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Dissertations / Theses on the topic 'Roughness prediction'

Author: Grafiati

Published: 4 November 2023

Last updated: 31 July 2025

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1

Munoz-Escalona, Patricia. "Surface roughness prediction when milling with square inserts." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519033.

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2

Shauche, Vishwesh. "Health Assessment based In-process Surface Roughness Prediction System." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298323430.

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3

Staheli, Kimberlie. "Jacking Force Prediction: An Interface Friction Approach based on Pipe Surface Roughness." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-07052006-203035/.

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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2007.<br>Dr. J. David Frost, Committee Chair ; Dr. G. Wayne Clough, Committee Co-Chair ; Dr. William F. Marcuson III, Committee Member ; Dr. Paul W. Mayne, Committee Member ; Dr. Susan Burns, Committee Member.

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4

Yamaguchi, Keiko. "Improved ice accretion prediction techniques based on experimental observations of surface roughness effects on heat transfer." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/14148.

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5

Sakthi, Gireesh. "WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-400462.

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The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU

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6

Srinivasan, Sriram. "Development of a Cost Oriented Grinding Strategy and Prediction of Post Grind Roughness using Improved Grinder Models." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78298.

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Irregularities in pavement profiles that exceed standard thresholds are usually rectified using a Diamond Grinding Process. Diamond Grinding is a method of Concrete Pavement Rehabilitation that involves the use of grinding wheels mounted on a machine that scraps off the top surface of the pavement to smooth irregularities. Profile Analysis Software like ProVAL© offers simulation modules that allow users to investigate various grinding strategies and prepare a corrective action plan for the pavement. The major drawback with the current Smoothness Assurance Module© (SAM) in ProVAL© is that it pr

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7

Celik, Kazim Arda. "Development Of A Methodology For Prediction Of Surface Roughness Of Curved Cavities Manufactured By 5-axes Cnc Milling." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608368/index.pdf.

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The surface quality is identified by surface roughness parameters. The average surface roughness (Ra) is used in this study, as it is the most commonly used roughness parameter in the industry. A particular curved cavity of a forging die is selected for the experimental study. Different milling methods are tested. The comparison studies are conducted between 3-axes and 5-axes milling, linear and circular tool path strategies and down and up milling. According to the experimental study, appropriate method is determined for the milling of a particular curved cavity of a forging die. The experime

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8

Cummings, Patrick. "Modeling the Locked-Wheel Skid Tester to Determine the Effect of Pavement Roughness on the International Friction Index." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1604.

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Pavement roughness has been found to have an effect on the coefficient of friction measured with the Locked-Wheel Skid Tester (LWT) with measured friction decreasing as the long wave roughness of the pavement increases. However, the current pavement friction standardization model adopted by the American Society for Testing and Materials (ASTM), to compute the International Friction Index (IFI), does not account for this effect. In other words, it had been previously assumed that the IFI's speed constant (SP), which defines the gradient of the pavement friction versus speed relationship, is an

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9

Mangin, Steven F. "Development of an Equation Independent of Manning's Coefficient n for Depth Prediction in Partially-Filled Circular Culverts." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1284488143.

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10

Levin, Ori. "Stability analysis and transition prediction of wall-bounded flows." Licentiate thesis, KTH, Mechanics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1663.

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<p>Disturbances introduced in wall-bounded .ows can grow andlead to transition from laminar to turbulent .ow. In order toreduce losses or enhance mixing in energy systems, afundamental understanding of the .ow stability is important. Inlow disturbance environments, the typical path to transition isan exponential growth of modal waves. On the other hand, inlarge disturbance environments, such as in the presence of highlevels of free-stream turbulence or surface roughness,algebraic growth of non-modal streaks can lead to transition.In the present work, the stability of wall-bounded .ows isinvest

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11

Ås, Sigmund. "Fatigue Life Prediction of an Aluminium Alloy Automotive Component Using Finite Element Analysis of Surface Topography." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Engineering Science and Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-758.

