Artificial neural network based on genetic learning for machining of polyetheretherketone composite materials

International Journal of Advanced Manufacturing Technology

Volumn 39, Issue 11-12, 2008, Pages 1101-1110

  António, C A Conceição ^a   Davim, J Paulo ^b   Lapa, Vítor ^b  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

ANN simulation; Genetic algorithm; Machining; Polymer matrix composites; Process monitoring

Indexed keywords

ABS RESINS; ALGORITHMS; BACKPROPAGATION; CARBON FIBER REINFORCED PLASTICS; COMPOSITE MATERIALS; COMPOSITE MICROMECHANICS; EDUCATION; EXPERIMENTS; FUNCTION EVALUATION; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; MACHINERY; MACHINING; METALLIC MATRIX COMPOSITES; NEURAL NETWORKS; POLYMER MATRIX COMPOSITES; PROCESS MONITORING; STATISTICAL TESTS;

ANN SIMULATION; ARTIFICIAL NEURAL NETWORKS; DESIGN VARIABLES; EXPERIMENTAL DATUMS; FITNESS FUNCTIONS; GENETIC LEARNINGS; HIDDEN LAYERS; LEARNING PROCESSES; MACHINING CONDITIONS; MACHINING PROCESSES; MEAN RELATIVE ERRORS; NUMERICAL RESULTS; OPTIMAL NUMBERS; ORTHOGONAL CUTTINGS; OUTPUT LAYERS; OUTPUT NEURONS; POLYETHERETHERKETONE COMPOSITES; REGULARIZATION TERMS; TESTING PHASES;

PROCESS ENGINEERING;

EID: 55649088977     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-007-1304-5     Document Type: Article

References (16)

1. Q Liu Y Altintas 1999 On-line monitoring of flank wear in turning with multi-layered feed-forward neural network Int J Mach Tool Manuf 39 1945 1959
2. SC Lin CJ Ting 1996 Drill wear monitoring using neural network Int J Mach Tool Manuf 36 465 475
3. S Das R Roy AB Chottopadhyay 1996 Evaluation of wear of turning carbide inserts using neuronal network Int J Mach Tool Manuf 36 789 797
4. SK Choudhury VK Jain CVV Rama Rao 1999 On-line monitoring of tool wear in turning using neural network Int J Mach Tool Manuf 39 489 504
5. J Briceno H El-Mounayri S Mukhopadhyay 2002 Selecting an artificial neuronal network for efficient modeling and accurate simulation of the milling process Int J Mach Tool Manuf 42 663 674
6. H Oktem T Erzurumlu F Erzincanli 2006 Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm Mater Design 27 735 744
7. YH Tsai JC Chen SJ Lou 1999 An in-process surface recognition system based on neural networks in end milling cutting operations Int J Mach Tool Manuf 39 583 605
8. PVS Suresh PV Rao SG Deshmukh 2002 A genetic algorithmic approach for optimization of surface roughness prediction model Int J Mach Tool Manuf 42 675 680
9. A Zhecheva S Malinov W Sha 2005 Simulation of microhardness profiles of titanium alloys after surface nitriding using artificial neural network Surface Coat Technol 200 2332 2342
10. N Altinkok R Koker 2006 Modelling of the prediction of tensile and density properties in particle reinforced metal matrix composites by using neural networks Mater Design 27 625 631
11. Childs TH, Maekawa K, Obikawa T, Yamane Y (2000) Metal machining: theory and application. Edward Arnold, London, pp 35-80
12. SL Mok CK Kwong WS Lau 2001 A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection molding Adv Manuf Technol 18 404 409
13. OB Nakhjavani M Ghoreishi 2006 Multi criteria optimization of laser percussion drilling process using artificial neural network model combined with genetic algorithm Mater Manuf Process 21 11 18
14. CC António CF Castro LC Sousa 2004 Optimisation of metal forming processes Comput Struct 82 1425 1433
15. Spears WM, DeJong SK (1991) On the virtues of parametrized uniform crossover. In: Belew RK, Booker LB (eds) Proceedings of Fourth International Conference on Genetic Algorithms. Morgan Kaufmann, pp 230-236
16. CA Conceição António J Paulo Davim 2002 Optimal cutting conditions in turning of particulate metal matrix composites based on experiment and a genetic search model Compos Part A Appl Sci Manuf 33 2 213 219