Analysis of the heat affected zone in CO2 laser cutting of stainless steel

This paper presents an investigation into the effect of the laser cutting parameters on the heat affected zone in CO2 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 L27 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

Analysis and modeling of the effects of process parameters on specific cutting energy in abrasive water jet cutting

The problem of cutting difficult-to-machine materials used in the aerospace industry, aircraft industry, and automobile industry, led to the development and application one of today’s most attractive technology for contour cutting - abrasive water jet cutting. For the efficient use of abrasive water jet cutting, it is of great importance to analyze the impact of process parameters on performance indicators, such as cutting quality, productivity, and costs. But also, from the energy utilization point of view, it is very important to analyze the impact of these parameters on the specific cutting energy which represents the amount of energy spent on the removal of material in the unit time. Having this in mind, this study presents the experimental results of abrasive water jet cutting of aluminum alloy with the aim of creating a mathematical model for estimating specific cutting energy as an important indicator of the degree of utilization of the available energy in the cutting process. The mathematical model of the specific cutting energy is explicitly represented as a non-linear function of the process parameters, obtained by the artificial neural network. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. TR 35034

Multiple criteria decision making of alternative fuels for waste collection vehicles in southeast region of Serbia

In this paper multiple criteria decision making approach of alternative fuels for waste collection vehicles in southeast region of Serbia was presented. Eight alternative fuels and advanced vehicle technologies were ranked according to thirteen criteria, including financial, socio-technical, and environmental. Assessment of alternatives was performed by using the weighted aggregated sum product assessment method and results were verified using multi-objective optimization on the basis of ratio analysis method. Considered criteria were obtained from previous researches and by assessment of professional experts from manufacturing industries, public utility companies, and academics institutions. The analysis showed that both biodiesel fuels - derived from used cooking oil or from vegetable oils are the best alternative fuels for Serbian waste collection vehicles in this point of time. Compressed natural gas-powered vehicles were also ranked high in this analysis, but due to the lack of financial capability for their purchase (especially in southeast region of Serbia), their gradual introduction into the waste collection fleet was proposed

Modeling of cutting temperature in the biomedical stainless steel turning process

Stainless steel is widely used as material in many industries and medicine. As biomedical material, it has been used for making devices, implants as well as tools and equipment in surgery and dentistry. The most of them is processed by turning. Modeling of temperature in the metal cutting process is very important step in understanding and analysis of the metal cutting process. The objective of this study is to develop an artificial neural network model which can be used successfully for accurate prediction of cutting temperature while performing turning of the biomedical stainless steel. Before the modeling, cutting temperature was measured, as one of the significant parameters in turning process, by using the infrared thermal imaging camera. Finally, based on the mathematical model, the effects of the turning parameters on the cutting temperature were examined