural Network." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012091. http://dx.doi.org/10.1088/1742-6596/2174/1/012091.

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Abstract In order to effectively meet the actual industrial production standards and improve the prediction accuracy of composite surface roughness, a prediction model of SiCp/Al composite surface roughness based on neural network is proposed. The influence parameters of surface roughness of SiCp/Al composites are analyzed from the cutting tool parameters, and the mathematical calculation of surface roughness of SiCp/Al composites is carried out. Using neural network technology, by determining various parameters of neural network, collecting and processing various data of material surface, the surface roughness prediction model of SiCp/Al composite is constructed to realize the surface roughness prediction of SiCp/Al composite. The experimental results show that the maximum error between the actual value and the predicted value of the surface roughness of composite materials from the prediction model established in this paper is only 0.013, and the average error percentage between the actual value and the predicted value is 0.705%, which can effectively improve the prediction accuracy of the surface roughness of composite materials and meet the standards of actual industrial production.

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15

Yang, Ching Been, Chyn Shu Deng, and Hsiu Lu Chiang. "The Establishment of a Prediction Model for Surface Roughness in Ultrasonic-Assisted Turning." Applied Mechanics and Materials 120 (October 2011): 119–25. http://dx.doi.org/10.4028/www.scientific.net/amm.120.119.

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During experimentation, traditional Taguchi analysis often uses L18 or L27 orthogonal array, with a considerable number of test sequences for Run 18 or Run 27. Both are time-consuming and expensive. A progressive Taguchi-neural network model is proposed in this study, which combines the Taguchi method with a neural network construction, and can construct a prediction model for surface roughness in ultrasonic-assisted turning. According to the results, the Stage-1 initial-network, due to its limited number of network training examples, generates good predictive ability results for regions near Taguchi factor level points. For learning and training examples from regions farther out, prediction results are increasingly unreliable. Comparably, the Stage-3 precision-network generates the more reliable predictions for global regions.

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16

Li, Qinghua, Chunlu Ma, Chunyu Wang, Zhengxi Lu, and Shihong Zhang. "Application of Combined Prediction Model in Surface Roughness Prediction." Journal of Nanoelectronics and Optoelectronics 17, no. 11 (November 1, 2022): 1511–16. http://dx.doi.org/10.1166/jno.2022.3335.

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In order to improve the stability work piece surface roughness prediction model after machining, LM algorithm, least squares algorithm and proportional conjugate gradient algorithm are used to build the prediction model of the cutting process of AL-7075 aluminum alloy. Two error analysis methods are used to compare the combination model with other single models. The study found that the forecasting model is more accurate and stable than the single forecasting model, and closer to the actual measurement results. The combination model provides a new way to predict the surface roughness of the work piece after machining and provides a theoretical basis for selecting the three main factors of machining.

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17

Ding, Ning, Chang Long Zhao, Xi Chun Luo, Qing Hua Li, and Yao Chen Shi. "An Intelligent Prediction of Surface Roughness on Precision Grinding." Solid State Phenomena 261 (August 2017): 221–25. http://dx.doi.org/10.4028/www.scientific.net/ssp.261.221.

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Precision grinding is generally used as the final finishing process, and it determines the surface quality of the machined component. It’s very difficult to achieve on-line measurement of the surface roughness. The purpose of this research was to study the surface roughness prediction and avoid the defect happening in the grinding process. A surface roughness prediction model was proposed in this paper, which presented the relationship between surface roughness and the wear condition of grinding wheel and grinding parameters. An AE sensor was used to collect the grinding signals during the grinding process to obtain the grinding wheel wear condition. Besides, a fuzzy neural network was used to obtain the prediction surface roughness. Grinding trials were performed on a high precision CNC cylindrical grinder (MGK1420) to evaluate the surface roughness prediction model. Experiment verified that the developed prediction system was feasible and had high prediction accuracy.

