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Journal articles on the topic 'Roughness prediction'

Author: Grafiati

Published: 4 November 2023

Last updated: 19 July 2025

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1

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

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2

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

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3

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 (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 surfac

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4

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 predi

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5

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 (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 calc

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6

Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (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

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7

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 pr

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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 (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 indica

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9

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 (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

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10

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 (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 g

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11

Sukkam, Chaiyakron, and Seksan Chaijit. "A Surface Roughness Prediction Model for SKT4 Steel Milling." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15499–504. http://dx.doi.org/10.48084/etasr.7612.

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Predicting surface roughness is critical in manufacturing processes like grinding, particularly for materials such as SKT4 steel, where a precise surface finish is imperative. Precise roughness prediction facilitates the optimization of process parameters to achieve the desired surface quality, consequently diminishing the need for supplementary operations such as grinding or polishing. This, in turn, decreases costs and lead times. This study aimed to develop a surface roughness prediction model tailored for milling SKT4 steel by designing experiments to analyze the influence of cutting param

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12

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 gri

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13

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 (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 variat

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14

Molinero-Pérez, Noelia, Laura Montalbán-Domingo, Amalia Sanz-Benlloch, and Tatiana García-Segura. "Dual Model for International Roughness Index Classification and Prediction." Infrastructures 10, no. 1 (2025): 23. https://doi.org/10.3390/infrastructures10010023.

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Existing models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-model approach that integrates pavement condition index (PCI), pavement distress types, climatic, and traffic data to improve IRI prediction. Using data from the Long-Term Pavement Performance database, a dual-model approach was developed: pavements were classified into groups based on key factors, and

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15

Lee, Hyeon-Uk, Chang-Jae Chun, and Jae-Mo Kang. "Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines." Mathematics 11, no. 22 (2023): 4682. http://dx.doi.org/10.3390/math11224682.

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This paper studies the application of artificial intelligence to milling machines, focusing specifically on identifying the inputs (features) required for predicting surface roughness. Previous studies have extensively reviewed and presented useful features for surface roughness prediction. However, applying research findings to actual operational factories can be challenging due to the additional costs of sensor installations and the diverse environments present in each factory setting. To address these issues, in this paper, we introduced effective features for predicting surface roughness i

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16

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 (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 th

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17

Gao, Shang, Haoxiang Wang, Han Huang, Zhigang Dong, and Renke Kang. "Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding." International Journal of Extreme Manufacturing 7, no. 3 (2025): 035103. https://doi.org/10.1088/2631-7990/adae67.

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Abstract Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials, including single-crystal silicon, silicon carbide, and gallium arsenide. Surface roughness and subsurface damage depth (SDD) are crucial indicators for evaluating the surface quality of these materials after grinding. Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions. This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semi

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18

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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19

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 pr

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20

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 t

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21

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 (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 th

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22

Li, Guo, Zheng Liangrui, and Feng Lang. "Intelligent prediction of surface roughness of PSZ ceramic grinding based on correlation analysis and CNN-BiLSTM neural network." Scientific Insights and Discoveries Review 4 (October 14, 2024): 313–22. http://dx.doi.org/10.59782/sidr.v4i1.154.

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Partially stabilized zirconia (PSZ) ceramics are widely used in aerospace industry and other fields due to their superior performance. Surface roughness is a key indicator for evaluating the grinding level of PSZ ceramics. In order to reduce the prediction error of grinding surface roughness, an acoustic emission prediction model for PSZ ceramic grinding surface roughness based on correlation analysis and convolution-bidirectional long short term memory neural network (CNN-BiLSTM) was proposed. By analyzing the correlation between the eigenvalues of grinding acoustic emission signals and the g

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23

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 feasibili

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24

Shi, Chaoyu, Bohao Chen, Yao Shi, and Jun Zha. "Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction." Applied Sciences 15, no. 11 (2025): 6027. https://doi.org/10.3390/app15116027.

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Grinding, as the most crucial finishing process for bearing rings, influences the surface integrity of bearings through the roughness of the ground surface. In order to improve the surface roughness of bearing ring grinding under multiple working conditions, a prediction model of bearing ring surface roughness based on feature extraction was proposed. Firstly, the signal was decomposed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm, and the sensitive components were selected based on the correlation coefficient between Intrinsic Mode Functions

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25

Hu, Zewen, Tao Wang, Hongcai Chen, Kanjian Zhang, and Haikun Wei. "Improved prediction of surface roughness in grinding process: a cascade of theoretical model and regularized extreme learning machine." Journal of Instrumentation 20, no. 06 (2025): P06008. https://doi.org/10.1088/1748-0221/20/06/p06008.

