Wybrane zagadnienia projektowania układów diagnostycznych obrabiarki i procesu skrawania

Sokołowski, A.

Artykuł - szczegóły

Tytuł artykułu

Wybrane zagadnienia projektowania układów diagnostycznych obrabiarki i procesu skrawania

Autorzy

Sokołowski A.

Identyfikatory

Warianty tytułu

Selected problems of designing of the machine tool and cutting process diagnostic systems

Języki publikacji

Abstrakty

Przedmiotem pracy jest zagadnienie wspomagania projektowania układów diagnostycznych obrabiarki i procesu skrawania. Dyskusja przeprowadzona w początkowej części pracy uzasadniła konieczność wspomagania projektowania układów diagnostycznych, co stanowiło o celowości i ukierunkowaniu podjętych badań. Przewodnią ideę badań odzwierciedla oryginalna koncepcja Inteligentnego Układu Diagnostycznego (IUD). Analizując koncepcję takiego układu zaproponowano implementację IUD w Inteligentnym Projektancie Układów Diagnostycznych (IPUD). Równocześnie uwypuklono znaczenie selekcji i integracji danych, co było przesłanką dokonania wyboru metod możliwych do zastosowania na tych etapach projektowania układów diagnostycznych. Dokonując wyboru metod uwzględniono specyficzne wymagania stawiane IUD/IPUD. Dążono również do zróżnicowania metod, potencjalnie pozwalającego na uogólnienia wniosków z przeprowadzonych badań. Analizowano metody proponowane w literaturze, metody dostępne w komercyjnych pakietach oprogramowania oraz metody dotychczas stosowane w diagnostyce obrabiarki i procesu skrawania. Zastosowano także oryginalne, opracowane lub zmodyfikowane przez autora rozwiązania. Przed przystąpieniem do realizacji badań przyjęto zasady prowadzenia testów i zaproponowano kryteria oraz sposoby oceny metod selekcji integracji danych. W zasadniczej części badań podjęto próby rozwiązania wybranych zadań (problemów) diagnostycznych. Rozpatrując każdy z problemów dokonywano opisu stanowiska pomiarowego oraz charakteryzowano przeprowadzone pomiary. Prowadzono również wybiórcze analizy aspektów technologicznych w celu uwypuklenia specyfiki lub stopnia złożoności rozwiązania zadań. W ramach pracy rozpatrywano problem klasyfikacji zużycia narzędzia podczas wiercenia wielowrzecionowego i podczas toczenia. Problem ten analizowano w odniesieniu do zróżnicowanych układów pomiarowych, tj. rozważany był typowy układ wieloczujnikowy oraz układ jednoczujnikowy. Dyskutowano także zastosowania metod selekcji i integracji w zadaniach aproksymacji, tj. rozważano problem diagnostyki przedmiotu obrabianego (problem tworzenia się zadziorów poobróbkowych) oraz problem diagnostyki odkształceń termicznych szlifierki. Zaproponowane w pracy kryteria i sposoby oceny oraz zasady realizacji testów pozwoliły na usystematyzowanie metod selekcji i integracji danych. Natomiast przeprowadzenie wielokierunkowych badań z zastosowaniem zasadniczo zróżnicowanych zadań oraz porównanie uzyskanych wyników umożliwiło weryfikację przydatności rozpatrywanych metod i wskazanie wytycznych oraz zasad ich jak najbardziej efektywnego zastosowania.

The subject of this work is related to aiding of machine tool and cutting process diagnostic system design. The discussion conducted in the first part of the dissertation justified necessity for aiding design of such systems. This justification depicted the aim and direction of the research to conduct, as well. The main idea of the research has been reflected by a concept of Intelligent Diagnostic System (IUD). Analysis of the above mentioned concept led to suggestion of implementing the IUD in the Intelligent Diagnostic System Designer (IPUD). Simultaneously, importance of data selection and integration was emphasized and methods that can be possibly applied at these staged of diagnostic system design have been selected. The selection of the methods has been done focusing on the specific requirements of the IUD/IPUD. Moreover, it has been decided to select possibly divers solutions that potentially allow generalizing the conclusions of the tests conducted. The selection was performed based on the literature review, commercially available software and the methods, which have been applied in the field of machine tool and cutting process diagnostics. Methods and algorithms developed by the Author of the dissertation have been considered, as well. Before conducting the tests, a set of rules for running such tests was assumed and criteria and means for data selection and integration method assessment were decided. In the main part of the dissertation several diagnostic tasks (problems) were analyzed. In each case, short characteristics of measuring set-up and recorder signals was presented. Then, technological aspects specific to each problem were emphasized in order to point at complexity of task to be solved. First, classification of cutting tool wear during multispindle drilling and turning was discussed. It can be underlined that not only different types of machining were analyzed but different types of measuring set-ups, i.e. multi-sensor and single-sensor, were considered, as well. Also, application of the data selection and integration methods for solving an approximation task was discussed. In this case, a problem of work piece diagnostics (i.e. a problem of burr formation) and a problem of thermal deformation of grinding machine tool were analyzed. The proposed in the dissertation rules for running the tests and proposed criteria and means of assessment allowed systematizing the data selection and integration methods. Whereas, the widely oriented research conducted based on the variety of diagnostic tasks together with careful comparison of the achieved results permitted verifying suitability of the consider methods and pointing at rules and settings for their the most effective application.

Słowa kluczowe

obrabiarki skrawanie Inteligentny Układ Diagnostyczny Inteligentny Projektant Układów Diagnostycznych diagnostyka maszyn sieci neuronowe algorytm genetyczny system logiki rozmytej wiercenie wrzecionowe toczenie szlifowanie

machine tool cutting intelligent diagnostic system Intelligent Diagnostic System Designer machine diagnostic neural networks genetic algorithms fuzzy logic system multispindle drilling turning sanding

Wydawca

Wydawnictwo Politechniki Śląskiej

Czasopismo

Zeszyty Naukowe. Mechanika / Politechnika Śląska

Rocznik

2003

Tom

z. 142

Strony

1--231

Opis fizyczny

Bibliogr. 231 poz.

Twórcy

autor

Sokołowski A.

Katedra Budowy Maszyn Politechniki Śląskiej, 44-100 Gliwice, ul. Konarskiego 18A, tel.: (0-32) 237-16-77, andrzej.sokolowski@polsl.pl

