Modeling of Cutting Parameters and Tool Geometry for Multi-Criteria Optimization of Surface Roughness and Vibration via Response Surface Methodology in Turning of AISI 5140 Steel

Abstract

AISI 5140 is a steel alloy used for manufacturing parts of medium speed and medium load such as gears and shafts mainly used in automotive applications. Parts made from AISI 5140 steel require machining processes such as turning and milling to achieve the final part shape. Limited research has been reported on the machining vibration and surface roughness during turning of AISI 5140 in the open literature. Therefore, the main aim of this paper is to conduct a systematic study to determine the optimum cutting conditions, analysis of vibration and surface roughness under different cutting speeds, feed rates and cutting edge angles using response surface methodology (RSM). Prediction models were developed and optimum turning parameters were obtained for averaged surface roughness (Ra) and three components of vibration (axial, radial and tangential) using RSM. The results demonstrated that the feed rate was the most affecting parameter in increasing the surface roughness (69.4%) and axial vibration (65.8%) while cutting edge angle and cutting speed were dominant on radial vibration (75.5%) and tangential vibration (64.7%), respectively. In order to obtain minimum vibration for all components and surface roughness, the optimum parameters were determined as Vc = 190 m/min, f = 0.06 mm/rev, κ = 60° with high reliability (composite desirability = 90.5%). A good agreement between predicted and measured values was obtained with the developed model to predict surface roughness and vibration during turning of AISI 5140 within a 10% error range.

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

“…There were other studies [62,67] which indicated the most important parameters were depth of cut on the temperature increase and TW progression. In [31,39,48,[61][62][63][64][65][66][67][68], it was reported that a correlation between cutting temperature and TW was found, mostly triggered by cutting conditions and cutting speed. Temperature based measurement provided significant contribution for detecting TW progress and El Hakim et al [39] indicated that in hard turning of high alloy steel with secondary hardening operation, temperature and flank TW demonstrated an increasing trend.…”

“…Monitoring of tool condition is a constraining and unstable process in machining operations [ 31 ]. TW is both inevitable and necessary for chip removing and manufacturing new product.…”

“…In order to obtain the desired results from the machining process, to keep the process variables under control and to avoid undesirable results, the necessary conditions must be provided to continue the process in a stable manner [ 31 ]. In this regard, vibrations occur because of the lack of rigidity in the machine, errors in the workpiece and cutting tool connecting procedure or depending on the altering cutting conditions during the machining process [ 36 ].…”

“…There were some studies in comparison with the studies which reported the cutting speed was the most effective parameters on temperature increase. Kuntoğlu et al [ 31 ] reported a study that depth of cut is the most effective parameter on the increase of cutting zone temperature. The reason was that high depth of cut values leads to large amount of metal chip removal, and the cutting tool undergoes a more restrictive force.…”

Analysis of the Possibility of Using Wavelet Transform to Assess the Condition of the Surface Layer of Elements with Flat-Top Structures

“…An example may be the ANOVA analysis [17] or the Monte Carlo method [18]. The ANOVA method is also used for multi-criteria optimization of the manufacturing process, in which the machinability indicator is not only surface roughness but also, for example, the vibration of the machining system [19]. Another example of optimization is the use of a hybrid WOA (Whale Optimization Algorithm) algorithm.…”