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

Abstract

The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

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

“…In order to achieve the assumed surface roughness in the manufacturing process, optimization can be carried out using a number of different methods and models. 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].…”

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

“…The shape memory materials are innovative materials with the high application potential. These materials can be used as the smart materials and multifunctional materials due to their memory shape and superelasticity properties [5][6][7]. But due to their specific properties NiTi alloys are known to be difficult-to-cut materials particularly by using conventional techniques.…”

“…But due to their specific properties NiTi alloys are known to be difficult-to-cut materials particularly by using conventional techniques. Their high ductility, high degree of strain hardening, poor thermal conductivity, very low "effective" elastic modulus and unconventional stress-strain behavior are the main properties responsible for their poor machinability [5,6,8,9].…”

“…Analyzing the relevant literature regarding the prediction and optimization of machining processes, it can be easily noticed that the current trend is the use of RSM, ANNs, GAs, and the Taguchi procedure for these purposes. The author of this study note that despite the many application possibilities of the MC method [5,10], its application for solving the problems related to the prediction and optimization of machining has not been given much attention in the literature.…”

Understanding Machining Process Parameters and Optimization of High-Speed Turning of NiTi SMA Using Response Surface Method (RSM) and Genetic Algorithm (GA)

“…The Signal-to-Noise Ratio (S/N), ANOVA, and Monte Carlo method were used to optimize the cutting parameters to minimize surface roughness (R a ) [17], cutting temperature (CT) [18], and cutting force (CF) [19], respectively. Moreover, the Monte Carlo method was also used to turn NiTi alloy to establish the surface roughness model [20]. Wang et al [21] designed the orthogonal experiment for milling Ni50.8Ti SMA and obtained the optimal cutting parameters for the minimum work hardening.…”