47 research outputs found
Turning of Carbon Fiber Reinforced Polymer (CFRP) Composites: Process Modeling and Optimization using Taguchi Analysis and Multi-Objective Genetic Algorithm
Carbon Fiber Reinforced Polymer (CFRP) composites have been widely used in aerospace, automotive, nuclear, and biomedical industries due to their high strength-to-weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties in non-oxidative atmospheres. Machining of CFRP composites has always been a challenge for manufacturers. In this research, a comparative study was performed between the optimal machining parameters of coated and uncoated carbide inserts obtained from the Multi-Objective Genetic Algorithm during turning of CFRP composites. It was found that coated carbide inserts provide lower tool wear and surface roughness, but higher cutting forces compared to those of uncoated carbide inserts during turning of CFRP composites. Taguchi Analysis was performed to investigate the effects of machining parameters (cutting speed, feed rate and depth of cut) on the output characteristics including cutting force, surface roughness and tool wear. The feed rate was found as the most significant machining parameter in turning of CFRP composites to minimize cutting force and surface roughness using both coated and uncoated carbide inserts. However, feed rate and cutting speed has been found as the most significant machining parameters for coated and uncoated carbide inserts respectively to minimize the tool wear. Regression Analysis has been performed to develop mathematical models for cutting force, surface roughness and tool wear as a function of cutting speed, feed rate and depth of cut. Higher R2 values and well fitted regression lines of normal probability plots in regression analysis indicate that the coefficients of mathematical models are statistically significant. The significance of this study is to emphasize the differences of performances between coated and uncoated carbide inserts during turning of CFRP composites in terms of cutting force, tool wear and surface roughness with the combination of different machining parameters (cutting speed, feed rate and tool wear) using data analysis tools such as Taguchi Analysis, Regression Analysis and Multi-Objective Optimization
Surface roughness modeling of CBN hard steel turning
Study in the paper investigate the influence of the cutting conditions parameters on surface roughness parameters during turning of hard steel with cubic boron nitrite cutting tool insert. For the modeling of surface roughness parameters was used central compositional design of experiment and artificial neural network as well. The values of surface roughness parameters Average mean arithmetic surface roughness (Ra) and Maximal surface roughness (Rmax) were predicted by this two-modeling methodology and determined models were then compared. The results showed that the proposed systems can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments modeling technique and artificial neural network can be effectively used for the prediction of the surface roughness parameters of hard steel and determined significantly influential cutting conditions parameters
Machinability of Carbon Fiber Reinforced Polymer (CFRP) Composites: Modeling and Optimization Using Taguchi Analysis and Multi-Objective Genetic Algorithm
Carbon Fiber Reinforced Polymer (CFRP) composites have been widely used in aerospace, automotive, nuclear, and biomedical industries due to their high strength to weight ratio, corrosion resistant durability and excellent thermo-mechanical properties in non-oxidative atmospheres. Machining of CFRP composites has always been a challenge for the manufacturers. In this study, turning operation has been performed on CFRP composites to investigate the effects of cutting parameters namely cutting speed, feed rate and depth of cut on the output characteristics including cutting force, surface roughness and tool wear using Taguchi Analysis. Regression Analysis has been used to develop mathematical model for cutting force, surface roughness and tool wear as a function of cutting speed, feed rate and depth of cut. A comparative study has been performed between coated and uncoated carbide inserts based on the optimal parameters in multi-objective optimization of cutting force, tool wear and surface roughness using Multi-Objective Genetic Algorithm (MOGA) during turning of CFRP composites in a CNC lathe machine. It was found that coated carbides provide lower tool wear and surface roughness, but higher cutting force compared to those of uncoated carbides during turning of CFRP composites. Feed rate has been found as the most significant parameters in turning of CFRP composites to minimize cutting force, tool wear and surface roughness. Cutting speed has been found very significant in tool wear when using uncoated carbide inserts
Optimization and modeling of process parameters in multi-hole simultaneous drilling using taguchi method and fuzzy logic approach
In industries such as aerospace and automotive, drilling many holes is commonly required to assemble different structures where machined holes need to comply with tight geometric tolerances. Multi-spindle drilling using a poly-drill head is an industrial hole-making approach that allows drilling several holes simultaneously. Optimizing process parameters also improves machining processes. This work focuses on the optimization of drilling parameters and two drilling processes-namely, one-shot drilling and multi-hole drilling-using the Taguchi method. Analysis of variance and regression analysis was implemented to indicate the significance of drilling parameters and their impact on the measured responses i.e., surface roughness and hole size. From the Taguchi optimization, optimal drilling parameters were found to occur at a low cutting speed and feed rate using a poly-drill head. Furthermore, a fuzzy logic approach was employed to predict the surface roughness and hole size. It was found that the fuzzy measured values were in good agreement with the experimental values; therefore, the developed models can be effectively used to predict the surface roughness and hole size in multi-hole drilling. Moreover, confirmation tests were performed to validate that the Taguchi optimized levels and fuzzy developed models effectively represent the surface roughness and hole size
