Effect of process parameters on the cutting quality in lasox cutting of mild steel

Sundar, M.; Nath, Ashish Kumar; Bandyopadhyay, Debasish; Chaudhuri, Sheli Sinha; Dey, P. K.; Misra, Dipten

doi:10.1007/s00170-008-1413-9

Cited by 26 publications

(11 citation statements)

References 9 publications

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“…LASOX cutting is found to cut a mild steel plate of thickness 50 mm with laser power output of only 2 kW [1]. In a recent study, Sundar et al [5] has investigated the influence of process parameters on cut geometry for LASOX cutting of 40 mm thick mild steel plate with 1 kW laser. But no study has been observed where soft computing techniques like artificial neural networks (ANN), genetic algorithm (GA), simulated annealing (SA) etc.…”

Section: Work Piecementioning

confidence: 98%

“…The experimental data for the present work has been obtained from the experiments carried out on LASOX cutting operation of 40 mm thick mild steel plate using an indigenous CO 2 laser machine at School of Laser Science and Engineering, Jadavpur University [5]. The independent operating variables for the experiment conducted are cutting speed (V, mm/min), gas pressure (PR, bar), laser power (P, W) and stand off distance (F, mm) respectively.…”

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

“…Average HAZ widths of top and bottom surfaces are measured by optical microscope (least count 0.001 mm) and the average kerf width of top and bottom surfaces is measured by low resolution microscope fitted with x-y table. The available 25 experimental datasets [5] is used in the present work for validation of SA-ANN hybrid model. Out of these 25 datasets, 20 sets have been considered for ANN training and the rest are used for ANN testing.…”

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

“…Range of the process parameters in experiment have been selected through a series of trial runs and the ranges are given in Table 1. Experiments have been conducted [5] in random order to avoid any systematic error. Resulting dataset contains 25 non-replicated experimental data.…”

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

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Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Chaki¹,

Ghosal

2011

Prod. Eng. Res. Devel.

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Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset.

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Section: Work Piecementioning

confidence: 98%

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

Section: Experimental Dataset For Present Studymentioning

confidence: 99%

See 2 more Smart Citations

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Chaki¹,

Ghosal

2011

Prod. Eng. Res. Devel.

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show abstract

“…LASOX is regarded more sensitive to nozzle structure and cutting parameters compared with conventional laser cutting methods. Sundar [9] developed a regression analysis model to describe the effect of the independent processing parameters on cut quality for LASOX. Artificial neural network was also used to optimize cutting parameters for LASOX [10].…”

Section: Introductionmentioning

confidence: 99%