Cutting temperature is very important parameter of cutting process. Around 90% of heat generated during cutting process is then away by sawdust, and the rest is transferred to the tool and workpiece. In this research cutting temperature was measured with artificial thermocouples and question of investigation of metal machinability from aspect of cutting temperature was analyzed. For investigation of material machinability during turning artificial thermocouple was placed just below the cutting top of insert, and for drilling thermocouples were placed through screw holes on the face surface. In this way was obtained simple, reliable, economic and accurate method for investigation of cutting machinability.
Dross free laser cutting is very important in the application of laser cutting technology. This paper focuses on the development of a fuzzy logic model to predict dross formation in CO2 laser oxygen cutting of mild steel. Laser cutting experiment, conducted according to Taguchi's experimental design using L25 orthogonal array, provided a set of data for the development of a fuzzy rule base. The predicting fuzzy logic model is based on using Mamdani-type inference system. Developed fuzzy logic model considered the cutting speed, laser power and assist gas pressure as inputs. Using this model the effects of the selected laser cutting parameters on the dross formation were investigated. Additionally, 3-D surface plots were generated to study the interaction effects of the laser cutting parameters. The analysis revealed that the cutting speed has the most significant effect, followed by laser power and assist gas pressure. The results indicated that the fuzzy logic modeling approach can be effectively used for the dross formation prediction in CO2 laser cutting of mild steel.
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. Keywords: analytic hierarchy process; artificial neural networks; CO2 laser cutting; cut quality characteristics; genetic algorithm; multi-objective optimization Višekriterijska optimizacija karakteristika kvalitete reza kod CO2 laserskog rezanja nehrđajućeg čelikaIzvorni znanstveni članak U ovom je radu predstavljena metodologija višekriterijske optimizacije karakteristika kvalitete reza kod CO2 laserskog rezanja AISI 304 nehrđajućeg čelika (korozijski postojanog čelika). Za predviđanje karakteristika kvalitete reza kao što su hrapavost površine reza, širina reza i zona utjecaja topline, kreirani su matematički modeli pomoću umjetnih neuronskih mreža. Eksperiment laserskog rezanja je planiran i izveden prema Taguchijevom L27 ortogonalnom nizu, a eksperimentalni podaci su korišteni za treniranje umjetnih neuronskih mreža pomoću Levenberg-Marquardtovog algoritma. Matematički modeli umjetnih neuronskih mreža su kreirani uzimajući u obzir snagu lasera, brzinu rezanja, tlak pomoćnog plina i položaj fokusa kao ulazne parametre. Problem
During the past few decades, special attention has been devoted to developing modern instruments and methods of monitoring the tribomechanical characteristics of technical systems. Today, various physical, chemical and tribological methods are used in tribomechanical systems diagnosis. Scientific experience in technical system exploitation and maintenance has shown that the most effective way to predict failure is based on parameters that are reliable indicators of wear. Analysis of oil samples, which contain particles due to the wear process, enables an evaluation of the tribology condition of the system in the early phases of its use. This paper deals with tribological tests that are part of the oil analysis and are used to access the condition of the system. Furthermore, the results of experimental research on the tribological characteristics of the oil sampled from engines and gear transmissions of the vehicles (Mercedes O 345, PUCH 300GD and PINZGAUER 710M) are shown. All of these road vehicle were in regular use by the Serbian armed forces. The performed research has revealed some significant changes in the tribological characteristics of oil for engine and gear transmission lubrication.. These changes directly depend on the condition of the entire engine and transmission elements, i.e. depend on their functional characteristics. The presented method of oil analysis should contribute to an early detection of failures due to friction and wear processes in vehicle engines and reduce the need for preventive maintenance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.