Wheel RPM Research Articles

Finite Element Analysis of Foldable Electric Vehicle

In the present work finite element analysis of foldable electric vehicle is carried out. With the use of finite element analysis, the magnitude of stresses and deflections at critical locations of the vehicle is evaluated. Dynamic analysis to simulate the vehicle behavior has also been carried out. The vehicle is proposed to accommodate a single passenger. The foldable vehicle is designed as per the regulations prescribed by the Automotive Resarch Association of India. Maximum vehicle speed of 25 kmph and dimensions in folded circumstances are the design constraints accounted for in the present work. Vehicle dimensions are computed using design constraints and the process mentioned in Automotive Industry Standard 049 (AIS-049). Materials for vehicle chassis, seat, hand rests, and centrally located telescopic support are steel 1018 and aluminum from speed and self-weight considerations. With the aid of the classical approach braking force, motor torque, wheel rpm, power, rolling resistance, and aerodynamic drag are calculated. Solidworks software is used for the preparation of computer-aided model. Finite element analysis of the foldable vehicle has been carried out to evaluate the static and dynamic stresses induced in the vehicle components. The meshing of the foldable vehicle is carried out using Ansys Workbench. From modal analysis, six mode shapes of the foldable vehicle are formulated, and corresponding frequencies and deflections are devised. A Mesh generator is used to mesh the foldable vehicle. The deflection and frequency magnitudes of the foldable vehicle evaluated are in good agreement with the experimental results available in the literature for similar materials.

Experimental investigations on electrical discharge grinding of composite material

Electrical Discharge Grinding is a very complex hybrid machining process of electrical discharge machining and grinding which is suitable for machining of flat surface materials by generating the spark between the non-abrasive rotating round tool and workpiece. In this process, mechanical and thermal energies are liable for improving the material removal rate and reducing the surface roughness owing to successful flushing of working gap. In the present work, an attempt has been initiated to develop Electrical Discharge Grinding setup and parametric study of input parameters such as current and wheel rpm on output parameters like material removal rate and surface roughness while the grinding the Al-SiC-B4C-Mg as a hybrid composite workpiece material using copper tool electrode..During the preliminary experimentation, it shows that output parameters increase while varying the current and wheel speed.

Multi-Response Optimization of Process Parameters for Grinding Aided Electrical Discharge Machining of Metal Matrix Composite

Electrical Discharge Machining (EDM) is a thermal energy based non-traditional shaping process for shaping of hard and brittle electrically conductive materials, but it suffers with low machinability and recast layer formations. The combination of grinding with EDM means enhancement in machining capability, but the process becomes highly complex. Therefore, the assortment of control factors for optimum results is greatly challenging for the industries. The objective of present study is to optimize the control factors such as current, pulse on-time, pulse off-time, wheel RPM and abrasive grit number (GN) to optimize the material removal rate (MRR) and average surface roughness (Ra) for Grinding Aided-EDM process. For this purpose, the simultaneous application of soft computing methods such as Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been employed. The results demonstrate that combination of ANN with GA effectively predicts the data and provides optimal results with adequate percentage errors in MRR and Ra positively.

Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

Cars can nowadays record several thousands of signals through the controller area network (CAN) bus technology and potentially provide real-time information on the car, the driver, and the surrounding environment. This paper proposes a new methodology for near-real-time analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, longitudinal and lateral acceleration. Data have been collected in a completely uncontrolled experiment involving 54 people, where over 2000 trips have been recorded without any type of predetermined driving instruction on a wide variety of road scenarios. While only few works have analyzed the driving behavior of more than 50 drivers using CAN bus data, we propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed, showing that by properly choosing a subsampling strategy it is possible to reduce the size of the database as much as 99% without impairing the clustering performance.

