Search

This paper is focused on the analysis and optimization of kerf width obtained in fiber laser cutting of 3mm thick S235 steel plate using oxygen as assist gas. Focus position, cutting speed and assist gas pressure were considered as important input parameters, and were systematically varied at 3 levels during experimentation, in accordance with Box-Behnken experimental design. A second order non-linear mathematical model for the prediction of kerf width was developed and analyzed with respect to three selected parameters. The unknown coefficients of the mathematical model were found by the method of least squares. The conducted analysis showed dominant effects of focus position and cutting speed with positive and negative correlation with kerf width, respectively. Focus position of 1 mm, cutting speed of 3.6 m/min and assist gas pressure of 0.75 bar should be used in order to achieve the smallest value of kerf width. To determine laser cutting parameters to obtain target kerf width value of 0.4 mm, and at the same time maximize material removal rate, laser cutting optimization model was defined and solved using genetic algorithm. It is shown that the combination of fiber laser cutting parameters: focus position f=1.4mm, cutting speed v=3.6m/min and assist gas pressure p=0.89 bar ensures maximum productivity of 4320 mm3/min, while ensuring the targeted kerf width value of 0.4mm. [ABSTRACT FROM AUTHOR]

Passivation is a chemical process in which the electrochemical condition of passivity is gained on the surface of metal alloys. Biomedical AISI 316LVM stainless steel (SS) can be passivized by means of nitric acid immersion in order to improve a protective oxide layer on the surface and consequently increase corrosion resistance of the SS in the physiological solutions. In this study, multiple regression analysis and artificial neural network (ANN) were employed for mathematical modeling of the AISI 316LVM SS passivation process after immersion in the nitric acid solution. The pitting potential, which represents the mea-sure of pitting corrosion resistance, was chosen as the response, while the passivation parameters were nitric acid concentration, temperature and passivation time. The comparison between experimental results and models predictions showed that only the ANN model provided statistically accurate predictions with a high coefficient of determination and a low mean relative error. Finally, based on the derived ANN equation, the effects of the passivation parameters on pitting potential were examined. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. ON174004 and Grant no. TR35034]

Modeling and optimization is an important aspect of machining processes. Optimization of cutting forces is critically important in turning operations because cutting forces are directly related to several machining performance characteristics. This paper focuses on modeless optimization approach for minimization of cutting force in polyamide turning process. The influence of four cutting parameters, such as cutting speed, feed rate, depth of cut and tool nose radius on main cutting force was analyzed on the basis of the standard L27 Taguchi's orthogonal array. The collected experimental results were analyzed with the help of analysis of means and analysis of variance. The near optimal turning parameters settings were determined considering Taguchi's robust design methodology.

Dimensional analysis is a tool used to understand and analyze various problems in engineering. This analysis is useful for calculating dimensionless parameters and gives an answer as to which group of parameters affects the problem. Dimensional analysis can be represented using Buckingham's p-theorem. This paper provides an overview of the literature on the application of dimensional analysis and Buckingham's p theorem for various processing processes. The review period is observed in the period from 2007 until today. The main focus remains of the production process, performance and parameters of process, dimension and years of publication. Review work in this area aims to provide ready-made information in one place that can be very useful to future researchers in this direction of research. [ABSTRACT FROM AUTHOR]

This paper presents Taguchi optimization methodology for identification of the robust conditions for minimization of the heat affected zone (HAZ) and burr height in CO2 laser cutting of AISI 304 stainless steel. The laser cutting experiment was designed and carried out on the basis of standard L27 Taguchi’s orthogonal array in which four laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position were arranged at three levels. From the analysis of means, analysis of variance and two-way interaction plots, the significant laser cutting parameters and the optimal combination of levels were identified. The results showed that the focus position is the most significant parameter affecting the HAZ and burr height, whereas the influence of the assist gas pressure can be neglected. The predicted responses at optimal laser cutting conditions were found to be in good agreement with the results from confirmation experiment.

In recent years, there is increasing interest in using artificial neural networks (ANNs) for modeling and optimization of machining processes. The advantages that ANNs offer are numerous and are achievable only by developing an ANN model of high performance. However, determining suitable training and architectural parameters of an ANN still remains a difficult task. These parameters are typically determined in trial and error procedure, where a large number of ANN models are developed and compared to one another. This paper presents the application of Taguchi method for the optimization of ANN model trained by Levenberg-Marquardt algorithm. A case study of a modeling resultant cutting force in turning process is used to demonstrate implementation of the approach. The ANN training and architectural parameters were arranged in L18 orthogonal array and the predictive performance of the ANN model is evaluated using the proposed equation. Using the analysis of variance (ANOVA) and analysis of means (ANOM) optimal ANN parameter levels are identified. Taguchi optimized ANN model has been developed and has shown high prediction accuracy. Analyses and experiments have shown that the optimal ANN training and architectural parameters can be determined in a systematic way, thereby avoiding the lengthy trial and error procedure.