Experimental Investigations on Machining of CFRP Composites: Study of Parametric Influence and Machining Performance Optimization
Carbon Fiber Reinforced Polymer (CFRP) composites are characterized by their excellent mechanical properties (high specific strength and stiffness, light weight, high damping capacity etc.) as compared to conventional metals, which results in their increased utilization especially for aircraft and aerospace applications, automotive, defense as well as sporting industries. With increasing applications of CFRP composites, determining economical techniques of production is very important. However, as compared to conventional metals, machining behavior of composites is somewhat different. This is mainly because these materials behave extremely abrasive during machining operations. Machining of CFRP appears difficult due to their material discontinuity, inhomogeneity and anisotropic nature. Moreover, the machining behavior of composites largely depends on the fiber form, the fiber content, fiber orientations of composites and the variability of matrix material. Difficulties are faced during machining of composites due to occurrence of various modes of damages like fiber breakage, matrix cracking, fiber–matrix debonding and delamination. Hence, adequate knowledge and in-depth understanding of the process behavior is indeed necessary to identify the most favorable machining environment in view of various requirements of process performance yields. In this context, present work attempts to investigate aspects of machining performance optimization during machining (turning and drilling) of CFRP composites. In case of turning experiments, the following parameters viz. cutting force, Material Removal Rate (MRR), roughness average (Ra) and maximum tool-tip temperature generated during machining have been considered as process output responses. In case of drilling, the following process performance features viz. load (thrust), torque, roughness average (of the drilled hole) and delamination factor (entry and exit both) have been considered. Attempt has been made to determine the optimal machining parameters setting that can simultaneously satisfy aforesaid response features up to the desired extent. Using Fuzzy Inference System (FIS), multiple response features have been aggregated to obtain an equivalent single performance index called Multi-Performance Characteristic Index (MPCI). A nonlinear regression model has been established in which MPCI has been represented as a function of the machining parameters under consideration. The aforesaid regression model has been considered as the fitness function, and finally optimized by evolutionary algorithms like Harmony Search (HS), Teaching-Learning Based Optimization (TLBO), and Imperialist Competitive Algorithm (ICA) etc. However, the limitation of these algorithms is that they assume a continuous search within parametric domain. These algorithms can give global optima; but the predicted optimal setting may not be possible to adjust in the machine/setup. Since, in most of the machines/setups, provision is given only to adjust factors (process input parameters) at some discrete levels. On the contrary, Taguchi method is based on discrete search philosophy in which predicted optimal setting can easily be achieved in reality.However, Taguchi method fails to solve multi-response optimization problems. Another important aspect that comes into picture while dealing with multi-response optimization problems is the existence of response correlation. Existing Taguchi based integrated optimization approaches (grey-Taguchi, utility-Taguchi, desirability function based Taguchi, TOPSIS, MOORA etc.) may provide erroneous outcome unless response correlation is eliminated. To get rid of that, the present work proposes a PCA-FuzzyTaguchi integrated optimization approach for correlated multi-response optimization in the context of machining CFRP composites. Application potential of aforementioned approach has been compared over various evolutionary algorithms
Monitoring of Tool Wear and Surface Roughness Using ANFIS Method During CNC Turning of CFRP Composite
Carbon fiber-reinforced plastic (CFRP) is gaining wide acceptance in areas including sports, aerospace and automobile industry . Because of its superior mechanical qualities and lower weight than metals, it needs effective and efficient machining methods. In this study, the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated for CFRP composite. For machining of CFRP, CNC turning operation with coated carbide tool is used. An ANFIS model with two MISO system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameter. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. However, Design of Experiments (DOE) confirmation of this experiment fails because of multi-collinearity problem in the dataset and insufficient experimental data points to predict the tool wear and surface roughness effectively using ANFIS methodology. Therefore, the result of this experiment do not provide a proper representation, and result in a failure to conform to a correct DOE approach
Modeling and optimization of parameters for minimizing surface roughness and tool wear in turning Al/SiCp MMC, using conventional and soft computing techniques
