Optimization of process parameters and performance for machining Inconel 718 in renewable dielectrics

Ming, Wuyi; Guo, Xudong; Zhang, Guojun; Hu, Shunchang; Liu, Zhen; Xie, Zhuobin; Zhang, Shengfei; Duan, Liuyang

doi:10.1016/j.aej.2023.07.075

Cited by 13 publications

(5 citation statements)

References 55 publications

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“…This analysis was applied to the results of all the recorded data, both for the recorded values of the parameters of the roughness of the machined surface Ra, as well as for the recorded values of the performance parameter of the electrical discharge process MRR. In the case of the recorded data where the presence of outliers or extreme values was confirmed, based on the analysis, and it was not possible to establish a normal distribution, even in cases where the normality of the distribution of the data was not proven even after the exclusion of the confirmed outliers, exponential and Box–Cox transformations were performed, which ensured the correctness of the statistical analysis of experimentally measured values of monitored output-dependent parameters Ra and MRR in micro-WEDM of HSS [ 70 , 71 , 72 , 73 , 74 ].…”

Section: Methodsmentioning

confidence: 99%

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology

Straka,

Čorný

2024

Micromachines

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When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach.

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Section: Methodsmentioning

confidence: 99%

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology

Straka,

Čorný

2024

Micromachines

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“…As mentioned in the technical literature, vegetable oils are highly biodegradable, renewable, and have a much lower impact on the environment compared to the mineral oil, which comply with the demands for a future greener industry [ 22 , 42 , 50 , 51 ]. Moreover, vegetable oils are compatible with the existing manufacturing facilities requiring none, or a minimum effort for the adaptation of the filtering systems.…”

Section: Methodsmentioning

confidence: 99%

Use of vegetable oils as dielectric fluids for electrical discharge machining. A case study

Radu,

Herghelegiu,

Tampu

et al. 2024

Heliyon

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“…In the field of engineering, physical models are considered to be useful in understanding the physical mechanisms inherent in objective phenomena [126][127][128]. In general, the accuracy of physical model predictions depends on the physical properties of the model, the initial conditions of the watershed, and the spatial and temporal resolution of the predictor and predictor variables.…”

Section: Vs Traditional ML and Physical Modelsmentioning

confidence: 99%

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Zhao,

Wang,

Bai

et al. 2024

Water

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Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.

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Optimization of process parameters and performance for machining Inconel 718 in renewable dielectrics

Cited by 13 publications

References 55 publications

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology

Use of vegetable oils as dielectric fluids for electrical discharge machining. A case study

A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

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