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18

Lu, Xiaohong, Xiaochen Hu, Hua Wang, Likun Si, Yongyun Liu, and Lusi Gao. "Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM." Industrial Lubrication and Tribology 68, no. 2 (March 14, 2016): 206–11. http://dx.doi.org/10.1108/ilt-06-2015-0079.

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Purpose – The purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision. Design/methodology/approach – A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model. Findings – The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718. Practical implication – Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718. Originality/value – The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.

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19

Zhang, Wenhe. "Surface Roughness Prediction with Machine Learning." Journal of Physics: Conference Series 1856, no. 1 (April 1, 2021): 012040. http://dx.doi.org/10.1088/1742-6596/1856/1/012040.

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20

Ahmed, Siddig E., and Mohammed B. Saad. "Prediction of Natural Channel Hydraulic Roughness." Journal of Irrigation and Drainage Engineering 118, no. 4 (July 1992): 632–39. http://dx.doi.org/10.1061/(asce)0733-9437(1992)118:4(632).

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21

Denkena, B., A. Abrão, A. Krödel, and K. Meyer. "Analytic roughness prediction by deep rolling." Production Engineering 14, no. 3 (April 30, 2020): 345–54. http://dx.doi.org/10.1007/s11740-020-00961-0.

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22

Ukar, E., A. Lamikiz, S. Martínez, I. Tabernero, and L. N. López de Lacalle. "Roughness prediction on laser polished surfaces." Journal of Materials Processing Technology 212, no. 6 (June 2012): 1305–13. http://dx.doi.org/10.1016/j.jmatprotec.2012.01.007.

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23

Zhang, Qing, Song Zhang, Jia Man, and Bin Zhao. "Effect Analysis and ANN Prediction of Surface Roughness in End Milling AISI H13 Steel." Materials Science Forum 800-801 (July 2014): 590–95. http://dx.doi.org/10.4028/www.scientific.net/msf.800-801.590.

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Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness.

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24

Kim, Dong Woo, Young Jae Shin, Kyoung Taik Park, Eung Sug Lee, Jong Hyun Lee, and Myeong Woo Cho. "Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks." Key Engineering Materials 364-366 (December 2007): 713–18. http://dx.doi.org/10.4028/www.scientific.net/kem.364-366.713.

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The objective of this research was to apply the artificial neural network algorithm to predict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed, cutting path interval and run-out were used as five input neurons; and artificial neural networks model based on back-propagation algorithm was developed to predict the output neuron-surface roughness. A series of experiments was performed, and the results were estimated. The experimental results showed that the applied artificial neural network surface roughness prediction gave good accuracy in predicting the surface roughness under a variety of combinations of cutting conditions.

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25

Najm, Sherwan Mohammed, and Imre Paniti. "Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Using ANN and SVR." International Journal of Precision Engineering and Manufacturing 22, no. 1 (November 13, 2020): 13–26. http://dx.doi.org/10.1007/s12541-020-00434-5.

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AbstractIn the present work, multiple forming tests were conducted under different forming conditions by Single Point Incremental Forming (SPIF). In which surface roughness, arithmetical mean roughness (Ra) and the ten-point mean roughness (Rz) of AlMn1Mg1 sheet were experimentally measured. Also, an Artificial Neural Network (ANN) was used to predict the (Ra) and (Rz) by adopting the data collected from 108 components that were formed by SPIF. Forming tool characteristics played a key role in all the predictions and their effect on the final product surface roughness. In the aim to explore the proper materials and geometry of forming tools, different ANN structures, different training, and transfer functions have been applied to predict (Ra) and (Rz) as an output argument. Furthermore, Support Vector Regression (SVR) with different kernel types have been used for prediction, together with Gradient Boosting regression to sort the effective parameters on the surface roughness. The input arguments were tool materials, tool shape, tool end/corner radius, and tool surface roughness (Ra and Rz). The actual data subjected to a fit regression model to generate prediction equations of Ra and Rz. The results showed that ANN with one output gives the best R-Square (R2). Levenberg-Marquardt backpropagation (Trainlm) training function recorded the highest value of R2, 0.9628 for prediction Ra using Softmax transfer function whereas 0.9972 for Rz by Log- Sigmoid transfer function. Furthermore, tool materials, together with tool surface (Ra), are playing a significant importance role, affecting the sheet surface roughness (Ra). Whereas tool roughness Rz was the critical parameter effected on the Rz of the product. Also, there was a significant positive effect of tool geometry on the sheet surface roughness.

