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Shashank Sharma

Title: Research Assistant Professor

Department: Center for Agile and Adaptive Additive Manufacturing

College: College of Engineering

Curriculum Vitae

Curriculum Vitae Link

Education

  • PhD, Indian Institute of Technology, Kanpur, 2020
    Major: Mechanical Engineering
    Specialization: FEM, Laser-matter interaction
    Dissertation: Numerical Simulation of Melt Hydrodynamics in Laser Metal Processing for Micro-scale Applications
  • MTech, Indian Institute of Technology Kanpur, 2015
    Major: Mechanical engineering
    Specialization: Laser-matter interaction
    Dissertation: Numerical simulation of Melt-hydrodynamics in selective laser melting
  • BTech, Indian Institute of Information Technology, Design and Manufacturing, 2014
    Major: Mechanical Engineering

Current Scheduled Teaching

No current or future courses scheduled.

Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching

No previous courses scheduled.

Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Intellectual Contributions

    Book

  • Dahotre, N.B., Sharma, S. (2022). Laser-Based Additive Manufacturing: Modeling, Simulation, and Experiments. Other. John Wiley VCH.

    • Book Chapter

  • Dahotre, N.B., Sharma, S., Joshi, S., Banerjee, R. (2022). Directed Energy Deposition. Other. 23A Materials Park, Ohio, ASM International.

    • Critical Review

  • Parsazadeh, M., Sharma, S., Dahotre, N.B. (2023). Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution. 135 (2023) 43. Amsterdam, Elsevier. https://doi.org/10.1016/j.pmatsci.2023.101102

    • Journal Article

  • Patil, S., Mani Krishna, K., Sharma, S., Joshi, S.S., Radhakrishnan, M., Banerjee, R., Dahotre, N.B. (2024). Thermo-mechanical process variables driven microstructure evolution during additive friction stir deposition of IN625. Additive Manufacturing.
  • Sharma, S., Krishna, K.M., Joshi, S.S., Radhakrishnan, M., Palaniappan, S., Dussa, S., Banerjee, R., Dahotre, N.B. (2023). Laser based additive manufacturing of tungsten: Multi-scale thermo-kinetic and thermo-mechanical computational model and experiments. Acta Materialia. 259 119244. Pergamon.
  • Dahotre, N.B., Sharma, S., Joshi, S., Radhakrishnan, M. (2023). Multiphysics Multi-scale Computational Framework for Linking Process-Structure-Property Relationships in Metal Additive Manufacturing: A Critical Review.
  • Sharma, S., Mandal, V., Tripathi, P., Jayabalan, B., Mukherjee, S., Singh, S., Ramkumar, J. (2023). Fabrication of ex-situ TiN reinforced IN718 composites using laser powder bed fusion (L-PBF): Experimental characterization and high-fidelity numerical simulations. Ceramics International.
  • Joshi, S.S., Sharma, S., Radhakrishnan, M., Pantawane, M.V., Patil, S.M., Jin, Y., Yang, T., Riley, D.A., Banerjee, R., Dahotre, N.B. (2022). A multi modal approach to microstructure evolution and mechanical response of additive friction stir deposited AZ31B Mg alloy. Scientific Reports. 12 (1) 13234. Nature Publishing Group UK London.
  • Dahotre, N.B., Joshi, S.S., Sharma, S. (2022). A new data-driven framework for prediction of molten pool evolution and lack of fusion defects in multi-track multi-layer laser powder bed fusion processes. International Journal of Advanced Manufacturing Technology.
  • Dahotre, N.B., Sharma, S., Radhakrishnan, M., Joshi, S. (2022). A Pseudo Thermo-Mechanical Model Linking Process Parameters to Microstructural Evolution in Multilayer Additive Friction Stir Deposition of Magnesium Alloy. Materials & Design.
  • Sharma, S., Krishna, K.M., Radhakrishnan, M., Pantawane, M.V., Patil, S.M., Joshi, S.S., Banerjee, R., Dahotre, N.B. (2022). A pseudo thermo-mechanical model linking process parameters to microstructural evolution in multilayer additive friction stir deposition of magnesium alloy. Other. 224 111412. Elsevier.
  • Dahotre, N.B., Sharma, S., Joshi, S. (2022). A Universal Predictor-Based Machine Learning Model for Optimal Process Maps in Laser Powder Bed Fusion Process. Other.
  • Joshi, S.S., Patil, S.M., Mazumder, S., Sharma, S., Riley, D.A., Dowden, S., Banerjee, R., Dahotre, N.B. (2022). Additive friction stir deposition of AZ31B magnesium alloy. Other. 10 (9) 2404--2420. Elsevier.
  • Joshi, S.S., Sharma, A., Sharma, S., Mazumder, S., Pantawane, M.V., Mantri, S.A., Banerjee, R., Dahotre, N.B. (2022). Cyclic thermal dependent microstructure evolution during laser directed energy deposition of H13 steel. Transactions of the Indian Institute of Metals. 75 (4) 1007--1014. Springer India New Delhi.
  • Mazumder, S., Pantawane, M.V., Patil, S., Radhakrishnan, M., Sharma, S., McKinstry, M., Dahotre, N. (2022). Thermokinetically Driven Microstructural Evolution in Laser‐Based Directed Energy‐Deposited CoCrMo Biomedical Alloy. Advanced Engineering Materials. 24 (11) 2200352.
  • Sharma, S., Pantawane, M., Sharma, A., Desari, S., Banerjee, S., Banerjee, R., Dahotre, N. (2021). Coarsening of martensite with multiple generations of twins in laser additively manufactured Ti6Al4V. Acta Materialia. 213
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Overall
Summative Rating		 Challenge and
Engagement Index		 Response Rate

out of 5
out of 7		 %
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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