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2020 | OriginalPaper | Buchkapitel

4. Intelligent Modelling of Hard Materials Machining

verfasst von : Manjunath Patel G. C., Ganesh R. Chate, Mahesh B. Parappagoudar, Kapil Gupta

Erschienen in: Machining of Hard Materials

Verlag: Springer International Publishing

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Abstract

In the Mid of 1950s, artificial intelligence was emerged to solve practical problems in engineering domain by using tools, developed based on human intelligence. Genetic algorithm ‘GA’, artificial neural network ‘ANN’, and fuzzy logic are some AI-based soft computing tools used to predict and assist in the control of manufacturing processes. Today, huge money is spent throughout the globe on the development of AI technology to assist manufacturing industries.

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Vorheriges Kapitel Experimentation, Modelling, and Analysis of Machining of Hard Material

Nächstes Kapitel Optimization of Machining of Hard Material

D.T. Pham, P.T.N. Pham, Artificial intelligence in engineering. Int. J. Mach. Tools Manuf. 39(6), 937–949 (1999)CrossRef

R.P. Cherian, L.N. Smith, P.S. Midha, A neural network approach for selection of powder metallurgy materials and process parameters. Artif. Intell. Eng. 14(1), 39–44 (2000)CrossRef

D.F. Hesser, B. Markert, Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manuf. lett. 19, 1–4 (2019)CrossRef

W.Y. Chang, C.C. Chen, S.J. Wu, Chatter analysis and stability prediction of milling tool based on zero-order and envelope methods for real-time monitoring and compensation. Int. J. Precis. Eng. Manuf. 20, 1–8 (2019)CrossRef

D. Luzeaux, Process control and machine learning: Rule-based incremental control. IEEE Trans. Autom. Control 39(6), 1166–1171 (1994)MathSciNetMATHCrossRef

R. Liu, B. Yang, E. Zio, X. Chen, Artificial intelligence for fault diagnosis of rotating machinery: A review. Mech. Syst. Signal Process. 108, 33–47 (2018)CrossRef

W. Kacalak, M. Majewski, New intelligent interactive automated systems for design of machine elements and assemblies, in International Conference on Neural Information Processing (Springer Berlin Heidelberg, 2012), pp. 115–122CrossRef

S. Nguyen, Y. Mei, M. Zhang, Genetic programming for production scheduling: A survey with a unified framework. Complex Intell. Syst. 3(1), 41–66 (2017)CrossRef

B. Çaliş, S. Bulkan, A research survey: Review of AI solution strategies of job shop scheduling problem. J. Intell. Manuf. 26(5), 961–973 (2015)CrossRef

10.

S. Sambath, P. Nagaraj, N. Selvakumar, Automatic defect classification in ultrasonic NDT using artificial intelligence. J. Non-destr. Eval. 30(1), 20–28 (2011)CrossRef

11.

H. Yano, T. Akashi, N. Matsuoka, K. Nakanishi, O. Takata, N. Horinouchi, An expert system to assist automatic remeshing in rigid plastic analysis. Toyota Tech. Rev. 46, 87–92 (1997)

12.

V. Dey, D.K. Pratihar, G.L. Datta, M.N. Jha, T.K. Saha, A.V. Bapat, Optimization of bead geometry in electron beam welding using a Genetic algorithm. J. Mater. Process. Technol. 209(3), 1151–1157 (2009)CrossRef

13.

P. Dutta, D.K. Pratihar, Modeling of TIG welding process using conventional regression analysis and neural network-based approaches. J. Mater. Process. Technol. 184(1–3), 56–68 (2007)CrossRef

14.

A.V. Goncharenko, Several models of artificial intelligence elements for airctaft control, in 2016 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC) (IEEE), pp. 224–227 (2016)

15.

L. Gonzalez, G. Montes, E. Puig, S. Johnson, K. Mengersen, K. Gaston, Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16(1), 97 (2016)CrossRef

16.

D.K. Pratihar, K. Deb, A. Ghosh, A genetic-fuzzy approach for mobile robot navigation among moving obstacles. Int. J. Approximate Reasoning 20(2), 145–172 (1999)MATHCrossRef

17.

T.V. Sibalija, S.Z. Petronic, V.D. Majstorovic, R. Prokic-Cvetkovic, A. Milosavljevic, Multi-response design of Nd: YAG laser drilling of Ni-based superalloy sheets using Taguchi’s quality loss function, multivariate statistical methods and artificial intelligence. Int. J. Adv. Manuf. Technol. 54(5–8), 537–552 (2011)CrossRef

18.

R. Teti, G. Caprino, Prediction of composite laminate residual strength based on a neural network approach. WIT Trans. Inf. Commun. Technol. 6, WIT Press. www.witpress.com. ISSN 1743-3517

19.

