Correlating Cutting Performance and Surface Roughness under Different Bias Using TiAlTaN Coated Milling Tools Article Swipe
Gustavo Pinto
,
David Almeida
,
F.J.G. Silva
,
Eduardo Moreira da Silva
,
Ricardo Alexandre
,
Filipe Fernandes
,
Andresa Baptista
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.53941/jmem.2025.100007
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.53941/jmem.2025.100007
Driven by the industry’s ongoing pursuit of continuous improvement in machining processes, the need to meet environmental targets, and the limited number of scientific studies addressing coatings doped with Tantalum (Ta), were the main motivations to undertake this investigation. This study investigates the impact of coating cutting tools with a TiAlTaN film deposited at two different bias voltages, −90 V and −135 V, and its relationship with cutting forces, surface roughness, and overall performance when machining AISI P20 steel. The choice of AISI P20 steel was deliberate, considering its widespread use in the mould manufacturing industry and the machining challenges posed by its mechanical properties. Throughout the study, during the machining process, the cutting forces in the three axes were measured, as well as the surface roughness of the AISI P20 steel after machining. A correlation was made between the cutting forces obtained and the roughness of the machined surface, considering the coated tools using bias of −90 V and −135 V. The results show that increasing bias level during the coating deposition has a positive effect on the tool machining performance. Also, there was a correlation between the cutting forces during the machining process and the surface roughness of the machined AISI P20 steel. The coated tool with −135 V bias induced lower cutting forces, in line with the lower roughness shown on the machined surface, when compared to the coated tool with −90 V bias.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.53941/jmem.2025.100007
- OA Status
- hybrid
- References
- 34
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4413224715Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.53941/jmem.2025.100007Digital Object Identifier
- Title
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Correlating Cutting Performance and Surface Roughness under Different Bias Using TiAlTaN Coated Milling ToolsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-11Full publication date if available
- Authors
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Gustavo Pinto, David Almeida, F.J.G. Silva, Eduardo Moreira da Silva, Ricardo Alexandre, Filipe Fernandes, Andresa BaptistaList of authors in order
- Landing page
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https://doi.org/10.53941/jmem.2025.100007Publisher landing page
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.53941/jmem.2025.100007Direct OA link when available
- Concepts
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Machining, Surface roughness, Materials science, Coating, Surface finish, Mechanical engineering, Metallurgy, Composite material, EngineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.roughness, | 70 |
| abstract_inverted_index.scientific | 23 |
| abstract_inverted_index.widespread | 89 |
| abstract_inverted_index.considering | 87, 150 |
| abstract_inverted_index.correlation | 135, 186 |
| abstract_inverted_index.deliberate, | 86 |
| abstract_inverted_index.improvement | 8 |
| abstract_inverted_index.motivations | 34 |
| abstract_inverted_index.performance | 73 |
| abstract_inverted_index.properties. | 104 |
| abstract_inverted_index.industry’s | 3 |
| abstract_inverted_index.investigates | 41 |
| abstract_inverted_index.performance. | 181 |
| abstract_inverted_index.relationship | 65 |
| abstract_inverted_index.environmental | 16 |
| abstract_inverted_index.manufacturing | 94 |
| abstract_inverted_index.investigation. | 38 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5102410997 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I38260057 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.41195167 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |