Experimental investigation on low-frequency vibration assisted micro-WEDM of Inconel 718

AbstractThe micro-wire electric discharge machining (micro-WEDM) has emerged as the popular micromachining processes for fabrication of micro-features. However, the low machining rate and poor surface finish are restricting wide applications of this process. Therefore, in this study, an attempt was made to improve machining rate of micro-WEDM with low-frequency workpiece vibration assistance. The gap voltage, capacitance, feed rate and vibrational frequency were chosen as control factors, whereas, the material removal rate (MRR) and kerf width were selected as performance measures while fabricating microchannels in Inconel 718. It was observed that in micro-WEDM, the capacitance is the most significant factor affecting both MRR and kerf width. It was witnessed that the low-frequency workpiece vibration improves the performance of micro-WEDM by improving the MRR due to enhanced flushing conditions and reduced electrode-workpiece adhesion

Artificial intelligence techniques for implementation of intelligent machining

For the past few years, the rapid progress and development of artificial intelligence (AI) based technologies have been analyzed for the applications of the intelligent manufacturing industry, i.e., industry 4.0. this has triggered a valuable transformation in means, models, and ecosystems within the manufacturing industry and AI development. With the advancement in manufacturing technology, there is a need to execute these technologies and AI more efficiently and cost-effectively. It can be possible by combining traditional manufacturing and machining technologies with recently developed intelligent manufacturing technologies comprising hardware and software techniques. This review paper discusses various AI implementation-based intelligent manufacturing industries with their architecture and technology systems based on the integration of AI with manufacturing and information communication. Furthermore, AI-based manufacturing application, their implementation, and current development in intelligent manufacturing have also been discussed

Experimental investigation on magnetorheological finishing process parameters

Magneto-rheological polishing (MRP) fluid was developed by MR fluid using a magnetic field, non-magnetic abrasives such as SiC and Al2O3, and carrier medium like oil. A magnetic polishing tool was developed using a super-strong permanent neodymium magnet (Nd2Fe14B) with 0.5-tesla magnetic intensity. This polishing tool was assembled to the vertical milling machine for the finishing workpieces. In the present research, magnetic materials (steel material) and non-ferromagnetic (copper) content were finishing using a developed MRP setup for experimental investigation. This research also investigated the parametric dependencies of different abrasives on the magneto-rheological finishing process. It determined the effect of magnetic particle concentration and abrasives on the surface roughness of ferromagnetic (stainless steel) and non-ferromagnetic material (copper). The final surface roughness value has reached 30 nm from its initial surface roughness of 800 nm for non-ferromagnetic (copper). For the magnetic material (stainless steel), the value is 50 nm from 1300 nm

Machine vision for the measurement of machining parameters: A review

Machining parameters have significant value in manufacturing and machining industries as they result in quality and dimensional accuracy of the product. The machining parameters are measured using various machine vision systems. In this review, machine vision and its various procedures have been discussed that are used to measure machining parameters, i.e., tool condition monitoring (TCM) tool wear and surface characteristics like surface roughness, surface defects, etc. Nowadays, Tool condition monitor is a significant machining parameter is developed in manufacturing and machining industries. The development of various techniques of machine vision explore in tool condition monitoring is of significant interest because of the improvement of non-tactile applications and computing hardware. The review also discusses the enhancement of machine vision systems in tool condition monitoring

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

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

Finite Element (FE) Shear Modeling of Woven Fabric Textile Composite

AbstractThis paper demonstrates the modeling of woven fabric textile composite under in-plain shear loading. The geometric modeling of fabric unit cell is modeled using TexGen textile modeling schema developed at the University of Nottingham. The yarns in the present scheme are treated as solid volume whose modeling depends upon various parameters such as yarn path, yarn cross- section, yarn surface. Periodic boundary conditions were identified to simulate the realistic nature of repetitive fabric unit cell.Transversely isotropic material law with non-linear transverse mechanical properties is incorporated using finite element (FE) simulation software ABAQUS®. This approach is initially validated for pure in-plain shear and compression loading; later on it is used to simulate the behavior of fabric under the combination of these loads which practically occurs during forming process. A successful prediction of shear force versus shear angle are made and was found that the majority of the energy being dissipated at higher shear angles due to yarn compaction. The scope of altering weave pattern and yarn characteristics is facilitated in this developed model

