Experimental investigation and optimization on machined surface of Si3N4 ceramic using hot SiC abrasive in HAJM

Pradhan, Subhadip; Dhupal, Debabrata; Das, Sudhansu Ranjan; Jena, Pankaj Charan

doi:10.1016/j.matpr.2020.12.066

Cited by 17 publications

(3 citation statements)

References 15 publications

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“…Pradhan has compared the experimental and CFD simulation results to predict erosion behavior during hot abrasive jet machining (HAJMing) [125]. Similarly, authors have modeled the HAJMing process using CFD analysis [126]. Amar et al developed the analytical and numerical models for abrasive water slurry jet machining (AWSJM) to fabricate the micro-channel using experimental study and CFD simulation, respectively [127].…”

Section: Applicationsmentioning

confidence: 99%

“…The process parameters like air pressure, the distance of nozzle tip, and the abrasive temperature are analyzed using variance, and Regression models are obtained. The Regression analysis and CFD analysis are carried out to discover the optimum machining parameters for HAJM [126]. The summary of MVRA-based modeling and simulation of the loose abrasive-based method is shown in Table 10.…”

Section: Applicationsmentioning

confidence: 99%

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Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Hashmi

Mali

Meena

et al. 2022

Metals

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Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., “LAMMPS Molecular Dynamics Simulator,” or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help.

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Hashmi

Mali

Meena

et al. 2022

Metals

View full text Add to dashboard Cite

show abstract

“…ANN model was used for better prediction, and the RSM was used to identify the optimal process parameters and analyze their interactions [23]. RSM was used to perform the regression analysis and optimize the multiresponse problems [24].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

Thangarasu¹,

Mohanraj

Devendran³

et al. 2023

Mathematical Problems in Engineering

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Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN-coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry-cutting conditions. DL algorithms such as adaptive neuro-fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single-point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid-based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

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Influence of Laser Modification of Nitride Ceramics Surface Layer on the Milling Cutters Reliability During the Finishing of Hardened Steel Workpieces

Kuzin,

Grigor’ev,

Fedorov

et al. 2023

Refract Ind Ceram

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Experimental investigation and optimization on machined surface of Si3N4 ceramic using hot SiC abrasive in HAJM

Cited by 17 publications

References 15 publications

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

Influence of Laser Modification of Nitride Ceramics Surface Layer on the Milling Cutters Reliability During the Finishing of Hardened Steel Workpieces

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