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CHARACTERIZATION OF CUTTING TOOL INSERTS WITH LASER SURFACE TEXTURING

*Corresponding author for this work
Research Output:
Contribution to journal
Article
Peer-review

Open access

Sustainable Development Goals

  • SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Publication metrics

Metrics

SciVal
Author count
6
SciVal
Paper percentile
45

Abstract

This study evaluates the performance of cutting inserts with four different surface conditions—three treated with laser surface texturing (LST) and one untreated—using a CNC milling machine. The objective was to identify the surface condition that optimally reduces energy consumption and tool wear, thereby extending tool life and improving the surface finish of the machined component. Given the widespread use of machining processes in industry, even marginal improvements can yield significant benefits. The laser texturing patterns tested included "S" shaped, crosshatch, and channel geometries. The experimental phase began with the identification of optimal laser parameters to achieve the desired texture depth and an area density of 16%. Each of the four insert types, including the untextured reference, was tested in five machining runs on 1018 steel plates. Key output variables were tool wear (evaluated by profile and radius), surface roughness of the machined part, and energy consumption during the process. Among the tested geometries, the "S" shaped texture exhibited the best overall performance. Compared to the untextured insert, it reduced tool wear by 2.4%, improved surface finish by 18%, and lowered energy consumption by 9.17%. These results highlight the potential of LST as a sustainable and effective technique to enhance machining efficiency and tool durability.

Publication Information

Output type

Research Output:
Contribution to journal
Article
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 80-88 (9 pages)

Journal (Volume, Issue Number)

International Journal of Modern Manufacturing Technologies (Volume 17, Issue 3 Special Issue)

Publication milestones

  • Published - 2025

Publication status

Published - 2025

ISSN

2067-3604

Publication IDs

  • Scopus: 105026919575