Systematic Literature Review: The Effect of Depth of Cut on Surface Roughness in the Lathe Process

Authors

  • Satria Sasikirana Universitas Negeri Semarang Author
  • Muhammad Ikhsanul Hakim Universitas Negeri Semarang Author
  • Dafa Indra Praditya Universitas Negeri Semarang Author
  • Bagas Ananta Satria Wicaksana Universitas Negeri Semarang Author
  • Nafhan Nazal Arifin Universitas Negeri Semarang Author
  • Danendra Falla Muhammad Universitas Negeri Semarang Author

Keywords:

Depth of Cut, , surface roughness, Turning Process, Cutting Parameters, Systematic Literature Review

Abstract

Surface roughness is a critical indicator of machining quality because it influences component friction, wear resistance, dimensional performance, and service life. Among the primary turning parameters, depth of cut affects material-removal productivity but may also increase cutting forces, vibration, and surface irregularities. This study systematically examined the effect of depth of cut on surface roughness in turning processes and identified the factors that influence this relationship. A systematic literature review was conducted by searching Scopus, ScienceDirect, Google Scholar, and other relevant academic sources using keywords related to depth of cut, surface roughness, turning processes, and cutting parameters. Articles were selected through identification, screening, eligibility assessment, and final inclusion stages. Twenty Scopus-indexed articles published between 2016 and 2026 met the inclusion criteria and were analyzed descriptively. The findings indicate that increasing the depth of cut generally increases surface roughness because of higher cutting forces, machining vibration, tool loading, and material deformation. However, the magnitude and direction of this effect vary according to workpiece material, feed rate, cutting speed, tool geometry, cooling conditions, and machine stability. Several studies found that feed rate contributed more strongly to surface roughness than depth of cut, demonstrating that machining parameters should not be evaluated independently. Therefore, optimal surface quality requires an integrated selection of depth of cut and other machining parameters. Future research should investigate advanced alloys and composite materials and develop artificial-intelligence-based prediction models to optimize machining productivity, tool life, and surface quality.

Downloads

Published

2026-07-24

Article ID

54450

Issue

Section

Articles