Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

Zhi, Gu; Kang, Min; Yang, Shoufeng

doi:10.3390/app7121268

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…With the size of an image, as provided by the user and the image pixel read by the software from the file, the software then calculates the total image Artificial intelligence has been widely used to find the ultrasonic cleaner's efficiency. Zhong et al [32], proposed a back propagation artificial neural network (BPANN) to find the material removal from an object that has been cleaned using ultrasonic cleaning. For the second series of experiments, a similar approach was used to find the efficiency of ultrasonic cleaning.…”

Section: Testing and Resultsmentioning

confidence: 99%

Optimum Driving of Ultrasonic Cleaner Using Impedance and FFT Analysis with Validation of Image Processing of Perforated Foils

et al. 2023

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Over the past decade, ultrasonic cleaners have been widely used in many industries. Now, this technology is finding its way into homes for vegetable, fruit, and clothes cleaning. In widely used ultrasonic cleaners, piezoelectric transducers are externally attached to the steel tank to generate ultrasonic waves inside the tank. Based on the impedance data of the piezoelectric transducers, the driving circuit was tuned to generate the required frequencies inside the cleaning tank. This paper discusses the design, development, and validation of an 800 mL tank capacity ultrasonic cleaner driven with a piezoelectric disc actuator. To achieve an optimum cleaning action without surface abrasion, several characteristics need to be considered in this complex relationship. The placement of transducers has been investigated according to the pressure distribution inside the liquid medium. The optimized ultrasonic cleaner design, along with a class-D half-bridge circuit, was developed to drive the ultrasonic transducer in the resonance frequency range. To validate the optimal design and driving frequency, the acoustic spectrum generated inside the tank was measured using a piezoelectric sensor and FFT analysis was performed. To validate the cleaning effect, a qualitative test based on aluminuim foil perforations was performed. The perforation area in the foils was quantitatively measured using image processing based on the YOLO V5 technique. The proposed image processing technique has an accuracy of 97 % in the detection of perforation areas in the aluminuim foil test.

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Section: Testing and Resultsmentioning

confidence: 99%

Optimum Driving of Ultrasonic Cleaner Using Impedance and FFT Analysis with Validation of Image Processing of Perforated Foils

et al. 2023

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“…The obtained experimental data, including MRR and surface roughness, were analyzed using statistical methods. The ideal parameter settings were determined by analyzing the experimental data [28,29]. To validate the optimal conditions, a confirmation experiment was carried out with the identical parameter settings as identified in the Taguchi L9 analysis.…”

Section: Experimental Designmentioning

confidence: 99%

Experimental investigation and parametric optimization of rotary ultrasonic machining of different bio-ceramic materials

Vishnoi,

Mamatha,

Singh

et al. 2024

Phys. Scr.

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Rotary ultrasonic machining (RUM) is a highly promising technique for machining bio-ceramic materials due to its ability to achieve high precision and superior surface quality. This research focuses on an experimental investigation and parametric optimization of RUM for different bio-ceramic materials, intending to optimize multiple machining responses simultaneously. The experimental setup involves utilizing a 3-axis CNC ultrasonic machine to machine three different workpieces with slot cutting. Various machining parameters, such as tool feed rate and tool rotating speed, are studied to determine their impacts. An orthogonal array design based on Taguchi optimization is used to execute the experiments effectively. Material removal rate (MRR) and surface roughness (SR) are monitored and statistically analyzed as a consequence of the responses. ANOVA demonstrates that the tool feed rate has a considerable impact on the output reactions, with material type and tool rotational speed also playing a role. For multiple response optimizations, the Taguchi-Grey method is used to achieve the best trade-off between MRR and SR. The results demonstrate that material type has the most substantial impact on surface roughness, followed by feed rate and spindle speed. In contrast, feed rate has the most significant effect on the material removal rate, followed by the material type and spindle speed. The optimal parameter settings for achieving the desired output parameter are determined. The confirmation experiments validate the effectiveness of the optimized parameters. The feed rate of 5 mm/min, and spindle speed of 2500 rpm were discovered to be the optimal condition for achieving maximum MRR and minimum Ra. The MRR and surface roughness values were measured as 1.7266 mm3/min and 1.5611 microns respectively.

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“…Zhong et al Zhong et al proposed a backpropagation artificial neural network. This method improves the accuracy of predicting the material removal rate of ultrasonic machining [13]. Shen et al applied Support Vector Fuzzy Adaptive Network as a parameter-free nonlinear regression technique to model material removal rate.…”

Section: Introductionmentioning

confidence: 99%

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

Tang

Liu

et al. 2022

Volume 2: 42nd Computers and Information in Engineering Conference (CIE)

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The exact removal of material in abrasive belt grinding determines the final machining quality of the workpiece. However, it is difficult to determine the removal state of materials in actual processing, which is affected by factors such as abrasive belt wear and processing errors. Therefore, a multi-scale attention convolutional neural network for material removal state prediction method is proposed based on the analysis of displacement data. First, the first-order difference and sliding-window expansion methods for displacement data are adopted, making it possible to use displacement data for deep learning, which is the premise of material removal state prediction. Then, the multi-scale convolutional neural network is Employed to extract important features of the displacement data. Due to the different importance of different features, Squeeze-and-Excitation Networks are used to independently assign the importance of features based on the loss function, so that the model pays more attention to those main features and ignores the secondary features, which can improve the convergence speed and prediction accuracy of the model. The K6 cross-validation of experiment results shows that this method can accurately predict the material removal state with an average prediction accuracy of 87.9%, which can be practically applied to the online prediction of the material removal state in industrial processing to further control the processing quality.

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Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

Cited by 6 publications

References 24 publications

Optimum Driving of Ultrasonic Cleaner Using Impedance and FFT Analysis with Validation of Image Processing of Perforated Foils

Optimum Driving of Ultrasonic Cleaner Using Impedance and FFT Analysis with Validation of Image Processing of Perforated Foils

Experimental investigation and parametric optimization of rotary ultrasonic machining of different bio-ceramic materials

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

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