Development of 3D Angle-Interlock Woven Preforms for Composites

The advent of three dimensional (3D) reinforcements has been mainly to overcome the issue of delamination and improve upon the damage tolerance properties by introducing fibres in the thickness direction for advanced composite applications. 3D preforms can be developed using various techniques. Angle-interlock weaving is one of them. This paper details about the efforts being put at CSIR-NAL for developing angle-interlock woven preforms. Four types of angle-interlock structures viz., layer-to-layer and through thickness (both with and without stuffer yarns) were developed using 6K, 400 Tex TC-33 grade Carbon tows on a custom designed handloom. The preforms without stuffer yarns had 4 layers of warp and were of 1.5± 0.2 mm thick. Preforms with stuffer yarns had 6 layers of warp (including 2 stuffer yarn layers) and were of 2.3±0.1 mm thick. Thermoset composites were prepared from these preforms using EPOLAM 2063 (an epoxy based resin system) by RTM process. The fibre weight fraction for these composites ranged from 0.53 to 0.58 and they were subjected to mechanical tests such as tensile, flexural and interlaminar shear strength. Test results showed improved response (in the warp direction) with respect to shear properties while the tensile and flexural properties were equivalent to that of the plain woven composites

Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance

Novel Design of Cocured Composite ‘T’ Joints with Integrally Woven 3D Inserts

Composites can be exploited to their full potential when cocured, wherein different parts are made and bonded together in a single cure operation to realise an integral structure. The key element in a typical cocured construction is T-joint, which forms the primary load transfer mechanism between the skin and stiffener in a structural assembly. T-joints are particularly vulnerable for pull off loads and researchers are looking at various techniques to improve the pull strength viz. stitching, tufting, 3D weaving, multilayer weaving, 3D braiding and the like. The present work uses a novel technique to improve the strength of T-joints by employing a hybrid design wherein an integral 3D ‘T’ insert is interleaved with a conventional T-joint. Inserts were woven using 3K and 6K carbon tows and incorporated in T-joints using CSIR-NAL proprietary process called ‘Vacuum Enhanced Resin Infusion Technology (VERITy)’ process. Several configurations of T-joints were tested in an UTM in the pull mode till the failure to assess the efficacy of integrally woven 3D inserts. It was observed that the initial failure load was nearly the same across the various T-joint configurations tested whereas the maximum failure loads were quite different. The normalised strength of T-joints with integrally woven 3D inserts in pull off mode was enhanced by about 30% when compared T-joints without the insert and thus vindicating the usage of integrally woven 3D insert in a cocured T-joint. The insert is conceived in such a way that it can be easily incorporated in the design of cocured structures

Determination of ‘Pressure Application Window’ (PAW) for the Vacuum Enhanced Resin Infusion Technology (VERITy) Process

Vacuum Enhanced Resin Infusion Technology (VERITY) is a hybridization of the Vacuum Assisted Resin Transfer Moulding (VARTM) and the autoclave molding processes. In this technique, innovative tooling concepts have been adopted and more importantly, an external pressure is applied at an appropriate time, after infusion has been completed. The application of external pressure ensures uniform consolidation, optimum fiber content, and low void content in the laminates. In the present study, a systematic approach has been taken to determine the pressure application window (PAW) for the RTM 120 resin system. Viscosity measurements, dielectric measurements and squeeze flow experiments were employed to determine the PAW. Parameters of void content and interlaminar shear strength (ILSS) have been chosen to evaluate the PAW determined

EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification

EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT THROUGH NEUROPHYSIOLOGIC TESTS

The cognitive signal processing is one of the important interdisciplinary field came from areas of life sciences, psychology, psychiatry, engi-neering, mathematics, physics, statistics and many other fields of research. Neurophysiologic tests are utilized to assess and treat brain injury, dementia, neurological conditions, and useful to investigate psychological and psychiatric disorders. This paper presents an ongoing research work on development of EEG interface device based on the principles of cognitive assessments and instrumentation. The method proposed engineering and science of cogni-tive signal processing in case of brain computer in-terface based neurophysiologic tests. The future scope of this study is to build a low cost EEG device for various clinical and pre-clinical applications with specific emphasis to measure the effect of cognitive action on human brain

Eeg based cognitive workload classification during nasa matb-ii multitasking

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification