The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform
Ramesh Rajguru - One of the best experts on this subject based on the ideXlab platform.
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Grey Fuzzy Multiobjective Optimization of Process Parameters for CNC Turning of GFRP/Epoxy Composites
Procedia Engineering, 2020Co-Authors: Hari Vasudevan, Naresh C. Deshpande, Ramesh RajguruAbstract:Abstract For obtaining close Fits and Tolerances, certain amount of machining has to be carried out on GFRP (Glass Fibre Reinforced Plastic) composites, produced by primary manufacturing processes. A number of cylindrical GFRP composite parts are finish machined by turning. These include axles, bearings, spindles, rolls and steering columns. There is always a tradeoff between quality and productivity during machining operations. Hence it becomes essential to evaluate the optimal cutting parameters setting in order to satisfy these opposing requirements. In this study, a hybrid multiobjective optimization algorithm involving grey and fuzzy coupled with Taguchi methodology is used. Four process parameters, each at three levels are selected for the study viz. cutting tool nose radius, cutting speed, feed rate and depth of cut. Surface roughness parameter Ra, tangential cutting force Fz and material removal rate MRR are the chosen output performance measures. The experimental plan is laid according to Taguchi's orthogonal array L27. Woven fabric based GFRP/ Epoxy tubes produced using hand layup process are finish turned using PCD cutting tool. Grey relational coefficients of the three performance measures are converted into a single multi performance characteristics index (MPCI) using Mamdani type fuzzy inference system. This MPCI is then optimized using Taguchi analysis. The parameter combination of A2B1C1D3, i.e. tool nose radius of 0.8 mm, cutting speed of 120 m/min, feed rate of 0.05 mm/rev and depth of cut of 1.6 mm, is evaluated as the optimum combination. The confirmatory experiment at these settings gave maximum value of MPCI, validating the results.
Rajesh Kumar Verma - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy rule based optimization in machining of glass fiber reinforced polymer (GFRP) composites
2012Co-Authors: Rajesh Kumar VermaAbstract:With the increasing use of Fiber Reinforced Polymer (FRP) composites outside the defense, space and aerospace industries; machining of these materials is gradually assuming a significant role. The current knowledge of machining FRP composites is in transition phase for its optimum economic utilization in various fields of applications. Therefore, material properties and theoretical mechanics have become the predominant research areas in this field. With increasing applications, economical techniques of production are indeed very important to achieve fully automated large-scale manufacturing cycles. Although FRP composites are usually molded, for obtaining close Fits and Tolerances and also achieving near-net shape, certain amount of machining has to be carried out. Due to their anisotropy, and non-homogeneity, FRP composites face considerable problems in machining like fibre pull-out, delamination, burning, etc. There is a remarkable difference between the machining of conventional metals and their alloys and that of composite materials. Further, each composite differs in its machining behavior since its physical and mechanical properties depend largely on the type of fibre, the fibre content, the fibre orientation and variabilities in the matrix material. Considerable amount of literature is readily available on the machinability of conventional metals/alloys and also polymers to some extent; with very limited work on FRP composites. Therefore, machining process optimization for all types FRP composites is still an emerging area of research. In this context, the present research highlights a multi-objective extended optimization methodology to be applied in machining FRP-polyester/epoxy composites with contradicting requirements of quality as well as productivity. Attempt has been made to develop a robust methodology for multi-response optimization in FRP composite machining 6 for continuous quality improvement and off-line quality control. Design of Experiment(DOE) has been be selected based on Taguchi’s orthogonal array design with varying process control parameters like: spindle speed, feed rate and depth of cut. Multiple surface roughness parameters of the machined FRP product along with Material Removal Rate (MRR) of the machining process have been optimized simultaneously. A Fuzzy Inference System (FIS) integrated with Taguchi’s philosophy has been proposed for providing feasible means for meaningful aggregation of multiple objective functions into an equivalent single performance index (MPCI). This Multi Performance Characteristic Index (MPCI) has been optimized finally. Detailed methodology of the proposed fuzzy based optimization approach has been illustrated in this reporting and validated by experiments.
Aman Tukrel - One of the best experts on this subject based on the ideXlab platform.
