B. Latha

Analysis on Drilling of Glass Fiber–Reinforced Polymer (GFRP) Composites Using Grey Relational Analysis

Glass fiber–reinforced polymer (GFRP) composite materials are an economic alternative to the engineering materials because of their superior properties. The present work focuses on the use of Grey relational analysis for optimizing the drilling parameters on the surface roughness and the thrust force in the drilling of GFRP composites. Taguchis L9, 3-level orthogonal array is used for the experimentation. Optimal machining parameters are determined by the Grey relational grade obtained from the Grey relational analysis for multiperformance characteristics. Experimental results show that the machining performance in the composite machining process can be improved at optimal drilling conditions.

Analysis of Thrust Force in Drilling Glass Fiber-Reinforced Plastic Composites Using Fuzzy Logic

Glass fiber-reinforced plastic (GFRP) composite materials are finding increased application in many engineering fields. Machining of these materials cannot be avoided to get accuracy and precision fits. Among the various machining processes used, drilling is one of the most important processes and is mainly used in joining of composite structures. A major problem which encountered in drilling of GFRP composite laminate is damage or delamination. The damage due to thrust force is the important concern in drilling, and it leads to poor machinability. In the present work, prediction of thrust force in drilling of composite materials is carried out using fuzzy logic. Fuzzy rule-based model is developed to predict the thrust force in drilling of GFRP composites. The effectiveness of the fuzzy model was compared with the response surface model. Good agreement is observed between the predictive model values and experimental values. The analysis of machining parameters on drilling is carried out using Pareto analysis of variance (Pareto–ANOVA) and analysis of variance (ANOVA).

Modeling and Analysis of Surface Roughness Parameters in Drilling GFRP Composites Using Fuzzy Logic

Glass fiber–reinforced composite materials are finding numerous applications in many engineering and domestic fields due to their excellent mechanical properties and corrosion resistance. Among the machining processes used, drilling is one of the most important processes and is mainly used in joining of composite structures. Maintaining of proper surface roughness in drilled holes is very important and is to be controlled. In the present work prediction of surface roughness in drilling of composite materials is carried out using fuzzy logic. In recent years, fuzzy logic in artificial intelligence has been used in manufacturing engineering for modeling and monitoring. An L27 orthogonal array is used for experimentation. A fuzzy rule–based model is developed to predict the surface roughness in drilling of glass fiber–reinforced plastic (GFRP) composites. Good agreement is observed between the model results and experimental values. The analysis of experimental results is carried out using Pareto analysis of variance (Pareto-ANOVA) and ANOVA and presented in detail.

Optimization of machining parameters in turning GFRP composites using a carbide (K10) tool based on the taguchi method with fuzzy logics

Glass fiber reinforced polymer (GFRP) composite materials are finding increased applications in a variety of engineering fields. Subsequently, the need for accurate, machining of composites has increased enormously. This paper discusses the application of the Taguchi method with fuzzy logic to optimize the machining parameters for machining of GFRP composites with multiple characteristics. A multi-response performance index (MRPI) was used for optimization. The machining parameters viz., work piece (fiber orientation), cutting speed, feed rate, depth of cut and machining time were optimized with consideration of multiple performance characteristics viz., metal removal rate, tool wear, and surface roughness. The results from confirmation runs indicated that the determined optimal combination of machining parameters improved the performance of the machining process.

MODELING AND OPTIMIZATION OF PROCESS PARAMETERS FOR DELAMINATION IN DRILLING GLASS FIBER REINFORCED PLASTIC (GFRP) COMPOSITES

Glass fiber-reinforced composite materials are used in varieties of applications due to their excellent properties. Drilling is an indispensable process for this kind of materials. Delamination due to drilling is an important concern and is to be reduced. In the present work, drilling tests were carried out on computer numeric control (CNC) drilling machine. The parameters considered for the drilling investigations were spindle speed, feed rate and diameter of the drill bits. Multiple regression analysis is used for the modeling of process parameters in drilling of GFRP composites. Taguchis S/N ratio analysis and desirability-based approach are used for the optimization of process parameters for studying the delamination in drilling of GFRP composites. The results revealed that the factor feed rate and drill diameter are the most influential parameters which affects the delamination in drilling of GFRP composites. The interaction between the parameters also affects the delamination in drilling of GFRP composites.

