Towards Optimization of Surface Roughness and Productivity Aspects during High-Speed Machining of Ti–6Al–4V

Abstract

Nowadays, titanium alloys are achieving a significant interest in the field of aerospace, biomedical, automobile industries especially due to their extremely high strength to weight ratio, corrosive resistance, and ability to withstand higher temperatures. However, titanium alloys are well known for their higher chemical reactive and low thermal conductive nature which, in turn, makes it more difficult to machine especially at high cutting speeds. Hence, optimization of high-speed machining responses of Ti–6Al–4V has been investigated in the present study using a hybrid approach of multi-objective optimization based on ratio analysis (MOORA) integrated with regression and particle swarm approach (PSO). This optimization approach is employed to offer a balance between achieving better surface quality with maintaining an acceptable material removal rate level. The position of global best suggested by the hybrid optimization approach was: Cutting speed 194 m/min, depth of cut of 0.1 mm, feed rate of 0.15 mm/rev, and cutting length of 120 mm. It should be stated that this solution strikes a balance between achieving lower surface roughness in terms of Ra and Rq, with reaching the highest possible material removal rate. Finally, an investigation of the tool wear mechanisms for three studied cases (i.e., surface roughness based, productivity-based, optimized case) is presented to discuss the effectiveness of each scenario from the tool wear perspective.

Multi-Response Optimization in High-Speed Machining of Ti-6Al-4V Using TOPSIS-Fuzzy Integrated Approach

“…However, these studies focused only on analyzing the influence of each cutting parameter and did not cover surface integrity or machining costs. To optimize the machining processes performance, many multi-objective techniques (e.g., genetic algorithm (GA), particle swarm optimization (PSO), harmony search (HS), artificial bee colony (ABC), ant colony optimization (ACO), fuzzy logic system (FLS)) have been successfully used in the open literature [11][12][13]. Thus, offering an optimized scenario that can cover different machining outputs and aspects is necessary, especially when machining titanium alloy or any other difficult-to-cut material.…”

Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

“…The dynamic development of science in the modern world is strongly correlated with the demand for more and more new construction materials with properties adapted to new technologies [ 1 , 2 , 3 , 4 , 5 ]. However, it should be remembered that the practical use of these materials is inextricably corresponded to the methods of their production and treatment.…”

“…Modern machining processes, especially of difficult-to-cut materials, which are widely used in industry, should ensure the best possible quality, efficiency, economy, reliability, and environmental friendliness of products [ 1 , 3 , 10 ].…”

“…The most unfavorable effects of this approach are the deterioration of product quality, an increase in operating costs and treatment time, a decrease in productivity, etc. [ 1 , 2 , 3 , 6 , 24 ].…”

“…The ability to predict surface roughness before machining has attracted great interest from many scientists, being the main goals of many research studies. The prediction of surface roughness is currently determined by using various techniques such as theoretical models [ 3 , 32 , 33 , 34 , 35 ], response surface methodology (RSM) [ 3 , 6 , 9 , 36 , 37 , 38 ], the Taguchi procedure [ 3 , 6 , 28 , 38 , 39 , 40 , 41 , 42 , 43 ], multiple linear regression equations [ 44 ], the Monte Carlo (MC) method [ 7 , 24 , 33 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ], artificial intelligence through the use of the artificial neural networks (ANNs) [ 1 , 3 , 26 , 29 , 30 , 53 , 54 , 55 , 56 ], genetic algorithms (GAs) [ 3 , 57 ], fuzzy logic (FL) [ 3 , 36 , 54 , 58 , …”