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26

Herwan, Jonny, Seisuke Kano, Oleg Ryabov, Hiroyuki Sawada, Nagayoshi Kasashima, and Takashi Misaka. "Predicting Surface Roughness of Dry Cut Grey Cast Iron Based on Cutting Parameters and Vibration Signals from Different Sensor Positions in CNC Turning." International Journal of Automation Technology 14, no. 2 (March 5, 2020): 217–28. http://dx.doi.org/10.20965/ijat.2020.p0217.

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During the turning process, cast iron is directly shattered to become particles. This mechanism means the surface roughness cannot be predicted using the kinematic equation. This paper provides surface roughness predictions using two methods, the multiple regression model (MRM) and artificial neural network (ANN). Cutting parameters and vibration signals are considered input variables in both methods. This work also overcomes the common sensor position limitation (tool shank) and provides a safe and efficient solution. The prediction values from MRM and ANN show accurate results compared to the measured surface roughness, with the average error of less than 8%. Furthermore, the proposed sensor position, at the turret bed, also exhibits similar prediction accuracy to a sensor at the tool shank, hence promising feasible industrial application.

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Chen, Yuan Ling, Bao Lei Zhang, Wei Ren Long, and Hua Xu. "Research on Surface Roughness Prediction Model for High-Speed Milling Inclined Plane of Hardened Steel." Advanced Materials Research 97-101 (March 2010): 2044–48. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.2044.

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As the factors influencing the workpiece surface roughness is complexity and uncertainty, according to orthogonal experimental results, the paper established Empirical regression prediction model and generalized regression neural networks (GRNN) for prediction of surface roughness when machining inclined plane of hardened steel in high speed , moreover, compared their prediction errors. The results show that GRNN model has better prediction accuracy than empirical regression prediction model and can be better used to control the surface roughness dynamically.

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28

Zhang, Ming, X. Q. Yang, and Bo Zhao. "On-Line Prediction Model of Ultrasonic Polishing Surface Roughness." Key Engineering Materials 455 (December 2010): 539–43. http://dx.doi.org/10.4028/www.scientific.net/kem.455.539.

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In order to solve the difficulty of on-line measuring the surface roughness of workpiece under ultrasonic polishing, the artificial neural networks and fuzzy logic systems are introduced into the on-line prediction model of surface roughness. The surface roughness identification method based on fuzzy-neural networks is put forward and used to the process of plane polishing. In the end, the on-line prediction model of surface roughness is established. The actual ultrasonic polishing experiments show that the accuracy of this prediction model is up to 96.58%, which further evidence the feasibility of the on-line prediction model.

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29

Hweju, Zvikomborero, Fundiswa Kopi, and Khaled Abou-El-Hossein. "Statistical evaluation of PMMA surface roughness." Journal of Physics: Conference Series 2313, no. 1 (July 1, 2022): 012030. http://dx.doi.org/10.1088/1742-6596/2313/1/012030.

