Quality and inspection of machining operations: Tool condition monitoring

Journal of Manufacturing Science and Engineering

Volumn 132, Issue 4, 2010, Pages 0410151-04101516

  Roth, John T ^a   Djurdjanovic, Dragan ^b   Yang, Xiaoping ^c   Mears, Laine ^d   Kurfess, Thomas ^d  

^a BEHREND COLLEGE   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c CUMMINS INC   (United States)

^d CLEMSON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITION MONITORING; INDUSTRIAL RESEARCH; MANUFACTURE; SIGNAL PROCESSING;

CONDITION-BASED MAINTENANCE; CUTTING OPERATIONS; MACHINING OPERATIONS; MANUFACTURING PROCESS; ON-BOARD SENSORS; PRODUCTION SYSTEM; SIGNAL PROCESSING CAPABILITY; TOOL CONDITION MONITORING;

CUTTING TOOLS;

EID: 77955332244     PISSN: 10871357     EISSN: 15288935     Source Type: Journal    
DOI: 10.1115/1.4002022     Document Type: Article

References (105)

1. Jardine, A. K. S., Lin, D., and Banjevic, D., 2006, "A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance," Mech. Syst. Signal Process., 20(7), pp. 1483-1510.
2. Thurston, M., and Lebold, M., 2001, "Standards Development for Condition- Based Maintenance Systems," Fifty-Fifth Meeting of the Society for Machinery Failure Prevention Technology.
3. Yang, Z., 2005, "Dynamic Maintenance Scheduling Using Online Information About System Condition," Ph.D. thesis, University of Michigan, Ann Arbor, MI.
4. Yang, Z., Djurdjanovic, D., and Jun, N., 2007, "Maintenance Scheduling for a Manufacturing System of Machines With Adjustable Throughput," IIE Trans., 39(12), pp. 1111-1125.
5. Boutros, T., and Liang, M., 2007, "Mechanical Fault Detection Using Fuzzy Index Fusion," Int. J. Mach. Tools Manuf., 47(11), pp. 1702-1714.
6. Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., König, W., and Teti, R., 1995, "Tool Condition Monitoring (TCM)-The Status of Research and Industrial Application," CIRP Ann., 44(2), pp. 541-567.
7. Rehorn, A. G., Jiang, J., and Orban, P. E., 2005, "State-of-the-Art Methods and Results in Tool Condition Monitoring: A Review," Int. J. Adv. Manuf. Technol., 26(7-8), pp. 693-710.
8. Sick, B., 2002, "On-Line and Indirect Tool Wear Monitoring in turning With Artificial Neural Networks: A Review of More Than a Decade of Research," Mech. Syst. Signal Process., 16, pp. 487-546.
9. Scheffer, C., and Heyns, P. S., 2001, "Wear Monitoring in Turning Operations Using Vibration and Strain Measurements," Mech. Syst. Signal Process., 15, pp. 1185-1202.
10. Ghasempoor, A., Moore, T. N., and Jeswiet, J., 2000, "Tool Wear Prediction in Turning," Proceedings of the 13th International Congress on Condition Monitoring and Diagnostic Engineering Management, p. 8.
11. Sun, J., Hong, G. S., Wong, Y. S., Rahman, M., and Wang, Z. G., 2006, "Effective Training Data Selection In Tool Condition Monitoring System," Int. J. Mach. Tools Manuf., 46(2), pp. 218-224.
12. Sun, J., Rahman, M., Wong, Y. S., and Hong, G. S., 2004, "Multiclassification of Tool Wear With Support Vector Machine by Manufacturing Loss Consideration," Int. J. Mach. Tools Manuf., 44(11), pp. 1179-1187.
13. Farrelly, F. A., Petri, A., Pitolli, L., Pontuate, G., Tagliani, A., and Novi Inverardi, P. L., 2004, "Statistical Properties of Acoustic Emission Signals From Metal Cutting Processes," J. Acoust. Soc. Am., 116(2), pp. 981-986.
14. Li, X., and Du, R., 2004, "Monitoring Machining Processes Based on Discrete Wavelet Transform and Statistical Process Control," Int. J. Wavelets, Multiresolut. Inf. Process., 2(3), pp. 299-311.
