Engenharia Mecânica
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/23
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2 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.
- Microhardness and residual stress of dissimilar and thick aluminum plates AA7181-T7651 and AA7475-T7351 using bobbin, top, bottom, and double-sided FSW methods(2020-05-12) Sergio Delijaicov; RODRIGUES, M.; FARIAS, A.; NEVES, M. D.; Roberto Bortolussi; MIYAZAKI, M.; BRANDÃO, F.Friction stir welding (FSW) is a relatively new manufacturing process (invented in 1991 at the Welding Institute, UK) and more than 5000 scientific articles have been published in the past 10 years in indexed journals demonstrating the robustness of the research. However, further research is necessary to ensure safe use of the technique for structural components, particularly with reference to the aeronautics industry. Residual stresses and their consequences on the life of welded products must be fully understood, in addition to their correlations with other properties such as hardness, strength, and microstructure. This paper is a part of the research being conducted to evaluate the impact of four FSW techniques (bobbin, top-sided, bottom-sided, and doublesided) on the mechanical properties in dissimilar thick joints (12.7 mm) of aluminum alloys (AA7181-T7651 and AA7475- T7351) used in the aeronautics industry. The residual stresses were measured using the incremental blind hole technique and analyzed using the integral method. The longitudinal residual stresses were all positive, with values between 100 and 200 MPa in the stir zone, whereas the transverse ones were all negative, with values between 0 and − 100 MPa. It was possible to verify that the Bobbin process produced lower values of residual stresses and demonstrated better stability in its distribution compared to all the other FSW methods tested.