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Abstract There is increased enthusiasm in polymer materials and yet limited research on single point diamond turning of Polymethyl methacrylate (PMMA) used to produce contact lenses. This study is a presentation of a statistical-based PMMA surface roughness prediction and parameter significance. The data utilized is obtained during dry single point diamond turning of PMMA. The experiment has been designed with the Central Composite Design (CCD) method and the Response Surface Methodology (RSM) has been used for surface roughness prediction and evaluation of cutting parameter importance. The surface roughness data used for regression model generation has been acquired manually using a profilometer. The resultant surface roughness (Ra) dataset has been split into training and testing datasets. The accuracy of the model has been determined based on the Mean Absolute Percentage Error (MAPE). Results have indicated that the generated regression model can predict surface roughness with 75.12 % accuracy. Furthermore, the order of parameter importance in decreasing order is as follows: feed rate, cutting speed, and depth of cut. The paired t-test results indicate that the difference between the averages of measured surface roughness and predicted surface roughness is not big enough to be statistically significant. Hence, the technique can be reliably utilized in predicting surface roughness during single point diamond turning of PMMA.

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30

Liu, Xubao, Yuhang Pan, Ying Yan, Yonghao Wang, and Ping Zhou. "Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters." Mathematics 10, no. 15 (August 5, 2022): 2788. http://dx.doi.org/10.3390/math10152788.

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Ground surface roughness is difficult to predict through a physical model due to its complex influencing factors. BP neural networks (BPNNs), a promising method, have been widely applied in the prediction of surface roughness. This paper uses the concept of BPNN to predict ground surface roughness considering the state of the grinding wheel. However, as the number of input parameters increases, the local optimum solution of the model that arises is more serious. Therefore, “identify factors” are designed to judge the iterative state of the model, whilst “memory factors” are designed to store the best weights during network training. The iterative termination conditions of the model are improved, and the learning rate and update rules of the weights are adjusted to avoid the local optimal solution. The results show that the prediction accuracy of the presented model is higher and more stable than the traditional model. Under three types of iteration steps, the average prediction accuracy is improved from 0.071, 0.065, 0.066 to 0.049, 0.042, 0.039 and the standard deviation of prediction decreased from 0.0017, 0.0166, 0.0175 to 0.0017, 0.0070, 0.0076, respectively. Therefore, the proposed method provides guidance for improving the global optimization ability of BPNNs and developing more accurate models for predicting surface roughness.

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31

Vencovský, Václav. "Roughness Prediction Based on a Model of Cochlear Hydrodynamics." Archives of Acoustics 41, no. 2 (June 1, 2016): 189–201. http://dx.doi.org/10.1515/aoa-2016-0019.

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Abstract The term roughness is used to describe a specific sound sensation which may occur when listening to stimuli with more than one spectral component within the same critical band. It is believed that the spectral components interact inside the cochlea, which leads to fluctuations in the neural signal and, in turn, to a sensation of roughness. This study presents a roughness model composed of two successive stages: peripheral and central. The peripheral stage models the function of the peripheral ear. The central stage predicts roughness from the temporal envelope of the signal processed by the peripheral stage. The roughness model was shown to account for the perceived roughness of various types of acoustic stimuli, including the stimuli with temporal envelopes that are not sinusoidal. It thus accounted for effects of the phase and the shape of the temporal envelope on roughness. The model performance was poor for unmodulated bandpass noise stimuli.

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32

Hu, Jin Ping, Yan Li, and Jing Chong Zhang. "Surface Roughness Prediction of High Speed Milling Based on Back Propagation Artificial Neural Network." Advanced Materials Research 201-203 (February 2011): 696–99. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.696.

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Prediction of surface roughness is an important research for machining quality analysis. In order to predict surface roughness in machining, increasing productivity under ensuring milling, the artificial neural network is introduced into milling area. To build high-speed milling surface roughness prediction model using BP neural network. Prediction results are compared with experimental value, which shows that this method can achieve better prediction accuracy. It has certain significance for parameters selection of high-speed milling and quality control of the surface.

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33

Cheng, Rong Kai, Yun Huang, and Yao Huang. "Experimental Research on the Predictive Model for Surface Roughness of Titanium Alloy in Abrasive Belt Grinding." Advanced Materials Research 716 (July 2013): 443–48. http://dx.doi.org/10.4028/www.scientific.net/amr.716.443.

