Optimization of Machining Time in the Turning of Welded Mild Steel Shafts for Enhanced Manufacturing Productivity

Authors

  • Lawal, H. O. Department of Production Engineering. University of Benin, Benin City, Nigeria
  • Achebo, J. I. Department of Production Engineering. University of Benin, Benin City, Nigeria
  • Uwoghiren, F. O. Department of Production Engineering. University of Benin, Benin City, Nigeria
  • Etin-Osa, C. E. Department of Production Engineering. University of Benin, Benin City, Nigeria

DOI:

https://doi.org/10.14738/tecs.1404.12018

Abstract

Manufacturing productivity depends largely on the ability to minimize machining time while maintaining product quality. In welded shaft production, machining operations performed after welding represent a significant portion of overall manufacturing cost. This study investigates the effects of spindle speed, feed rate, and depth of cut on machining time during turning of welded mild steel shafts. The shaft specimens were first joined through welding and then machined using a structured experimental design based on Response Surface Methodology (RSM). Statistical and optimization techniques were employed to determine the most influential machining variables and identify optimal parameter combinations for reducing machining time. Results indicate that spindle speed and feed rate exert the greatest influence on machining duration, while depth of cut contributes to overall productivity through increased material removal rates. The optimized machining conditions (cutting speed: 300 m/min, feed rate: 0.160 mm/rev, depth of cut: 0.288 mm) significantly reduced production time to 39.431 seconds without compromising the integrity of the welded component. The study provides practical guidance for efficient machining of welded shafts in manufacturing and repair operations

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Published

2026-07-23

How to Cite

Lawal, H. O., Achebo, J. I., Uwoghiren, F. O., & Etin-Osa, C. E. (2026). Optimization of Machining Time in the Turning of Welded Mild Steel Shafts for Enhanced Manufacturing Productivity. Transactions on Engineering and Computing Sciences, 14(04), 111–120. https://doi.org/10.14738/tecs.1404.12018