P.G. Manjunath, P. Krishna, Prediction and optimization of dimensional shrinkage variations in injection molded parts using forward and reverse mapping of artificial neural networks. Adv. Mater. Res. 463, 674–678 (2012)CrossRef

20.

M. Patel, P. Krishna, A review on application of artificial neural networks for injection moulding and casting processes. Int. J. Adv. Eng. Sci. 3(1), 1–12 (2013)

21.

M.G. Patel, P. Krishna, M.B. Parappagoudar, Prediction of squeeze cast density using fuzzy logic based approaches. J. Manuf. Sci. Prod. 14(2), 125–140 (2014)

22.

M.G.C. Patel, P. Krishna, M.B. Parappagoudar, Prediction of secondary dendrite arm spacing in squeeze casting using fuzzy logic based approaches. Arch. Foundry Eng. 15(1), 51–68 (2015)CrossRef

23.

I. Mukherjee, P.K. Ray, A review of optimization techniques in metal cutting processes. Comput. Ind. Eng. 50(1–2), 15–34 (2006)CrossRef

24.

D.K. Pratihar, Soft computing. Alpha Science International, Ltd. (2007)

25.

S. Shanmuganathan, Artificial neural network modelling: An introduction, in Artificial neural network modelling (Springer Cham, 2016), pp. 1–14

26.

K. Shanmukhi, P.R. Vundavilli, B. Surekha, Modeling of ECDM micro-drilling process using GA-and PSO-trained radial basis function neural network. Soft. Comput. 19(8), 2193–2202 (2015)CrossRef

27.

R.H.L. Da Silva, M.B. da Silva, A. Hassui, A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals. Machining Sci. Technol. 20(3), 386–405 (2016)CrossRef

28.

P.R. Vundavilli, M.B. Parappagoudar, S.P. Kodali, S. Benguluri, Fuzzy logic-based expert system for prediction of depth of cut in abrasive water jet machining process. Knowl.-Based Syst. 27, 456–464 (2012)CrossRef

29.

B. Surekha, P.R. Vundavilli, M.B. Parappagoudar, A. Srinath, Design of genetic fuzzy system for forward and reverse mapping of green sand mould system. Int. J. Cast Met. Res. 24(1), 53–64 (2011)CrossRef

30.

B. Surekha, P.R. Vundavilli, M.B. Parappagoudar, Forward and reverse mappings of the cement-bonded sand mould system using fuzzy logic. Int. J. Adv. Manuf. Technol. 61(9–12), 843–854 (2012)CrossRef

31.

G.C.M. Patel, A.K. Shettigar, P. Krishna, M.B. Parappagoudar, Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process. Appl. Soft Comput. 59, 418–437 (2017)CrossRef

32.

G.C.M. Patel, A.K. Shettigar, M.B. Parappagoudar, A systematic approach to model and optimize wear behaviour of castings produced by squeeze casting process. J. Manuf. Process. 32, 199–212 (2018)CrossRef

33.

J. Wang, Y. Ma, L. Zhang, R.X. Gao, D. Wu, Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 48, 144–156 (2018)CrossRef

34.

D. Weimer, B. Scholz-Reiter, M. Shpitalni, Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 65(1), 417–420 (2016)CrossRef

35.

M. Rahman, Q. Zhou, G.S. Hong, On-line cutting state recognition in turning using a neural network. Int. J. Adv. Manuf. Technol. 10(2), 87–92 (1995)CrossRef

36.

J.D. Thiele, S.N. Melkote, Effect of cutting edge geometry and workpiece hardness on surface generation in the finish hard turning of AISI 52100 steel. J. Mater. Process. Technol. 94, 216–226 (1999)CrossRef

37.

D.E. Dimla Sr., Application of perceptron neural networks to tool state classification in a metal turning operation. Eng. Appl. Artif. Intell. 12, 471–477 (1999)CrossRef

38.

Y.K. Chou, C.J. Evans, M.M. Barash, Experimental investigation on CBN turning of hardened AISI 52100 steel. J. Mater. Process. Technol. 124, 274–283 (2002)CrossRef

39.

T. Özel, Y. Karpat, Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int. J. Mach. Tools Manuf. 45(4–5), 467–479 (2005)CrossRef

40.

V.N. Gaitonde, S.R. Karnik, L. Figueira, J.P. Davim, Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. Int. J. Adv. Manuf. Technol. 52(1–4), 101–114 (2011)CrossRef

41.

K.L. Petri, R.E. Billo, B. Bidanda, A neural network process model for abrasive flow machining operations. J. Manuf. Syst. 17(1), 52–64 (1998)CrossRef

42.

