Tool wear control through cognitive paradigms

Procedia CIRP

Volumn 33, Issue , 2015, Pages 221-226

  D'Addona, D M ^a   Matarazzo, D ^a   Sharif Ullah, A M M ^b   Teti, R ^a  

^a UNIVERSITY OF NAPLES FEDERICO II   (Italy)

^b KITAMI INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

DNA Based Computing; Neural Network; Tool Wear

Indexed keywords

COMPLEX NETWORKS; DNA; EFFICIENCY; IMAGE PROCESSING; MACHINING CENTERS; MANUFACTURE; NEURAL NETWORKS; VIDEO CAMERAS; WEAR OF MATERIALS;

AUTOMATED ENVIRONMENTS; DNA-BASED COMPUTING; FINISHED PRODUCTS; MACHINING EFFICIENCY; MACHINING PARAMETERS; OPTICAL MONITORING; PRODUCTION PROCESS; TOOL WEAR;

CUTTING TOOLS;

EID: 84939779286     PISSN: 22128271     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procir.2015.06.040     Document Type: Conference Paper

References (16)

1. D'Addona, D.M., Teti, R. Image data processing via neural networks for tool wear prediction, Procedia CIRP, Vol. 12, 2013, 252-267.
2. Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., Koenig, W., Teti, R. Tool Condition Monitoring. (TCM)-The Status of Research and Industrial Application, Annals of the CIRP, vol. 44/2, 1995, pp. 541-567.
3. Fahlman, S.E., Lebiere, C. An Empirical Study of Learning Speed in Back Propagation Networks, CMU Technical Report, 1990, CMU-CS-88-162.
4. D'Addona, D., Segreto, T., Simeone, A., Teti, R. ANN Tool Wear Modelling in the Machining of Nickel Superalloy Industrial Products, CIRP Journal of Manufacturing Science and Technology, Special Section on Innovative and Cognitive Manufacturing Engineering, 2011, 4/1: 33-37.
5. Leone, C., D'Addona, D., Teti, R. Tool Wear Modelling through Regression Analysis and Intelligent Methods for Nickel Base Alloy Machining, CIRP Journal of Manufacturing Science and Technology, Elsevier, ISSN 1755-5817, 2011, 4/3: 327-331.
6. Ferretti, S., Caputo, D., Penza, M., D'Addona, D.M. Monitoring systems for zero defect manufacturing, Procedia CIRP, 2013, Vol. 12, 258-263.
7. D'Addona, D., Teti, R. Genetic Algorithm-based Optimization of Cutting Parameters in Turning Processes, Procedia CIRP, Vol. 7, 2013, ISSN 22128271: 323-328.
8. Pham, D. T., et al. Optimizing Neural Networks for identification of Wood Defects Using the Bees Algorithm, 2006.
9. Di Foggia, M., D'Addona, D.M., Identification of critical key parameters and their impact to zero-defect manufacturing in the investment casting process, Procedia CIRP, Vol. 12, 2013: 264-269.
10. Alberts B., Bray D., Lewis J., Raff M., Roberts K., Watson J.D. Molecular Biology of the Cell (3rd Edition). Garland Science: New York, 1994, pp. 223-290.
11. Ullah, A.M.M.S., Yano, A., Higuchi, M. Protein Synthesis Algorithm and New Metaphor for Selecting Optimal Tools, JSME International Journal, Series C, 40(3), 1997, pp. 540-546.
12. Ullah, A.M.M.S. A DNA-based computing method for solving control chart pattern recognition problems, CIRP Journal of Manufacturing Science and Technology, 3(4), 2010, pp. 293-303.
13. Crick, F. Central Dogma of Molecular Biology, Nature, 227(8), 1970, pp. 561-563.
14. Cornish-Bowden, A. Nomenclature for incompletely specified bases in nucleic acid sequences: recommendations 1984, Nucleic Acids Research, 13(9), (1985), 1984, 3021-3030.
15. Eldra P. Solomon, Linda, Berg R., Diana W. Martin. Biology, 8th Edition, International Student Edition. Thomson Brooks/Cole. ISBN 978-0495317142.
16. Ullah, A.M.M.S., D'Addona, D.M., Nobuyuki Arai. DNA based computing for understanding complex shapes, Biosystems, Volume 117, 2014 40-53.