Engenharia Mecânica
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/23
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3 resultados
Resultados da Pesquisa
- Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes(2020-08-01) FARIAS, ADALTO DE; ALMEIDA, SÉRGIO LUIZ RABELO DE; Sergio Delijaicov; SERIACOPI, VANESSA; Ed Claudio BordinassiThe aim of this work was to identify the occurrence of machine tool wear in carbide inserts applied in a machine turning center with two steel materials. Through the data collected with an open-source communication protocol during machining, eighty trials of twenty runs each were performed using central composite design experiments, resulting in a data set of eighty lines for each tested material. The data set consisted of forty lines with the tool wear condition and forty lines without. Machining parameters were set to be in the range of the usual industrial values. The cutting parameters in the machining process were cutting speed, feed rate, cutting depth, and cutting fluid applied in the abundance condition and without cutting fluid (dry machining). The collected data were the spindle motor load, X-axis motor load, and Z-axis motor load in terms of the percentage used. AISI P20 and AISI 1045 steels workpieces were tested with both new and worn inserts, and a flank tool wear of 0.3 mm was artificially induced by machining with the same material before the data collecting experiment. Two approaches were used in order to analyze the data and create the machine learning process (MLP), in a prior analysis. The collected data set was tested without any previous treatment, with an optimal linear associative memory (OLAM) neural network, and the results showed 65% correct answers in predicting tool wear, considering 3/4 of the data set for training and 1/4 for validating. For the second approach, statistical data mining methods (DMM) and data-driven methods (DDM), known as a self-organizing deep learning method, were employed in order to increase the success ratio of the model. Both DMM and DDM applied along with the MLP OLAM neural network showed an increase in hitting the right answers to 93.8%. This model can be useful in machine monitoring using Industry 4.0 concepts, where one of the key challenges in machining components is finding the appropriate moment for a tool change.
- MQL Strategies Applied in Ti-6Al-4V Alloy Milling-Comparative Analysis between Experimental Design and Artificial Neural Networks(2020-08-30) PASCHOALINOTO, NELSON WILSON; BATALHA, GILMAR FERREIRA; Ed Claudio Bordinassi; FERRER, JORGE ANTONIO GILES; LIMA FILHO, ADERVAL FERREIRA DE; RIBEIRO, GLEICY DE L. X.; CARDOSO, CRISTIANOThis paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.
- The effect of process parameters and cutting tool shape on residual stress of SAE 52100 hard turned steel by high speed machining(2020-04-26) PASCHOALINOTO, N. W.; Ed Claudio Bordinassi; Roberto Bortolussi; Fabrizio Leonardi; Sergio DelijaicovThis study focused on determining the residual stress of SAE 52100 hard-turned steel. The objective was to evaluate and compare the effects of the cutting-edge geometry and cutting parameters (cutting speed, feed rate, and cutting depth) on the residual stresses of three different conventional inserts: S-WNGA08 0408S01020A 7025, T-WNGA08 0408T01020A 7025, and S-WNGA432S0330A 7025. Tests were performed on 60 samples of SAE 52100 hardened steel with an average hardness of 58.5 HRC. The circumferential residual stresses of the samples were measured by X-ray diffraction. A full factorial design of experiments with three factors and two levels each with two central points and a replicate was used for a statistical analysis. The most significant results were as follows: For all inserts, the measured residual stresses were compressive, which extended the tool lifespan. The residual stresses of the Type-S inserts were significantly influenced by the cutting speed and depth, and those of the Type-T insert were significantly influenced by the feed rate and cutting depth. In addition, the residual stresses of the insert 3 were more compressive than those of the other two types of inserts. In other words, residual stresses are more compressive for inserts with larger chamfer angles even as the principal residual stress profiles were all compressive. This work has also shown that it is possible to determine a significant statistical relationship between cutting forces and residual stresses, allowing force measurements to predict the residual stress without any information on process parameters.