Investigating the influence of cutting fluid on tool life and power usage during AISI-4130 steel turning
Purpose This study evaluates the impact of cutting fluids on energy consumption and tool life in machining, focusing on sustainable practices to reduce environmental impact and improve efficiency. By comparing vegetable-based soyabean oil with mineral-based blasocut oil, the study assesses their effects on power usage and tool life. Design/methodology/approach This study introduces a novel approach by applying both response surface methodology (RSM) and artificial neural network (ANN) models to validate the performance of vegetable-based cutting fluids, specifically soyabean oil, in machining operations. Findings Results indicate that soyabean oil reduces energy use by 9% and extends tool life by 29% compared to blasocut oil, with strong alignment between model predictions and actual results. Research limitations/implications The findings, though specific to certain fluids and conditions, suggest that soyabean oil offers a viable eco-friendly alternative for machining processes. Practical implications Adoption of such fluids could lower greenhouse gas emissions, reduce dependency on mineral oils and benefit farmers by creating additional demand for vegetable oils. Originality/value This dual-model validation of cutting fluid performance marks an innovative contribution to sustainable machining, supporting the adoption of greener, resource-efficient manufacturing practices. This study underscores the potential of vegetable-based cutting fluids to enhance sustainability in manufacturing.
Authors
- Nilesh C. Ghuge (ORCID: https://orcid.org/0000-0002-0544-2006)
- Dattatray D. Palande
Institutions
- Indian Numismatic, Historical and Cultural Research Foundation (IN)
Publication Details
- Journal
- Frontiers in Engineering and Built Environment
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1108/febe-07-2024-0035
- Citations
- 4
- Primary Topic
- Advanced machining processes and optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.85