Artificial neural network modelling of erosion-abrasion-based hybrid machining of aluminium-silicon carbide-boron carbide composite

The erosion-abrasion-based hybrid machining (EAHM) is newly developed machining process, which comprises the erosion-based machining such as electro-discharge grinding (EDG) and abrasion-based machining such as diamond grinding (DG) for machining of difficult to machine hard and brittle materials. The aim of this study is to develop an artificial neural network (ANN) model for EAHM process during machining of aluminium-silicon carbide-boron carbide (Al%SiC%B4C) composite workpiece. The ANN model has been trained and tested with experimental observations, which are collected after experimentations. The experiments were conducted on EDM machine considering the effect of pulse current, pulse on-time, pulse off-time, wheel RPM and abrasive grit number on the material removal rate and average surface roughness. It has been found that the developed ANN model was significantly predicted the responses within the acceptable limit. Such developed model is further used to study the effect of process parameters on the performance measures.

Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding

In this paper, a fuzzy logic artificial intelligence technique is delineate to predict the material removal rate (MRR) and average surface roughness (Ra) during abrasive-mixed electro-discharge diamond surface grinding (AMEDDSG) of Nimonic 80A. Though, Nimonic 80A superalloy is extensively used in aerospace and automotive industries due to its high corrosion, fracture toughness, oxidation, and temperature resistance characteristics, being a difficult-to-cut material, its machining is a challenging job. The hybrid machining processes like AMEDDSG can be competently used for machining of Nimonic 80A. The face-centered central composite design is used consummate the experiments and then experimental data are used to establish fuzzy logic Mamdani model to predict the MRR and Ra with respect to changes in the input process parameters viz. wheel RPM, abrasive concentration, pulse current and pulse-on-time. The results of confirmation experiments reveal an agreement between the fuzzy model and experimental results with 93.89 % accuracy implying that the established fuzzy logic model can be precisely used for predicting the performance of the AMEDDSG process. An increase in wheel RPM, pulse current, and pulse-on-time from their low level to high level contributes to increased MRR by 83.89, 71.01, 17.02 %, respectively. Also, an increase in wheel RPM contributes to reduced Ra values by 5.96 %. Abrasive concentration increase from 0 to 4 g/L improves MRR by 24.03 %. The 17.10 % improvement in surface finish is achieved by increasing abrasive concentration from 0 to 8 g/L.

Machining of a hybrid–metal matrix composite using an erosion–abrasion-based compound wheel in electrical discharge grinding

ABSTRACTMachining of the composites made of matrix and reinforcement is always difficult for manufacturing industries due to their unusual properties. Among various existing traditional and non-traditional machining processes, erosion-based machining process i.e., Electrical Discharge Grinding (EDG) and the abrasion-based process i.e., Diamond Grinding (DG) have been shown their potential to machine such difficult-to-machine materials. The aims of the present study are to analyze the performances of the erosion–abrasion-based compound wheel during machining of the hybrid–metal matrix composite made of Aluminum–Silicon Carbide–Boron Carbide (Al/SiC/B4C) by the stir casting method. The performances of the compound wheel have been tested on the EDM machine in the face grinding mode. The role of pulse current, pulse on-time, pulse off-time, wheel RPM, and abrasive grit number have been analyzed on the material removal rate (MRR) and average surface roughness (Ra). The experimental results showed that the machining with compound wheel gives higher MRR with better surface finish as compared to the uniform wheel. It has also been observed that MRR and Ra are highly affected by the pulse current, pulse on-time, and wheel RPM.

Application of Soft Computing Techniques for Modeling and Optimization of Slotted-Electrical Discharge Diamond Face Grinding Process

An innovative hybrid machining called as slotted-electrical discharge diamond face grinding (S-EDDFG) has been developed by comprising the electrical discharge face grinding and diamond face grinding. Due to complexity in S-EDDFG process, the selection of suitable combinations of input parameters is a challenging task for manufacturers. In recent, soft computing techniques are gaining more attention of researchers for modeling and optimization for the process parameters of complex manufacturing processes. In the present paper, two soft computing techniques known as artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied for modeling and optimization of process parameters (pulse current, pulse on-time, pulse off-time, wheel RPM and grit number) during machining of Aluminium–Silicon Carbide–Graphite (Al/SiC/Gr) composite. The results shown that two hidden layer ANN architecture was more appropriate to predict the material removal rate and average surface roughness during S-EDDFG of Al/SiC/Gr workpiece. The optimal results obtained by combined approach of ANN–NSGA-II are experimentally validated and found that optimal data are under acceptable limits.

