Composite materials are used in varieties of applications and are mainly used in structural components. Drilling is one of the important operations in composite structure, often a final operation during assembly. Delaminations in drilling of composite structures are a serious problem and lead to rejection and impose heavy loss. In the present investigation, delamianation associated with drilling has been studied using experimental design approach. Two different cutting tools were tried for experimentation. The experiments were conducted by using high-speed steel-made twist drill and 4-flute cutter. Empirical models were developed for predicting delamination factor in drilling GFRP composites. Regression analysis and analysis of variance (ANOVA) were used for analysis. The adequacies of the developed models have been verified by calculating correlation coefficient. The effect of cutting parameters on drilling such as spindle speed, feed, and their interactions are studied in detail.

Medium density fiberboard (MDF) panels are an industrial wood product which belongs to wood-based composite. MDF panels are made with wood fibers clued together with resin by heat and pressure. Nowadays the MDF products are preferred over solid wood in many applications due to certain comparative advantage and mainly used in furniture industry. This article discusses the use of Taguchi and response surface methodologies for predicting the surface roughness in drilling MDF material by a physical vapor deposition TiN coated carbide step drill bits. The experiments are conducted based on Taguchi’s experimental design technique. The drilling parameters used are spindle speed, feed rate, and drill diameter. The effect of drilling parameters on the surface roughness is evaluated and discussed in detail. The second order model has been established between the drilling parameters and the surface roughness using response surface methodology. The experimental result reveals that the most significant drilling parameter for the surface roughness is feed rate followed by the cutting speed. The predicted values and measured values are fairly close which indicates that the developed model can be effectively used to predict the surface roughness in the drilling of MDF panels. The predicted results are validated by using validation experiments.

Two different alternatives over conventional leaf spring are developed in this work, such as composite and hybrid composite-based leaf springs. In composite type, carbon-epoxy based multi-leaf springs are used and the combination of metal and carbon fiber material is used to develop hybrid composite leaf springs. The outputs namely deflection, maximum stress, and fatigue tests are determined using the finite element analysis approach. Besides, the optimal outcomes are set as the target for hybrid deep neural network-rat swarm optimization based prediction. The findings shows that the hybrid composite leaf spring provides better and enhanced results than the composite leaf spring.