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Titanium alloys have been applied to aerospacemedical and other fields. The surface roughness of titanium alloy about these areas is very high. Based on the results of orthogonal test, belt grinding surface roughness prediction model of TC4 Titanium alloy is established using linear regression method. The significant tests of regression equation are conducted and proved that the prediction model has a significant. The results indicate that the model has reliability on the prediction of surface roughness, abrasive belt grinding pressure has certain influence on the surface roughness, and grain size of belt and the belt linear speed have high significant influence on surface roughness and the influence coefficient are-0.9378 and-0.2317. While the contact wheel hardness and workpiece axial feeding speed have no significant influence on surface roughness.

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34

Ding, Ning, Long Shan Wang, and Guang Fu Li. "Study of Intelligent Prediction Control of Surface Roughness in Grinding." Key Engineering Materials 329 (January 2007): 93–98. http://dx.doi.org/10.4028/www.scientific.net/kem.329.93.

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A surface roughness intelligent prediction control system during grinding is built. The system is composed of fuzzy neural network prediction subsystem and fuzzy neural network controller. In the fuzzy neural network prediction subsystem, the vibration data are added to the inputs besides the grinding condition, such as feed and speed, so as to improve the dynamic performance of the prediction subsystem. The fuzzy neural network controller is able to adapt grinding parameters in process to improve the surface roughness of machined parts when the roughness is not meeting requirements. Experiment verifies that the developed prediction control system is feasible and has high prediction and control accuracy.

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35

Wang, Jing He, Shen Dong, H. X. Wang, Ming Jun Chen, Wen Jun Zong, and L. J. Zhang. "Forecasting of Surface Roughness and Cutting Force in Single Point Diamond Turning for KDP Crystal." Key Engineering Materials 339 (May 2007): 78–83. http://dx.doi.org/10.4028/www.scientific.net/kem.339.78.

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The method of single point diamond turning is used to machine KDP crystal. A regression analysis is adopted to construct a prediction model for surface roughness and cutting force, which realizes the purposes of pre-machining design, prediction and control of surface roughness and cutting force. The prediction model is utilized to analyze the influences of feed, cutting speed and depth of cut on the surface roughness and cutting force. And the optimal cutting parameters of KDP crystal on such condition are acquired by optimum design. The optimum estimated values of surface roughness and cutting force are 7.369nm and 0.15N, respectively .Using the optimal cutting parameters, the surface roughness Ra, 7.927nm, and cutting force, 0.19N, are obatained.

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36

Wu, Tian-Yau, and Chi-Chen Lin. "Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints." Applied Sciences 11, no. 5 (February 28, 2021): 2137. http://dx.doi.org/10.3390/app11052137.

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The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

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37

YU, J., Y. NAMBA, and M. SHIOKAWA. "FRACTAL ROUGHNESS CHARACTERIZATION OF SUPER-GROUND Mn-Zn FERRITE SINGLE CRYSTALS." Fractals 04, no. 02 (June 1996): 205–11. http://dx.doi.org/10.1142/s0218348x96000285.

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The surface of superground Mn-Zn ferrite single crystal may be identified as a self-affine fractal in the stochastic sense. The rms roughness increased as a power of the scale from 102 nm to 106 nm with the roughness exponent α=0.17±0.04, and 0.11±0.06, for grinding feed rate of 15 and 10 μm/rev, respectively. The scaling behavior coincided with the theory prediction well used for growing self-affine surfaces in the interested region for magnetic heads performance. The rms roughnesses increased with increase in the feed rate, implying that the feed rate is a crucial grinding parameter affecting the supersmooth surface roughness in the machining process.

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38

Guo, Xiong Hua, Mao Fu Liu, and Chang Rong Zhao. "Surface Roughness Prediction in Precision Surface Grinding of Nano-Ceramic Coating Based on Improved ANFIS." Applied Mechanics and Materials 44-47 (December 2010): 2293–98. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2293.