Bibliografia

1. [Ali i Zhang, 1999] Ali Y.M., Zhang L.C. : Surface roughness prediction of ground components using a fuzzy logic approach. Journal of Materials Processing Technology 89-90, 1999
2. [Antic, et al., 2001] Antic A., Hodolic J., Gatalo R., Stevie M. : Contribution to development of multisensor system for tool monitoring. Proceedings of the 12th International DAAAM Symposium, Jena 2001
3. [Arabas, 2000] Arabas J. : Algorytmy ewolucyjne : przegląd tematyki. Conference Proceedings „Methods of Artificial Intelligence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gliwice 2000
4. [Azouzi і Guillot, 1996] Azouzi R., Guillot M. : Control and optimisation of the turning process using a new hybrid neural network. Proceedings of the 1996 Japan - USA Symposium on Flexible Automation, Vol. 2, Boston 1996
5. [Bahre et al., 1992] Bahre D., Rosenberger U., Warnecke G. : Monitoring of cutting processes by vibration analysis : Exemplary applications in grinding and turning processes. Sensors and Signal Processing for Manufacturing, ASME, PED, Vol. 55, 1992
6. [Bajtyngier і Jemielniak, 1988] Bajtyngier T., Jemielniak K. : Diagnostyka stanu noża tokarskiego oparta na bezpośrednich pomiarach sił skrawania. I Naukowa Szkoła Nadzorowania i Diagnostyki Systemów Obróbkowych, Prace Naukowe Instytut Technologii Budowy Maszyn Politechniki Wrocławskiej nr 38, seria : Konferencje nr 14, Wrocław 1988
7. [Balazinski i Jemielniak, 1998] Balazinski M., Jemielniak K. : Tool condition monitoring using fuzzy decision support system. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warszawa 1998
8. [Barschdorff і Monostori, 1991] BarschdorffD., Monostori L. : Seminar on learning in LMS. The first CIRP workshop on the Intelligent Manufacturing Systems, Budapest 1991
9. [Barton і Thangaraj, 1991] Barton R.P., Thangaraj A.R. : A neural network approach to drilled hole quality monitoring based on machine spindle vibrations. Trans, of NAMRI/SME, Vol. 19, 1991
10. [Basil, 1994] Basil S.A. : Modelling and monitoring drill bit transverse motion. First S.M. Wu Symposium on Manufacturing Science, SME, Chicago 1994
11. [Bavarian, 1992] Bavarian B. : Tutorial on artificial neural networks for intelligent manufacturing and robotics systems. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
12. [Berger et al., 2001] Berger B., Bêlai C., Anand D. : Toeplitz matrices and cutting state identification. Proceedings of the Eighth International Congress on Sound and Vibration, vol. 2, Hong Kong (China) 2001
13. [Bhar et al., 1994] Bhar B., Motavalli S., Arfi T., Srinivas G. : Integrating vibration and vision system for monitoring cutting process. First S.M. Wu Symposium on Manufacturing Science, SME, Chicago 1994
14. [Bobkowski, 1997] Bobkowski J. : Intebgentne przetworniki pomiarowe w diagnostyce procesów przemysłowych. II Krajowa Konferencja Naukowo - Techniczna Diagnostyka Procesów Przemysłowych, Łagów 1997
15. [Burczyński, 2000] Burczyński T. : Evolutionary algorithms : applications in mechanics. Conference Proceedings „Methods of Artificial Intelbgence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gliwice 2000
16. [Burke, 1989] Burke L.I. : Automated identification of tool wear states in machining processes : an appbcation of self-organising neural networks. Ph.D. Dissertation, Department of Mechanical Engineering, University of Cabfomia at Berkeley, 1989
17. [Byrne et al., 1995] Byrne G., Domfeld D., Inasaki I., Ketteler G., Konig W., Teti R. : Tool Condition Monitoring (TCM) - The status of research and industrial application. Annals of the CIRP, Vol. 44/2/ 1995
18. [Campos et al., 1994] Campos Bicudo L.A.M., Sokołowski A., Gomes de Oliveira J.F., Domfeld D.A. : Compensation of thermal deformation on cylindrical grinding machines using neural networks. Proceedings of The 1994 Japan-U.S.A. Symposium on Flexible Automation, Kobe 1994
19. [Campos et al., 1995] Campos Bicudo L.A.M., Sokołowski A., Gomes de Obveira J.F., Domfeld D.A. :Thermal compensation of grinding machines using neural networks. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
20. [Carpenter et al., 1991] Carpenter G. A., Grossberg S., Rosen D. : ART 2-А : An adaptive resonance algorithm for rapid category learning and recognition. Neural Networks, Vol. 4, 1991
21. [Carpenter і Grossberg, 1987] Carpenter G.A., Grossberg S. : ART 2 : Self¬organisation of stable category recognition codes for analogue input patterns. Applied Optics 26, 1987
22. [Cempel, 1980] Cempel C. : Redukcja zbioru danych w diagnostyce maszyn. Zagadnienia Eksploatacji Maszyn, Zeszyt 4/44/1980
23. [Chem, 1993] Chem G.L. : Analysis of burr formation and breakout in metal cutting. Ph.D. Dissertation, Department of Mechanical Engineering, University of California at Berkeley, 1993
24. [Choi et al., 1990] Choi G. S., Wang Z. X., Domfeld D. A., Tsujino K. : Development of an intebigent on-line tool wear monitoring system for turning operations. Proceedings of the 1990 Japan - USA Symposium on Flexible Automation, Kyoto 1990
25. [Cholewa et al., 1988] - Cholewa W. i inni : Programowany analizator sygnałów diagnostycznych PAS6. Instytut Mechaniki i Podstaw konstrukcji Maszyn, Pobtechnika Śląska, Zeszyt 109, Gliwice 1988
26. [Cholewa i Kiciński, 1995] Cholewa W., Kiciński J. : Metody identyfikacji reguł dla diagnostycznych systemów doradczych. XXXV Sympozjon „Modelowanie w Mechanice”, Gliwice 1995
27. [Cholewa i Sobpiwko, 1985] Cholewa W., Solipiwko A. : Optymalizacja przestrzeni cech w wibroakustycznych badaniach diagnostycznych. VII Szkoła Diagnostyki, Politechnika Poznańska, Poznań - Rydzyna 1985
28. [Cholewa, 2000] Cholewa W. : Dynamiczne systemy doradcze. Conference Proceedings „Methods of Artificial Intelbgence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gbwice 2000
29. [Ciupke, 2001] Ciupke K. : Metoda selekcji i redukcji informacji w diagnostyce maszyn. Zeszyt Naukowy KPKM nr 118, Gbwice 2001
30. [Clabian et al., 1997] Clabian M., Pfutzner H., Раку F., Rauscher H. : Determination of decisive inputs of a neural network for sleep apnea classification. Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks, Stokholm 1997
31. [CODEF] Consortium on Deburring and Edge Finishing (CODEF), The University of Cabfomia at Berkeley, http://lma.berkelev.edu/codei7
32. [Colgan et al., 1992] Colgan J., Chin H., Danai K., Hayashi S.R. : On-line tool breakage detection in turning : A multi-sensor method. Sensors and Signal Processing for Manufacturing, ASME, PED, Vol. 55, November 1992