Experimental Investigation of Electro-Discharge Face Grinding of Metal Matrix Composite (Al/SiC)

Surface quality and process productivity are the challenging performance measures in grinding of difficult-tomachine materials these are measured in terms of Surface Roughness (Ra) and Material Removal Rate (MRR). Metal Matrix composites are the materials having aggregate properties of constituents like higher strength to weight ratio, toughness, stiffness and hardness. Because of having these superior properties MMCs are highly desirable in applications ranging from commercial aircraft to sports equipment but still facing challenges in machining of MMCs. Machining of MMCs by conventional machining process results poor surface finishand frequent tool failure due to presence of ceramic particles. Grinding of MMCs by conventional grinding process produces poor surface finish and low material removal rate due to frequent wheel loading. Electro-Discharge Face Grinding (EDFG) process is used on work specimen of the material Al/SiC MMC to investigate the effects of Wheel speed, Gap current, Pulse on-time and Pulse off-time on Surface Roughness (Ra) and Material Removal Rate (MRR). Surface Roughness (Ra) increases with increase in Wheel RPM, Gap current and Pulse on-time. MRR also increases with increase in Gap current and Pulse on-time while it is almost constant on increasing Wheel RPM. SEM micrograph of machined surface is taken for analysis of surface texture.

A Hybrid Methodology of ANN-NSGA-II for Optimization of the Process Parameters of Slotted-Electrical Discharge Abrasive Grinding Process

Slotted-Electrical Discharge Abrasive Grinding (S-EDAG) is a new innovative hybrid machining process, which is developed by comprising the Electrical Discharge Grinding (EDG) and Abrasive Grinding (AG) where both the processes occur alternatively. The selection of the suitable combination of process parameters is much difficult due to non-linear relation between input and output. The aim of this study is to apply a hybrid approach of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm (NSGA-II) to optimize the process parameters of S-EDAG process during machining of Al/SiC/B4C composite. ANN architecture with back propagation algorithm has been used for modeling, which is trained and tested with experimental data. The experimental data have been collected after exhaustive experimentation considering the effect of current, pulse duration, pulse interval, wheel RPM and abrasive grit number on material removal rate (MRR) and average surface roughness (Ra). The developed ANN model has been coupled with NSGA-II to generate the population and evaluate the objective functions during optimization. The results have been shown that hybrid approach of ANN-NSGA-II provides set of optimal solution that helps to manufacturer to select the appropriate combination of input parameters and reduce the product manufacturing cost.

Slotted-electrical discharge diamond cut-off grinding of Al/SiC/B4C hybrid metal matrix composite

Advanced manufacturing industries need materials with high strength and low weight in the fields of advanced engineering, such as automobiles and aeronautics. Metal matrix composites (MMCs) are one of the advanced engineering materials that meet the above requirements. To enhance the properties of MMCs, researchers added an additional phase of reinforcements into single reinforced MMCs; such developed MMCs are known as hybrid MMCs. The additional phase of reinforcements enhances the properties of MMCs, but simultaneously leads to rapid tool wear and poor machinability. This study developed an innovative hybrid machining process (HMP) consisting of electrical discharge grinding and diamond grinding in such a way that both the processes occur alternately with equal intervals due to the rotation of a slotted abrasive grinding wheel. The performance of the hybrid process was tested on an Al/SiCp/B4Cp work-piece in cut-off grinding mode. The experiments were conducted on an electrical discharge machining machine, which consists of a separate attachment on a vertical column to rotate the wheel. Pulse current, pulse on-time, pulse off-time, wheel RPM, and abrasive grit number were taken as input parameters while material removal rate (MRR) and average surface roughness were taken as output parameters. Result were shown that the HMP gives higher MRR with better surface finish as compared to the constituent processes. Pulse current ranging from 3 A to 21 A, pulse on-time ranging from 30 μs to 200 μs, and pulse off-time ranging from 15 μs to 90 μs were also found to be more suitable for higher MRR, and a wheel RPM at 1300 RPM was more suitable for higher MRR with better surface finish.