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For improving surface integrity and machining quality after precision grinding of the parts of nano-ceramic coating, and investigating its prediction technique of surface roughness, the prediction model of surface roughness in precision surface grinding of nano-ceramic coating based on adaptive network-based fuzzy inference system (ANFIS) was proposed in this paper. Then, the proposed prediction model was improved by hybrid Taguchi genetic algorithm (HTGA). At last, by comparative analysis of prediction results from traditional BP neural network model, simple ANFIS model and improved ANFIS model, the effectiveness of the proposed model was verified using grinding parameters and measured surface roughness in grinding tests as training and testing samples. It showed that the prediction accuracy of the improved ANFIS model proposed in this paper was higher, and it was an effective prediction method of surface roughness in precision grinding of nano-ceramic coating.

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39

Vidakis, Nectarios, Markos Petousis, Nikolaos Vaxevanidis, and John Kechagias. "Surface Roughness Investigation of Poly-Jet 3D Printing." Mathematics 8, no. 10 (October 13, 2020): 1758. http://dx.doi.org/10.3390/math8101758.

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An experimental investigation of the surface quality of the Poly-Jet 3D printing (PJ-3DP) process is presented. PJ-3DP is an additive manufacturing process, which uses jetted photopolymer droplets, which are immediately cured with ultraviolet lamps, to build physical models, layer-by-layer. This method is fast and accurate due to the mechanism it uses for the deposition of layers as well as the 16 microns of layer thickness used. Τo characterize the surface quality of PJ-3DP printed parts, an experiment was designed and the results were analyzed to identify the impact of the deposition angle and blade mechanism motion onto the surface roughness. First, linear regression models were extracted for the prediction of surface quality parameters, such as the average surface roughness (Ra) and the total height of the profile (Rt) in the X and Y directions. Then, a Feed Forward Back Propagation Neural Network (FFBP-NN) was proposed for increasing the prediction performance of the surface roughness parameters Ra and Rt. These two models were compared with the reported ones in the literature; it was revealed that both performed better, leading to more accurate surface roughness predictions, whilst the NN model resulted in the best predictions, in particular for the Ra parameter.

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40

Tangjitsitcharoen, Somkiat, and Angsumalin Senjuntichai. "Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process." Applied Mechanics and Materials 121-126 (October 2011): 2059–63. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2059.

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In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

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41

Huiping, Zhang, Zhang Hongxia, and Lai Yinan. "Surface Roughness and Residual Stresses of High Speed Turning 300 M Ultrahigh Strength Steel." Advances in Mechanical Engineering 6 (January 1, 2014): 859207. http://dx.doi.org/10.1155/2014/859207.

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Firstly, a single factor test of the surface roughness about tuning 300 M steel is done. According to the test results, it is direct to find the sequence of various factors affecting the surface roughness. Secondly, the orthogonal cutting experiment is carried out from which the primary and secondary influence factors affecting surface roughness are obtained: feed rate and corner radius are the main factors affecting surface roughness. The more the feed rate, the greater the surface roughness. In a certain cutting speed rang, the surface roughness is smaller. The influence of depth of cut to the surface roughness is small. Thirdly, according to the results of the orthogonal experiment, the prediction model of surface roughness is established by using regressing analysis method. Using MatLab software, the prediction mode is optimized and the significance test of the optimized model is done. It showed that the prediction model matched the experiment results. Finally, the surface residual stress test of turning 300 M steel is done and the residual stress of the surface and along the depth direction is measured.

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42

Wang, Yahui, Yiwei Wang, Lianyu Zheng, and Jian Zhou. "Online Surface Roughness Prediction for Assembly Interfaces of Vertical Tail Integrating Tool Wear under Variable Cutting Parameters." Sensors 22, no. 5 (March 3, 2022): 1991. http://dx.doi.org/10.3390/s22051991.

