Search results

This research work focuses on implementing this methodology in reducing the rejection rate of the turbocharger component problem that occurs during the manufacturing process. Using design, measure, analyze, improve and control (DMAIC) processes, it has been identified that clamping pressure on the component is one factor that affects quality. The impact of clamping pressure is studied to arrive at the ideal clamping pressure in which the rejection rate is the least.

Quality is the keyword in manufacturing where the production of a defect-free component is the most sought out objective. The definition of quality keeps getting refined throughout the years, from making products with no defects to minimizing rejection and scrap in the manufacturing process. Production facilities, to achieve this purpose, have adopted various methods and use of the DMAIC of Six Sigma methodology is one among them.

The study identified the fault causing the defect and suggested the methods to correct the fault. The suggestions would result in reducing the losses arising due to this and similar rejection causes.

With the adoption of DMAIC, it is found that misalignment of top and side clamp pressure is zero. When the side clamp pressure is at 75 PSI, and top clamp pressure is changed from 90 PSI to 95 PSI, the mean of responses is greater than the side clamp pressure of 80 PSI. Therefore, from the three-combination top clamp pressure of 100 PSI and the side clamp pressure of 75 PSI is the optimal condition.

Neuroeconomics and neurofinance are emerging as intriguing fields of research, despite sharing ambiguity with the concepts of neuroscience. The relationship among the concepts of economics, finance and neuroscience is not explicitly defined in the past literature, which distorts the use of neuroeconomics and neurofinance approaches in real-world practice for financial decision-making. The purpose of this paper is to consolidate the literature in the field of neuroeconomics and neurofinance to set up the research agenda for the upcoming scholarship in the field.

The purpose of this paper is to consolidates the extant literature in the fields of neuroeconomics and neurofinance by conducting an extensive systematic literature review to investigate the current state and define the relationship between economics, finance and neuroscience.

This paper identifies and explains the explicit relationship between different sub-fields of neuroscience with neuroeconomics and neurofinance and providing instances for future research studies.

The exclusive and extensive literature survey in the form of systematic literature review is undertaken for understanding the fields of neuroeconomics and neurofinance and is the key highlight of this paper. Another, interesting fact lies with matching the literature in neuroeconomics and neurofinance with further sub-fields of neuroscience such as neurophysiology, neuroanatomy, molecular neuroscience and cognitive neuroscience.

This study aims to reduce the interfacial tension by increasing the better adhesion between different immiscible polymers phases and also to evaluate the mechanical, thermal and thermo-mechanical behavior of the immiscible polymer blends.

The polymer blend composite (PBC) was prepared using a twin-screw extruder followed by injection molding. Two different kinds of PBC with compatibilizer (Ethylene-n-Butylacrylate-Glycidyl methacrylate) of varying compositions like polybutylene terephthalate + poly trimethylene terephthalate and polybutylene terephthalate (30% glass filled) + poly trimethylene terephthalate were prepared and material behavior at various test conditions were studied. The effect of glass fiber reinforcement on polymer blend and the interlocking effect by the compatibilizer between the polymer phases were also assessed.

Mechanical behavior of PBC was estimated by tensile, flexural and angular impact tests. Likewise, the thermal deflection was studied with the help of heat deflection temperature test. Thermo-mechanical behavior likes storage modulus, loss modulus and loss tangent were studied using the dynamic mechanical analysis test. Morphological analysis was characterized using field emission scanning electron microscopy.

This in turn makes the process easy to obtain the PBC having inherent mechanical, thermal and thermo-mechanically stable. And, it also enhances the mechanical properties like tensile, flexural and impact strength. Simultaneously, posse’s excellent heat deflection and thermo-mechanical behavior over a temperature range of 35–140ºC at a constant frequency of 5 Hz.

The purpose of this study is to deals with the effect of premixed dual metal sulfides (tin disulfide + iron disulfide) as a replacement for antimony trisulfide on the tribological performance of brake friction materials.