15. Li, X., and Yao, X., 2005, "Multi-Scale Statistical Process Monitoring in Machining," IEEE Trans. Ind. Electron., 52(3), pp. 924-927.
16. Chen, X., and Li, B., 2007, "Acoustic Emission Method for Tool Condition Monitoring Based on Wavelet Analysis," Int. J. Adv. Manuf. Technol., 33, pp. 968-976.
17. Gao, H., and Xu, M., 2005, "Intelligent Tool Condition Monitoring System for Turning Operations," Lect. Notes Comput. Sci., 3498, pp. 883-889.
18. Jemielniak, K., and Bombiński, S., 2006, "Hierarchical Strategies in Tool Wear Monitoring," Proc. Inst. Mech. Eng., Part B, 220(3), pp. 375-381.
19. Salgado, D. R., and Alonso, F. J., 2007, "An Approach Based on Current and Sound Signals for In-Process Tool Wear Monitoring," Int. J. Mach. Tools Manuf., 47(14), pp. 2140-2152.
20. Min, B. K., O'Neal, G., Koren, Y., and Pasek, Z., 2002, "Cutting Process Diagnostics Utilising a Smart Cutting Tool," Mech. Syst. Signal Process., 16(2-3), pp. 475-486.
21. Achiche, S., Balazinski, M., Baron, L., and Jemielniak, K., 2002, "Tool Wear Monitoring Using Genetically-Generated Fuzzy Knowledge Bases," Eng. Applic. Artif. Intell., 15(3-4), pp. 303-314.
22. Wang, L., Mostafa, G. M., and Kannatey-Asibu, E., Jr., 2002, "Hidden Markov Model-Based Tool Wear Monitoring in Turning," ASME J. Manuf. Sci. Eng., 124(3), pp. 651-658.
23. Salgado, D. R., and Alonso, F. J., 2006, "Tool Wear Detection in Turning Operations Using Singular Spectrum Analysis," J. Mater. Process. Technol., 171(3), pp. 451-458.
24. Lu, M.-C., and Kannatey-Asibu, E., Jr., 2002, "Analysis of Sound Signal Generation Due to Flank Wear in Turning," ASME J. Manuf. Sci. Eng., 124(4), pp. 799-808.
25. Scheffer, C., and Heyns, P. S., 2004, "An Industrial Tool Wear Monitoring System for Interrupted Turning," Mech. Syst. Signal Process., 18(5), pp. 1219-1242.
26. Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N., and Lee, J., 2007, "Similarity Based Method for Manufacturing Process Performance Prediction and Diagnosis," Comput. Ind., 58(6), pp. 558-566.
27. Cohen, L., 2000, Time-Frequency and Time-Scale Analysis, Prentice-Hall, Englewood Cliffs, NJ.
28. Jeong, J., and Williams, W. J., 1992, "Kernel Design for Reduced Interference Distributions," IEEE Trans. Signal Process., 40(2), pp. 402-412.
29. Djurdjanovic, D., Lee, J., and Ni, J., 2003, "Watchdog Agent-An Infotronics- Based Prognostics Approach for Product Performance Degradation Assessment and Prediction," Adv. Eng. Inf., 17(3-4), pp. 109-125.
30. Cakir, M. C., and Isik, Y., 2005, "Detecting Tool Breakage in Turning AISI 1050 Steel Using Coated and Uncoated Cutting Tools," J. Mater. Process. Technol., 159(2), pp. 191-198.
31. Balazinski, M., Czogata, E., Jemielniak, K., and Leski, J., 2002, "Tool Condition Monitoring Using Artificial Intelligence Methods," Eng. Applic. Artif. Intell., 15(1), pp. 73-80.
32. Jemielniak, K., 2006, "Tool Wear Monitoring Based on a Non-Monotonic Signal Feature," Proc. Inst. Mech. Eng., Part B, 220(2), pp. 163-170.
33. Du, R., and Yeung, K., 2006, "Fuzzy Transition Probability: A New Method for Monitoring Progressive Faults. Part 2: Application Examples," Eng. Applic. Artif. Intell., 19(2), pp. 145-155.