33. [Constantinides і Bennett, 1987] Constantinides N., Bennett S. : An investigation of methods for on-line estimation of tool wear. Int. J. Mach. Tools Manufact., Vol. 27, No. 2, 1987
34. [Czech et al., 1997] Czech M., Wodecki J., Klarecki K., Śliwka J., Dyrbuś G. : Optymalizacja wierteł do obróbki stab austenitycznej. Prace Naukowe Katedry Budowy Maszyn Nr 2/97, Politechnika Śląska w Gbwicach, Gbwice 1997
35. [Davis, 1991] Davis L. : Handbook of Genetic Algorithms. Van No strand Reinhold, New York 1991
36. [DeVor et al., 1996] DeVor R.E., Kapoor S.G., Hibner M., Kim D., Reutzel K. : A process model-based approach for machine tool and cutting process diagnostics. Proceedings of the 1996 Japan - USA Symposium on Flexible Automation, Vol. 2, Boston 1996
37. [Diagnostyka techniczna, 1997] Diagnostyka techniczna. Odwrotne modele diagnostyczne. Cholewa W., Kiciński J. (red.), Wydawnictwo Pobtechniki Śląskiej, Gliwice 1997
38. [Diagnostyka techniczna, 2001] Diagnostyka techniczna. Metody odwracania nieliniowych modeb obiektów. Cholewa W., Kiciński J. (red.), Katedra Podstaw Konstrukcji Maszyn, Pobtechnika Śląska, Zeszyt 120, Gliwice 2001
39. [Diei i Domfeld, 1987] Diei E.N., Domfeld D.A. : Acoustic emission sensing of tool wear in face milling. J. Eng. Industry, Vol. 109, August 1987
40. [Dimla et al., 1997] Dimla D.E., Lister P.M., Leighton N.J. : Application of back- propagation neural network to tool condition monitoring in a metal turning process. MATADOR CONFERENCE, Manchester, 1997
41. [Diniz et al., 1992] Diniz A.E., Liu J.J., Domfeld D.A. : Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
42. [Dmochowski, 1981] Dmochowski J. : Podstawy obróbki skrawaniem. PWN, Warszawa 1981
43. [Domański et al., 1995] Domański P.D., Tadej W., Arabas J. : A comparison of the genetic and neural approaches to the fuzzy logic based modelling. Proceeding of the Second International Symposium on Methods and Models in Automation and Robotics, Międzyzdroje, 1995
44. [Domfeld, 1992] Domfeld D. : Intelligent deburring of precision components. Proceedings of the International Conference on Industrial, Electronics, Control, Instrumentation and Automation, Vol. 2., 1992
45. [Du et al., 1992] Du R. X., Elbestawi M.A., Li S. : Tool condition monitoring in turning using fuzzy set theory. Int. J. Mach. Tools Manufact. Vol. 32, No. 6, 1992
46. [Elanayar і Shin, 1992] Elanayar S., Shin Y.C. : Robust tool wear estimation via radial basis function neural networks. Neural Networks in Manufacturing and Robotics, ASME, PED, Vol. 57, November 1992
47. [Emel і Kannatey-Asibu, 1991] Emel. E., Kannatey-Asibu Jr E. : Acoustic emission monitoring of the cutting process - negating the influence of varying condition. Journal of Engineering Materials and Technology, ASME, Vol. 113, October 1991
48. [Fan і Wu, 1992] Fan H.T., Wu S.M. : Case studies on modelling manufacturing processes using artificial neural networks. Neural Networks in Manufacturing and Robotics, ASME, PED, Vol. 57, 1992
49. [Gawlik i Karbowski, 1997] Gawlik J., Karbowski К. : Prognozowanie stanu ostrza skrawającego z zastosowaniem sieci neuronowych. Mechanik Nr 4/1997
50. [Gawlik i Zębala, 1995] Gawlik J., Zębala W. : New concept of edge wear prediction in the precision cutting. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
51. [Gibiec, 2000] Gibiec M. : Artificial intelligence in diagnostic systems. Conference Proceedings „Methods of Artificial Intelligence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gliwice 2000
52. [Gillespie і Blotter, 1976] Gillespie L.K., Blotter P.T. : The formation and properties of machining burr. Journal of Engineering for Industry, Vol. 98, No. 1, 1976
53. [Gindy і Al-Habaibeh, 1997] Gindy N., Al-Habaibeh A. : Condition monitoring of cutting tools using artificial neural networks. MATADOR CONFERENCE, Manchester, 1997
54. [Giusti et al., 1987] Giusti F., Santochi M., Tantussi G. : On - line sensing of flank and crater wear of cutting tools. Annals of the CIRP, Vol. 36/1/ 1987
55. [Goldberg, 1989] Goldberg D.E. : Genetic Algorithms in Search, Optimization and Machine Learning. Addison - Wesley Co., Redwood City, USA, 1989
56. [Govekar і Grabec, 1998] Govekar E., Grabec I. : Classification of cutting tool wear by chaotic characteristics. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw 1998
57. [Guo і Domfeld, 1998] Guo Y., Domfeld D.A., : Finite element analysis of drilling bun minimization with a backup material. Trans. ofNAMRI/SME, Vol. 26, 1998
58. [Gupta і Rao, 1994] Gupta M.M., Rao D.H. : On the principles of fuzzy neural networks. Fuzzy Sets and Systems, Vol. 61, 1994
59. [Hamrol i Twardowski, 1995] Hamrol A., Twardowski, P. : Diagnosis of ceramic tool point during turning of hardened steel. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
60. [Hamrol i Twardowski, 1998] Hamrol, A., Twardowski, P. : Shape error control for turning hardened steel based on neural network model. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw, 1998
61. [Hamrol, 1998] Hamrol A. : Sterowanie jakością procesu obróbki na podstawie sygnałów diagnostycznych. Technologia Maszyn, IV Naukowe Warsztaty Profesorskie TM’98 i III Forom Prac Badawczych, Wyd. Pol. Koszalińskiej, Koszalin 1998
62. [Hertz et al., 1991] Hertz J., Krogh A., Palmer R.G. : Introduction to the Theory of Neural Computation. Addison - Wesley Co., Redwood City, USA, 1991
63. [Heyden, 1996] Heyden van der M.J. : Neural network and statistics : a brief overview. Proceedings of the International Workshop on Neural Networks HELNET’94 - ’95, Vol. I/II, VU University Press, Amsterdam 1996
64. [Holland, 1975] Holland J. H. : Adaptation in Natural and Artificial Systems. Ann Arbor : The University of Michigan Press, 1975
65. [Hong at al., 1992] Hong S.Y., Ni J., Wu S.M. : Analysis of drill failure modes by multi-sensors on a robotic end effector. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
66. [Homik, 1991] Hornik К. : Approximation capabilities of multilayer feedforward networks. Neural Networks, Vol. 4, 1991
67. [Inasaki, 1995] Inasaki I. : Intelligent monitoring and supervision in grinding. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
68. [Inasaki, 1998] Inasaki I. : Sensor fusion for monitoring and controlling grinding processes. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw 1998
69. [Jacob і Ball, 1997] Jacob P.J., Ball A.D. : Structuring a RBF classifier using genetic algorithm or a forward selection heuristic. Proceedings of the 1997 International Conference on Engineering Applications of Neural Networks, Stokholm 1997
