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In this article, Aluminum (Al2219) as a matrix, and particles of n-B4C and MoS2 were chosen as reinforcements. By means of the stir casting technique, the unhybrid and hybrid nano metal matrix composites were prepared. The turning experiment was done through CCD design of experiment with TiN coated carbide insert using a Computer Numerically Controlled Lathe. Also, for turned inserts tool wear measurement was done using a Mitutoyo profile projector with digital readout. By ANOVA the significant contribution for tool wear of each parameter can be identified and the confirmation test is performed in sort to validate the tool wear results. Furthermore, the formation of chips during turning nano MMCs (unhybrid and hybrid) are studied for different feed rates (f) and cutting speeds (v) at 0.5 mm constant depth of cut. The outcome showed that both increases in cutting speed and feed rate increase the tool wear. For Al 2219, unhybrid and hybrid nano metal matrix composites feed rate is the important factor. The value of tool wear of the Al2219 matrix is minimal and for unhybrid nano composite is maximum. The leading wear mechanism in unhybrid nano composite is abrasion. Moreover, with the addition of 2%MoS2 in the hybrid nanocomposite the tool wears is decreased. The development of Build Up Edge (BUE) was seen on the flank face of the cutting tool for hybrid nanocomposite at 0.5 mm depth of cut, (v = 114.64 m/min) and (f = 0.441 mm/rev). The obtained % error among the modeled and experimental values is <5% and it is well within the limit. The analysis of chip formation was studied for nanocomposites at lower as well as higher feed rates, cutting speeds, and constant depth of cut during turning. [ABSTRACT FROM AUTHOR]

The mono metal matrix composite (MMC) (Al2219/2% n-B4C) and hybrid MMCs (Al2219 + 2% n-B4C +2% MoS2) were prepared through liquid metallurgical. An effort is made in the current study to investigate the effect of addition of molybdenum disulphide (2% MoS2) particles on tool wear. Uncoated carbide tool exhibits the highest flank wear observed during Al2219 + 2% n-B4C composite; moreover, it is due to higher cutting speed and occurrence of hard ceramics (n-B4C) particles in the nano MMCs. In the case of a hybrid composite, the presence of MoS2 particles avoids metal-to-metal contact and reduces the flank wear and during turning with coated carbide tool obtains a higher surface quality. The depth of cut is independent of the adhesion effect. The influenced zone (affected area) is higher with an increase in depth of cut; for 0.25, 0.5, 0.75 and 1.0 mm; however, the effect on surface roughness (Ra) is similar. [ABSTRACT FROM AUTHOR]

The current study examines the erosive behaviour of a 65% NiCrAlY + 35% TiO2 plasma-sprayed coating on titanium-15 substrate at various temperatures. X-ray diffraction and scanning electron microscopy were used to characterize the coating. We assessed the coating's porosity, microhardness, surface abrasiveness, and adhesive power. At impact angles of 30° and 90°, solid particle erosion studies were conducted at various temperatures of 300°C, 500°C, and 700°C. The Al2O3 erodent was used in the hot air jet erosion tester to conduct the testing. Using an optical profilometer, the erosion volume loss of substrates with and without coatings was measured. At the various test temperatures used, it was noted that the coating's erosion resistance was found to be higher than the substrate's. For both impact angles, of 30° and 90°, it was discovered that the coating's erosion resistance improved with temperature, going from 300°C to 700°C. It can be seen from the shape of the degraded coating surface that the coating exhibits ductile behavior as a whole. [ABSTRACT FROM AUTHOR]

This paper presents financial time series forecasting with multistage wavelet transform (WT). First, the time series data is processed through WT with different mother wavelet functions to extract high frequency and low frequency coefficients. Later, standard particle swarm optimization (PSO) algorithm is utilized to find optimal regression models in order to predict future samples. Mean square error (MSE) is opted as cost function for PSO to find optimal coefficients of the regression model. This study further extended to various mother wavelet functions and their decomposition levels to investigate their impacts on time series prediction. These investigations help to data scientists for selection of process parameters and variables. Further, the impact of control parameters of PSO is also discussed to show the importance in the search mechanism especially in regression problems., Competing Interests: Conflict of interestAuthors declared that no conflicts of interest., (© The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2022.)