This paper presents the use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the optimum process parameters in plasma arc cutting of stainless steel. Two input process parameters, cutting speed and plasma gas pressure are considered and experiments are conducted based on Taguchi L9 orthogonal array. After performing the experiments, the surface roughness, cut perpendicularity and kerf width are measured. The analysis of variance (ANOVA) are performed in order to identify the effect of each input process parameters on the output responses. The results indicate that TOPSIS method is appropriate for solving multi-criteria optimization of process parameters. Results also showed that cutting speed of 2500 mm/min and plasma gas pressure of 6 bar are the optimum combination of process parameters.

Original scientific paper The paper presents analysis of the influence of cutting parameters on surface roughness during CO2 laser cutting process of tungsten alloy by using nitrogen as assist gas, based on control charts made by statistical process control (SPC) approach. Dependent variable is surface roughness, while independent variables are laser power and cutting speed. The control chart used within this paper is a variation of the moving means chart of experimental data samples, that calculates mean and range values using the three consecutive individual values. Applying the criteria often used in the SPC methods for the assessment of "out of control" situations, it may be inferred that increasing the cutting speed leads to worsening of control status for the process with lower laser power used.

The surface roughness of the end product is a very important indicator of laser cutting quality. The paper reports a comparison of surface roughness during CO2 laser cutting of tungsten alloy plate using oxygen as assist gas, based on control charts made by statistical process control (SPC) approach. Dependent variable is surface roughness, while independent variables are laser power and cutting speed. The control chart used within this paper is a variation of the ichart of experimental data samples, where using evaluation of moving range of the two consecutive values, in order to estimate value of standard error by average moving range and Hartley's constant d2. Applying the criteria often used in the SPC methods for the assessment of "out of control" situations, it may be inferred that the observed differences in surface roughness during CO2 laser cutting could be used to advice on the more appropriate laser power and cutting speed for the laser cutting quality. Keyword: laser cutting process; statistical process control; control chart; surface roughness; tungsten alloy This Publication has to be referred as: Begic-Hajdarevic, D[erzija]; Pasic, M[ugdim]; Vucijak, B[ranko] & Cekic, A[hmet] (2016). Statistical Process Control of Surface Roughness during CO2 Laser Cutting using Oxygen as Assist Gas, Proceedings of the 26th DAAAM International Symposium, pp.0247-0255, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-07-5, ISSN 1726-9679, Vienna, Austria DOI:10.2507/26th.daaam.proceedings.034

The cut quality is of great importance during the laser cutting process. The quality of laser cut mainly depends on an appropriate selection of process parameters. In this paper, the effect of process parameters was analysed on the laser cut quality of an uncommon alloy, the tungsten alloy (W ≈ 92.5 % and the remainder Fe and Ni) sheet with thickness of 1 mm. This alloy has a wide application in different industrial areas, e.g. in medical applications, the automobile sectors, and the aircraft industry. This paper introduces a developed back-propagation artificial neural network (BP- ANN) model for the analysis and prediction of cut quality during the CO2 laser cutting process. In the presented study, three input process parameters were considered such as laser power, cutting speed and assist gas type, and two output parameters such as kerf width and average surface roughness. Amongst the 42 experimental results, 34 data sets were chosen for training the network, whilst the remaining 8 results were used as test data. The average prediction error was found to be 5.5 % for kerf width and 9.5 % for surface roughness. The results of the predicted kerf width and surface roughness by the BP-ANN model were compared with experimental data. Based on the results of the study, it was shown that the proposed artificial neural network model could be a useful tool for analysing and predicting surface roughness and kerf width during CO2 laser cutting processes.

The abrasive water jet (AWJ) cutting technique is one of the most rapidly improving technological methods of cutting materials. In this paper a mathematical model for analysis and prediction of surface roughness during AWJ cutting of aluminium plate is developed based on experimental observations. Dependent variable in the model is average surface roughness, while independent variables are depth of cut in cutting process, traverse speed and abrasive mass flow rate. To evaluate results, analysis of variance (ANOVA) method is performed. Obtained regression model results suggest that regression modelling can be useful tool for analysing surface roughness in abrasive water jet cutting process.

This paper defines mathematical models of value changes for surface roughness (Ra, μm) and heat affected zone width (HAZ, mm) during high-alloyed steel 1.4828 laser cutting using oxygen as an assistance gas. For the definition of appropriate mathematical models, multiple linear regression analysis is used, with four independent variables that were varied at five levels. Following parameters are varied: cutting speed (V), assist gas pressure (p), focus position (fs) and stand-off (Nd). In comparison between the model and the experimental results, it can be concluded that the effects of specified parameters on cut quality, productivity and thus the legitimacy of this technology for cutting high-alloyed steels are well described by the obtained mathematical models. © 2015 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of DAAAM International Vienna.