Juniper Elbow Co. Inc | United States
Mrs. Sravya Yelamanchili is an Industrial and Mechanical Engineer with over a decade of hands-on experience in advanced manufacturing especially in the naval and marine systems domain where she has engineered high-integrity marine closures such as watertight, airtight, and ballistic hatches, doors, and scuttles. Her professional journey includes roles such as Methods Engineer and Engineering Manager, during which she designed detailed assembly protocols, developed configurable BOM frameworks, led ERP (MACOLA, Syteline) migrations, instituted SOPs, and drove continuous improvement programs using lean and Six Sigma principles. Her certifications include Six Sigma Green Belt and NDT Level II (Visual Testing). Her research work spans surface roughness modeling robotics in welding, and process parameter optimization. She has authored peer-reviewed publications and is cited in international journals; her current h-index is [h-index value, 4 with approximately citation count, 115 total citations. She has been recognized for technical leadership and Gold Medal achievement in her undergraduate degree. With deep expertise in process standardization, lean transformation, and engineering leadership, she continues to drive innovation in marine manufacturing, with particular interest in improving first-pass yield, reducing production lead times, and integrating advanced tooling and robotics to raise quality and throughput.
Featured Publications
Yelamanchili, S. (2025, September 22). Use of additive technologies for the accelerated production of spare parts for marine hatches. International Journal of Science and Research.
Yelamanchili, S. (2025, September 10). Assessing the impact of robotic welding on the productivity of watertight-door manufacturing. International Journal of Scientific Engineering and Science.
Yelamanchili, S., Prasad, M. V. R. D., & Tejaswi, K. S. (2014, November 10). Study of the influence of process parameters on surface roughness when Inconel 718 is dry turned using CBN cutting tool by artificial neural network approach. International Journal of Materials, Mechanics and Manufacturing.