Preliminary Study on Slotted-Electrical Discharge Diamond Face Grinding of Metal Matrix Composite

Metal matrix composites (MMCs) are highly required in field of advanced engineering. Due to the abrasive nature of the reinforcements into matrix, machining of the MMCs is yet a challenge for manufacturing industries. In this paper, a new hybrid machining process named as slotted-electrical discharge diamond face grinding (S-EDDFG) has been proposed for machining of the end surface of the electrically conductive hard and brittle materials. The performance of the developed process has been tested on EDM machine with application of the compound grinding wheel. The role of discharge voltage, current, pulse on-time, pulse off-time and wheel RPM has been investigated on MRR and Ra during machining of the Al/SiC composite. The experimental results show that MRR increases and Ra value decreases with increase of the wheel RPM due to enhances in the flushing efficiency of the dielectric.

Design, Development & Experimental Investigation of Electro-Discharge Diamond Surface Grinding of Ti-6Al-4V

Abstract: Ti-6Al-4V are used extensively in aerospace, medical, marine and surgical implants etc. but it is hard to machine. Machining of advanced difficult-to-machine very hard materials (Ti-6Al-4V, composites and ceramics) is a big challenge. By conventional machining processes, their machining is not only costly but results in poor surface finish and shorter tool life. To meet these challenges, new hybrid machining process (HMP) has been developed. This article is focused on hybrid machining process comprising of conventional surface grinding along with electro-discharge machining between the periphery of metal bonded diamond grinding wheel and flat rectangular shape workpiece. This process has the advantage of shaping advance engineering materials and difficult-to- machine very hard materials. The experimental investigations of various input parameters like wheel RPM, duty factor, current and pulse on-time on material removal rate of Ti-6Al-4V in EDDSG process have been reported here on newly self designed & fabricated set up. Keywords: Electro-Discharge Diamond Surface Grinding (EDDSG), Hybrid Machining Process (HMP), Ti-6Al-4V.

Study of the Parameters in Electro-Discharge Diamond Face Grinding through Response Surface Methodology Approach

The present study investigates the relationship of process parameters in electro-discharge diamond face grinding (EDDFG) of tungsten carbide and cobalt composite (WC-Co). The central composite rotatable design had been utilized to plan the experiments and response surface methodology (RSM) was employed for developing experimental models. Analysis on machining characteristics of EDDFG was made based on the developed models. In this study, wheel RPM, current, pulse on-time, and duty factor are considered as input process parameters. The process performances such as material removal rate (MRR) and average surface roughness (Ra) were evaluated. Analysis of variance test had also been carried out to check the adequacy of the developed regression models. The observed optimal process parameter settings are wheel RPM of 1500, current of 6.9029 A, pulse on-time of 137. 8208 µs, and duty factor of 0.79 for achieving maximum MRR and minimum Ra; finally, the results were experimentally verified. A good agreement is observed between the results based on the RSM model and the actual experimental observations. The error between experimental and predicted values at the optimal combination of parameter settings for MRR and Ra lie within 6.18% and 12.33%, respectively.

Simultaneous Optimization of Multiple Quality Characteristics in Electrical Discharge Diamond Cut-Off Grinding (EDDCG)

This paper presents the simultaneous optimization of multiple quality characteristics (Material removal rate and Average surface roughness) using Taguchi method for electrical discharge diamond cut-off grinding (EDDCG) during machining of cemented carbides material. The experiments are carried out on a self-developed electrical discharge diamond grinding setup in cut-off mode, In the present work four input parameters ( current, pulse on-time, duty factor and wheel RPM ) were selected in which each parameter was at three levels. The total degree of freedom (dof) has been calculated without considering the effects of interaction among the different control factors. Based on design of experiments (DOE), a standard L9 orthogonal array (OA) was taken for experimentation. The result shows the considerable improvement of 13.999dB in multiple S/N ratio as compared to initial condition. Also, the comparison of results from single and multi-objective optimization has been presented.