This case study deals with modeling of plasma cutting process using artificial neural network (ANN), with the aim of simulation the impact of noise on its performance. The input parameters used in this study were reduced to three cutting parameters which consisted of strength of current (I), cutting speed (V), and material thickness (s). The ten-point height of irregularities (Rz), which is one of the basic characteristics of the surface quality, was adopted as the output parameter (response). The data for this research were gathered from literature. A feed-forward three-layer ANN was created, with backpropagation and algorithm for supervised learning. For the hidden layer neurons sigmoidal type of non-linearity was selected, while a linear activation function was selected for the output layer. ANN training was carried out using Levenberg-Marquardt algorithm with Bayesian regularization. The trained and tested ANN on the original data set showed a satisfactory level of prediction accuracy. In order to simulate an experiment with noise, measured values of the surface roughness were corrected. The correction was performed by adding randomly selected numbers to each measured value, within the range - 0.1 to + 0.1 μm. With the previously selected architecture and the same other parameters, retraining of ANN was carried out. The analysis showed that the ANN model trained on the data with noise has similar performance as the ANN model trained on the original data, which indicates the robustness of this type of ANN.

The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots. [ABSTRACT FROM AUTHOR]

For an efficient use of laser cutting technology, it is of great importance to analyze the impact of process parameters on different performance indicators, such as cut quality criteria, productivity criteria, costs as well as environmental performance criteria (energy and resource efficiency). Having this in mind, this study presents the experimental results of CO2 laser fusion cutting of AISI 304 stainless steel using nitrogen, with the aim of developing a semi-empirical mathematical model for the estimation of process efficiency as an important indicator of the achievable energy transfer efficiency in the cutting process. The model was developed by relating the theoretical power needed to melt the volume per unit time and used laser power, where the change of kerf width was modeled using an empirical power model in terms of laser cutting parameters such as laser power, cutting speed, and focus position. The obtained results indicated the dominant effect of the focus position on the change in process efficiency, followed by the cutting speed and laser power. In addition, in order to maximize process efficiency and simultaneously ensure high cut quality without dross formation, a laser cutting optimization problem with constraints was formulated and solved. Also, a multi-objective optimization problem aimed at simultaneous optimization of process efficiency and material removal rate was formulated and solved, where the determined set of Pareto non-dominated solutions was analyzed by using the entropy method and multi-criteria decision analysis method, that is, the Technique for Order of Preference by Similarity to Ideal Solution. The optimization results revealed that in order to enhance process efficiency and material removal rate, while ensuring high cut quality without dross formation, focusing the laser beam deep into the bulk of material is needed with particular trade-offs between laser power and cutting speed levels at high pressure levels of nitrogen. [ABSTRACT FROM AUTHOR]

Taking full advantage of what laser cutting technology offers in terms of achieving superb quality cuts at low cost and high production rates requires the optimization of laser cutting parameters. This implies the need to formulate and solve different laser cutting optimization problems. In this article, an optimization model for CO2 laser cutting of mild steel is developed. The laser cutting optimization problem was explicitly formulated as a single-objective optimization problem with five non-linear constraints of the equality, inequality and range type. The goal was to determine the laser cutting parameter values so as to maximize the material removal rate while simultaneously considering practical process constraints related to dross formation, kerf width, perpendicularity deviation, surface roughness and severance energy. Two crossed experimental designs of different resolutions were performed in order to define six mathematical models, which were used in the formulation of the optimization problem. For the purpose of optimization, the exhaustive iterative search algorithm was applied, since it determines solutions whose optimality is guaranteed in the given discrete space of input variable values. The practical usability of the developed laser cutting optimization model and the effectiveness of the applied optimization approach were proved while solving a real case study aimed at the optimization of laser cutting parameters for cutting parts for the furnace industry. [ABSTRACT FROM AUTHOR]

Surface roughness is one of the main indicators of quality assessment of finished parts processed by laser cutting. This paper presents the experimental results regarding surface roughness of laser cuts in high-power CO2 laser cutting of AA5754 aluminum alloy by using nitrogen as the assist gas. Based on carrying out the full factorial design experiment, the collected data were used for the development of an artificial neural network prediction model of surface roughness in terms of laser cutting parameters such as cutting speed, laser power and assist gas pressure. In addition to the modeling and analysis of the interdependencies between the considered process inputs and surface roughness, this paper presents results regarding single- and multi-objective optimization, determined by the use of a genetic algorithm, by considering the surface roughness as the main criterion, as well as other criteria such as kerf width, assist gas consumption and material removal rate. [ABSTRACT FROM AUTHOR]