34. Srinivasa-Rao, C., Nageswara-Rao, D., and Someswara-Rao, R., 2006, "Online Prediction of Diffusion Wear on the Flank Through Tool Tip Temperature in Turning Using Artificial Neural Networks," Proc. Inst. Mech. Eng., Part B, 220(12), pp. 2069-2076.
35. Oraby, S. E., Al-Modhuf, A. F., and Hayhurst, D. R., 2005, "A Diagnostic Approach for Turning Tool Based on the Dynamic Force Signals," ASME J. Manuf. Sci. Eng., 127(3), pp. 463-475.
36. Scheffer, C., Engelbrecht, H., and Heyns, P., 2005, "A Comparative Evaluation of Neural Networks and Hidden Markov Models for Monitoring Turning Tool Wear," Neural Comput. Appl., 14(4), pp. 325-336.
37. Du, R., and Yeung, K., 2004, "Fuzzy Transition Probability: A New Method for Monitoring Progressive Faults. Part 1: The Theory," Eng. Applic. Artif. Intell., 17(5), pp. 457-467.
38. Yu, G., Qui, H., Djurdjanovic, D., and Lee, J., 2006, "Feature Signature Prediction of a Boring Process Using Neural Network Modeling With Confidence Bounds," Int. J. Adv. Manuf. Technol., 30(7-8), pp. 614-621.
39. Choi, Y. J., Park, M. S., and Chu, C. N., 2008, "Prediction of Drill Failure Using Features Extraction in Time and Frequency Domains of Feed Motor Current," Int. J. Mach. Tools Manuf., 48(1), pp. 29-39.
40. Subramanian, K., 1977, "Sensing of Drill Wear and Prediction of Drill Life," ASME J. Eng. Ind., 99, pp. 295-301.
41. Jantunen, E., 2002, "A Summary of Methods Applied to Tool Condition Monitoring in Drilling," Int. J. Mach. Tools Manuf., 42(9), pp. 997-1010.
42. Velayudham, A., Krishnamurthy, R., and Soundarapandian, T., 2005, "Acoustic Emission Based Drill Condition Monitoring During Drilling of Glass/ Phenolic Polymeric Composite Using Wavelet Packet Transform," Mater. Sci. Eng., A, 412(1-2), pp. 141-145.
43. Heinemann, R., Hinduja, S., and Barrow, G., 2007, "Use of Process Signals for Tool Wear Progression Sensing in Drilling Small Deep Holes," Int. J. Adv. Manuf. Technol., 33(3-4), pp. 243-250.
44. Panda, S. S., Singh, A. K., Chakraborty, D., and Pal, S. K., 2006, "Drill Wear Monitoring Using Back Propagation Neural Network," J. Mater. Process. Technol., 172(2), pp. 283-290.
45. Fu, L., Ling, S.-F., and Tseng, C.-H., 2007, "On-Line Breakage Monitoring of Small Drills With Input Impedance of Driving Motor," Mech. Syst. Signal Process., 21(1), pp. 457-465.
46. Ertunc, H. M., and Oysu, C., 2004, "Drill Wear Monitoring Using Cutting Force Signals," Mechatronics, 14(5), pp. 533-548.
47. Choi, Y. J., and Chung, S. C., 2006, "Monitoring of Micro-DrillWear by Using the Machine Vision System," Trans. NAMRI/SME, 34, pp. 134-150.
48. Abu-Mahfouz, I., 2003, "Drilling Wear Detection and Classification Using Vibration Signals and Artificial Neural Network," Int. J. Mach. Tools Manuf., 43(7), pp. 707-720.
49. Al-Sulaiman, F. A., Baseer, M. A., and Sheikh, A. K., 2005, "Use of Electrical Power for Online Monitoring of Tool Condition," J. Mater. Process. Technol., 166(3), pp. 364-371.
50. Brophy, B., Kelly, K., and Byrne, G., 2002, "AI-Based Condition Monitoring of the Drilling Process," J. Mater. Process. Technol., 124(3), pp. 305-310.
51. Franco-Gasca, L. A., Herrera-Ruiz, G., Peniche-Vera, R., Romero-Troncoso, R. D. J., and Leal-Tafolla, W., 2006, "Sensorless Tool Failure Monitoring System for Drilling Machines," Int. J. Mach. Tools Manuf., 46(3-4), pp. 381- 386.