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Abstract Surface roughness is a key indicator of product quality, and developing a precise prediction model contributes to optimizing processes and enhancing production efficiency. Current models for predicting surface roughness primarily include theoretical and data-driven models. However, due to the complex nature of the grinding process, theoretical models that rely on simplified assumptions often fail to estimate surface roughness accurately. Additionally, data-driven models lack physical interpretation and exhibit a high dependence on data, which limits their practical application. To add

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26

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 (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 wo

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27

Hweju, Zvikomborero, Fundiswa Kopi, and Khaled Abou-El-Hossein. "Statistical evaluation of PMMA surface roughness." Journal of Physics: Conference Series 2313, no. 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 su

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28

Zhang, Yan, Yashuang Zhang, Liaoyuan Zhang, Wenhui Li, Xiuhong Li, and Kun Shan. "The Development and Experimental Validation of a Surface Roughness Prediction Model for the Vertical Vibratory Finishing of Blisks." Coatings 15, no. 6 (2025): 634. https://doi.org/10.3390/coatings15060634.

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The surface roughness of blisks during vibratory finishing is a critical evaluation index for their processing effect. Establishing a surface roughness prediction model helps reveal the processing mechanism and guide the optimization of process parameters. Therefore, based on wear theory and the least squares centerline system, a relationship between the surface roughness and material removal depth was established, and a scratch influence factor was introduced to correct the impact of surface scratches on the theoretical model. Interaction parameters between the blisk and granular media were o

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29

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 (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 t

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30

Ling, Le, Xuejian Zhang, Xiaobing Hu, et al. "Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network." Machines 12, no. 2 (2024): 100. http://dx.doi.org/10.3390/machines12020100.

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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 intelligen

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31

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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32

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

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33

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

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34

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

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35

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 (2012): 1305–13. http://dx.doi.org/10.1016/j.jmatprotec.2012.01.007.

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36

Sakamoto, Ryo, Ryutaro Tanaka, Isaí Espinoza Torres, Israel Martínez Ramírez, Katsuhiko Sekiya, and Keiji Yamada. "Prediction of Surface Roughness Components in Turning with Single Point Tool—Measurement of Tool Edge Contour and Prediction of its Position During Cutting—." International Journal of Automation Technology 18, no. 3 (2024): 382–89. http://dx.doi.org/10.20965/ijat.2024.p0382.

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Surface roughness is affected by the tool geometry, feed rate, overcutting by built-up edge, and tool vibration in the depth of the cut direction. However, dividing the roughness value into each component is difficult. Therefore, a new prediction method for the position of the tool contour on the roughness curve is proposed to divide the measured roughness value into components. This proposed method consists of two processes. In one, the roughness curve is divided into the roughness curve formed during each revolution of the work material regardless of the clarity of the feed marks. The other

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37

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. Experimen

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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

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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 cuttin

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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 (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 expl

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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 (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

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Vencovský, Václav. "Roughness Prediction Based on a Model of Cochlear Hydrodynamics." Archives of Acoustics 41, no. 2 (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

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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 mod

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YU, J., Y. NAMBA, and M. SHIOKAWA. "FRACTAL ROUGHNESS CHARACTERIZATION OF SUPER-GROUND Mn-Zn FERRITE SINGLE CRYSTALS." Fractals 04, no. 02 (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 affectin

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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 (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 Rey

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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 t

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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 (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 fa

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Vidakis, Nectarios, Markos Petousis, Nikolaos Vaxevanidis, and John Kechagias. "Surface Roughness Investigation of Poly-Jet 3D Printing." Mathematics 8, no. 10 (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 a

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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

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Cui, Pengcheng, Zhanqiang Liu, Xinglin Yao, and Yukui Cai. "Effect of Ball Burnishing Pressure on Surface Roughness by Low Plasticity Burnishing Inconel 718 Pre-Turned Surface." Materials 15, no. 22 (2022): 8067. http://dx.doi.org/10.3390/ma15228067.

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The low plasticity burnished surface roughness is significantly affected by the low plasticity burnishing (LPB) parameters. This research proposed the analytical prediction model to predict the LPBed surface roughness and optimal LPB pressure based on Hertz contact mechanics and the slip-line field theory. In this study, the surface formatted process was divided into the smoothing stage (SS) and the indentation stage (IS). The smoothing mechanism of SS and the deterioration mechanism of IS were analyzed theoretically. The analytical prediction model for the LPBed surface roughness was proposed

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