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<p>A 6082 aluminium alloy has been characterized with regard to the influence of surface roughness on fatigue strength.</p><p>Fatigue life testing of smooth specimens was used to establish reference curves for the material in extruded and forged T6 condition. The extruded material was found to have better fatigue strength than the forged material, although the cyclic stress-strain response was similar for both. The forged material was tested in T5, T6 and T7 tempers, showing no significant difference in fatigue strength.</p><p>Surface roughness was created by circumferential grinding of cylind

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12

Edwards, Matthew. "Prediction and control of rolling noise in buildings." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI097.

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Les nouveaux bâtiments dans les zones urbaines sont divisés en zones commerciales et en zones habitables. Les magasins commerciaux se trouvent généralement au rez-de-chaussée, les résidences privées aux étages supérieurs. Cette utilisation a révélé des perturbations critiques dues au bruit généré par les chariots de livraison lorsque les bâtiments sont principalement occupés (par exemple tôt le matin). Ces chariots génèrent des vibrations à basse fréquence (moins de 100 Hz) qui se propagent facilement dans la structure du bâtiment et aux étages supérieurs, perturbant les habitants qui s'y trou

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13

França, Thiago Valle [UNESP]. "Estudo da predição da circularidade e rugosidade de peças retificadas utilizando as redes neurais artificiais." Universidade Estadual Paulista (UNESP), 2005. http://hdl.handle.net/11449/90809.

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Made available in DSpace on 2014-06-11T19:24:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-01-19Bitstream added on 2014-06-13T18:21:15Z : No. of bitstreams: 1 franca_tv_me_bauru.pdf: 6693171 bytes, checksum: a1e62f81ea86d1eeed97e598cb2bb8f5 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)<br>Atualmente, a fabricação é caracterizada pela sua complexidade, pluralidade de disciplinas e crescente demanda de novas ferramentas e técnicas para a solução de difíceis problemas. As redes neurais ar

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14

Zhang, Yuanyuan. "Friction prediction for rough surfaces in an elastohydrodynamically lubricated contact." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI063.

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Le frottement à l’interface des surfaces influence les performances des éléments mécaniques. Le frottement a été étudié expérimentalement dans la plupart des études. Dans ce travail, le frottement est prédit à l'aide d'une simulation numérique dans des conditions de contact rugueux avec une lubrification élastohydrodynamique (EHL). La technique classique Multigrille fonctionne bien pour limiter le temps de calcul et les besoins en mémoire. Cependant, le choix de la grille grossière a une influence importante sur la robustesse du code et son efficacité pour résoudre le problème brut. Dans la pr

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França, Thiago Valle. "Estudo da predição da circularidade e rugosidade de peças retificadas utilizando as redes neurais artificiais /." Bauru : [s.n.], 2005. http://hdl.handle.net/11449/90809.

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Resumo: Atualmente, a fabricação é caracterizada pela sua complexidade, pluralidade de disciplinas e crescente demanda de novas ferramentas e técnicas para a solução de difíceis problemas. As redes neurais artificiais oferecem uma nova e diferente alternativa para investigar e analisar os desafiadores tópicos relacionados à manufatura. Desta forma, estudou-se neste trabalho os assuntos relacionados à aplicação das redes neurais na predição da circularidade e rugosidade da peça retificada pela análise de algumas variáveis de saída do processo. Foram empregados nos ensaios de usinagem: um fluido

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16

Cherguy, Oussama. "Vers une modélisation de la topographie des surfaces générées par le procédé de toilage." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2023. http://www.theses.fr/2023ECDL0030.

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Le toilage appartient à la famille des procédés de finition – superfinition abrasives. Il permet d’obtenir des surfaces ayant une très bonne rugosité. En outre, il s’agit d’une très bonne alternative pour la production à grande échelle, car c’est un procédé très efficace et stable. Malheureusement, le toilage reste un procédé difficile à optimiser et nécessite de nombreux essais avant de trouver les conditions optimales. La modélisation numérique du procédé de toilage est donc une excellente alternative aux optimisations empiriques qui prennent beaucoup de temps. Ce travail propose des méthode

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17

Logins, Andris. "High speed milling technological regimes, process condition and technological equipment condition influence on surface quality parameters of difficult to cut materials." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/164122.