70. [Jammu et al., 1993] Jammu V.B., Danai K., Malkin S. : Unsupervized neural network for tool breakage detection in turning. Annals of the CIRP, Vol. 42/1/ 1993
71. [Jawahir і Fei, 1993] Jawahir I.S., Fei J. : A comprehensive evaluation of tool inserts for chip control using fuzzy modelling of machinability parameters. Trans, of NAMRI/SME, Vol. 21, 1993
72. [Jemielniak і Kosmol, 1995] Jemielniak K., Kosmol J. : Tool and process monitoring - State of art and future prospects. Proceedings of VI Workshop on Diagnostics and Supervision in Flexible Manufacturing, Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej nr 61, Seria : Konferencje 26, Wrocław 1995
73. [Jemielniak, 1995] Jemielniak К : Catastrophic tool failure detection based on signals from feed force sensors. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
74. [Jemielniak, 1998] Jemielniak K. : Commercial tool condition monitoring systems. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw 1998
75. [Jemielniak, 2001] Jemielniak K. : Automatyczna diagnostyka stanu narzędzia i procesu skrawania. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa 2001
76. [Jędrzejewski, 1997] Jędrzejewski J. : Trends in development of intelligent manufacturing systems. MANUFACTURING CONTROL OPTIMIZATION, Proceedings of the VIII Workshop on Supervising and Diagnostics of Machining Systems, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 1997
77. [Jin i Murakawa, 2001] Jin M., Murakawa M. : Development of practical ultrasonic vibration cutting tool system. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
78. [John et al., 1994] John G., Kohavi R., Pfleger K. : Irrelevant features and the subset selection problem. 11th International Conference on Machine Learning, New Bruswick, Morgan Kaufman 1994
79. [Kacalak і Wawryn, 1995] Kacalak W., Wawryn K. : A fuzzy compensation of disturbances in automated manufacturing. Intelligent Engineering Systems Through Artificial Neural Networks, Vol. 5, Fuzzy Logic And Evolutionary Programming, ASME Press, 1995
80. [Kacalak, 1995] Kacalak W. : Metodyka tworzenia algorytmów wnioskowania rozmytego do optymalizacji i sterowania w procesach szlifowania. Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej - XVIII Naukowa Szkoła Obróbki Ściernej, Wrocław 1995
81. [Kacalak, 1995a] Kacalak W. : Optymalizacja cykli współrzędnościowego szlifowania otworów z zastosowaniem sieci neuronowych . Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej - XVIII Naukowa Szkoła Obróbki Ściernej, Wrocław 1995
82. [Kacalak, 1996] Kacalak W. : Metody i zastosowania sztucznej inteligencji do diagnostyki, optymalizacji i sterowania w procesach szlifowania. Materiały XIX Naukowej Szkoły Obróbki Ściernej, Łódź 1996
83. [Kang et al., 2001] Kang M.C., Kim J.S., Kim H.J. : A monitoring technique using a multi-sensor in high speed machining. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
84. [Karin, 1991] Karin E.D. : A simple procedure for pruning back propagation trained neural networks. IEEE Transactions on Neural Networks 1 (2), 1991
85. [Ketteler, 1999] Ketteler G. : Analysis of requirements for manufacturing systems. Proceedings of the Second International Workshop on Intelligent Manufacturing Systems, Leuven, Belgium 1999
86. [Khan, 1993] Khan E. : NeuFuz : The integration of fuzzy logic and neural nets offers designers tremendous application advantage - Today. WESCON’93, Conference Record, Moscone Convention Center, San Francisco 1993
87. [Kim і Domfeld, 1999] Kim J., Domfeld D.A. : Experimental study of burr formation in drilling of intersecting holes with gun drill and twist drills. Technical Papers of the NAMRC, SME, 1999
88. [Kim і Klamecki, 1990] Kim S., Klamecki B.E. : Milling cutter wear monitoring using spindle shaft vibration. Monitoring and Control for Manufacturing Processes, ASME, PED, Vol. 44, 1990
89. [Kim, 2000] Kim J. : Development of a drilling burr control chart. Research Reports 1999/2000, Laboratory for Manufacturing Automation, University of California at Berkeley, Berkeley 2000
90. [Kishawy і Elbestawi, 1997] Kishawy H.A., Elbestawi M.A. : On mechanics and properties of chip formation during machining of hardened steel. MATADOR CONFERENCE, Manchester, 1997
91. [Ko і Cho, 1994] Ko T.J., Cho D.W. : Adaptive optimization of face milling operations using neural networks. First S.M. Wu Symposium on Manufacturing Science, SME, Chicago 1994
92. [Kohonen, 1988] Kohonen T. : Self-Organisation and Associative Memory. Springer Verlag, Berlin 1988
93. [Kônig et al., 1992] Kônig W., Kutzner K, Schehl U. : Tool monitoring of small drills with acoustic emission. Int. J. Mach. Tools Manuf., Vol. 32, No. 4, 1992
94. [Kônig et al., 1995] Kônig W., Domfeld D.A., Rehse M., Sokołowski A. : On designing of a tool wear monitoring system for multispindle drilling machine. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
95. [Kopać i Sali, 2001] S Kopać J., Sali S. : Tool wear monitoring during turning process. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
96. [Korbicz et al., 1994] Korbicz J., Obuchowicz A., Uciński D. : Sztuczne sieci neuronowe. Podstawy i zastosowania. Akademicka Oficyna Wydawnicza PLJ, Warszawa 1994
97. [Korbicz, 2000] Korbicz, J. : Optymalizacja modelowania neuronowego w diagnostyce procesów. II Międzynarodowy Kongres Diagnostyki Technicznej "Diagnostyka 2000", vol. 1, Warszawa, 2000
98. [Kosko, 1992] Kosko B. : Neural Networks and Fuzzy Systems - A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall International Editions, 1992
99. [Kosmol (red), 1996] Adamczyk Z., Jemielniak K., Kosmol J., Sokołowski A. : Automatyzacja wytwarzania. Monitorowanie ostrza skrawającego. Metody konwencjonalne i sieci neuronowe. Pod redakcją J. Kosmola, Wydawnictwo Naukowo- Techniczne, Warszawa 1996
100. [Kosmol at al., 2002] Kosmol J., Sokołowski A., Niedbała M. : Machine tool as mechatronic system. 1 st Intern. Congress on Mechatronics Incorporating ICANOV 2002 MECH2K2 (wydano na CD), Austria / Linz 2002
101. [Kosmol i Sokołowski, 1991] Kosmol J., Sokolowski A. : Adaptive control in wheel sets machining. Proceedings of International Conference "Modelling & Simulation", ASME, Vol.l, New Orleans 1991
102. [Kosmol i Sokołowski, 2000a] Kosmol J., Sokolowski A. : Machine tool as mechatronic system. Materiały Międzynarodowej Konferencji "Mechatronika 2000", vol. 1, Warszawa 2000
103. [Kosmol, 1995] Kosmol J. : Automatyzacja obrabiarek i obróbki skrawaniem. Wydawnictwa Naukowo - Techniczne, Warszawa 1995