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Optimization of turning parameters for the finest surface roughness characteristics using desirability function analysis coupled with fuzzy methodology and ANOVA
Materials Today: Proceedings, 2020Co-Authors: Kalpesh Tank, Nishith Shetty, Gaurang Panchal, Aman TukrelAbstract:Abstract Evolution of technology is as essential as it is inevitable, and as technology improves so does the advancements in the materials used. Fundamentally, the need for light yet strong material is the one we need to address. One such material is Glass Fiber Reinforced Plastic (GFRP). This composite material is being widely used in various industries like aerospace, chemical and construction industries. Eventually, this calls for an economical manufacturing method for GFRP in order to facilitate an automated and therefore, quick production of this composite. However, it is also vital to maintain the accuracy in these methods to obtain close Fits and Tolerances. This paper is an attempt to demonstrate the implementation of Desirability Functional Analysis combined with Fuzzy logic to optimize the machining parameters for turning of GFRP. Aim of the experimentation was to achieve the best surface characteristics for the given input parameters. The input parameters which we selected were cutting speed, tool nose radius, feed rate and depth of cut at three different levels of each. L27 orthogonal array based on Taguchi philosophy was used to conduct the experiment and output parameters selected were three surface roughness characteristics Ra, Rt and Rsm. Optimum combination of input parameters for best surface finish characteristics is obtained at A1B3C1D2, i.e. tool nose radius at level 1 (0.4mm), cutting speed at level 3 (200mm/min), feed rate at level 1 (0.05mm/rev) and depth of cut at level 2 (1mm). Analysis of Variance (ANOVA) was also performed to find the input parameter of which the impact is maximum on the output response and found it to be feed rate. A confirmation test is also carried out to validate the results obtained from this optimization. Therefore, the implementation of Desirability Functional Analysis coupled with Fuzzy logic is an effective approach in optimizing the machining parameters of GFRP.
Hari Vasudevan - One of the best experts on this subject based on the ideXlab platform.
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Grey Fuzzy Multiobjective Optimization of Process Parameters for CNC Turning of GFRP/Epoxy Composites
Procedia Engineering, 2020Co-Authors: Hari Vasudevan, Naresh C. Deshpande, Ramesh RajguruAbstract:Abstract For obtaining close Fits and Tolerances, certain amount of machining has to be carried out on GFRP (Glass Fibre Reinforced Plastic) composites, produced by primary manufacturing processes. A number of cylindrical GFRP composite parts are finish machined by turning. These include axles, bearings, spindles, rolls and steering columns. There is always a tradeoff between quality and productivity during machining operations. Hence it becomes essential to evaluate the optimal cutting parameters setting in order to satisfy these opposing requirements. In this study, a hybrid multiobjective optimization algorithm involving grey and fuzzy coupled with Taguchi methodology is used. Four process parameters, each at three levels are selected for the study viz. cutting tool nose radius, cutting speed, feed rate and depth of cut. Surface roughness parameter Ra, tangential cutting force Fz and material removal rate MRR are the chosen output performance measures. The experimental plan is laid according to Taguchi's orthogonal array L27. Woven fabric based GFRP/ Epoxy tubes produced using hand layup process are finish turned using PCD cutting tool. Grey relational coefficients of the three performance measures are converted into a single multi performance characteristics index (MPCI) using Mamdani type fuzzy inference system. This MPCI is then optimized using Taguchi analysis. The parameter combination of A2B1C1D3, i.e. tool nose radius of 0.8 mm, cutting speed of 120 m/min, feed rate of 0.05 mm/rev and depth of cut of 1.6 mm, is evaluated as the optimum combination. The confirmatory experiment at these settings gave maximum value of MPCI, validating the results.
Jiejian Feng - One of the best experts on this subject based on the ideXlab platform.
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A 2D approach for quantifying footwear fit
2020Co-Authors: Channa P. Witana, Ravindra S. Goonetilleke, Jiejian FengAbstract:Fit is an important consideration when purchasing footwear, even though fitting footwear to feet is still rather cumbersome and very unscientific. Some researchers have proposed methodologies to quantify the degree of fit so that matching shoes to feet can be performed without trying them on. This paper reports an experimental study to show the feasibility of such a method. Twenty participants wore and rated the fit of three different dress shoes. By matching the foot outlines to the last outlines, the dimensional differences were quantified and plotted. The plots revealed four distinct minimums, and the forefoot and midfoot fit ratings were strongly correlated with the dimensional differences at these four locations. These locations and the corresponding dimensions can give manufacturers information about the degree of fit so that in the long term, it may be possible to generate a plot similar to the ISO Fits and Tolerances Chart, for the design and selection of good-fitting footwear.