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[ES] La calidad superficial en las piezas mecanizadas depende del acabado superficial, resultado de las marcas dejadas por la herramienta durante el proceso de corte. Las aproximaciones teóricas tradicionales indican que estas marcas están relacionadas con los parámetros de corte (velocidad de corte, avance, profundidad de corte...), el tipo de máquina, el material de la pieza, la geometría de la herramienta, etc. Pero no todos los tipos de mecanizado y selección de materiales pueden dar un resultado ambiguo. Hoy en día, de manera progresiva, se están utilizando las técnicas de fresado de Alta

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18

Pelletier, Jon D., and Jason P. Field. "Predicting the roughness length of turbulent flows over landscapes with multi-scale microtopography." COPERNICUS GESELLSCHAFT MBH, 2016. http://hdl.handle.net/10150/618956.

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The fully rough form of the law of the wall is commonly used to quantify velocity profiles and associated bed shear stresses in fluvial, aeolian, and coastal environments. A key parameter in this law is the roughness length, <i>z</i><sub>0</sub>. Here we propose a predictive formula for <i>z</i><sub>0</sub> that uses the amplitude and slope of each wavelength of microtopography within a discrete-Fourier-transform-based approach. Computational fluid dynamics (CFD) modeling is used to quantify the effective <i>z</i><sub>0</sub> value of sinusoidal microtopography as a function of the amplitude a

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19

Moraru, Laurentiu Eugen. "Numerical Predictions and Measurements in the Lubrication of Aeronautical Engine and Transmission Components." University of Toledo / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1125769629.

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20

Ead, Richard M. "Predicting the effects of sea surface scatter on broad band pulse propagation with an ocean acoustic parabolic equation model." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FEad.pdf.

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21

Yan, Dinghong. "Measurement and prediction of surface roughness in finish turning." 1994. http://hdl.handle.net/1993/18605.

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22

HUANG, YEN-PIN, and 黃硯斌. "Surface Roughness and Contour Error Prediction Using Neural Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/th3435.

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碩士<br>國立中正大學<br>通訊工程研究所<br>107<br>In precision machining industry, computer numerical control (CNC) machine tools play an important role. In order to improve the quality and accuracy of the workpiece, the requirements for machining precision are getting higher. The selection and optimization of machining parameters are one of the main factors that affect the machining precision. Machining parameters are usually determined by the trial-and-error method. This method not only consumes time and manpower, but also results in just acceptable but not optimized parameter values. In order to solve this

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23

Wu, Shi-Chang, and 吳士昌. "Data Mining Assisted Neural Network in Surface Roughness Prediction System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/z5855p.

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碩士<br>中原大學<br>工業與系統工程研究所<br>99<br>Intelligent control theory has been studied in modern research and widely applied in various fields. With the rapid technological advances, however, intelligent control system becomes more complex and is difficult for researchers to define accurately. As a result, data mining is proposed to solve problems in the relevant areas. In this study, the neural algorithm as the main theory background, Data mining is assisted into neural prediction system, the data analysis and identify each one of the relationship, When the target can be determined by making the relat

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24

Lin, Chi-Chen, and 林豈臣. "Surface Roughness Prediction and Cutting Parameter Optimization in Milling Process." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5311038%22.&searchmode=basic.

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碩士<br>國立中興大學<br>機械工程學系所<br>107<br>In this study, the spindle and vise vibrations as well as the spindle current were measured synchronously during the process of milling Inconel 718. The surface roughness (represented by Ra) of workpiece was investigated by determining the correlation among the Ra, the signals of vibration, the cutting parameters, and the current signals, under the different combinations of cutting parameters. The prediction models of workpiece surface roughness were built through the Elman neural network. In the experiment, the features of signals were extracted through the E

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25

Jenq, Ren-Wen, and 鄭人文. "The Prediction of Roughness in Turning by a Dynamic Simulating System." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/22986636465928816390.

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26

Chuang, Kai-Yi, and 莊凱驛. "Development of Performance Prediction Models for Pavement Roughness－Using LTPP Database." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/10580082115674133135.