H.C. Zhang, S.H. Huang, Applications of neural networks in manufacturing: a state-of-the-art survey. Int. J. Product. Res. 33(3), 705–728 (1995)MATHCrossRef

43.

V.S. Sharma, S. Dhiman, R. Sehgal, S.K. Sharma, Estimation of cutting forces and surface roughness for hard turning using neural networks. J. Intell. Manuf. 19(4), 473–483 (2008)CrossRef

44.

M. Mia, N.R. Dhar, Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network. Measurement 92, 464–474 (2016)CrossRef

45.

F.J. Pontes, A.P. de Paiva, P.P. Balestrassi, J.R. Ferreira, M.B. da Silva, Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays. Expert Syst. Appl. 39(9), 7776–7787 (2012)CrossRef

46.

M. Mia, N.R. Dhar, Response surface and neural network based predictive models of cutting temperature in hard turning. J. Adv. Res. 7(6), 1035–1044 (2016)CrossRef

47.

M. Mia, M.H. Razi, I. Ahmad, R. Mostafa, S.M. Rahman, D.H. Ahmed, P.R. Dey, N.R. Dhar, Effect of time-controlled MQL pulsing on surface roughness in hard turning by statistical analysis and artificial neural network. Int. J. Adv. Manuf. Technol. 91(9–12), 3211–3223 (2017)CrossRef

48.

X. Wang, W. Wang, Y. Huang, N. Nguyen, K. Krishnakumar, Design of neural network-based estimator for tool wear modeling in hard turning. J. Intell. Manuf. 19(4), 383–396 (2008)CrossRef

49.

I. Asiltürk, M. Çunkaş, Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst. Appl. 38(5), 5826–5832 (2011)CrossRef

50.

B.A. Beatrice, E. Kirubakaran, P.R.J. Thangaiah, K.L.D. Wins, Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application. Procedia Eng. 97, 205–211 (2014)CrossRef

51.

S.N. Sivanandam, S.N. Deepa, Principles of Soft Computing (Wiley, 2007)

52.

H. Kurtaran, B. Ozcelik, T. Erzurumlu, Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm. J. Mater. Process. Technol. 169(2), 314–319 (2005)CrossRef

53.

P.K. Yarlagadda, E.C.W. Chiang, A neural network system for the prediction of process parameters in pressure die casting. J. Mater. Process. Technol. 89, 583–590 (1999)CrossRef

54.

S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, 1994)

55.

S. Rajasekaran, G.V. Pai, Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications (with cd) (PHI Learning Pvt. Ltd., 2003)

56.

J.Y. Yu, Q. Li, J. Tang, X.D. Sun, Predicting model on ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter based on BP neural network. China Foundry 8(3), 286–289 (2011)

57.

L.H. Jiang, A.G. Wang, N.Y. Tian, W.C. Zhang, Q.L. Fan, BP neural network of continuous casting technological parameters and secondary dendrite arm spacing of spring steel. J. Iron. Steel Res. Int. 18(8), 25–29 (2011)CrossRef

58.

M.S. Ozerdem, S. Kolukisa, Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys. Mater. Des. 30(3), 764–769 (2009)CrossRef

59.

M.P.G. Chandrashekarappa, P. Krishna, M.B. Parappagoudar, Forward and reverse process models for the squeeze casting process using neural network based approaches. Appl. Comput. Intel. Soft Comput. 2014, 12 (2014)

60.

J.K. Kittur, G.M. Patel, M.B. Parappagoudar, Modeling of pressure die casting process: an artificial intelligence approach. Int. J. Metalcast. 10(1), 70–87 (2016)CrossRef

61.

G.C.M. Patel, P. Krishna, M.B. Parappagoudar, An intelligent system for squeeze casting process—soft computing based approach. Int. J. Adv. Manuf. Technol. 86(9–12), 3051–3065 (2016)

62.

E. Abhilash, M.A. Joseph, P. Krishna, Prediction of dendritic parameters and macro hardness variation in permanent mould casting of Al-12% Si alloys using artificial neural networks. Fluid Dyn. Mater. Process. 2, 211–220 (2006)

63.

L. Zhang, L. Li, H. Ju, B. Zhu, Inverse identification of interfacial heat transfer coefficient between the casting and metal mold using neural network. Energy Convers. Manag. 51(10), 1898–1904 (2010)CrossRef

Titel: Intelligent Modelling of Hard Materials Machining
verfasst von: Manjunath Patel G. C.
Ganesh R. Chate
Mahesh B. Parappagoudar
Kapil Gupta
Verlag: Springer International Publishing
Buch: Machining of Hard Materials
Print ISBN: 978-3-030-40101-6

Electronic ISBN: 978-3-030-40102-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-40102-3_4

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