104. [Kostka, 1998] Kostka P. : Badanie wrażliwości cech sygnałów diagnostycznych. III Krajowa Konferencja Naukowo - Techniczna : „Diagnostyka Procesów Przemysłowych”, Jurata 1998
105. [Krimmel et al., 2000] Krimmel O., Tonnessen K., Wen Li Z. : Potentials and limitations of tool condition monitoring for MoS2 and TiN-coated drills with acoustic emission in dry machining. Proceedings of the XI Workshop on Supervising and Diagnostics of Machining Systems, Design and Optimization of Intelligent Machine Tools, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2000
106. [Kubowicz i Sokołowski, 1997] Kubowicz K., Sokołowski A. : Modelowanie zadziorów poobróbkowych w procesie wiercenia. Prace Naukowe Katedry Budowy Maszyn Nr 2/97, Pobtechnika Śląska w Gbwicach, Gbwice 1997
107. [Kumuluca et al., 1997] Kumuluca A., Tulunay E., Altinay O., Tulunay Y. : Relative significance of inputs to a neural network. Proceedings of the 1997 International Conference on Engineering Appbcations of Neural Networks, Stokholm 1997
108. [Leem і Dreyfus, 1992] Leem C.S., Dreyfus S. : A neural network with automatic feature selection for on-line monitoring of tool wear in metal-cutting. Proceedings of the 2nd Pacific Rim International Conference on Artificial Intelligence, Seoul 1992
109. [Leżański, 1998] Leżański P. : A neuro-fiizzy system for diagnostics of cylindrical grinding process. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw 1998
110. [Li et al., 1992] Li S., Elbestawi M.A., Du R. X. : A fuzzy logic approach for multi¬sensor process monitoring in machining. Sensors and Signal Processing for Manufacturing, ASME, PED, Vol. 55, 1992
111. [Li i Kim, 1992] Li C.J., Kim T. : A new feedforward neural network structural and weight learning algorithm. Neural Networks in Manufacturing and Robotics, ASME, PED, Vol. 57, 1992
112. [Liang і Domfeld, 1989] Liang S.Y., Domfeld D.A. : Tool wear detection using time series analysis of acoustic emission. J. Eng. Industry, Vol. Ill, 1989
113. [Lierath et al., 1988] Lierath F., Рорке H., Goldau H. : Sensorsystem fur die Werkzeuguberwachung auf der basis des Korperschalls. Prace Naukowe Instytutu Technologu Budowy Maszyn Pobtechniki Wrocławskiej, Seria : Konferencje nr 13, Wrocław 1988
114. [Lin i Lee, 1991] Lin C. T., Lee G. C. S. : Neural-network-based fuzzy logic control and decision system, IEEE Transaction on Computers, Vol. 40, No 12, 1991
115. [Mahdi і Zhang, 2001] Mahdi M., Zhang L. : A finite element model for the orthogonal cutting of fibre-reinforced composite materials. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
116. [Mańczak, 1976] Mańczak К. : Technika planowania eksperymentu. WNT, Warszawa 1976
117. [Marsumura et al., 1993] Marsumura T., Obikawa T., Shirakashi T., Usui E. : Autonomous turning operation planning with predicting tool wear and surface roughness. Trans. ofNAMRI/SME, Vol. 21, 1993
118. [Mechanik, 1989] Diagnozowanie stanu narzędzi w autonomicznych stacjach obróbkowych. Oprać, napodst. VDI-Z 12/1988, Mechanik 6/1989
119. [Mehrotra et al., 1991] Mehrotra K.G., Mohan C.K., Ranka S. : Bounds on the number of samples needed for neural learning. IEEE Transactions on Neural Networks, Vol. 2, No. 6, 1991
120. [Mehta et al., 1983] Mehta N.K., Pandey P.C., Chakravarti G. : An investigation of tool wear and the vibration spectrum in milling. Wear, Vol. 91, 1983
121. [Michalewicz, 1992] Michalewicz Z. : Genetic Algorithms + Data Structure = Evolutionary Programs. Springer-Verlag, AI Series, New York 1992
122. [Min et al., 2001] Min S., Kim J., Domfeld D.A. : Development of a drib burr control chart for low alloy steel AISI4118. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
123. [Moczulski, 1997] Moczulski W. : Metody pozyskiwania wiedzy dla potrzeb diagnostyki maszyn. Zeszyty Naukowe Politechniki Śląskiej, seria : MECHANIKA, z. 130, Gbwice 1997
124. [Monostori, 1992] Monostori L. : Artificial neural networks in intelligent manufacturing. Robotics & Computer-Integrated Manufacturing, Vol. 9, No. 6, 1992
125. [Monostori, 1993] Monostori L. : Intelligent manufacturing through neural networks. Proceedings of the International Conference on Intelligent Autonomous Systems, Pittsburgh 1993
126. [Monostori, 1995] Monostori L. : Hybrid AI approaches to supervision and control of manufacturing processes. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
127. [Mori et al., 1992] Mori K., Kasashima N., Yamane T., Nakai T. : An intelligent vibration diagnostic system for cylindrical grinding. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
128. [Morrison, 1990] Morrison D.F. : Wielowymiarowa analiza statystyczna. Państwowe Wydawnictwa Naukowe, Warszawa 1990
129. [Nakayama i Arai, 1987] Nakayama К., Arai M. : Burr Formation in Metal Cutting. Annals of the CIRP, Vol. 36/1/ 1987
130. [Narayanaswami і Domfeld, 1994] Narayanaswami R., Domfeld D.A. : Design and process planing strategies for burr minimization and deburring. Trans, of NAMRI/SME, Vol. 22, 1994
131. [Narayanaswami i Sokołowski, 1994] Narayanaswami R, Sokolowski A. : Minimizing burr length in face milling using genetic algorithm. Research Reports 1993/1994, Laboratory for Manufacturing Automation, University of California at Berkeley, Berkeley 1994
132. [Oishi і Yasuda, 1997] Oishi K., Yasuda T. : Machinability of reisen impregnated sintered iron. MATADOR CONFERENCE, Manchester, 1997
133. [Orantek i Burczyński, 2000] Orantek P., Burczyński T. : Hybrid evolutionary algorithms aided by sensitivity information in identification and structural optimisation. Conference Proceedings „Methods of Artificial Intelligence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gliwice 2000
134. [Osowski, 1996] Osowski S. : Sieci neuronowe w ujęciu algorytmicznym. WNT, Warszawa 1996
135. [Pao, 1989] Pao Yoh-Han : Adaptive Pattern Recognition and Neural Networks. Addison - Wesley Co., Redwood City, USA, 1989
136. [Parlos et al., 1992] Parłoś A.G., Muthusami J., Fernandez B., Atiya A.F., Tsai W.K. : An adaptive Backpropagation learning algorithm for multilayer neural networks. Proceedings of the Artificial Neural Networks in Engineering Conference, St. Louis 1992
137. [Peterson і Ladage, 1992] Peterson G.E., Ladage R.N. : On using sensitivity analysis to prone the inputs to a neural network. Proceedings of the Artificial Neural Networks in Engineering Conference, St. Louis 1992
138. [Pettersson et al., 2001] Pettersson L., Hakansson L., Cleasson L, Olsson S. : Active control of machine-tool vibration in a CNC lathe based on an active tool holder shank with embedded piezo ceramic actuators. Proceedings of the Eighth International Congress on Sound and Vibration, vol. 2, Hong Kong (China) 2001