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碩士<br>淡江大學<br>土木工程學系碩士班<br>95<br>Performance predictive models have been used in various pavement design, evaluation, rehabilitation, and network management activities. As pavement design evolves from traditional empirically based methods toward mechanistic-empirical, the equivalent single axle load (ESAL) concept used for traffic loads estimation is no longer adopted in the recommended Mechanistic-Empirical Pavement Design Guide. The success of the new design guide considerably depends upon the accuracy of pavement performance predictions. Thus, this study will first investigate its goodness

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Liang, Hung-Wei, and 梁宏維. "Intelligent genetic algorithm (IGA) for modeling and prediction of surface roughness." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/k39z7x.

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碩士<br>國立虎尾科技大學<br>機械與機電工程研究所<br>97<br>In this dissertation, high performance in IGA arises mainly from intelligent crossover based on orthogonal experimental designs. Using OED with both orthogonal array (OA) and factor analysis, it can analyze the effect of several factors simultaneously. Accurate estimation of surface roughness of workpieces in turning operations play an important role in the manufacturing industry. It is used to assess the workpiece in the end milling process of performance criterion. For example：surface seals, ball bearing, gear, cam and journal. Surface roughness has

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28

Sahoo, Smrutiranjan. "Prediction of machining parameters for optimum Surface Roughness in turning SS 304." Thesis, 2011. http://ethesis.nitrkl.ac.in/2340/1/project_report_8th_sem_smrutiranjan_sahoo.pdf.

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In any machining process, apart from obtaining the accurate dimensions, achieving a good surface quality is also of utmost importance. A machining process involves many process parameters which directly or indirectly influence the surface quality of the product. Surface roughness and waviness in turning process are caused due to various parameters of which feed, speed, depth of cut are important ones. A precise knowledge of these optimum parameters would facilitate reduce the machining costs and improve product quality. Extensive study has been conducted in the past to optimize the process par

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29

Gupta, Parag. "An analytical model for in-process prediction of surface roughness in finish turning." 1991. http://hdl.handle.net/1993/18282.

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30

Xie, Po-Syong, and 謝博雄. "Remoting Real-Time Prediction Grinding Force and Surface Roughness in the Grinding Process." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/45123150678262204869.

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碩士<br>國立臺灣大學<br>機械工程學研究所<br>101<br>Grinding processing is the common processing method in the precision processing, which plays the most important part in the final stage of the processing because it relates to surface accuracy and scale quality. In general grinding, excess grinding force will rough workpiece'' surface, and which has to be measured after grinding. In order to judge the trends of grinding force and surface roughness efficiently, this study will cooperate with SkyMars, establishing the model of grinding force and surface roughness to estimate the quality of workpiece.

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31

Yu, Chung-He, and 余忠河. "A Study of Applying MR Control in Gray Online Modeling Surface Roughness Prediction." Thesis, 2017. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22105CYCU5030063%22.&searchmode=basic.

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碩士<br>中原大學<br>工業與系統工程研究所<br>105<br>In nowadays product property of diverse and few and short life time, the need of the quality becomes more and more strict. Milling in a common way in processing, and the roughness will affect the damage level of cutting tool and the rate of manufacturing plan, to grasp the roughness will affect the quality and in order to get the accuracy of roughness lots of scholar wish to use predict, monitor the process variation and decrease the cost on the other hand. Under the different process environment and plan, there are lots of element that cannot be grasped. The

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32

Lyu, Peng-Hua, and 呂朋樺. "A Study of Surface Roughness Prediction System using Fuzzy Regression and Taguchi Method." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/07744168256323458179.

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碩士<br>中原大學<br>工業與系統工程研究所<br>98<br>Intelligent control theory has been studied in modern research and widely applied in various fields. With the rapid technological advances, however, intelligent control system becomes more complex and is difficult for researchers to define accurately. As a result, fuzzy theory is proposed to solve problems in the relevant areas. Although the fuzzy theory can be used to solve complex issues and make accurate definitions, two main issues occur in the process of the building fuzzy systems including defining appropriate membership functions in the fuzzy IF- THEN r

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33

Chen, Yu-Chen, and 陳宥辰. "Study on the Correlationship and Prediction Model of Surface Roughness and Machining Parameters." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/cb3q7s.