52. Patra, K., Pal, S. K., and Bhattacharyya, K., 2007, "Application of Wavelet Packet Analysis in Drill Wear Monitoring," Mach. Sci. Technol., 11(3), pp. 413-432.
53. Ertunc, H. M., Loparo, K. A., and Ocak, H., 2001, "Tool Wear Condition Monitoring in Drilling Operations Using Hidden Markov Models (HMMs)," Int. J. Mach. Tools Manuf., 41, pp. 1363-1384.
54. Oh, Y. T., Kwon, W. T., and Chu, C. N., 2004, "Drilling Torque Control Using Spindle Motor Current and its Effect on Tool Wear," Int. J. Adv. Manuf. Technol., 24, pp. 327-334.
55. Al-Sulaiman, F., Sheikh, A., and Baseer, M., 2004, "Empirical Models of Mechanical and Electrical Drilling Power of Mild Steel," Proc. Inst. Mech. Eng., Part B, 218(9), pp. 1181-1189.
56. Newland, D. E., 1999, "Ridge and Phase Identification in the Frequency Analysis of Transient Signals by Harmonic Wavelets," ASME J. Vibr. Acoust., 121(2), pp. 149-155.
57. Romberg, T. M., Cassar, A. G., and Harris, R. W., 1984, "A Comparison of Traditional Fourier and Maximum Entropy Spectral Methods for Vibration Analysis," ASME J. Vib., Acoust., Stress, Reliab. Des., 106(1), pp. 36-39.
58. Chandrasekharan, V., Kapoor, S. G., and DeVor, R. E., 1995, "A Mechanistic Approach to Predicting the Cutting Forces in Drilling: With Application to Fiber-Reinforced Composite Materials," ASME J. Eng. Ind., 117(4), pp. 559- 570.
59. René de Jesús, R.-T., Herrera-Ruiz, G., Terol-Villalobos, I., and Jáuregui- Correa, J., 2003, "Driver Current Analysis for Sensorless Tool Breakage Monitoring of CNC Milling Machines," Int. J. Mach. Tools Manuf., 43(15), pp. 1529-1534.
60. Amer, W., Grosvenor, R. I., and Prickett, P. W., 2006, "Sweeping Filters and Tooth Rotation Energy Estimation (TREE) Techniques for Machine Tool Condition Monitoring," Int. J. Mach. Tools Manuf., 46(9), pp. 1045-1052.
61. Zhang, J., and Chen, J., 2007, "Tool Condition Monitoring in an End-Milling Operation Based on the Vibration Signal Collected Through a Microcontroller- Based Data Acquisition System," Int. J. Adv. Manuf. Technol., 39(1-2), pp. 445-448.
62. Kang, E.-G., Park, S.-J., and Lee, S.-J., 2005, "Development of In Situ System to Monitor the Machining Process Using a Piezo Load Cell," Int. J. Adv. Manuf. Technol., 25(7-8), pp. 647-651.
63. Dini, G., and Tognazzi, F., 2007, "Tool Condition Monitoring in End Milling Using a Torque-Based Sensorized Toolholder," Proc. Inst. Mech. Eng., Part B, 221(1), pp. 11-23.
64. Roth, J., 2006, "Using the Eigenvalues of Multivariate Spectral Matrices to Achieve Cutting Direction and Sensor Orientation Independence," ASME J. Manuf. Sci. Eng., 128(1), pp. 350-354.
65. Suprock, C., and Roth, J., 2007, "Methods for On-Line Directionally Independent Failure Prediction of End Milling Cutting Tools," Mach. Sci. Technol., 11, pp. 1-43.
66. Bhattacharyya, P., Sengupta, D., and Mukhopadhyay, S., 2007, "Cutting Force-Based Real-Time Estimation of Tool Wear in Face Milling Using a Combination of Signal Processing Techniques," Mech. Syst. Signal Process., 21(6), pp. 2665-2683.
67. Xiaoli, L., 2001, "Detection of Tool Flute Breakage In End Milling Using Feed-Motor Current Signatures," IEEE/ASME Trans. Mechatron., 6(4), pp. 491-498.