139. [Piramuthu, 1996] Piramuthu S. : The effect of feature selection on backpropagation in Feed-Forward Neural Networks. Proceedings of the International Workshop on Neural Networks HELNET’94 - ’95, Vol. I/II, VU University Press, Amsterdam 1996
140. [Praca zbiorowa, 2000] Praca zbiorowa : Biocybernetyka i inżynieria biomedyczna 2000, tom 6 : Sieci neuronowe. Red. Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R., Akademicka Oficyna Wydawnicza EXIT, Warszawa 2000
141. [Projekt Badawczy, 1993] Projekt badawczy KBN nr 707319101 „Badania nad wykorzystaniem sygnałów wibroakustycznych do nadzorowania zużycia ostrza skrawającego”. Projekt zrealizowany pod kierunkiem prof. J.Kosmola, Politechnika Śląska w Gliwicach, Gliwice 1993 - 1995
142. [Projekt Badawczy KBN, 1997] Projekt badawczy KBN nr 7 T07 D 010 08 „Inteligentny system wspomagania projektowania układów nadzorujących stan procesu skrawania”. Projekt zrealizowany pod kierunkiem dra inż. A. Sokołowskiego, Politechnika Śląska w Gliwicach, Gliwice 1995-1997
143. [Projekt Badawczy KBN, 1999] Projekt badawczy KBN nr 7 TO7 D 005 17 „Inteligentne narzędzia w diagnostyce procesu skrawania”. Projekt realizowany pod kierunkiem dra inż. A. Sokołowskiego, Politechnika Śląska w Gliwicach, 1999 (projekt w trakcie realizacji)
144. [Projekt PSA/RENAULT] Projekt badawczy “Drill state diagnostics”. Projekt zlecony przez Renault, France oraz PSA Peugeot Citroen, France, projekt zrealizowany w The University of California at Berkely pod kierunkiem Prof. D.A. Domfelda, Berkely 1992-1993
145. [Rangwala і Domfeld, 1990] Rangwala S., Domfeld D.A. : Sensor integration using neural networks for intelligent tool condition monitoring. Journal of Engineering for Industry, Vol. 112, August 1990
146. [Rao, 1986] Rao S.B. : Tool wear monitoring through the dynamics of stable turning. J. Eng. Industry, Vol. 108, August 1986
147. [Richter i Śpiewak, 1990] Richter F., Śpiewak S.A. : A system for on-line detection and prediction of catastrophic tool failure in milling. Trans, of NAMRI/SME, Vol. 17, 1990
148. [Rumelhardt et al., 1986] Rumelhart D.E., Hinton G.E., Williams R.J. : Learning internal representation by error propagation. Parallel Distributing Processing, The MIT Press, 1986
149. [Rutkowska et al., 1997] Rutkowska D., Piliński M., Rutkowski M. : Sieci neuronowe, algorytmy genetyczne i systemy rozmyte. Wydawnictwa Naukowe PWN, Warszawa 1997
150. [Sakakura i Inasaki, 1992] Sakakura M., Inasaki I. : A neural network approach to the decision-making process for grinding operations. Annals of the CIRP, Vol. 36/1/ 1992
151. [Sarwal і Srinath, 1992] Sarwal P., Srinath M.D. : A conjugate gradient algorithm for learning in multilayer perceptrons. Proceedings of the Artificial Neural Networks in Engineering Conference, St. Louis 1992
152. [Shiraishi, 1988] Shiraishi M. : Scope of in-process measurement, monitoring and control techniques in machining processes - Part 1 :In-process techniques for tools. Precision Engineering, Butterworth & Co Ltd, November 1988
153. [Sietsma і Dow, 1991] Sietsma J., Dow R.J.F. : Creating artificial neural networks that generalize. Neural Networks, Vol. 4, No. 1, 1991
154. [Sobczak і Malina, 1985] Sobczak W., Malina W. : Metody selekcji i redukcji informacji. WNT, Warszawa 1985
155. [Sokołowski et al., 1992] Sokołowski A., Liu J.J., Kosmol J. : On the correlation between the vibration measurement and tool wear in turning. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
156. [Sokołowski et al., 1994] Sokołowski A., Vincent P., Domfeld D.A. : Drill state monitoring during multispindle machining. Trans, of NAMRI/SME, Vol. 22, 1994
157. [Sokołowski et al., 1994a] Sokołowski A., Narayanaswami R., Domfeld D.A. : Prediction of burr size using neural networks and fuzzy logic. The Japan-U.S.A. Symposium on Flexible Automation, Kobe 1994
158. [Sokołowski et al., 1995] Sokołowski A., Kosmol J., Domfeld D.A. : Vibration measurement as a means for tool wear monitoring; Part 2 : Neural network application. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
159. [Sokolowski et al., 1996] Sokołowski A., Wodecki J., Czech M. : Burr formation modelling in metal cutting. International Conference on "Development of Metal Cutting DMC 96", Kosice 1996
160. [Sokołowski et al., 1996a] Sokołowski A., Kolka A., Kosmol J. : Projekt badawczy: Inteligentny system wspomagania projektowania układów nadzorujących stan procesu skrawania. Prace Naukowe Katediy Budowy Maszyn Nr 1/96, Politechnika Śląska w Gliwicach, Gliwice 1996
161. [Sokołowski et al., 1997] Sokołowski A., Kolka A., Kosmol J. : An approach to support designing of monitoring systems. MANUFACTURING CONTROL OPTIMIZATION, Proceedings of the VIII Workshop on Supervising and Diagnostics of Machining Systems, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 1997
162. [Sokołowski et al., 1997a] Sokołowski A., Kolka A., Kosmol J. : A new approach to aid designing of monitoring systems. MATADOR CONFERENCE, Manchester, 1997
163. [Sokołowski et al., 1997b] Sokołowski A., Kolka A., Kosmol J. : Wspomaganie projektowania układów diagnostycznych. Rozdział w pozycji książkowej „Optymalizacja sterowania wytwarzaniem”, Seria : Inżynieria Maszyn, pod redakcją J. Jędrzejewskiego, vol. 2, Nr 2/3, 1997
164. [Sokołowski i Domfeld, 1994] Sokołowski A., Domfeld, D.A. : Intelligent system for cutting parameter optimization and design of cutting process monitoring systems. First S.M. Wu Symposium on Manufacturing Science, SME, Chicago 1994
165. [Sokołowski і Domfeld, 1994a] Sokołowski A., Domfeld D.A. : On designing tool wear monitoring systems using supervised and unsupervised neural networks. Mechatronics; The Basis for New Industrial Development, Computational Mechanics Publications, Southampton, Boston 1994
166. [Sokołowski і Gude, 1999] Sokołowski A., Gude M. : Optimisation of curvature of laminate plates with genetic algorithm. Prace Naukowe Katedry Budowy Maszyn Nr 2/99, Politechnika Śląska w Gliwicach, Gliwice 1999
167. [Sokołowski i Kosmol, 1991] Sokołowski A., Kosmol J. : Utilization of vibration measurements of machine tool elements in the monitoring of the cutting tool condition. Proceedings of 4th World Meeting on Acoustic Emission and 1st International Conference on Acoustic Emission in Manufacturing, Boston 1991
168. [Sokołowski і Kosmol, 1994] Sokołowski A., Kosmol J. : On application of neural networks, fuzzy logic and genetic algorithm for design of tool wear monitoring systems. Proceedings of the III-rd International Conference „Achievements in the Mechanical and Material Science”, Gliwice 1994
