Process parameters optimization on machining force and delamination factor in milling of GFRP composites using grey relational analysis

313-320<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:" times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";="" mso-ansi-language:en-gb;mso-fareast-language:en-us;mso-bidi-language:ar-sa"="" lang="EN-GB">In this study, the optimization of process parameters for milling of glass fiber reinforced polymer (GFRP) composites using grey relational analysis has been investigated. Experiments are conducted using helix angle, spindle speed, feed rate, depth of cut and fiber orientation angle as typical process parameters. The grey relational analysis (GRA) is adopted to obtain grey relational grade for milling process with multiple characteristics namely machining force and delamination factor. Analysis of variance (ANOVA) is performed to get the contribution of each parameter on the performance characteristics and it is observed that fiber orientation angle and feed rate are the most significant process parameters that affect the milling of GFRP composites. The experimental results reveal that, the helix angle of 25o, spindle speed of 3000 rpm, feed rate of 500 mm/min, depth of cut of 1 mm and fiber orientation angle of 15o is the optimum combination for lower machining force and lower delamination factor. The experimental results for the optimal setting show that there is considerable improvement in the process.</span

Free vibration analysis of a composite reinforced with natural fibers employing finite element and experimental techniques

Composites have been extensively used in the modern era in wide range of applications. This article deals with the study of vibration characteristics of natural fiber reinforced composite beam in which Aloe vera fiber is reinforced in epoxy matrix and is used in combination with glass-epoxy. Finite element method of analysis is adopted using Ansys software. Effects of several parameters like laminate stacking sequence, support conditions, material hybridization, and number of layers, etc. on the natural frequency of natural fiber reinforced composite beam are analyzed. Experimental study on Aloe vera fiber reinforced composite beam is also conducted for validation

ACTSEA : annotated corpus for Tamil & Sinhala emotion analysis

The purpose of text emotion analysis is to detect and recognize the classification of feeling expressed in text. In recent years, there has been an increase in text emotion analysis studies for English language since data were abundant. Due to the growth of social media large amount data are now available for regional languages such as Tamil and Sinhala as well. However, these languages lack necessary annotated corpus for many NLP tasks including emotion analysis. In this paper, we present our scalable semi-automatic approach to create an annotated corpus named ACTSEA for Tamil and Sinhala to support emotion analysis. Alongside, our analysis on a sample of the produced data and the useful findings are presented for the low resourced NLP community to benefit. For ACTSEA, data were gathered from twitter platform and annotated manually after cleaning. We collected 600280 (Tamil) and 318308 (Sinhala) tweets in total which makes our corpus largest data collection which is currently available for these languages