68. Dutta, R., Paul, S., and Chattopadhyay, A., 2006, "The Efficacy of Back Propagation Neural Network With Delta Bar Delta Learning in Predicting the Wear of Carbide Inserts in Face Milling," Int. J. Adv. Manuf. Technol., 31(5-6), pp. 434-442.
69. Yao, Y., Liu, C., Yuan, Z., and Lu, Y., 2006, "Robustness Improvement of Tool Life Estimation Assisted by a Virtual Manufacturing Cell," J. Mater. Process. Technol., 172(3), pp. 445-450.
70. Fish, R. K., Ostendorf, M., Bernard, G. D., and Castanon, D. A., 2003, "Multilevel Classification of Milling Tool Wear With Confidence Estimation," IEEE Trans. Pattern Anal. Mach. Intell., 25(1), pp. 75-85.
71. Zhu, R., DeVor, R. E., and Kapoor, S. G., 2003, "A Model-Based Monitoring and Fault Diagnosis Methodology for Free-Form Surface Machining Process," ASME J. Manuf. Sci. Eng., 125(3), pp. 397-404.
72. Alaniz-Lumbreras, P. D., Gómez-Loenzo, R. A., de Jesús Romero-Troncoso, R., del Rocío Peniche-Vera, R., Jáuregui-Correa, J. C., and Herrera-Ruiz, G., 2006, "Sensorless Detection of Tool Breakage in Milling," Mach. Sci. Technol., 10, pp. 263-274.
73. Peng, Y., 2006, "Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage," ASME J. Manuf. Sci. Eng., 128(1), pp. 154-166.
74. Ghosh, N., Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., and Chattopadhyay, A. B., 2007, "Estimation of Tool Wear During CNC Milling Using Neural Network-Based Sensor Fusion," Mech. Syst. Signal Process., 21(1), pp. 466-479.
75. Ritou, M., Garnier, S., Furet, B., and Hascoet, J.-Y., 2006, "A New Versatile In-Process Monitoring System for Milling," Int. J. Mach. Tools Manuf., 46(15), pp. 2026-2035.
76. Zuperl, U., and Cus, F., 2004, "Tool Cutting Force Modeling in Ball-End Milling Using Multilevel Perceptron," J. Mater. Process. Technol., 153-154, pp. 268-275.
77. Tansel, I. N., Bao, W. Y., Reen, N. S., and Kropas-Hughes, C. V., 2005, "Genetic Tool Monitor (GTM) for Micro-End-Milling Operations," Int. J. Mach. Tools Manuf., 45(3), pp. 293-299.
78. Kuljanic, E., and Sortino, M., 2005, "TWEM, a Method Based on Cutting Forces-Monitoring Tool Wear In Face Milling," Int. J. Mach. Tools Manuf., 45(1), pp. 29-34.
79. Xu, M., Schuyler, C. K., Fussel, B. K., and Jerard, R. B., 2006, "Experimental Evaluation of a Smart Machining System for Feedrate Selection and Tool Condition Monitoring," Trans. NAMRI/SME, 34, pp. 151-158.
80. Shao, H., Wang, H. L., and Zhao, X. M., 2004, "A Cutting Power Model for Tool Wear Monitoring in Milling," Int. J. Mach. Tools Manuf., 44(14), pp. 1503-1509.
81. Tönshoff, H. K., Friemuth, T., and Becker, J. C., 2002, "Process Monitoring in Grinding," CIRP Ann., 51(2), pp. 551-571.
82. Karpuschewski, B., and Inasaki, I., 2006, "Monitoring Systems for Grinding Processes," Condition Monitoring and Control for Intelligent Manufacturing, Springer, London, p. 83.
83. Inasaki, I., and Karpuschewski, B., 2001, "Abrasive Processes," Chapter in Sensors in Manufacturing, Wiley, New York, p. 236.
84. Dornfeld, D. A., Lee, Y., and Chang, A., 2003, "Monitoring of Ultraprecision Machining Processes," Int. J. Adv. Manuf. Technol., 21(8), pp. 571-578.
85. Aguiar, P. R., Serni, P. J. A., Bianchi, E. C., and Dotto, F. R. L., 2004, "In- Process Grinding Monitoring by Acoustic Emission," IEEE International Conference on Acoustics, Speech, and Signal Processing.