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碩士<br>國立勤益科技大學<br>機械工程系<br>106<br>High speed and high precision machining has become the most important technology in manufacturing industry. The surface roughness of high precision components is regarded as the important characteristics of the product quality. On the other hand, regenerative chatter occurring in high speed machining could damage the machined surface and restricts the process efficiency and the longevity of cutting tools. To avoid chatter and increase machining precision, most of the engineers have selected the appropriate cutting conditions according the machining stability l

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Lu, Huai-Shiun, and 呂淮熏. "The Study for Prediction of Surface Roughness and Optimization of Parameters in Side Milling." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/30304129487733738281.

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博士<br>國立高雄第一科技大學<br>工程科技研究所<br>95<br>Side milling is a machining process in end milling operations that utilizes the peripheral cutting edge of an end mill to perform broad surface machining upon the vertical wall of workpieces. It is a common manufacturing process used in molding and mechanical components. The corresponding performance is closely related to several cutting parameters, such as cutting speed, feed per tooth, axial depth of cut, radial depth of cut, overhang length, and flank wear of peripheral cutting edge. Conventionally, a cutting condition selected by operators is mostly bas

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Chuang, Ying-Chuan, and 莊英川. "The Development of a Neural Network Surface Roughness Prediction System in End Milling Operations." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/43089858513853363389.

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碩士<br>中原大學<br>工業與系統工程研究所<br>104<br>Because of progressions of industries as well as innovations of products, the requirement for CNC (Computer Numerical Control) machining process of computer assisted manufacturing and demands for accuracy of dimensions is growing up, which makes studies about CNC milling process essential topics when it comes to automatic industries. After CNC machining, the influence of surface roughness affects the quality profoundly, and many savants wish to employ approaches of prediction to curtail the time consumed by setting parameters and obtain ideal surface roughnes

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Wang, You-Xuan, and 王又萱. "The Development of an In-process BPN Surface Roughness Prediction System in Drilling Operations." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/45550562002031989854.

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碩士<br>中原大學<br>工業與系統工程研究所<br>101<br>With the invention of the Computer Numerical Control (CNC) and development of the material technology, the engineering of advanced manufacturing is greatly improved. These advantages accelerate the development of manufacturing industry, which becomes a stable foundation of the automation. However, CNC nowadays are widely used in different kinds of industries, which mainly focus on how to minimize the cost and maximize the production and profit. These strategies play important roles in reaching entrepreneur’s goals and visions. At this point, a right decision

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Lei, Kai-Wei, and 雷凱崴. "Analysis and Prediction for Surface Roughness of Milling Using Vibration Signal and Artificial Neural Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/46924490466228751179.

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碩士<br>國立中興大學<br>機械工程學系所<br>105<br>This study primarily investigates the correlation among the cutting parameters, the surface roughness level of S45C steel through the milling process and the vibration signals that are recorded synchronously. With different combinations of cutting parameters, such as: feed rate of per cut, cutting depth and clamping torgue of vise, the different levels of surface roughness are predicted by using the artificial neural network (ANN). The vibrations are measured by the accelerometers which are mounted on the spindle and the vise. The features of vibration signa

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Chen, Jiu-Hong, and 陳久弘. "Using Grey Relational Analysis and Neural Networks in Surface Roughness Prediction System of Milling Operations." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/34777212759273232655.

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碩士<br>中原大學<br>工業工程研究所<br>100<br>With the development of the electronics industry, the demands of computer numerical control (CNC) machine for milling operations are increased. Accompanying with the requirements of product quality, CNC milling technology becomes an important issue of various automation industries. Controlling of the surface roughness has a significant impact to the quality in CNC milling operations. In order to achieve accurate required surface roughness, many researchers hope to take advantages of the predictable manner, and to reduce the additional cost and improve the qualit

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Huang, Chung-Hsiung, and 黃忠雄. "Intelligent Genetic Algorithm (IGA) for modeling and prediction of hematopoietic stem cells (HSCs) and roughness." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/m5uh93.

