Filtreler
A Novel, Nelder-Mead Optimization Approach, based on Neuro-regression modeling for the Energy Efficiency Parameters of End Milling Process

ŞEYDA İŞBİLİR

Makale | 2021 | Journal of Artificial Intelligence and Data Science1 ( 1 ) , pp.96 - 105

Global crises are increasing day by day due to the rapid depletion of energy supplies around the planet. One of the goals of engineering is to prevent this situation by developing innovative solutions to this rapid energy consumption that has disappeared in the world. A solution could be to reduce the energy consumption of the machines that are used during production. In this study, a new design technique based on the neuro-regression approach and non-linear regression modeling was offered as an alternative to Taguchi design to reduce energy consumption. Thus, a cutting parameter optimization model was created to examine the effects . . . of the constraint conditions on energy consumption. The cutting power, the surface roughness of the part, and tool life were handled as objective functions(constraint conditions). First of all, the multiple non-linear regression modeling was created using design variables in end milling . These design variables were determined as spindle rotational speed, feed rate power, radial cut depth, axial cut depth, and cutting speed. Then, objective functions were brought to the proper minimum optimal levels due to this optimization modeling. As a result of the optimization model built with design variables, accurate modeling was achieved in this work by studying several optimization models utilized to optimize the minimum objective functions, which play a significant role in reducing energy consumption in end milling. After the optimization, the maximum value was found as 110.791. At the end of the study, some options of direct search method to maximize and minimize results were applied Daha fazlası Daha az

Modeling and optimum design for wire electrical discharge machining of γ titanium aluminide alloy

ÖMER FARUK BÜYÜKYAVUZ

Makale | 2021 | Journal of Artificial Intelligence and Data Science1 ( 1 ) , pp.89 - 95

Wire electrical discharge machining (WEDM) of γ titanium aluminide is the subject of the current research. Due to the large number of process variables and sophisticated stochastic process mechanisms, selecting the best machining parameter combinations for increased cutting efficiency and accuracy is a difficult task in WEDM. In general, there is no perfect combination that can produce the fastest cutting speed and the finest surface finish quality at the same time. For this purpose, the data were selected from a literature study. This study describes an attempt to devise a suitable machining technique for achieving the highest poss . . .ible process criteria yield. To model the machining process, a stochastic optimization method, differential evolution, has been performed. Cutting speed, surface roughness, and wire offset are the three most important criteria that have been used as indicators of process performance. The response characteristics can be predicted as a function of six different control parameters, namely pulse on time, pulse off time, peak current, wire tension, dielectric flow rate, and servo reference voltage. The limitations of the candidate models are checked after the R 2 training, R2 testing and R2 valiadtion values are calculated to reveal whether the model is realistic. Optimization results are 3.02 mm/min, 2.36 µm, and 0.13 mm for the maximum cutting speed, the minimum surface roughness, and minimum wire offset, respectively. It is shown that the machining model is suitable and that the optimization technique meets practical requirements Daha fazlası Daha az

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