In this study, brake friction materials were developed by using premixed dual metal sulfides as a replacement for antimony trisulfide in the formulation. The brake friction materials were developed in the form of standard brake pads as per the industrial practice. Thermal stability was measured for varying ingredients and developed brake pads using thermogravimetric analysis. The physical, mechanical and thermal properties of the developed brake pads were tested as per the industrial standards. The tribological properties were analyzed using the Chase test as per SAE J661. Worn surface analysis was done using a scanning electron microscope.

The experimental results indicate that the brake pads filled with premixed dual metal sulfides had good thermal stability, physical, chemical and mechanical properties with stable friction and less wear rate due to better lubrication preventing friction undulations.

This paper explains the influence of premixed dual metal sulfides as a replacement for antimony trisulfide in brake pads formulation to enhance the tribological performance by preventing friction undulations.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

This paper aims to deal with the effect of natural barytes purity levels on the tribological performance of brake pads.

In this study, brake pads were developed by varying three different natural barytes without varying other ingredients. The brake pads were developed as per the standard industrial practice. The physical, mechanical and thermal properties of the developed brake pads were tested as per the industrial standards. The tribological properties were analyzed using a full-scale inertia brake dynamometer. Worn surface analysis was done using scanning electron microscope coupled with elemental mapping.

The experimental results indicate that the brake pads filled with natural barytes 95% purity had good physical, chemical and mechanical properties with stable friction and less wear rate due to reduced impurity level preventing frictional undulations.

This paper explains the effect of the purity level of natural barytes in brake pads formulation to enhance the tribological performance by altering tribofilms and preventing friction undulations.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.

The study aims to study the extent of research done in luxury marketing in an emerging economy like India by conducting a bibliometric analysis. A period of 21 years has been considered to present a comprehensive picture for results and analysis. Key findings indicate the gaps and scope of further research for academics in India and abroad. The findings indicate a dearth of research by scholars and academicians in luxury, counterfeit and masstige, especially when there is a surge of the upper middle class in India. More specifically, Indian-grown luxury brands also present a massive scope for future research.

This study aims to investigate the effects of layering sequence, aspect ratios, fiber orientation angles, loading types and boundary conditions on the buckling behavior of glass/carbon/hybrid fiber-reinforced epoxy laminated composites using the differential quadrature (DQ) approach.

In total, different hybrid combinations are considered separately for angle-ply, cross-ply and antisymmetric layup schemes. For this purpose, a DQ code is developed using MATLAB.

The results obtained from the study reveal that the lamination arrangements and varying loading conditions of the hybrid laminated structures have a significant effect on the buckling performance of these materials.

This is the first study to investigate how the critical buckling loads of glass fiber/epoxy and carbon fiber/epoxy laminated hybrid composite plates are influenced by fiber orientation angles, aspect ratios, stacking sequences, loading conditions and boundary conditions using the DQ method. This study contributes to the literature in this regard.

The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech. It proposes a convolutional neural network-based capsule network (CNN-CapsNet) model and outlining the performance of the system in recognition of gait and speech variations. The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.

This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNN and used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint. The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.

This research work provides recognition of signal, biometric-based gait recognition and sound/speech analysis. Empirical evaluations are conducted on three aspects of scenarios, namely fixed-view, cross-view and multi-view conditions. The main parameters for recognition of gait are speed, change in clothes, subjects walking with carrying object and intensity of light.

The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices. It can also act as a pre-requisite tool to analyze, identify, detect and verify the malware practices.

This proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

The purpose of this article covers the design and manufacture of porous materials that can be used in different engineering applications by additive manufacturing.

The most important design parameters of the porous materials are the cell structure and wall thickness. These two design criteria are difficult to control in porous materials produced by conventional production methods. In the study, two different wall thicknesses and four different pore diameters of the porous structure were determined as design parameters.

A compression test was applied to the produced samples. Also, the densities of the produced samples were compared. As a result of the study, changes in mechanical properties were observed according to the cell wall thickness and pore size.

The originality of the study is that, unlike traditional porous structure production, the pore structure and cell wall thicknesses can be produced in desired dimensions. In addition, a closed pore structure was tried to be produced in the study. Studies in the literature generally have a tube-type pore structure.