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碩士<br>國立虎尾科技大學<br>機械與機電工程研究所<br>97<br>Genetic algorithm used in engineering widely, including automatic control, system optimization design in recent years. Intelligent Genetic Algorithm(IGA) with Fuzzy Neural Network(FNN) is used to model and predict the workpiece surface roughness for the end milling process. IGA is powerful by using the Orthogonal Experimental Design’s algorithm. It can effectively reason to near-optimal solutions. Surface roughness is very important in the manufacturing industry. It is used to assess the workpiece in the end milling process of performance criterion

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40

Yang, Shang-wei, and 楊上緯. "Prediction of Partition Coefficients for VolatileOrganic Compounds and Surface Roughness byIntelligent Genetic Algorithm (IGA) Method." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/nffj8k.

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碩士<br>國立虎尾科技大學<br>自動化工程研究所<br>99<br>In recent years, modeling and forecasting is widely used in many places, such as stock index, the projected seasonal waves, motor controller application object tracking ... and so on. In this paper, IGA and adaptive Neuro-Fuzzy Inference System (FNN) method as a basis to predict the distribution coefficients of volatile organic compounds for medicinal chemistry and environmental chemistry of volatile organic compounds in terms of the distribution coefficient can be used to study organic compounds in in vivo migration behavior and the environment, so this is

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Chang, Huang-Jie, and 張黃傑. "The Development of a Gray In-process Adaptive Learning Surface Roughness Prediction System in Milling Operations." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/80452657783741706708.

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碩士<br>中原大學<br>工業工程研究所<br>100<br>With competitions between enterprises, it is importance and necessary to control costs and quality of products. Therefore, the development of prediction system is tending to become a significant role in last decades. The models of the system including model simulation, statistical prediction, regression analysis and various soft computing have been generally used in the prediction system. its accuracy is also increasing gradually. However, the major component of building the models is a large number of data. Under the circumstance, the prediction system is diffi

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Fan, Chen-Lun, and 范振倫. "Surface Roughness Prediction based on Markov Chain and Deep Neural Network for Wire Electrical Discharge Machining." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/24zse3.

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碩士<br>國立中央大學<br>資訊工程學系<br>107<br>Industry 4.0 Smart Manufacturing is a hot topic recently. Today's global manufacturing industry is committed to smart manufacturing through industrial Internet of Things, big data analytics, and Cyber Physical System (CPS) technologies to improve product performance and product quality by saving production time and cost. This paper explores Virtual Metrology (VM) research to predict product quality before or after the production process has not been completed, without the need of product measurement. Specifically, this paper focuses on the Surface Roughness pre

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Huang, Wei-Cheng, and 黃偉誠. "The Application of Gray Theory on Prediction of the Surface Roughness and Wheel Redress in Surface Grinding." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/71165338177814365383.

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碩士<br>大同大學<br>機械工程研究所<br>89<br>In this study the Grey System Theory is applied to predict the surface roughness (Ra) and redress cycle of the wheel in surface grinding process. The Ra value of workpieces varied with grinding feedrate and depth were collected in experiments. And then, the data were used to create the roughness prediction model. Furthermore, the result of the prediction will used to forecast the redress cycle time of grinding wheel.Finally, the prediction accuracy were verified by experiments to prove the proposed method is useable . Experimental results show that the

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Liao, Hsueh-Yu, and 廖學佑. "Artificial Intelligence Machining Surface Roughness Prediction Model and MRR Optimization – A Case Study of Plastic Injection Mold Milling." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/nbzk5j.

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碩士<br>國立中興大學<br>機械工程學系所<br>103<br>Technical products have characteristics such as short life cycle, high surface quality request and large quantity. Therefore, manufacturing factories gradually tend to use general types machine tool equipped with intelligent management, intelligent design and monitoring systems. Recently, with technological development, there are more and more indirect sensors which are normally used, and computer efficiency is higher than the past. The obstacles of hardware are gradually removed. Accordingly, in the field of manufacturing, many researchers use artificial neur

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Chan, Chun-Hsu, and 詹淳旭. "The Development of a Surface Roughness Prediction System in Milling Operations by Implementing RPSO and LM in BPN." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/44884015470416043627.

