ASSESSMENTS OF ENERGY EFFICIENCY OF THE GREEN MQL-ASSISTED MULTI-ROLLER BURNISHING PROCESS

Abstract

Roller burnishing is a prominent solution for machining hardened steels and
most investigations focused on improving the burnished quality. However, the
impacts of process parameters on the energy efficiency (EF) under the minimum
quantity lubrication condition (MQL) have not been considered. The purpose of
this investigation is to analyze the impacts of burnishing factors, including the
burnishing speed (S), depth of penetration (D), the air pressure (P), and the flow
rate (Q) on the EF of the minimum quantity lubrication-assisted internal roller
burnishing (MQLAIB) process. The EF model of were proposed with the aid of the
adaptive neuro-based-fuzzy inference system (ANFIS). The results indicated that
the S was found to be the most effective factor, followed by the D, Q, and P,
respectively. The developed EF model could be applied to forecast the response
values for the MQLAIB process.