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Monitoring surface quality during machining has considerable practical significance for the performance of high-value products, particularly for their assembly interfaces. Surface roughness is the most important metric of surface quality. Currently, the research on online surface roughness prediction has several limitations. The effect of tool wear variation on surface roughness is seldom considered in machining. In addition, the deterioration trend of surface roughness and tool wear differs under variable cutting parameters. The prediction models trained under one set of cutting parameters fail when cutting parameters change. Accordingly, to timely monitor the surface quality of assembly interfaces of high-value products, this paper proposes a surface roughness prediction method that considers the tool wear variation under variable cutting parameters. In this method, a stacked autoencoder and long short-term memory network (SAE–LSTM) is designed as the fundamental surface roughness prediction model using tool wear conditions and sensor signals as inputs. The transfer learning strategy is applied to the SAE–LSTM such that the surface roughness online prediction under variable cutting parameters can be realized. Machining experiments for the assembly interface (using Ti6Al4V as material) of an aircraft’s vertical tail are conducted, and monitoring data are used to validate the proposed method. Ablation studies are implemented to evaluate the key modules of the proposed model. The experimental results show that the proposed method outperforms other models and is capable of tracking the true surface roughness with time. Specifically, the minimum values of the root mean square error and mean absolute percentage error of the prediction results after transfer learning are 0.027 μm and 1.56%, respectively.

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43

Ding, Ning, Yi Chen Wang, Ding Tong Zhang, Yu Xiang Shi, and Jian Shi. "Surface Roughness Prediction Model of Cylinder Grinding." Applied Mechanics and Materials 157-158 (February 2012): 123–26. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.123.

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Based on the theory of roughness during cylinder grinding and the theory of fuzzy-neural network, a surface roughness intelligent prediction model is developed in this paper. The feed, speed, and the vibration data are the inputs for the model. An accelerometer is used to gather the vibration signal in real time. The model is used in the grinding experiment, and verifies the feasibility of the proposed model.

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44

Cebeci, Tuncer, and David A. Egan. "Prediction of transition due to isolated roughness." AIAA Journal 27, no. 7 (July 1989): 870–75. http://dx.doi.org/10.2514/3.10194.

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45

Kong, Dongdong, Junjiang Zhu, Chaoqun Duan, Lixin Lu, and Dongxing Chen. "Bayesian linear regression for surface roughness prediction." Mechanical Systems and Signal Processing 142 (August 2020): 106770. http://dx.doi.org/10.1016/j.ymssp.2020.106770.

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46

Grzenda, Maciej, and Andres Bustillo. "The evolutionary development of roughness prediction models." Applied Soft Computing 13, no. 5 (May 2013): 2913–22. http://dx.doi.org/10.1016/j.asoc.2012.03.070.

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47

Reddy, B. Sidda, G. Padmanabha, and K. Vijay Kumar Reddy. "Surface Roughness Prediction Techniques for CNC Turning." Asian Journal of Scientific Research 1, no. 3 (April 15, 2008): 256–64. http://dx.doi.org/10.3923/ajsr.2008.256.264.

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48

Jesuthanam, C. P., S. Kumanan, and P. Asokan. "SURFACE ROUGHNESS PREDICTION USING HYBRID NEURAL NETWORKS." Machining Science and Technology 11, no. 2 (May 29, 2007): 271–86. http://dx.doi.org/10.1080/10910340701340141.

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49

Singh, Sanjay Kumar, K. Srinivasan, and D. Chakraborty. "Acoustic characterization and prediction of surface roughness." Journal of Materials Processing Technology 152, no. 2 (October 2004): 127–30. http://dx.doi.org/10.1016/j.jmatprotec.2004.03.023.

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50

Brezocnik, M., M. Kovacic, and M. Ficko. "Prediction of surface roughness with genetic programming." Journal of Materials Processing Technology 157-158 (December 2004): 28–36. http://dx.doi.org/10.1016/j.jmatprotec.2004.09.004.

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