Simul Banerjee

Development of a Grinding Fluid Delivery Technique and Its Performance Evaluation

In grinding process, a stiff air layer is generated around the wheel due to rotation of porous grinding wheel at high speed. This stiff air layer restricts fluid to reach deep inside the grinding zone. Conventional method of fluid delivery system is not capable of penetrating this stiff air layer, and thus cannot control grinding temperature and thermal defects effectively. In this work, formation of stiff air layer has been studied experimentally by measuring the variation of air pressure around grinding wheel periphery at different conditions. A pneumatic barrier setup has been developed first time for restricting the stiff air layer around grinding wheel. It is found from analysis of variance (ANOVA) that both pneumatic barrier position and orientation have significant effects on suppressing air layer pressure around the grinding wheel. Using the pneumatic barrier, reduction of air pressure up to 53% has been observed experimentally. Hence, it reduces wastage of grinding fluid, leading to less environmental problem. Surface grinding experiments using the pneumatic barrier setup shows remarkable reduction in forces and surface roughness over flood cooling conditions expectedly due to better penetration of fluid in the grinding zone, showing its applicability.

Predictive Modeling and Investigation on the Formation of Stiff Air-Layer around the Grinding Wheel

In grinding process, a stiff air layer is generated around the wheel periphery due to high surface speed of the porous grinding wheel. This stiff air layer restricts grinding fluid to reach deep inside the grinding zone. The formation of stiff air layer has been studied experimentally by obtaining the variation of air pressure around the wheel periphery under different wheel speeds. With the help of the experimental data, a mathematical model has been developed to predict the pressure of the stiff layer of air under different wheel speed. From the model it is found that at close proximity to the wheel, air pressure obtained is very high, and this establishes quantitatively the formation of stiff air layer around the grinding wheel.

Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization

Two-stage soft computing ((SVM-TLBO)-(PLM-TLBO-pseudo PARETO)) based virtual system of manufacturing process - EDM is developed.Virtual data generator of EDM process learned by support vector machine (SVM) with internal parameters (C, ? and ?) tuned by teaching learning based optimization (TLBO) is reported.Modifications namely population based termination criteria, initialize population with high dispersion and way of choosing teacher in case of multiple best learners performing same score, over standard TLBO are suggested. Further, a comparison between performances of TLBO and PSO in model development is studied.A simple procedure for pseudo Pareto front development by modified TLBO is proposed.Inverse solution procedure for selection of optimum available machine parameter setting corresponding to specific output combination is elaborated. Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses - MRR and ASR are still difficult. Advanced structural risk minimization based learning system - support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM - C, ? and ? are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination.

Improving Grindability of Inconel 600 Using Alumina Wheel through Pneumatic Barrier Assisted Fluid Application

A Stiff Air Layer, Formed around a Rotating Grinding Wheel, Tends to Restrict Grinding Fluid Penetrate Deep inside the Grinding Zone. for this, much Fluid Is Wasted, and Thermal Defects in Grinding May Not Be Controlled. in the Present Experimental Work, a Flood Cooling System with Pneumatic Barrier Is Used for Delivering Grinding Fluid. the Pneumatic Barrier Breaks the Stiff Air Layer, and Therefore, Grinding Fluid Reaches Grinding Zone. an Exotic Nickel Base Alloy, Inconel 600, Is Taken for Surface Grinding Experiments Using an Infeed of 30 µm under Different Environmental Conditions. the Effect of Different Experimental Conditions Is Investigated in Respect of Grinding Force, Chip Formation, Surface Quality and Wheel Condition. the Experimental Result Shows that Grinding Forces and Wheel Wear Are Remarkably Reduced, and Favourable Chips and Good Surface Quality Are Obtained with the Use of Flood Cooling along with Pneumatic Barrier Setup Compared to other Environmental Conditions.

Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN

Delamination in the drilling of polyester composite reinforced with chopped fiberglass is a problematic phenomenon. The material’s structural integrity is reduced by delamination, which results in poor tolerance during assembly and is a primary reason for decreased performance. Surface roughness is another important factor to consider when drilling fiber-reinforced plastics, as surface roughness causes failures by inducing high stresses in rivets and screws. Due to the random orientation of fiberglass and the non-homogenous, anisotropic properties of this material, an exact mathematical model has not been developed yet. Instead, modelling by artificial neural networks (ANNs) is adopted. In the present work, a multilayer perception ANN architecture has been developed with a feed-forward back-propagation algorithm. The algorithm uses material thickness, drill diameter, spindle speed, and feed rate as input parameters and delamination factor (Fd) at the entrance of the drilled hole, average surface roughness (Ra), and root mean square surface roughness (Rq) as the output parameters. The ANN model is then used to develop response surfaces to examine the influence of various input parameters on different response parameters. The developed model predicts that surface roughness increases with increases in feed rate and that a smaller-diameter drill will be advantageous in reducing surface roughness. A reduced feed rate will minimize delamination as well.