86. Liao, T. W., Hua, G., Qu, J., and Blau, P. J., 2006, "Grinding Wheel Condition MonitoringWith Markov Model-Based Clustering Methods," Mach. Sci. Technol., 10, pp. 511-538.
87. Warren Liao, T., Ting, C.-F., Qu, J., and Blau, P. J., 2007, "A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring," Int. J. Mach. Tools Manuf., 47(3-4), pp. 580-592.
88. Liao, T. W., Tang, F., Qu, J., and Blau, P. J., 2008, "Grinding Wheel Condition Monitoring With Boosted Minimum Distance Classifiers," Mech. Syst. Signal Process., 22(1), pp. 217-232.
89. Lee, D. E., Hwang, I., Valente, C. M. O., Oliveira, J. F. G., and Dornfeld, D. A., 2006, "Precision Manufacturing Process Monitoring With Acoustic Emission," Int. J. Mach. Tools Manuf., 46(2), pp. 176-188.
90. Oliveira, J. F. G., and Valente, C. M. O., 2004, "Fast Grinding Process Control With AE Modulated Power Signals," CIRP Ann., 53(1), pp. 267-270.
91. Furutani, K., Ohguro, N., Hieu, N. T., and Nakamura, T., 2002, "In-Process Measurement of Topography Change of Grinding Wheel by Using Hydrodynamic Pressure," Int. J. Mach. Tools Manuf., 42(13), pp. 1447-1459.
92. Meyer, L., Heinzel, C., and Brinksmeier, E., 2004, "Monitoring of Grinding Processes Using a Sensor Equipped Grinding Wheel," Annals of the WGP., 11(1), pp. 41-44.
93. Brinksmeier, E., Heinzel, C., and Meyer, L., 2005, "Development and Application of a Wheel Based Process Monitoring System in Grinding," CIRP Ann., 54(1), pp. 301-304.
94. Hosokawa, A., Mashimo, K., Yamada, K., and Ueda, T., 2004, "Evaluation of Grinding Wheel Surface by Means of Grinding Sound Discrimination," JSME Int. J., 47(1), pp. 51-57.
95. Guo, C., Campomanes, M., Mcintosh, C., Becze, C., and Malkin, S., 2004, "Model-Based Monitoring and Control of Continuous Dress Creep-Feed Form Grinding," CIRP Ann., 53(1), pp. 263-266.
96. Warkentin, A., and Bauer, R., 2003, "Analysis of Wheel Wear Using Force Data in Surface Grinding," Transaction of the Canadian Society for Mechanical Engineering, 27(3), pp. 193-204.
97. Kwak, J.-S., and Ha, M.-K., 2004, "Detection of Dressing Time Using the Grinding Force Signal Based on the Discrete Wavelet Decomposition," Int. J. Adv. Manuf. Technol., 23(1-2), pp. 87-92.
98. Couey, J. A., Marsh, E. R., Knapp, B. R., and Vallance, R. R., 2005, "Monitoring Force in Precision Cylindrical Grinding," Precis. Eng., 29(3), pp. 307- 314.
99. Chiu, N., 1993, "Computer Simulation for Form Grinding Process," Ph.D. dissertation, University of Massachusetts.
100. Chiu, N., and Malkin, S., 1994, "Computer Simulation for Creep-Feed Form Grinding," Trans. NAMRI/SME, 22, pp. 119-126.
101. Malkin, S., 1989, Grinding Technology: Theory and Applications of Machining With Abrasives, Wiley, New York.
102. Snoeys, R., and Peters, J., 1974, "The Significance of Chip Thickness in Grinding," CIRP Ann., 23, pp. 227-237.
103. Nuttall, A. H., 1994, "Detection Performance of Power-Law Processors for Random Signals of Unknown Location, Structure, Extent, and Strength," Naval Undersea Warfare Center Division, Naval Undersea Warfare Center Technical Report No. 10751.
104. Freund, Y., and Schapire, R. E., 1997, "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting," J. Comput. Syst. Sci., 55(1), pp. 119-139.
105. Kim, Y., 2003, "Averaged Boosting: A Noise-Robust Ensemble Method," Advances in Knowledge Discovery and Data Mining, Springer, New York, p. 565.