Investigating the Effect of Cutting Speed on Heat Affected Zone in Laser Cutting Process of Stainless Steel (original) (raw)
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Optimization of laser cutting parameters on stainless steel to achieve minimum surface damage
Stainless steels have a wide range of applications in various industries due to its high corrosion resistance, desirable formability and high strength in elevated temperatures. Also, laser cutting is a non-contact method that is of interest due to the high precision, small kerf, high cutting speed and the ability to cut complicated shapes that are hard to cut by mechanical processes. In order to use this method, the process parameters shall be thoroughly selected. In this study, three main parameters: laser power, cutting speed and focal position were selected. Considering the literature review and the effects were investigated on the heat affected zone (HAZ) width. Taguchi orthogonal array L16 was employed with levels of variation of input parameters. Cutting was performed using 00 watt Nd:A laser with argon shielding gas, on stainless steel 316. HAZ width and micro-hardness were measured for all samples and the results were analyzed by ANOVA method to find the optimal parameters. In order to verify the results, 3 etra eperiments were conducted using optimal parameters. Moreover, a mathematical model was obtained to predict the HAZ width. Comparison of the results from the model and eperimental data showed that the method can predict the HAZ width with insignificant error.
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This paper presents an investigation into the effect of the laser cutting parameters on the heat affected zone in CO 2 laser cutting of AISI 304 stainless steel. The mathematical model for the heat affected zone was expressed as a function of the laser cutting parameters such as the laser power, cutting speed, assist gas pressure, and focus position using the artificial neural network. To obtain experimental database for the artificial neural network training, laser cutting experiment was planned as per Taguchi's L 27 orthogonal array with three levels for each of the cutting parameter. Using the 27 experimental data sets, the artificial neural network was trained with gradient descent with momentum algorithm and the average absolute percentage error was 2.33%. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 6.46%. Statistically assessed as adequate, the artificial neural network model was then used to investigate the effect of the laser cutting parameters on the heat affected zone. To analyze the main and interaction effect of the laser cutting parameters on the heat affected zone, 2-D and 3-D plots were generated. The analysis revealed that the cutting speed had maximum influence on the heat affected zone followed by the laser power, focus position and assist gas pressure. Finally, using the Monte Carlo method the optimal laser cutting parameter values that minimize the heat affected zone were identified.
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OPTIMISATION OF SHEET METAL CUTTING PARAMETERS OF LASER BEAM MACHINE
This paper is about optimization of sheet metal cutting parameter of Laser Beam Machining (LBM) process parameters on surface roughness while machining Stainless Steel (SS 304). Laser Beam Machining (LBM) is a non conventional process in which material removal takes place through melting and vaporization of metal when the laser beam comes in contact with the metal surface. There are so many process parameters which affect the quality of machined surface cut by LBM. But, the laser power, cutting speed, assist gas pressure, nozzle distance, focal length, pulse frequency and pulse width are most important. However, the important performance measures in LBM are Surface Roughness (SR), Material Removal Rate (MRR), kerf width and Heat Affected Zone (HAZ). Experiments are carried out using L9 Orthogonal array by varying laser power, cutting speed and assist gas pressure for stainless steel SS 304 material.
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Samples of stainless steel X5CrNi*18*10 (1.4301) with thickness 3 mm were cut on a CO2 laser cutting system Mazak Super Turbo-X 48 Mk2 1800 W using N2 as assisting gas and the combined effects of the gas pressure, power, frequency, efficiency and feed rate on surface roughness have been studied. The study was conducted in an operational manufacturing environment and was based on the design of a 2 full factorial experiment. It was observed that feed rate had a major effect on roughness variation. Using DOE a complex relationship was find to show roughness Rz variation according to these parameters. The results show ability DOE to optimize laser cutting processes. Key-Words: optimize laser cutting process, stainless steel X5CrNi*18*10, DOE-design of experiment
Experimental Studies on Austenitic Stainless Steel Using Co 2 Laser Cutting Machine
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Laser machining operation is a thermal, separation process, well suitable for several engineering industrial applications. High cutting speed, superior cut quality and low machining costs made laser cutting to become competitive to existing methods of contour cutting. Austenitic stainless steel is a significant engineering metal and it is complex to cut by oxy–fuel formed oxides and high melting point. So, austenitic stainless steel is mostly appropriate to be cut by laser. The cutting process parameters are highly affects the laser cut quality. In this research 1.9 mm austenitic stainless steel is cut with co2 laser. Laser power, cutting speed, gas pressure and focal distance are to be varied. The goal of this research is to narrate these conditions to formations of burr and surface roughness of cut edge. These relationships are engendered and approved with a mathematical model, which is used to forecast and reduce burr height and minimizing the cut edge surface roughness. © 2015 E...
The Effect of Selected Technological Parameters of Laser Cutting on the Cut Surface Roughness
Tehnicki vjesnik - Technical Gazette, 2018
The theoretical part of the paper presents some basics of the laser cutting technology and principles. The characteristics of the CO2 laser beam and the parameters entering the laser cutting process are described. The conventionally used material-S235JR steel, method of its laser cutting and the effect of process technological parameters on the cut area characteristics are presented as well. Experimental investigations were performed on samples made of the S235JR steel, with application of different laser cutting technological parameters, while observing the surface roughness Rz and Ra. Roughness results are displayed as images of the scanned cutting surface geometry and graphically for each sample, with comparison between the individual test samples. Effects of the position of the laser beam focus point, the cutting speed and assist gas pressure on the cut surface roughness are analyzed. It was concluded that the laser cutting parameters impose significant influence on the cut surface quality.
International Journal of ADVANCED AND APPLIED SCIENCES, 2017
High power CO2 laser cutting of 6 mm thick C45 steel sheets is investigated with the aim of evaluating the effect of the various laser cutting parameters such as laser power and cutting speed, on the laser cutting quality. In this study, cutting quality was evaluated by measuring the thickness of the Heat Affected Zone (HAZ), the microhardness beneath the cut surface (HV) and the cut section roughness. A simple and practical model was proposed to predict the thickness of the HAZ and the microhardness as a function of two, namely parameters; laser power and cutting speed. The adequacy of the proposed models was tested by analysis of variance (ANOVA). The Experimental data were compared with modelling data to verify the capability of the proposed model. The results indicate that laser power and cutting speed are determinant cutting-parameters on the HAZ thickness and microhardness beneath the cut section.
Multi-objective optimization of cut quality characteristic in CO2 laser cutting stainless steel
Tehnicki vjesnik-Technical Gazette, 2015
Original scientific paper In this paper, multi-objective optimization of the cut quality characteristics in CO2 laser cutting of AISI 304 stainless steel was discussed. Three mathematical models for the prediction of cut quality characteristics such as surface roughness, kerf width and heat affected zone were developed using the artificial neural networks (ANNs). The laser cutting experiment was planned and conducted according to the Taguchi's L27 orthogonal array and the experimental data were used to train single hidden layer ANNs using the Levenberg-Marquardt algorithm. The ANN mathematical models were developed considering laser power, cutting speed, assist gas pressure, and focus position as the input parameters. Multi-objective optimization problem was formulated using the weighting sum method in which the weighting factors that are used to combine cut quality characteristics into the single objective function were determined using the analytic hierarchy process method.