A simple procedure for searching pareto optimal front in machining process: electric discharge machining

Optimum control parameter setting in complex and stochastic type processes is one of the most challenging problems to the process engineers. As such, effective model development and determination of optimal operating conditions of electric discharge machining process (EDM) are reasonably difficult. In this apper, an easy to handle optimization procedure, weight-varying multiobjective simulated annealing, is proposed and is applied to optimize two conflicting type response parameters in EDM--material removal rate (MRR) and average surface roughness (Ra) simultaneously. A solution set is generated. The Pareto optimal front thus developed is further modeled. An inverse solution procedure is devised so that near-optimum process parameter settings can be determined for specific need based requirements of process engineers. The results are validated.

Study of the Behavior of Air Flow around a Grinding Wheel under the Application of Pneumatic Barrier

In grinding process, a stiff air layer is generated around the wheel due to rotation of the porous grinding wheel at a high speed. This stiff air layer restricts fluid to reach deep inside the grinding zone. Conventional method of fluid delivery system is not capable of penetrating this stiff air layer, and generally, results in wastage of large amount of grinding fluid that leads to environmental pollution. Several attempts have been made to adopt certain means to improve better penetration of grinding fluid inside the grinding zone. In this work, formation of stiff air layer has been studied experimentally by measuring the variation of air pressure around grinding wheel periphery at different conditions. A pneumatic barrier set-up has been developed first time for restricting the stiff air layer around grinding wheel. The reduction of air pressure around grinding wheel has been observed at various pneumatic pressures. Using the pneumatic barrier, maximum reduction of air pressure up to 53% has been observed experimentally. This pneumatic barrier system reduces deflection of grinding fluid away from wheel, and hence, the reduction in wastage of grinding fluid, leading to less problem related to environment.

Multi-objective optimisation of abrasive water jet machining responses by simulated annealing and particle swarm

Optimum control parameter settings in complex and stochastic type processes are one of the most challenging problems to the process engineers. As such, effective model development and determination of optimal operating conditions of abrasive water jet machining process (AWJM) are reasonably difficult. In this article new modifications are proposed on two single-objective optimisation techniques, simulated annealing and particle swarm optimisation and applied to optimise two response parameters in AWJM – material removal rate (MRR) and depth of cut (DOC) simultaneously. For the optimisation purpose statistical models are developed from experimental data obtained from AWJ machining of amorphous material – borosilicate glass. Optimum results are validated and it is suggested to employ particle swarm-based proposed multi-objective optimisation technique in present case for newly modelled system due to its fast convergence and low memory space requirement.

Analyzing Process Capability of Drilling on Glass Fiber Reinforced Polyester (GFRP) Composites with Taguchi Loss Function

The delamination is a major problem associated with the drilling of chopped glass fiber reinforced polyester (GFRP) composites. It reduces the structural integrity of the material, results in poor assembly tolerance and has the potential for long term performance deterioration. Thus it is important to evaluate the process capability of drilling on this material. It will thereby reveal the spectrum of delamination to be accepted from the view point of process capability for a chosen range of material thickness, drill diameter, speed and feed rate combinations. In the present analysis, therefore, Taguchi loss function is derived for the particular process based on the experimental data. Process capability index is then evaluated. The range of delamination factor is determined based on the consideration that process capability index should be one or more. The results are presented.

Evaluation for Chaos in EDM Generated Surface Topography

Machined surface carries the inherent features of machining process. Investigation of surface topography generated by machining process is helpful to extract the features of surface development process. In the present study, roughness profiles measured on machined surface generated by EDM are considered as time series and used for extraction of inherent features of surface topography through phase space reconstruction. Presence of self-similarity in surface topography is assessed by estimating a second order fractal dimension, called as correlation dimension. Saturation of correlation exponents with the increase of embedding dimension indicates the presence of chaos in surface topography.