169. [Sokołowski і Kosmol, 1995] Sokolowski A., Kosmol J. : Cutting process supervision : review of artificial intelligence application. Proceedings of VI Workshop on Diagnostics and Supervision in Flexible Manufacturing, Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej nr 61, Seria : Konferencje 26, Wrocław 1995
170. [Sokołowski i Kosmol, 1995a] Sokołowski A., Kosmol J. : Diagnostyka narzędzia za pomocą sieci neuronowej. IV Naukowa Szkoła Nadzorowania i Diagnostyki Systemów Obróbkowych, Sztuczna Inteligencja w Diagnostyce i Nadzorowaniu, Prace Naukowe Instytutu Technologii Maszyn i Automatyzacji Politechniki Wrocławskiej 57, seria: Konferencje 23, Wrocław 1995
171. [Sokołowski i Kosmol, 1995b] Sokołowski A., Kosmol J. : Sieć neuronowa typu Feed Forward Back Propagation jako narzędzie do selekcji i integracji cech sygnałów pomiarowych. XXXIV Sympozjon „Modelowanie w Mechanice”, Zeszyty Naukowe Politechniki Śląskiej, seria: Mechanika, z. 121, Gliwice 1995
172. [Sokołowski i Kosmol, 1995с] Sokołowski A., Kosmol J. : Vibration measurement as a means for tool wear monitoring; Part 1 : Conventional method application. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
173. [Sokołowski і Kosmol, 1995d] Sokołowski A., Kosmol J. : Koncepcja inteligentnego czujnika w zastosowaniu do identyfikacji zużycia ostrza skrawającego. VIII Krajowa Konferencja Metrologii, Warszawa 1995
174. [Sokołowski i Kosmol, 1995e] Sokołowski A., Kosmol J. : An example of Feed Forward Back Propagation and ART-2A neural networks application for tool wear monitoring. Proceeding of the Second International Symposium on Methods and Models in Automation and Robotics, Międzyzdroje, 1995
175. [Sokołowski і Kosmol, 1996] Sokołowski A., Kosmol J. : Designing intelligent diagnostic systems. Proceedings of the International Conference on "Computer Integrated Manufacturing - СІМ 96", vol. I, Zakopane 1996
176. [Sokołowski i Kosmol, 1996а] Sokołowski A., Kosmol J. : Intelligent Monitoring System Designer. Proceedings of the 1996 Japan - USA Symposium on Flexible Automation, Vol. 2, Boston 1996
177. [Sokołowski і Kosmol, 1996b] Sokołowski A., Kosmol J. : Przykłady zastosowań wybranych metod sztucznej inteligencji w diagnostyce procesu obróbki skrawaniem. Materiały Kongresu Diagnostyki Technicznej KDT'96, Gdańsk 1996
178. [Sokołowski i Kosmol, 1997] Sokołowski A., Kosmol J.: Koncepcja i realizacja inteligentnego systemu wspomagania projektowania układów diagnostycznych. Zeszyty Naukowe Katedry Mechaniki Stosowanej, z. 3, Gliwice 1997
179. [Sokołowski i Kosmol, 1998] Sokołowski A., Kosmol J. : Feature selection for bun¬height estimation. Proceedings of the V International Conference on Monitoring and Automatic Supervision in Manufacturing, Warsaw 1998
180. [Sokołowski і Kosmol, 1998a] Sokołowski A., Kosmol J.: Wybrane testy metod selekcji i integracji cech sygnałów pomiarowych. Technologia Maszyn, IV Naukowe Warsztaty Profesorskie TM’98 i III Forum Prac Badawczych, Wyd. Pol. Koszalińskiej, Koszalin 1998
181. [Sokołowski i Kosmol, 1999] Sokołowski A., Kosmol J. : On some aspects of productivity and quality improvement in machining. Refereed papers presented at the 8th International Conference on "Productivity & Quality Research", Vaasa, Finland 1999
182. [Sokołowski і Kosmol, 1999a] Sokołowski A., Kosmol J. : Artificial intelligence in cutting process monitoring. Advances in Technology of Machines and Mechanical Equipment, Poland, vol. 23, No 3, 1999
183. [Sokołowski і Kosmol, 2000] Sokołowski A., Kosmol J. : Selected aspects of practical implementation of Intelligent Diagnostic System. II Międzynarodowy Kongres Diagnostyki Technicznej "Diagnostyka 2000", vol. 2, Warszawa 2000
184. [Sokołowski i Kosmol, 2001] Sokołowski A., Kosmol J. : Complex approach to vibration based cutting tool diagnostics. Proceedings of the Eighth International Congress on Sound and Vibration, vol. 2, Hong Kong (China) 2001
185. [Sokołowski і Kosmol, 2001a] Sokołowski A., Kosmol J. : On a specific approach to application of artificial neural networks for machine tool monitoring. Proceedings of the 4th International Conference "Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques", Paryż 2001
186. [Sokolowski і Kosmol, 2001b] Sokolowski A., Kosmol J. : Selected examples of cutting process monitoring and diagnostics. Journal of Materials Processing Technology, vol. 113/1-3 (2001), Special Issue, Korea 2001
187. [Sokolowski і Rehse, 1994] Sokolowski A., Rehse M. : Feature selection task in tool wear monitoring - genetic algorithm approach. Research Reports 1993/1994, Laboratory for Manufacturing Automation, University of California at Berkeley, Berkeley 1994
188. [Sokolowski, 1993] Sokolowski A. : Comparison between neural networks and fuzzy logic for tool wear detection. Research Reports 1992/1993, Laboratory for Manufacturing Automation, University of California at Berkeley, Berkeley 1993
189. [Sokolowski, 1995] Sokolowski A. : Nadzorowanie stanu ostrza skrawającego z zastosowaniem sieci neuronowych. Rozprawa doktorska, Katedra Budowy Maszyn, Politechnika Śląska w Gliwicach, Gliwice 1995
190. [Sokołowski, 1996] Sokołowski A. : Teoretyczne i praktyczne aspekty zastosowań sieci neuronowej Feed Forward Back Propagation. Prace Naukowe Katedry Budowy Maszyn Nr 1/96, Politechnika Śląska w Gliwicach, Gliwice 1996
191. [Sokołowski, 2000] Sokołowski A. : Konstruowanie sztucznych sieci neuronowych. Conference Proceedings „Methods of Artificial Intelligence in Mechanics and Mechanical Engineering”, AIMECH’2000, Gliwice 2000
192. [Sokołowski, 2001] Sokołowski A. : Application of neural networks and neuro-fuzzy logic for burr modelling. Proceedings of the 12th International DAAAM Symposium, Jena 2001
193. [STATISTICA] STATISTICA for Windows, Computer program manual, ver. 5.1 D, StatSoft, Inc., 1996
194. [STATISTICA-NN] STATISTICA - Neural Networks, User Guide and Descriptions, ver. 3.0 B, Trajan Software Ltd., StatSoft, Inc., 1998
195. [Stein et al., 1993] Stein J., Narayanaswami R., Ho S., Lam A., Babu M., Park L, Afeal A., Domfeld D. : Intelligent deburring of precision components. Proceedings of the Symposium on Deburring and Surface Finishing, SME, 1993
196. [Stein і Domfeld, 1997] Stein J., Domfeld D. : An architecture for integrated design and manufacturing of precision mechanical components, Trans, of NAMRI/SME, Vol. 25, 1997
