Amit Kumar

Bio: Amit Kumar is an academic researcher from Indian Institute of Technology Ropar. The author has contributed to research in topics: Fuzzy logic & Sericulture. The author has an hindex of 4, co-authored 11 publications receiving 110 citations.

Biomechanical characterization of spider webs

Abstract: In light of recent focus on the behaviour of the natural structures for revolutionary technological growth, spider web seems to have seized considerable attention of product designer due to its amazing behaviour. In present work, mechanism behind the structural integrity of the spider web along with the materialistic analysis of its constituent silk threads has been extensively investigated. The nanoindentation tool both in static and dynamic mode has been utilized for complete analysis of the mechanical behaviour of the spiral and radial threads separately. Both the average elastic modulus and hardness of the radial silk thread is higher than the spiral silk thread which reveals the radial silk thread is the major structural component of the web. The sustainability of spider webs under storm, windy conditions and during the impact of pray has been investigated under dynamic conditions. The radial silk thread exhibits elastic like response and the spiral silk thread exhibits viscous like response in a wide frequency range (1–200 Hz). The damping characteristic of the radial and spiral silk threads, an important parameter to investigate the energy dissipation properties of the materials has also been investigated in windy conditions.

Improved nanomechanical and in-vitro biocompatibility of graphene oxide-carbon nanotube hydroxyapatite hybrid composites by synergistic effect

Abstract: Hydroxyapatite (HAP) is an attractive bio-material for new bone growth process, hard tissue repair, bioactivity, osteoblast adhesion and proliferation due to its physicochemical resembles natural apatite. The intrinsic brittleness and poor mechanical properties of HAP restrict it for potential clinical applications. This problem is undertaken by exploiting the unique properties of carbon nanofillers (carbon nanotube (CNTs), graphene oxide (GO), graphene oxide-carbon nanotube (GCNTs) hybrid) which are used as reinforcement for preparing the carbon nanofillers based HAP composites. The nanomechanical and in-vitro biocompatibility of carbon nanofiller reinforced HAP composites have been studied. Carbon nanofiller reinforced HAP composites led to an improvement in nanomechanical and biocompatibility properties. The nanoindentation hardness and elastic modulus of GCNTs-HAP composites are significantly higher than other carbon nanofiller reinforced composites and pristine HAP powder. The in-vitro cytotoxicity of the prepared carbon nanofillers reinforced HAP composites is examined using MTT-assay on the MDCK cell line. The prepared GCNTs-HAP composites containing 2% of GCNTs nanofiller show higher cell viability, improved compatibility, and superior one cell proliferation induction than the other carbon nanofillers and HAP. These findings will provide the new prospects for utilizing the GO and its hybrid in HAP composites in bone repair, regeneration, augmentation and implantation.

Structural properties and their influence on the prey retention in the spider web

Abstract: Orb webs absorb the impact energy of prey and transmit vibratory information to the spider with minimal structural damage. The structural properties of the web and the arrangement of threads within the web affect transmission time during the prey impact. The objective of the present study is to determine damping, stiffness, and transmissibility of healthy and damaged spider webs. Experimental results show that stiffness and transmissibility diminish from the inner to outer spiral threads and gradient variation in the structural properties of spiral threads enhances signal transmission capability toward the centre regardless of the position of prey impact within the healthy web. Spiral threads exhibit excellent prey retention properties due to their stretching capability. Kinetic energy produced by prey is absorbed in the threads, which help the spider to analyse the prey retention properties and also determine the response time. The minor damage (up to 25%) does not alter the basic characteristics of the web due to self-adjustment of tension within the web. Damping, natural frequency, stiffness and transmissibility decrease with the increase in the percentage of damaged web. The present study addresses the structural sustainability of the spider web irrespective of minor damages and also provides guidance in designing the structures under impact. This article is part of the theme issue 'Bioinspired materials and surfaces for green science and technology'.

Mimicking high strength lightweight novel structures inspired from the trabecular bone microarchitecture.

Abstract: Nature's evolution of a billion years has advanced flawless functionality in limitless optimized structures like bone structural adaptation in various physiological behaviours. In this study, porou...

On the Mechanistic Origins of Toughness in Bone

Abstract: One of the most intriguing protein materials found in nature is bone, a material composed of assemblies of tropocollagen molecules and tiny hydroxyapatite mineral crystals that form an extremely tough, yet lightweight, adaptive and multifunctional material. Bone has evolved to provide structural support to organisms, and therefore its mechanical properties are of great physiological relevance. In this article, we review the structure and properties of bone, focusing on mechanical deformation and fracture behavior from the perspective of the multidimensional hierarchical nature of its structure. In fact, bone derives its resistance to fracture with a multitude of deformation and toughening mechanisms at many size scales ranging from the nanoscale structure of its protein molecules to the macroscopic physiological scale.

A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management

Abstract: Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

Abstract: Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in machine systems that are inaccessible or unable to be installed in external sensors, the VS-based approach is impracticable. Otherwise, motor current signals (CSs) are easily measured by the inverters that are the available components of those systems. Therefore, the motor CS-based bearing fault diagnosis approach has attracted considerable attention from researchers. However, the performance of this approach is still not good as the VS-based approach, especially in the case of fault diagnosis for external bearings (the bearings that are installed outside of the electric motors). Accordingly, this article proposes a motor CS-based fault diagnosis method utilizing deep learning and information fusion (IF), which can be applied to external bearings in rotary machine systems. The proposed method uses raw signals from multiple phases of the motor current as direct input, and the features are extracted from the CSs of each phase. Then, each feature set is classified separately by a convolutional neural network (CNN). To enhance the classification accuracy, a novel decision-level IF technique is introduced to fuse information from all of the utilized CNNs. The problem of decision-level IF is transformed into a simple pattern classification task, which can be solved effectively by familiar supervised learning algorithms. The effectiveness of the proposed fault diagnosis method is verified through experiments carried out with actual bearing fault signals.