Abstract:
Stainless steels are widely used in industries such as aerospace, biomedical and
automotive due to their corrosion resistance and excellent mechanical properties. However,
their machinability remains a major challenge, requiring advanced techniques to optimize
the machining process. The use of traditional cutting fluids poses risks to health and the
environment, hence the growing interest in alternative solutions such as minimum quantity
lubrication (MQL). An even more promising approach is to use nanofluid-assisted MQL
(NF-MQL) to improve machining performance.
In this study, we used the response surface (RSM) and artificial neural networks (ANN)
methodology to establish models for predicting system behavior. Single and multi-objective
optimization methods, such as the Taguchi approach, the desirability function (DF), as well
as the NSGA-II and TOPSIS algorithms, were used to determine optimal machining
conditions.
The experiment was carried out on machining AISI 316L stainless steel in dry conditions,
MQL and NF-MQL, evaluating key parameters such as surface roughness (Ra), cutting
temperature (Tc), flank wear (Vb) and cutting force (Fz). The results showed that optimizing
cutting parameters had a significant impact on process performance, with reduced tool wear,
lower energy consumption and improved quality of machined parts. The integration of
nanofluids into MQL has proven to be particularly effective, providing better lubrication,
reduced cutting temperature and less tool wear, while promoting sustainable and
environmentally friendly machining.
