Parametric Optimization to Minimise the Surface Roughness on the Machining of GFRP Composites

The present investigation focuses on the parametric influence of machining parameters on the surface finish obtained in turning of glass fiber reinforced polymer （GFRP） composites. The experiments were conducted based on Taguchi＇s experimental design technique. Response surface methodology and analysis of variance （ANOVA） were used to evaluate the composite machining process to perform the optimization. The results revealed that the feed rate was main influencing parameter on the surface roughness. The surface roughness increased with increasing the feed rate but decreased with increasing the cutting speed. Among the other parameters, depth of cut was more insensitive. The predicted values and measured values were fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of GFRP composites with 95% confidence intervals. Using such model could remarkablely save the time and cost.

A Construction of Variable Strength Covering Arrays

Covering arrays(CA)of strength t,mixed level or fixed level,have been applied to software testing to aim for a minimum coverage of all t-way interactions among components.The size of CA increases with the increase of strength interaction t,which increase the cost of software testing.However,it is quite often that some certain components have strong interactions,while others may have fewer or none.Hence,a better way to test software system is to identify the subsets of components which are involved in stronger interactions and apply high strength interaction testing only on these subsets.For this,in 2003,the notion of variable strength covering arrays was proposed by Cohen et al.to satisfy the need to vary the size of t in an individual test suite.In this paper,an effective deterministic construction of variable strength covering arrays is presented.Based on the construction,some series of variable strength covering arrays are then obtained,which are all optimal in the sense of their sizes.In the procedure,two classes of new difference matrices of strength 3 are also mentioned.

Aerial vehicle guidance based on passive machine learning technique

Purpose-The purpose of this paper is to develop a new guidance scheme for aerial vehicles based on artificial intelligence.The new guidance scheme must be able to intercept maneuvering targets with higher probability and precision compared to existing algorithms.Design/methodology/approach-A simulation setup of the aerial vehicle guidance problem is developed.A model-based machine learning technique known as Q-learning is used to develop a new guidance scheme.Several simulation experiments are conducted to train the new guidance scheme.Orthogonal arrays are used to define the training experiments to achieve faster convergence.A wellknown guidance scheme known as proportional navigation guidance(PNG)is used as a base model for training.The new guidance scheme is compared for performance against standard guidance schemes like PNG and augmented proportional navigation guidance schemes in presence of sensor noise and computational delays.Findings-A new guidance scheme for aerial vehicles is developed using Q-learning technique.This new guidance scheme has better miss distances and probability of intercept compared to standard guidance schemes.Research limitations/implications-The research uses simulation models to develop the new guidance scheme.The new guidance scheme is also evaluated in the simulation environment.The new guidance scheme performs better than standard existing guidance schemes.Practical implications-The new guidance scheme can be used in various aerial guidance applications to reach a dynamically moving target in three-dimensional space.Originality/value-The research paper proposes a completely new guidance scheme based on Q-learning whose performance is better than standard guidance schemes.