Department of Mechanical engineering

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    Orientation dependent mechanical properties of commercially pure (cp) titanium
    (2014-12) Kumar, Gulshan
    The present investigation is an attempt at correlating the crystallographic orientation and mechanical properties of hexagonal commercially pure titanium (cp-titanium). Annealed cp-titanium sheets are subjected to tensile deformation along the rolling direction, along 45° to the rolling direction and along 90° to the rolling direction respectively. Crystallographic textures and mechanical properties of these cp-titanium samples are investigated in the present study. The hardness of different grains/orientations is estimated through nanoindentation, grain average misorientation, orientation estimated elastic stiffness and Taylor factor measurements. It is observed that the hardness of the grains close to basal orientation is higher compared to non-basal orientations. It is further observed that the estimated bulk mechanical properties of cp-titanium have a direct relationship with the volume fraction of basal grains/orientations.
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    A review of the applications of machine learning for prediction and analysis of mechanical properties and microstructures in additive manufacturing
    (ACM Digital Library, 2024-12) Challa, Jagat Sesh; Singh, Amit Rajnarayan
    This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors such as process parameters, defects, cooling rates, thermal histories, and machine stability have a prominent impact on AM products’ properties and microstructure. It is difficult to establish the relationship between these AM factors and the AM end product properties and microstructure. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This article discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM-fabricated material’s microstructures and acceleration of microstructure prediction procedures are discussed in this article.
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    Experimental investigations of TIG welding based additive manufacturing process for improved geometrical and mechanical properties
    (IOP, 2019) Kala, Prateek
    The welding based additive manufacturing process has a potential for producing functional 3D metallic component in a cost effective manner. Out of many welding based alternatives available TIG (Tungsten Inert Gas) based additive manufacturing process is one of the efficient processes. This article aims at developing TIG welding based additive manufacturing process for producing metallic parts with improved geometrical and mechanical properties. In this work authors have identified a process parameter condition by which components with good geometrical properties can be produced. The work reports least bead width deposited, for 1.2 mm filler wire, using wire arc based additive manufacturing system. The study performed on residual stress analysis of the deposited material showed compressive residual stresses throughout the sample. Usually welding process produces tensile stresses in the specimen which may reduce the product life. The compressive stresses reported in this study are considered good as they tend to increase product life. Authors have also addressed the reason for this unusual but favourable behaviour. This work would also help to develop automated TIG welding based metal deposition system to produce thin walled structures with improved mechanical and geometrical properties.
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    Investigation of mechanical strength of 2D nanoscale structures using a molecular dynamics based computational intelligence approach
    (World Scientific, 2015) Sangwan, Kuldip Singh
    A molecular dynamics (MD) based computational intelligence (CI) approach is proposed to investigate the Young modulus of two graphene sheets: Armchair and Zigzag. In this approach, the effect of aspect ratio, the temperature, the number of atomic planes and the vacancy defects on the Young modulus of two graphene sheets are first analyzed using the MD simulation. The data obtained using the MD simulation is then fed into the paradigm of a CI cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of Young modulus of two graphene structures. We find that the MD-based-CI model is able to model the Young modulus of two graphene structures very well, which compiles in good agreement with that of experimental results obtained from the literature. Additionally, we also conducted sensitivity and parametric analysis and found that the number of defects has the most dominating influence on the Young modulus of two graphene structures.