Influence of drill geometry on thrust force in drilling GFRP composites

In this article, influence of drill geometry on thrust force in drilling GFRP composites is analyzed. Glass fiber-reinforced composite materials are finding increased application in different fields such as automobile, ship building, sport goods, etc., due to their excellent properties. Drilling is indispensable process and it cannot be avoided for joining composite structures. In this study, drilling experiments are conducted on these composite materials using CNC drilling machine. Three different drill bits are used for the experimentation. The response analyzed is thrust force. The influence of drill geometry on thrust force in drilling of composite materials is carried out using three different drill bits, namely, ‘Brad and Spur’ drill, ‘multifaceted’ drill, and ‘step’ drill. The analyses of the results are carried out using effect graphs and three-dimensional graphs. The results indicated that the step drills are performing better than the other drills considered.

Optimization of delamination factor in drilling GFR–polypropylene composites

ABSTRACT Glass fiber-reinforced polypropylene composites often replace the conventional materials due to their special or unique mechanical properties. As the applications of these composites increase for a number of industries, drilling of these composites is inevitable for subsequent composite product manufacturing stage. In the drilling of composites, the thrust force is induced during the drilling operation; as a result, it causes damage. This damage is characterized by the delamination factor, which depends on the machining parameters such as speed of the spindle, feed rate, and drill diameter. The study on the delamination in the drilling of glass fiber-reinforced polypropylene is limited and has been carried out comprehensively. The effect of machining parameters on delamination in the drilling of glass fiber-strengthened polypropylene (GFR-PP) composites is studied through the Box–Bhenken design. Response surface method, along with the desirability analysis, is used for modeling and optimization of delamination factor in the drilling. The result proves that the models are effectively used to forecast the delamination in the drilling of GFR-PP composites. Also, the result indicates that the foremost issue that influences the delamination is the feed rate.

Thrust Force Analysis in Drilling Glass Fiber Reinforced/Polypropylene (GFR/PP) Composites

Glass fiber reinforced thermoplastic (GFRTP) composites are an important alternate to the conventional engineering materials owing to their good application-oriented properties. Drilling is unavoidable and an important operation used in automotive and aerospace industries in the assembly stage. The reduction of thrust force is required to minimize delamination. This paper examines the parameters that influence the thrust force on drilling glass fiber reinforced polypropylene (GFR/PP). The experiments are conducted using the Box–Behnken experimental design method. An empirical relation is established for determining the thrust induced in the drilling of GFRTP. The factors that affect the drilling process and their interaction are analyzed and presented in detail.

Fuzzy rule-based modeling of machining parameters for surface roughness in turning carbon particle-reinforced polyamide

Carbon particle-reinforced polyamides (PAs) are finding increasing applications in many engineering fields. Machining of these materials is needed for obtaining near net shape and precision fit. In this work, application of fuzzy logic technique along with the Taguchi’s orthogonal array is used in turning of PA6 to develop a model for predicting surface roughness. There are many factors affecting the surface roughness. Among these factors the machining parameters that play a dominant role are cutting speed, feed, and depth of cut. The adequacy of the fuzzy rule-based model is verified through coefficient of determination. The correlation that exists between the experimental value and the fuzzy model is on the higher side; hence, the fuzzy logic technique can be effective when estimating the surface roughness in turning of PA6.

Application of Artificial Neural Network for the Prediction of Surface Roughness in Drilling GFRP Composites

Composite materials are used in different fields, due to their excellent properties. Glass fiber reinforced composite materials are used in aerospace, automobile, sport goods, etc. Joining by drilling operation is necessary for this composite to perform assembly. Surface roughness of the holes plays an important role in mechanical joints. Good surface leads to the precision fits and efficient joints. The present article discusses the use of artificial neural network (ANN) for the prediction of surface roughness in drilling glass fiber reinforced plastic (GFRP) composites. The experiments are carried out on computer numeric control machining center. The results indicated that the well-trained ANN model could able to predict the surface roughness in drilling of GFRP composites.