Alumina-enriched sunflower bio-oil in machining of Hastelloy C-276: a fuzzy Mamdani model-aided sustainable manufacturing paradigm
With the increasing emphasis on sustainable manufacturing practices, eco-friendly lubricants have gained significant attention to moderate the friction coefficient at the tool-work interface. In line with this, the contemporary study aimed to examine the viability of Alumina-enriched sunflower bio-oil as a metalworking fluid. Different volume fractions of Alumina nanoparticles (varying from 0 to 1 vol%) were mixed with sunflower bio-oil, and the physical properties, for instance, contact angle and dynamic viscosity, were analyzed to determine the optimal concentration of Alumina. Subsequently, machining experiments were executed on Hastelloy C-276 under various lubricating conditions, including dry cutting, compressed air, sunflower bio-oil, and 0.6 vol% Alumina-sunflower bio-oil. A comparative analysis among these lubricating mediums demonstrated that sunflower bio-oil with a 0.6 vol% Alumina concentration outperformed others, resulting in a significant reduction of surface roughness, and tool wear by 73.31%, and 82.14% respectively when compared to dry machining. Besides, the utilization of 0.6 vol% Alumina-sunflower bio-oil has demonstrated a reduction of 17.86% in total machining cost, along with reductions of 15.44% in energy consumption and carbon emissions, when compared to dry machining. Finally, a Taguchi-designed experiment consisting of sixteen trials was performed in different lubricating conditions, and a Fuzzy-Mamdani model was employed to achieve a sustainable machining environment. The sustainability assessment results indicated that a cutting speed of 75 m/min, feed of 0.05 mm/tooth, depth of cut of 0.15 mm, and the utilization of the 0.6 vol% Alumina-sunflower bio-oil resulted in the most sustainable machining environment, with the highest Multi-Performance Characteristics Index of 0.75.
Authors
- Rustem Zairov (ORCID: https://orcid.org/0000-0002-1699-6741)
- Shantanu Debnath (ORCID: https://orcid.org/0000-0002-7353-5255)
- Binayak Sen (ORCID: https://orcid.org/0000-0001-8282-5548)
- Gurbhej Singh (ORCID: https://orcid.org/0000-0001-9903-2552)
- Abhijit Bhowmik (ORCID: https://orcid.org/0000-0001-9409-0972)
- Muhammad Imam Ammarullah (ORCID: https://orcid.org/0000-0002-8845-7202)
- Vishwesh Mishra (ORCID: https://orcid.org/0000-0003-1067-5709)
Institutions
- Diponegoro University (ID)
- Chennai Mathematical Institute (IN)
- Kazan Federal University (RU)
- Indian Institute of Technology Madras (IN)
- Government Medical College, Amritsar (IN)
- SRKR Engineering College
- Chitkara University (IN)
- GLA University (IN)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1038/s41598-024-80254-z
- Citations
- 32
- Primary Topic
- Advanced machining processes and optimization
- Type
- article
- Field-Weighted Citation Impact
- 6.80