An approach to the influence of the machining process on power consumption and surface quality during the milling of 304L austenitic stainless steel

Abstract

Increasing the quality of a machined product and minimizing energy consumption is a primary objective for all industries, given their significant impact on manufacturing costs and the environment. The choice of the machining process and the optimal cutting parameters to meet this requirement is the objective of this experimental study, which deals with the effects of the cutting parameters and the machining process on the energy consumption and surface condition during the milling of AISI 304L austenitic steel. This article presents a multi-objective optimization method based on the response surface methodology and Grey's weighted relational analysis. Based on this approach, the down milling cutting parameters indicate that the cutting speed is the most influential parameter on energy consumption (62.71%), while the feed rate is the most influential factor in roughness (47.20%). For up milling, the cutting speed is the most important factor influencing surface roughness (29.07%) and also energy consumption (64.09%).It has also been found that the cutting power can be reduced by 39% for down milling and 16% for up milling compared to the maximum value. On the other hand, the quality of the machined surface can be improved by 58.5% for down milling and by 60% for up milling.

Multi objective optimization of process parameters in hard turning of AISI 52100 steel with surface irregularities using GRA-PCA

“…Model terms with p-values less than 0.05 are considered significant. With a model F-value of 3.04, the model is significant [58]. In this case, feed, depth of cut, and the interactions of cutting speed & feed (ν × f), feed &depth of cut (f × d) are significant.…”

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

“…The steps of this methodology are known and presented in several studies. 12,[13][14][15] In what follows, the study relates to this sequence S 8 with the combination F 1 -F 5 -F 6 .…”

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

“…The regression coefficient b 0 is associated with each term, and k is the number of independent variables. Finally, ϵ represents the residual error (HAMZA et al, 2022). Mathematical models of energy consumption and cost as a function of cutting parameters are developed using Minitab 17.0.…”

“…These models allow for a thorough representation of the complex relationships that can exist between different variables. Indeed, they offer a general, versatile form for modelling these relationships, which can be described mathematically by equation (13) (Abolghasemian et al, 2020; Hamza et al, 2022). This approach allows us to better understand the cross-influences of the different variables on the performance of the system under study and to better identify the key factors that influence this performance. where Y is the desired performance response and x i (1.2,…, n ) are the milling input cutting parameters.…”

“…The regression coefficient b 0 is associated with each term, and k is the number of independent variables. Finally, ε represents the residual error (HAMZA et al, 2022).…”