Multi-criteria optimization of the turning parameters of Ti-6Al-4V titanium alloy using the Response Surface Methodology

Abstract

The paper depicts an application of Response Surface Methodology (RSM) for predicting selected parameters in turning of Ti-6Al-4V titanium alloy using polycrystalline diamond tool. Response surface plots that are generated by the model helps in determining the optimum combination of input factors (cutting speed vc and feed rate f) for best possible surface roughness (Sa), cutting force (Fc)and temperature (T) for dry and cooling turning. The methodology of multi-criteria optimization was used to establish the interaction between input parameters and given responses

Impact of Cutting Data on Cutting Forces, Surface Roughness, and Chip Type in Order to Improve the Tool Operation Reliability in Sintered Cobalt Turning

“…Trung showed that increasing the cutting speed does not have as great an effect on cutting forces as increasing the feed rate [13]. Kluz et al have shown that in dry turning, an increase in the feed rate leads to the appearance of a chemical reaction [14]. Grzesik et al proved the influence of tool wear on friction resulting from thermal effects including thermal softening [15].…”

Process Stability Analysis during Trochoidal Milling of AZ91D Magnesium Alloy Using Different Toolholder Types

“…A significant amount of available research is focused on the analysis of the machining stability of aluminium alloys [27][28][29][30][31]. On the other hand, difficult-to-machine materials are very popular in scientific research [32,33]. Research in the field of magnesium alloys is performed much less often and is carried out along standard straight paths [34][35][36].…”

Hardware implementation of Monte Carlo and RSM method for the optimization of cutting force during turning of NiTi shape memory alloy

“…The ability to predict and optimization cutting force before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction and optimization of cutting force is currently determined by using various techniques such as theoretical models [14][15], FE method [4,[15][16][17], the Taguchi procedure [1,2,11,[17][18][19][20], response surface methodology (RSM) [13,[21][22][23], the Multi-Objective Ant Lion Optimizer MOALO [21] the multi-response TOPSIS method [3,19], artificial intelligence through the use of the artificial neural networks (ANNs) [15,, genetic algorithms (GAs) [18] and fuzzy logic (FL) [26]. any research works show the use of these methods in the forecasting and also optimization of cutting force [13].…”

“…any research works show the use of these methods in the forecasting and also optimization of cutting force [13]. Researchers usually do not use only one modeling approach in their works, but look for a mutual compilation of the above strategies [18,21,23]. The benefits of using cutting force prediction methods include an increase in the productivity and competitiveness of the production process [1,2,27].…”