Aluminium alloy with silicon carbide particulate (Al/SiCp) reinforced metal matrix composite (MMC) are used within a variety of engineering applications due to their excellent properties in comparison with non-reinforced alloys. This presented work attempted the development of predictive modeling and optimization of process parameters in the turning of Al/SiCp MMC using a titanium nitride (TiN) coated carbide tool. The surface roughness Ra as product quality and tool wear VB for improved tool life were considered as two process responses and the process parameters were cutting speed v, feed f, and depth of cut d. Two modeling techniques viz., response surface methodology (RSM) and artificial neural network (ANN) were employed for developing Ra and VB predictive models and their predictive capabilities compared. Four different RSM models were tried out viz., linear, linear with interaction, linear with square, and quadratic models. The linear with interaction model was found to be better in terms of predictive performance. The optimum operating zone was identified through an overlaid contour plot generated as a response surface. Parameter optimization was performed for minimizing Ra and VB as a single objective case using a genetic algorithm (GA). The minimum Ra and VB obtained were 2.52 μm and 0.31 mm, respectively. Optimizations of multi-response characteristics were also performed employing desirability function analysis (DFA). The optimal parameter combination was obtained as v = 50 m/min, f = 0.1 mm/rev and d = 0.5 mm being the best combined quality characteristics. The prediction errors were found as 4.98 % and 3.82 % for Ra and VB, respectively, which showed the effectiveness of the method
Studies on some aspects of composite machining
In this technological era, globalization has brought new challenges for the manufacturing industries, towards improving quality and productivity simultaneously, by reducing costs and increasing the performance of the machine tools. Process simulation is one of the most important aspects in any manufacturing/production context. With upcoming worldwide applications of Glass Fiber Reinforced Polymer (GFRP) composites; machining has become an important issue which needs to be investigated in detail. Process efficiency is measured in the sense of different objective functions or process output responses weather they are acceptable for a given targeted value or tolerance. Therefore, finding the best optimal parameter combination can lead towards improvement of the overall process efficiency. The performance of the process can be improved by applying optimization to the simulation model with respect to its process parameters. Single objective optimization method often creates conflict, when more than one response variables need to be optimized simultaneously. In order to minimize cost and to maximize production rate simultaneously; multi-objective optimization approach should be explored. In this thesis, multi-objective optimization methods have been reported to study some aspects of machining of composite material i.e. Glass Fiber Reinforced Polymer (GFRP) composite. The various process parameters used were cutting speed, feed rate, and depth of cut. Optimal cutting condition has been aimed to be evaluated to satisfy contradicting multi-requirements of product quality as well as productivity. This thesis has intended towards focusing two important aspects (i) when it comes to improve productivity, material removal rate has been considered and for (ii) quality of the machined composite product, various surface roughness characteristics of statistical importance have been investigated
Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel
Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems
An experimental and simulation study on parametric analysis in turning of inconel 718 and GFRP composite using coated and uncoated tools
Process simulation is one of the important aspects in any
manufacturing/production context because it generates the scenarios to gain insight into process performance in reasonable time and cost. With upcoming worldwide applications of Inconel 718 and Glass Fiber Reinforced Polymer (GFRP) composites, machining has become an important issue which needs to be investigated in detail. In turning of hard materials (such as Inconel 718), cutting tool environment features high-localized temperatures (~1000ºC) and high stress (~700 MPa) due to contact between cutting tool and work piece. The tool may experience
repeated impact loads during interrupted cuts and the work piece chips may chemically interact with the tool materials. Therefore, the use of coated tool is preferred for turning of Inconel 718. It is observed that performance of machining process is influenced by different machining parameters such as spindle speed, depth of cut and feed rate as in case of turning. Material removal rate (MRR) and flank wear in turning of Inconel 718 using physical vapour deposition (PVD) and chemical vapour deposition (CVD) coated on carbide insert tool are reported. A simulation model based on finite element approach is proposed using DEFORM 3D software. The simulation results are validated with experimental results. The results indicate that simulation model can be effectively used to predict the flank wear and MRR in turning of Inconel 718. For simultaneous optimization of multiple responses, a fuzzy inference system (FIS) is used to convert multiple responses into a single equivalent response so that uncertainty and fuzziness in data can be addressed in an effective manner. The single response characteristics so generated is known as Multi Performance characteristic Index (MPCI). A non-linear empirical model has been developed using regression analysis between MPCI and process parameters. The optimal process parameters are obtained by a recent population-based optimization method known as imperialistic competitive algorithm (ICA). Analysis of variance (ANOVA) is performed to identify the most influencing factors for all the performance characteristics. The optimal conditions of process parameters during turning of Inconel 718 and GFRP composites are reported. It is observed that flank wear is combatively less when machined with PVD coated tool
than CVD coated tool in turning of both Inconel 718 and GFRP composite