5 research outputs found
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