Experimental study and parameter optimisation of electro-discharge diamond face grinding

Electro-discharge diamond face grinding (EDDFG), a hybrid machining process comprising diamond face grinding (DFG) and electro-discharge machining (EDM), has been developed for machining of electrically conductive difficult-to-machine very hard materials. The process employs simultaneous synergetic interactive effect of abrasion action and electro-discharge action. A face grinding setup for electro-discharge diamond grinding (EDDG) process is developed. The effect of wheel RPM, current, pulse on-time and duty factor on material removal rate (MRR), wheel wear rate (WWR) and average surface roughness (Ra), while machining high speed steel (HSS) workpiece, are investigated. It is observed that the MRR increase with increasing wheel RPM, current and pulse on-time, while it decreases with an increase in duty factor. The most significant factor affecting the EDDFG robustness has been identified as pulse on-time and duty factor.

Neural network modelling and multi objective optimisation of electrical discharge diamond cut-off grinding (EDDCG)

This paper presents the development of neural network model and parameter optimisation of electrical discharge diamond cut-off grinding (EDDCG) during machining of cemented carbides for material removal rate (MRR) and average surface roughness (Ra). The experiments were carried out on a self developed electrical discharge diamond cut-off grinding (EDDCG) attachment on conventional EDM machine. The numbers of experiments were selected based on full factorial design of experimental procedure. After experimentation the data set were divided into a training set and testing set for ANN modelling. The ANN back propagation algorithm with four inputs (current, pulse on-time, duty factor, wheel RPM) two outputs (MRR, Ra) and one hidden layer with 15 neurons is proposed to establish the process model. The model after proper training was found to be capable of predicting the response parameters. The predicted results from ANN model are discussed to get the insight of the process. Further, multi-objective optimisation of EDDCG is done using GRA coupled with entropy measurement method.

English

Electro-Discharge Diamond Grinding (EDDG), a hybrid machining process comprising diamond grinding and electrodischarge grinding, has been developed for machining of electrically conductive difficult-to-machine very hard materials. The process employs simultaneous synergetic interactive effect of abrasion action and electro-discharge action. A self-designed face grinding setup is attached on ELEKTRA PULS EDM machine to use it as Electro-Discharge Diamond Face Grinding (EDDFG). This paper investigates optimization design of an EDDFG process performed on high speed steel (HSS).The major performance characteristics selected to evaluate the processes are material removal rate (MRR) and wheel wear rate (WWR), and the corresponding EDDFG parameters are wheel RPM, current, pulse ontime and duty factor. In this study, since the process is with multiple-performance characteristics, therefore, the grey relational analysis that uses grey relational grade as performance index is specially adopted to determine the optimal combination of EDDFG parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively described. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively be used to obtain the optimal combination of EDDFG parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the machining performance of EDDFG process.

Indirect measurement of grinding wheel run-out using acoustic emission

The roundness of a plunge ground cylindrical workpiece is a function of both the grinding wheel run-out and the waveshift. Waveshift determines how roundness profiles of the workpiece overlap in consecutive revolutions and is the difference between the ratio of the wheel rpm to the workpiece rpm and the next highest integer. In this paper, Acoustic Emission (AE) is evaluated as a possible method of monitoring wheel run-out, by measuring its amplitude at the wheel frequency. Variation of the AE signal at wheel frequency with waveshift is similar to model predictions of the variation in the normal force at wheel frequency and it reaches a maximum at a waveshift of 0.5. Tests to establish the linearity of the AE measurement with the amount of run-out show some scatter that needs to be understood before the technique can be successfully applied.

Wheel slip measurement in 2WD tractor

A microcontroller-based slip sensor was developed for a 2WD tractor to indicate slip values during on-farm use. The ‘zero condition’ considered for the development of slip sensor was – tractor supplied with a driving torque to propel any device across a tarmacadam surface while delivering zero net traction (self-propelled condition). This sensor comprised of four components: power supply; sensing of throttle position, gear position, and wheel rpm; processing of collected data; and display unit. Power was taken from the tractor battery. Rotary potentiometer and proximity switches were installed on the tractor to measure throttle position and wheel revolution, respectively. The performance of developed slip sensor was evaluated both on tarmacadam surface as well as in the field. The variations between indicated and actual slip were found to be within 0–5% for both the surfaces, thus indicating the accuracy of slip measurement by the developed slip sensor.