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碩士<br>中原大學<br>工業與系統工程研究所<br>101<br>An artificial neural network (ANN) was developed and has been widely applied for years. One of the most applications is the supervised back propagation neural network (BPN). A traditional BPN adopted the steepest descent method to train and adjust weight values between neurons. It has the following shortcomings: (1) apt to convergence to local minima (2) slowly updated weight values and had long learning time (3) it is possible to become divergence results if the learning speed value increases too much and too fast. This study implements regrouping particle

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ho, ka-king, and 何家敬. "Adaptive Network-Based Fuzzy Inference System for Prediction of Workpiece Surface Roughness in End Milling Using Genetic-Based Learning Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/77207990673716195157.

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碩士<br>國立高雄第一科技大學<br>機械與自動化工程所<br>95<br>In this paper , genetic-based learning algorithm method of adaptive-network based fuzzy inference system(ANFIS) was used to predict the work piece surface roughness. Spindle speed , feed rate and depth of cut are the input variable , and the final output is the surface roughness . The premise part of the ANFIS is applied by Gauss membership function , and the consequent part is applied by the Takagi-Sugeno (TS) fuzzy model to inference. Taguchi-genetic algorithm was used to train the parameters of both the Gauss membership function in the premise part and

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Chen, Yan-Siang, and 陳彥翔. "Prediction for Surface Roughness and Remaining Useful Life of Grinding Wheel Using Grinding Vibration Signal and Long Short-Term Memory Network." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5311044%22.&searchmode=basic.

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碩士<br>國立中興大學<br>機械工程學系所<br>107<br>The surface quality of the grinding process is affected by many factors, such as grinding wheel speed, grinding depth, feed rate, machine vibration and grinding wheel wear. The workpiece surface quality and the grinding wheel state are assessed based on the experience of operators by observing the produced sparks and noise in the traditional machining. This makes it difficult to control the grinding quality levels, and thus results in the increase of time and material costs. Therefore, it is crucial to predict the surface roughness of workpiece and to monitor

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Jenwei, Zhang, and 張仁威. "Modeling and Predicting of Surface Roughness of Silicon Wafer Grinding." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/79918682631059825391.

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碩士<br>大葉大學<br>機械工程研究所碩士班<br>95<br>Manufacturing of silicon wafer starts with growth of silicon ingots. Typical processes to turn a silicon ingot to silicon wafers include slicing, edge profiling, grinding, lapping, polishing, etching etc. Grinding lapping and polishing are used to flatten the rough surface of wafer caused by slicing. Lapping and polishing processed for flatting wafers. In this study models for predicting the roughness of silicon wafers are developed. The prediction models are based on the grinding parameters and grit size of the grinding wheel. Box-Benkhen experiment design is

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Chang, Chu-Hsien, and 張竹賢. "Application of regression neuron in predicting surface roughness in end milling operations." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/43940163227936329924.

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碩士<br>中原大學<br>工業與系統工程研究所<br>98<br>Every industry hopes to reduce the cost, the waste and promote the productivity and efficiency to achieve the goal and vision. For achieving this goal, quality control is the most important role in manufacturing processing. Therefore, how to reach quality control efficiency must using some kinds of method to reduce the fail rate and promote the productivity, ex: Quality 7 Tools, 6 sigma and QCC etc. However, using this methods to achieve quality control, we usually through workpiece measurement. In the manufacturing industry, the surface roughness is an i

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LO, SHIH-HSUAN, and 羅世軒. "Predicting Surface Roughness from Machining Vibration Signals Based on Deep Learning Technique." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ygr3yr.

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碩士<br>靜宜大學<br>資訊管理學系<br>107<br>The application of the intelligent monitoring system could effectively improve the surface roughness during the surface milling of the product. Based on ease of installation and cost effectiveness, the external accelerator collected vibration signals generated during the production process on the processing machine to predict the surface roughness of the product. More accurate predictions could be obtained by combining deep learning prediction models. In this study, three models of FFT-DNN, FFT-LSTM and 1D-CNN were used to investigate training model and predictio

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