Tapan Kumar Mahanta

In this current work, nanocrystalline calcium-deficient hydroxyapatite (CDHA) was synthetically produced using calcium hydroxide and diammonium phosphate through a wet chemical synthesis route. X-ray diffraction (XRD) analysis and electron microscopy demonstrated the nano-level (~47 nm) of the produced CDHA. Then, the nano-CDHA was reinforced into ZE41 Mg alloy by friction stir processing (FSP) aimed at manufacturing degradable bone implants. Microstructures clearly demonstrated the development of a fine-grained structure (7.4 ± 6.9 µm) in addition to incorporating nano-CDHA into ZE41 Mg alloy. FSP resulted in decreased intermetallic phases, which suggests increased solubility of zinc into magnesium due to FSP. XRD analysis of the composite confirms the development of a basal-dominated texture in the composite. From the potentiodynamic polarization studies, corrosion performance was assessed using simulated body fluid (SBF). The composite exhibited noble behavior by demonstrating a lower corrosion current density (-1.23 ± 0.8 × 10-4 A/cm2) compared with ZE41 alloy (-4.75 ± 1.1 × 10-4 A/cm2). Lower weight loss was observed for the composite after 72 hours of immersion in SBF, indicating improved degradation resistance due to the incorporated nano-CDAH and grain refinement. The lower corrosion rate measured for the composite (11.4 ± 2.1 mm/year) is 40% lower compared with ZE41 alloy (19.3 ± 1.8 mm/year) based on weight loss data. The results demonstrate the feasibility of FSP to develop ZE41-nano-CDHA composite with better corrosion control.

Using friction-assisted solid-state processes in the surface engineering of metals and alloys addresses the challenges involved in liquid-state methods. In the present work, friction surface alloying (FSA) was used to deposit Al-Zn alloy coating as a layer on pure Al substrate. Zn powder was filled in the holes produced longitudinally in a pure Al rod and was used as the consumable rod. Processing was done at 1100 rpm with 25 mm/min feed rate at a worktable vertical feed of 5 mm/min. Due to the generated heat during the friction surfacing, the zinc reinforcement was melted and a solid solution alloy of Al-Zn was produced on pure Al. The cross-sectional microstructure clearly indicated a strong metallurgical bonding at the alloy-substrate interface. Phase analysis carried out by X-ray diffraction studies confirms the developed Al-Zn alloy in the coated surface. Hardness measurements at the cross-section and at the surface demonstrated improvement (75.2±8.3 HV0.1) compared with the substrate (36.5±7.1 HV0.1) due to the formation of the alloy. Corrosion experiments by polarization studies revealed significant improvement in the corrosion resistance for the produced surface alloy as reflected from the lower corrosion current density (1.39×10–4 A/cm2) compared with the substrate (1.83×10–3 A/cm2). Higher tensile strength (245.5±5.2 MPa) was measured for the developed alloy than the substrate (182.1±6.9 MPa) without losing the ductility significantly. The results demonstrate the potential of FSA to develop surface alloys within the solid-state itself.

This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications.

The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.

The acrylic acid treated rattan fiber reinforced epoxy (RF/Epoxy) composite has been fabricated by taking various weight percentages (5, 10, 15, 20, and 25 wt%) of fibers using hand layup followed by compression molding technique. The optimum fiber loading has been determined by regression analysis and it is found that 18 wt% of rattan fiber gives maximum tensile strength. The interaction of rattan fiber with epoxy matrix has been studied by Fourier transform infrared spectroscopy and X-ray diffraction. Scanning electron microscope (SEM) has been used to study the morphology of chemically modified rattan fiber and RF/Epoxy composite. The mechanical, thermal, and dynamic mechanical thermal properties of 18 wt% RF/Epoxy composite have been studied as per ASTM standards. From the results, it is observed that RF/Epoxy composite gives best properties at optimum (18 wt%) fiber loading. The maximum tensile strength and flexural strength of the composite are found to be 47.5 and 121 MPa, respectively, which is better in comparison to other natural fiber composites used for manufacturing different automobile body parts. Hence, it can be concluded that 18 wt% RF/Epoxy composite can be considered as a potential candidate for automotive body parts.

The present study deals with the fabrication of polyvinyl alcohol composites reinforced with 10, 20, 30, 40, 50, 60, 70 wt% of date palm leaf fibers. Among these 60 wt% is providing good sound absorption because of the increase of porosity. Next to acoustic behaviour of composites analysed by varying thickness from 20-40mm by keeping density constant (60 wt %). An impedance tube setup is used for the measurement of the sound absorption coefficient. The sound absorption properties of composite samples increased significantly by increasing the thickness of materials with a constant density. Initially, sound absorption was measured without an air gap behind the samples and it was observed that date palm leaf fibers composite behaves like a good sound energy absorber in the frequency range of 1500-6300 Hz. Another experiment performed by introducing the air gap behind the samples varies from (10-30mm). This configuration dissipating more energy of sound waves as compared to without air gap because air is also absorbing some amount of energy. Air gap with this sample can be used as a new method for the fabrication of natural fiber-reinforced composites.

Making a decision is one of the crucial tasks for choosing the best optimal solution for various problems related to the decision making. Decision can be made easily for the problems with one or two input criteria and many choices/alternatives as solution. Whereas, it’s very difficult to take the decision for the problems with more than two input criteria and many choices/alternative as solution. This type of problems can be solved with Multi-Criteria Decision-Making (MCDM) approaches. There are many MCDM approaches in the field of decision making research. This study considers two efficient MCDM approaches namely Simple Additive Weighting (SAW) method and Weighted Product Method (WPM) to solve the multi criteria decision making problems. These two MCDM approaches are implemented for underground mining method selection problem for the Indian metal mine X as a case study. For the specified mine SAW and WPM provides the Room and Pillar as a suitable mining method for extraction of the minerals.

Environmental impact assessment is essential to meet the objectives of sustainable product design. The environmental impact should be measured at the initial stages of product design to minimize the impact. In this paper, we demonstrate the application of a computer-aided design (CAD) tool to measure the environmental impact during the design stage. SolidWorks software uses the Life Cycle Assessment (LCA) model to measure the environmental impact such as energy consumption, carbon footprint, and air-water pollution. The spur gear model is used to demonstrate the process of assessing environmental impact using the CAD tool. The input parameters such as material, manufacturing process, region, transportation method, and product usage play an essential role in the environmental impact. The results indicate that the environmental impact can be reduced by optimizing the parameters by selecting alternatives using the CAD tool at the initial design stage.