197. [Such et al., 1999] Suh J.H., Kumara S.R.T., Mysore S. P. : Machinery fault diagnosis and prognosis : Application of advanced signal processing techniques. Annals of the C1RP, Vol. 48/1/ 1999
198. [Sung і Kuo, 1992] Sung M.H., Kuo L.E. : The use of Back-Propagation neural networks to construct the membership functions. Proceedings of the 2nd Pacific Rim International Conference on Artificial Intelligence, Seoul 1992
199. [Sunil et al., 1990] Sunil Elanayar V.T., Shin Y. C., Kumara S. : Machining condition monitoring for automation using neural networks. Monitoring and Control for Manufacturing Processes, ASME, PED, Vol. 44, 1990
200. [Susmaga et al., 1999] Susmaga R., Słowiński R., Greco S., Matarazzo B. : Computation of reducts for multi-attribute and multi - criteria classification. Intelligent Information Systems, IPI PAN, Ustroń 1999
201. [Szabo et al., 1989] Szabo B., Du R., Wu S.M. : Tool wear monitoring for milling processes using DDS methodology. Trans, of NAMRI/SME, Vol. 17, 1989
202. [Tadeusiewicz, 1993] Tadeusiewicz R. : Sieci neuronowe. Akademicka Oficyna Wydawnicza, Warszawa 1993
203. [Tadeusiewicz, 2000] Tadeusiewicz R. : Sieci neuronowe jako wciąż jeszcze nie w pełni wykorzystane narzędzie diagnostyki. II Międzynarodowy Kongres Diagnostyki Technicznej "Diagnostyka 2000", vol. 1, Warszawa, 2000
204. [Takagi i Hayashi, 1991] Takagi H., Hayashi I. : NN-driven fuzzy reasoning. International Journal of Approximate Reasoning, Vol. 5, No. 3, 1991
205. [Takagi і Sugeno, 1985] Takagi T., Sugeno M. : Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, 1985
206. [Takahashi et al., 1993] Takahashi H., Tomita E., Kawabata T. : Separability of internal representation in multilayer perceptrons with application to learning. Neural Networks, Vol. 6, 1993
207. [Takata et al., 1986] Takata S., Ahn J.H., Miki M.,Miyao Y., Sata T. : A sound monitoring system for fault detection of machine and machining states. Annals of the CIRP, Vol. 35/1/ 1986
208. [Takata et al., 1987] Takata S., Nakajima T., Ahn J.H., Sata T. : Tool breakage monitoring by means of fluctuation in spindle rotational speed. Annals of the CIRP, Vol. 36/1/ 1987
209. [Takeshita і Inasaki,1991] Takeshita H., Inasaki I. : Monitoring of endmill cutter with an AE-sensor. Proceedings of 4th World Meeting on Acoustic Emission and 1st International Conference on Acoustic Emission in Manufacturing, Boston, 1991
210. [Tang et al., 2001] Tang Y., Sun Q., Lu W. : Tool wear detection for microdrilling process with discrete wavelet transform. Proceedings of the Eighth International Congress on Sound and Vibration, vol. 2, Hong Kong (China) 2001
211. [Tansel i Mc Laughlin, 1991] Tansel I. N., Me Laughlin C. : On-line monitoring oftool breakage with unsupervised neural networks. Trans. ofNAMRI/SME, Vol. 20, 1991
212. [Tamg et al., 1994] Tamg Y.S., Li T.C., Chen M.C. : On-line drilling chatter recognition and avoidance using an ART-2A neural network. International Journal of Machine Tools & Manufacture, Design, Research and Application, vol. 34, no 7, 1994
213. [TCM Workshop, 1993] Proceedings : Workshop on Tool Condition Monitoring (TCM). Second Meeting of the CIRP Working Group on TCM, edited by W. Weis and D. Domfeld, Edinburgh 1993
214. [Teshima et al., 1993] Teshima T., Shibasaka T., Takuma M., Yamamoto A., Iwata K. : Estimation of cutting tool life by processing tool image data with neural network. Annals of the CIRP, Vol. 42/1/ 1993
215. [Teti і Domfeld, 1989] Teti R., Domfeld D. : Modelling and experimental analysis of acoustic emission from metal cutting. J. Eng. Industry, Vol. Ill, August 1989
216. [Tyrlik et al., 1989] Tyrlik T., Kosmol J., Sokolowski A : Komputerowa obróbka dyskretnych sygnałów pomiarowych - pakiet DATA-DYN. Materiały VII Konferencji MiSPWK, Warszawa 1989
217. [Ueda i Manabe, 1993] Ueda К., Manabe К. : Rigid - plastic FEM analysis of three - dimensional deformation field in chip formation process. Annals of the CIRP, Vol. 42/1/ 1993
218. [Urbaniak, 2001] Urbaniak M. : Neural network approach to analyse of grinding wheel cutting surface. Proceedings of the 12th International DAAAM Symposium, Jena 2001
219. [Virck i Tłusty, 1992] Virck K.C., Tłusty J. : Adaptive thresholding for cutter breakage in milling. Sensors and Signal Processing for Manufacturing, ASME, PED, Vol. 55, 1992
220. [Wang і Domfeld, 1992] Wang X., Domfeld D.A. : In-process tool wear monitoring using neural networks. Proceedings of the 1992 Japan - USA Symposium on Flexible Automation, San Francisco 1992
221. [Wang і Mendel, 1992] Wang L.X., Mendel J.M. : Generating fuzzy rales by learning from examples. IEEE Transactions on Systems, Man and Cybernetics, Vol. 22, No. 6, 1992
222. [Westkamper et al., 1995] Westkamper E., Lange D., Hoffineister H.W. : Safe abrasive process through adaptive control under highly chaotic conditions. Proceedings of the IV International Conference on Monitoring and Automatic Supervision in Manufacturing, Miedzyszyn/Warszawa 1995
223. [Westkamper et al., 1996] Westkamper E., Lange D., Schmidt T. : Modelling the grinding process with regression models and artificial neural networks. Production Engineering, Research and Development in Germany, Vol. III/1, 1996
224. [Yager, 1994] Yager R.R. : Modelling and formulating fuzzy knowledge bases using neural networks. Neural Networks, Vol. 7 No. 8, 1994
225. [Yamasaki, 1996] Yamasaki H. (editor) : Intelligent Sensors, Handbook of Sensors and Actuators. ELSEVIER, Amsterdam, 1996
226. [Yao і Fang, 1993] Yao Y.L., Fang X.D. : Assessment of chip forming patterns with tool wear progression in machining via neural networks. Int. J. Mach. Tools Manufact., Vol. 33, No. 1, 1993
227. [Yerramareddy et al., 1993] Yerramareddy S., Lu S.C-Y., Arnold KF. : Developing empirical models from observational data using artificial neural networks. Journal of Intelligent Manufacturing, Vol. 4, No. 1, February 1993
228. [Zacksenhouse, 2001] Zacksenhouse, M. : How blind can neural networks based diagnostic be ?. Proceedings of the 4th International Conference "Acoustical and Vibratory Surveillance Methods and Diagnostic Techniques", Paryż 2001
229. [Zadeh, 1983] Zadeh L. A. : A Computational Approach to Fuzzy Quantifiers in Natural Languages. Comp & Maths with Appls, Vol. 9, No 1, 1983
230. [Żurada et al., 1994] Żurada J.M., Malinowski A., Cloete I. : Sensitivity analysis for minimisation of input data dimension for feedforward neural network. IEEE Symposium CAS, London 1994
231. [Żurada, 1992] Żurada J.M. : Introduction to Artificial Neural Systems. West Publishing Company, 1992

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