nach oben

Neural Computing and Applications

Erschienen in:

01.12.2005

Surface roughness prediction in turning using artificial neural network

verfasst von: Surjya K. Pal, Debabrata Chakraborty

Erschienen in: Neural Computing and Applications | Ausgabe 4/2005

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this work, a back propagation neural network model has been developed for the prediction of surface roughness in turning operation. A large number of experiments were performed on mild steel work-pieces using high speed steel as the cutting tool. Process parametric conditions including speed, feed, depth of cut, and the measured parameters such as feed and the cutting forces are used as inputs to the neural network model. Roughness of the machined surface corresponding to these conditions is the output of the neural network. The convergence of the mean square error both in training and testing came out very well. The performance of the trained neural network has been tested with experimental data, and found to be in good agreement.

Vorheriger Artikel Revisiting squared-error and cross-entropy functions for training neural network classifiers

Nächster Artikel A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Albrecht AB (1956) How to secure desired surface finish in turning operations. American Machinist 133–136

Ansell CT, Taylor J (1962) The surface finishing properties of a carbide and ceramic cutting tool. In: Proceedings of the 3rd international MTDR conference

Chandiramani KL, Cook NH (1964) Investigations into the nature of surface finish and its variation with cutting speed. Trans ASME J Eng Ind 86–87:134–140

Karmakar A (1970) Factors influencing surface finish during fine turning. In: Proceedings of 4th all India machine tool design and research conference, pp 123–128

Selvam MS (1975) Tool vibrations and its influence on surface roughness in turning. Wear 35:149–157CrossRef

Sundaram RM, Lambert BK (1981) Mathematical models to predict surface finish in fine turning of steel. Int J Prod Res 19:547–564

Mital A, Mehta M (1988) Surface finish prediction models for fine turning. Int J Prod Res 26(12):1981–1876

Shiraishi M, Sato S (1990) Dimensional and surface roughness controls in a turning operation. Trans ASME J Eng Indus 112:78–83

Rangwala SS, Dornfield DA (1989) Learning and optimization of machining operations using computing abilities of neural networks. IEEE Trans Sys Man Cybern 19(2):299–214CrossRef

10.

Azouzi A, Guillot M (1990) On-line optimization of turning process using an inverse process neuro controller. Trans ASME J Manuf Sci Eng 120:101–108

11.

Fang XD, Jawahir IS (1994) Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters. Int J Prod Res 32(4):833–849

12.

Grezsik W (1996) A revised model for predicting surface roughness in turning. Wear 194:143–148CrossRef

13.

Thomas M, Beauchamp Y, Youssef AY, Masounave J (1996) Effect of tool vibrations on surface roughness during lathe dry turning process. Comput Ind Eng 31(3/4):637 – 644CrossRef

14.

Abouelatta OB, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118:269–277CrossRef

15.

Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tools Manuf 41:1251–1263CrossRef

16.

Lin WS, Lee BY, Wu CL (2001) Modeling the surface roughness and cutting force for turning. J Mater Process Technol 108:286–293CrossRef

17.

Ho SY, Lee KC, Chen SS, Ho SJ (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42:1441–1446CrossRef

18.

Grezsik W, Brol S (2003) Hybrid aprroach to surface roughness evaluation in multistage machining processes. J Mater Process Technol 134:265–272CrossRef

19.

Puertas-Arbizu I, Luis-Pérez CJ (2003) Surface roughness prediction by factorial design of experiments in turning processes. J Mater Process Technol 143–144:390–396CrossRef

20.

Risbood KA, Dixit US, Sahasrabudhe AD (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132:203–214CrossRef

21.

Lee SS, Chen JC (2003) On-line surface roughness recognition system using artificial neural network system in turning operations. Int J Adv manuf Technol 22:498–509CrossRef

22.

Kohli A, Dixit US (2004) A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25:118–129CrossRef

23.

Monostori L (1993) A step towards intelligent manufacturing: modelling and monitoring of manufacturing processes through artificial neural network. Ann CIRP 42(1):485–493

24.

Das S, Roy R, Chottopadhyay AB (1996) Evaluation of wear of turning carbide inserts using neural network. Int J Mach Tools Manuf 36:789–797CrossRef

25.

Dimla DE, Lister JPM, Leighton NNJ (1997) Neural network solutions to the tool condition monitoring problem in metal cutting-a critical review of methods. Int J Mach Tools Manuf 37(9):1219–1241CrossRef

26.

Choudhury SK, Jain VK , Rama-Rao CHVV (1999) On-line monitoring of tool wear in turning using neural network. Int J Mach Tools Manuf 39:489–504CrossRef

27.

Chien WT, Tsai CS (2003) The investigation on prediction of tool wear and the determination of optimum cutting conditions in machining 17–4 PH stainless steel. Int J Machine Tools Manuf 140:340–345CrossRef

28.

Haykin S (2003) Neural networks: a comprehensive foundation, 2nd edn. Pearson Education

Titel: Surface roughness prediction in turning using artificial neural network
verfasst von: Surjya K. Pal
Debabrata Chakraborty
Publikationsdatum: 01.12.2005
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2005
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-005-0468-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2005

Neural network analysis of ophthalmic arterial doppler signals with Uveitis disease

A Review of data fusion models and architectures: towards engineering guidelines

The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling

Revisiting squared-error and cross-entropy functions for training neural network classifiers

Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

Information Retrieval and Categorisation using a Cell Assembly Network