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The purpose of this paper is to find out the optimum machining parameters using Taguchi technique with principal component analysis (PCA) during end milling of GFRP composites.

In multi-objective optimization, weight criteria of each objective are important for producing better and accurate solutions. This method has been employed for simultaneous minimization of surface roughness, cutting force and delamination factor. Experiments were planned using Taguchi’s orthogonal array with the machining parameters, namely, helix angle of the end mill cutter, spindle speed, feed rate and depth of cut were optimized with considerations of multiple response characteristics, including machining force, surface roughness and delamination as the responses. PCA is adopted to find the weight factors involved for all objectives. Finally analysis of variance concept is employed on multi-SN ratio to find out the relative significance of machining parameter in terms of their percentage contribution.

The multi-SN ratio is achieved by the product of weight factor and SN ratio to the performance characteristics in the utility concept. The results show that a combination of machining parameters for the optimized results has helix angle of 35°, machining speed of 4,000 m/min, feed rate of 750 mm/rev and depth of cut of 2.0 mm.

Effect of milling of GFRP composites on delamination factor, surface roughness and machining force with various helix angle solid carbide end mill has not been analysed yet using PCA techniques.

The purpose of this paper is to optimise the process parameters, namely, fibre orientation angle, helix angle, spindle speed, and feed rate in milling of glass fibre-reinforced plastic (GFRP) composites using grey relational analysis (GRA) and desirability function analysis (DFA).

In this work, experiments were carried out as per the Taguchi experimental design and an L27 orthogonal array was used to study the influence of various combinations of process parameters on surface roughness and delamination factor. As a dynamic approach, the multiple response optimisation was carried out using GRA and DFA for simultaneous evaluation. These two methods are best suited for multiple criteria evaluation and are also not much complicated.

The process parameters were found optimum at a fibre orientation angle of 15°, helix angle of 25°, spindle speed of 6,000 rpm, and a feed rate of 0.04 mm/rev. Analysis of variance was employed to classify the significant parameters affecting the responses. The results indicate that the fibre orientation angle is the most significant parameter preceded by helix angle, feed rate, and spindle speed for GFRP composites.

An attempt to optimise surface roughness and delamination factor together by combined approach of GRA and DFA has not been previously done.

Social media is noted for its usefulness and contribution to destination marketing and management. Social media data is particularly valued as a source to understand issues such as tourist behavior and destination marketing strategies. Among the social media platforms, Twitter is one of the most utilized in research. Its use raises two issues: the challenge of obtaining historical data and the importance of qualitative data analysis. Regarding these issues, the chapter argues that retrieving tweets using hashtags and keywords on the Twitter website provides a corpus of tweets that is valuable for research, especially for qualitative inquiries. In addition, the value of qualitative analysis of Twitter data is presented, demonstrating, among other things, how such an approach captures in-depth information, enables appreciation and inclusion of the nonconventional language used on social media, distinguishes between “noise” and useful information, and recognizes information as the sum of all parts in the data.

Compared with cusp height and area deviation ratio, volume error (VE) caused by the layer height could represent the stair-case effect more comprehensively. The proposed relative volume error (RVE)-based adaptive slicing method takes VE rather than cusp height as slicing criteria, which can improve part surface quality for functionalized additive manufacturing.

This paper proposes a volumetric adaptive slicing method of manifold mesh for rapid prototyping based on RVE. The pre-height sequences of manifold mesh are first preset to reduce the SE by dividing the whole layer sequence into several parts. A breadth-first search-based algorithm has been developed to generate a solid voxelization to get VE. A new parameter RVE is proposed to evaluate the VE caused by the sequence of the layer positions. The RVE slicing is conducted by iteratively adjusting the layer height sequences under different constraint conditions.

Three manifold models are used to verify the proposed method. Compared with uniform slicing with 0.2 mm layer height, cusp height-based method and area deviation-based method, the standard deviations of RVE of all three models are improved under the proposed method. The surface roughness measured by the confocal laser scanning microscope proves that the proposed RVE method can greatly improve part surface quality by minimizing RVE.

This paper proposes an RVE-based method to balance the surface quality and print time. RVE could be calculated by voxelized parts with required accuracy at a very fast speed by parallel.

This paper aims to predict the effect of combustor inlet area ratio (CIAR) on the lean blowout limit (LBO) of a swirl stabilized can-type micro gas turbine combustor having a thermal capacity of 3 kW.

The blowout limits of the combustor were predicted predominantly from numerical simulations by using the average exit gas temperature (AEGT) method. In this method, the blowout limit is determined from characteristics of the average exit gas temperature of the combustion products for varying equivalence. The CIAR value considered in this study ranges from 0.2 to 0.4 and combustor inlet velocities range from 1.70 to 6.80 m/s.

The LBO equivalence ratio decreases gradually with an increase in inlet velocity. On the other hand, the LBO equivalence ratio decreases significantly especially at low inlet velocities with a decrease in CIAR. These results were backed by experimental results for a case of CIAR equal to 0.2.

Gas turbine combustors are vulnerable to operate on lean equivalence ratios at cruise flight to avoid high thermal stresses. A flame blowout is the main issue faced in lean operations. Based on literature and studies, the combustor lean blowout performance significantly depends on the primary zone mass flow rate. By incorporating variable area snout in the combustor will alter the primary zone mass flow rates by which the combustor will experience extended lean blowout limit characteristics.

This is a first effort to predict the lean blowout performance on the variation of combustor inlet area ratio on gas turbine combustor. This would help to extend the flame stability region for the gas turbine combustor.

The lean blowout (LBO) limit of the combustor is one of the important performance parameters for any gas turbine combustor design. This study aims to predict the LBO limits of an in-house designed swirl stabilized 3kW can-type micro gas turbine combustor.

The experimental prediction of LBO limits was performed on 3kW swirl stabilized combustor fueled with methane for the combustor inlet velocity ranging from 1.70 m/s to 6.80 m/s. The numerical prediction of LBO limits of combustor was performed on two-dimensional axisymmetric model. The blowout limits of combustor were predicted through calculated average exit gas temperature (AEGT) method and compared with experimental predictions.

The results show that the predicted LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity.

This LBO limits predictions will use to fix the operating boundary conditions of 3kW can-type micro gas turbine combustor. This methodology will be used in design stage as well as in the testing stage of the combustor.

This is a first effort to predict the LBO limits on micro gas turbine combustor through AEGT method. The maximum uncertainty in LBO limit prediction with AEGT is 6 % in comparison with experimental results.

This study aims to the service life of TA15 alloy by solving the problem of the binding force between the matrix and AlTiSiN coating. The effect of a plasma nitriding (PN) interlayer on the magnetron-sputtered AlTiSiN coating was also investigated in detail.

The double-glow plasma alloying (DGPA) and magnetron sputtering (MS) techniques were combined as a new approach to realize a bilayer on TA15 consisting of an AlTiSiN layer with a PN interlayer. A TiN interlayer was formed via co-diffusion during the PN conducted at 1050°C for 3 h.

The PN interlayer can effectively improve the adhesion between coating and matrix; the PN/AlTiSiN coating presented excellent adhesion (80.1 N) and anti-wear property with a nano-hardness of 18.62 GPa. The resulting three-dimensional wear-track morphology exhibited a shallow depth and a narrow width.

The novel combination of the DGPA and MS technologies, using an infiltration layer rather than a coating one as the intermediate layer, can effectively enhance the adhesion between AlTiSiN coating and TA15 matrix. Meanwhile, the gradient layer can effectively improve both surface bearing and wear resistance.