Sensitivity Analysis of Tool Wear in Drilling of Titanium Aluminides

Beranoagirre, A.; Urbikaín, Gorka; Marticorena, Raúl; Bustillo, Andrés; Lacalle, Luís Norberto López de

doi:10.3390/met9030297

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…Machining is more efficient when executed at higher speeds, feed rates and depths of cut [18,19,20]. Simultaneously, due to the fact that the relationships between the said factors show a nonlinear character, there is a growing interest in the application of mathematical analytical methods [2,18,21,22] or artificial intelligence systems [15,23,24,25] in modelling machining processes.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

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Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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“…The wear state of a tool will directly affect the machining accuracy, surface quality, and production efficiency of the parts. Therefore, the technology of tool condition monitoring (TCM) is of great significance for ensuring the quality of processing and realizing continuous automatic processing [6][7][8][9]. TCM methods are divided into direct measurement methods and indirect measurement methods.…”

Section: Introductionmentioning

confidence: 99%

Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

et al. 2019

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To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

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“…For the past few years, research on rare earth (RE) microalloyed steel has attracted much attention [14,15,16]. First of all, the doping of RE elements can improve the comprehensive mechanical properties of microalloyed steels due to their effects on the purification of steel liquid, improvement of phase transformation, and modification of second phases, which has been investigated extensively [16,17,18,19,20,21,22,23,24,25,26]. Secondly, the addition of RE elements has an effect on corrosion properties of microalloyed steels by promoting the formation of passive corrosion product films [22,27,28,29], and modifying inclusions [25,30].…”

Section: Introductionmentioning

confidence: 99%

Mechanism Understanding of the Role of Rare Earth Inclusions in the Initial Marine Corrosion Process of Microalloyed Steels

Tang

Liu

et al. 2019

Materials

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In this study, the corrosion behavior of rare earth (RE) microalloyed steels was first evaluated through potentiodynamic polarization tests and corrosion weight loss experiments, and then the corrosion morphologies were observed by scanning electron microscope (SEM). After immersion in a NaCl solution, the sulfides (or oxygen sulfides) dissolved preferentially, followed by corrosion at the boundary between the Fe matrix and oxides. Afterwards, the inclusions fell off as a whole, which promoted pitting nucleation. The first principle modeling demonstrated that the work functions of various kinds of inclusions descended in the following order: La2Zr2O7 > LaAlO3 > (La2O3 ≈ Fe ≈ La2O2S) > La2S3, which provided a theoretical explanation to the dissolution behaviors of inclusions. That is, inclusions containing sulfur tend to dissolve preferentially, whereas the oxides do not dissolve easily. Subsequently, the surface current distributions were detected by the scanning vibrating electrode technique (SVET), which provided more microscopic insight into the role of inclusions in the corrosion propagation. Results showed that the active sites of pitting nucleation accelerated the transverse propagation of corrosion. Finally, local corrosion spread to the whole surface as uniform corrosion.

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Sensitivity Analysis of Tool Wear in Drilling of Titanium Aluminides

Cited by 14 publications

References 29 publications

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Mechanism Understanding of the Role of Rare Earth Inclusions in the Initial Marine Corrosion Process of Microalloyed Steels

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