Parameter estimation for abrasive water jet machining process using neural networks

International Journal of Advanced Manufacturing Technology

Volumn 40, Issue 5-6, 2009, Pages 497-502

  Parikh, Pratik J ^a   Lam, Sarah S ^b  

^a State University   (United States)

^b State University of New York at Binghamton   (United States)

Author keywords

Abrasive water jet machining process; Backpropagation network; Neural networks; Radial basis function network

Indexed keywords

ABRASIVES; ATTITUDE CONTROL; BACKPROPAGATION; ESTIMATION; FEEDFORWARD NEURAL NETWORKS; FUNCTIONS; IMAGE SEGMENTATION; JETS; LEARNING ALGORITHMS; MACHINING; MACHINING CENTERS; PARAMETER ESTIMATION; PROBABILITY DENSITY FUNCTION; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS;

ABRASIVE MASS FLOW RATES; ABRASIVE PARTICLES; ABRASIVE WATER JET MACHINING PROCESS; BACKPROPAGATION NETWORK; CRITICAL VARIABLES; EMPIRICAL MODELS; HIGH-VELOCITY JETS; MATERIAL COMBINATIONS; MATERIAL REMOVAL PROCESS; NON-LINEAR REGRESSIONS; OPERATING CONDITIONS; PROCESS PARAMETERS; PROCESS PERFORMANCE; PUMP PRESSURES; RADIAL BASIS FUNCTION NETWORK; TRAVERSE RATES;

NEURAL NETWORKS;

EID: 59749084814     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-007-1363-7     Document Type: Article

References (18)

1. Blickwedel H, Guo NS, Haferkamp H, Louis H (1991) Prediction of abrasive jet cutting performance and quality. In: Saunders D (ed) Proc 10th International Conference (Jet Cutting Technology). Elsevier, London, pp 163-179
2. Brown J (1998) Advanced machining technology handbook. McGraw-Hill, New York
3. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, New York
4. K Hornik M Stinchcombe H White 1989 Multilayer feedforward networks are universal approximators IEEE Trans Neural Netw 2 359 366
5. NK Jain VK Jain 2001 Modeling of material removal in mechanical type advanced machining processes: a state-of-art review Int J Mach Tools Manuf 41 1573 1635
6. JJR Jegaraj NR Babu 2005 A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter Int J Mach Tools Manuf 45 1443 1450
7. R Kovacevic M Fang 1994 Modeling of the influence of the abrasive waterjet cutting parameters on the depth of cut based on fuzzy rules Int J Mach Tools Manuf 3 1 55 72
8. E Lemma R Deam L Chen 2005 Maximum depth of cut and mechanics of erosion in AWJ oscillation cutting of ductile materials J Mater Process Technol 160 188 197
9. R Mohan R Kovacevic K Damarla 1994 Real-time monitoring of abrasive waterjet nozzle wear using neural networks technology The Society of Mechanical Engineers Transaction XXII 253 258
10. Momber AW, Kovacevic R (1998) Principles of abrasive water jet machining. Springer, New York
11. J Park IW Sandberg 1991 Universal approximation using radial basis function networks Neural Comput 3 246 257
12. Shaw MC (1996) Principles of abrasive processing. Oxford Science Publications, New York
13. Singh PJ (1995) Development of a window-based expert system for abrasive water jet cutting. Proc 8th American Water Jet Conference, Water Jet Technical Association (TJ Labus (ed.)) 2:717-726
14. CP Sitarama N Ramesh Babu 2000 A hybrid approach for selection of optimal process parameters in abrasive water jet cutting Proc Inst Mech Eng, B J Eng Manuf 214 B9 781 791
15. Summers DA (1995) Waterjetting technology. E & FN Spon (an imprint of Chapman & Hall), Oxford, Great Britain
16. Tsoukalas L, Uhrig R (1997) Fuzzy and neural approaches in engineering. Wiley, New York
17. JM Twomey AE Smith 1998 Bias and variance of validation methods for function approximation neural networks under conditions of sparse data IEEE Trans Syste Man Cybern 28 3 417 430
18. Xiaosong G, Popovic D, Schulz-Ekloff G (1995) The backpropagation neural network being capable of real-time identification. Proc 4th IEEE Conference on Control Applications, IEEE, New York, NY, pp 572-577