Unit Cell Model of Woven Fabric Textile Composite for Multiscale Analysis

AbstractThis paper presents a micromechanical unit cell model of 5-Harness satin weave fabric textile composite for the estimation of in- plane elastic properties. Finite element modeling of unit cell at mesoscopic level has been recommended over employing costly experimental setup for such sophisticated materials. The unit cell is identified based upon its ability to enclose the characteristic periodic repeat pattern in the fabric weave. Modeling of unit cell and its analysis for this new model are developed using an open source software, TexGen and a commercially available finite element software ABAQUS®. The scope of altering weave pattern and yarn characteristics is facilitated in this developed model. Several parametric studies were carried out in order to ascertain the effectiveness of the model and to investigate the effects of various geometric parameters such as yarn spacing, yarn width, fabric thickness and fibre volume fraction on the mechanical behavior of woven composites. Present analysis reveals that the values of Young's and shear modulus increased with increasing in the fabric parameters such as yarn width and fabric thickness. On the other hand it is decreased when the spacing between the yarns increased. A good comparision was obtained between the predicted results and available experimental and theoretical data in open literature for the developed unit-cell model and its suitability is tested for multi-scale analysis. The potential advantage of the present scheme lies in its ability which permits the textile modeling from building of textile fabric model to its solution including mesh generation undertaken using an integrated scripting approach thus requiring far less human time than traditional finite element models

Investigation of wear behavior of nanoalumina and marble dust-reinforced dental composites

In the present work, the effects of adding nanoalumina and marble dust on the wear behavior of dental composites were investigated. The hardness of dental composite was determined using Vickers micro-hardness tester. A two-body abrasive wear test was performed on the dental wear simulator under the medium of artificial saliva. The experiments were performed as per the Taguchi orthogonal array and steady state condition by varying parameters such as filler content, normal load, sliding velocity, and number of cycles. The hardness results indicated that the incorporation of 5 wt. % of nanoalumina increased the hardness of the dental composite by 12%, whereas the incorporation of 5 wt. % of marble dust increased the hardness of the dental composite by 7%. Also, for the experiments as per the Taguchi orthogonal array, the mean volumetric wear in the case of nanoalumina-filled dental composite was 9.6% less than that of marble dust-filled dental composite. However, in both the cases, the volumetric wear increased with the increase in normal load, sliding speed, and number of cycles but decreased with the increase in filler content. Analysis of variance (ANOVA) of the results indicated that normal load was less significant compared to filler content, sliding speed, and number of cycles

ANN-NSGA-II dual approach for modeling and optimization in abrasive mixed electro discharge diamond grinding of Monel K-500

Hybrid machining processes (HMPs) have attracted the attention of investigators from both academia and industry due to their enhanced process performance and capabilities while machining so-called difficult-to-cut materials. In this paper, the dual approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was used to model and optimize a new HMP called as Abrasive Mixed Electro Discharge Diamond Grinding (AMEDDG). Due to complex nature of AMEDDG process, the choice of an appropriate coalition of input factors is an effortful job for machinist. The central composite rotatable design was used to plan the experiments and ANN model was established to observe the effect of input machining parameters viz. Wheel speed, powder concentration, pulse current, and pulse-on-time on material removal rate (MRR) and average surface roughness (Ra). The established ANN model was found to be capable of forecasting the output responses within tolerable limits for the chosen set of machining parameters. An ANN-NSGA-II based dual approach was applied for multi-objective optimization of control factors in AMEDDG, and experimental validation directs that optimal data was in tolerable limits. Keywords: Hybrid machining, Electric discharge grinding, ANN, Genetic algorithm, Modelling, Optimizatio

Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study

Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization of the μ-EDM is proposed as an alternative to overcome the process limitations. Conversely, it complicates the process nature and poses a challenge for modelling and predicting critical process responses. Therefore, in this work, two distinct, nonparametric, previously unreported, workpiece material independent models using a Generalized Regression Neural Network (GRNN) and Gaussian Process Regression (GPR) were developed and compared to assess their performance with limited training data. Various smoothing factors and kernels were tested for GRNN and GPR, respectively. The prediction of models was compared in terms of the mean absolute percentage error, root mean square error, and coefficient of determination. The results showed that GPR outperforms GRNN and accurately predicts the μ-EDM process responses. The GRNN’s performance was better for less stochastic output with a discernible pattern than other outputs. The Automatic Relevance Determination (ARD) squared exponential kernel was found to be the best performing kernel among those chosen. GPR models can be used with reasonable accuracy to predetermine critical process outputs as they have R2 values above 0.90 for both training and validation data for all outputs. This work paves the way for future industrial implementation of GPR to model and predict the outputs of complex hybrid machining processes