Cu-Catalyzed ligand-free synthesis of rosuvastatin based novel indole derivatives as potential anticancer agents

Rosuvastatin based novel indole derivatives designed as potential anti-cancer agents were synthesized via a newly developed ligand-free, simple, straightforward and inexpensive one-pot method. The methodology involved a Cu-catalyzed coupling-cyclization of a rosuvastatin based alkyne with o-iodoanilides in the presence of CuI and K2CO3 in PEG-400. Three of the synthesized compounds showed promising anti-proliferative activities against cancer cell lines and an increase of p21 mRNA expression and apoptotic effects in zebrafish embryos/larvae.Peer reviewe

Progressive tool flank wear monitoring by applying discrete wavelet transform on turned surface images

In this paper, a method for on-machine tool progressive monitoring of tool flank wear by processing the turned surface images in micro-scale has been proposed. Micro-scale analysis of turned surface has been performed by using discrete wavelet transform. A novel methodology for proper selection of mother wavelets and its decomposition level dependent on the feed rate parameter has also been shown in this research. The selected mother wavelets are utilized to decompose the turned surface images at the chosen decomposition level and two features, namely, GRMS and Energy are extracted as the highly repeatable descriptors of tool flank wear. An exponential correlation of GRMS and Energy values with progressive tool flank wear are found with average coefficient of determination values as 0.953 and 0.957, respectively

Progressive tool condition monitoring of end milling from machined surface images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error

Tool Condition Monitoring in Turning by Applying Machine Vision

In this paper, a method for predicting progressive tool flank wear using extracted features from turned surface images has been proposed. Acquired turned surface images are analyzed by using texture analyses, viz., gray level co-occurrence matrix (GLCM), Voronoi tessellation (VT), and discrete wavelet transform (DWT) based methods to obtain information about waviness, feed marks, and roughness from machined surface images for describing tool flank wear. Two features from each texture analyses are extracted and fed into support vector machine (SVM) based regression models for predicting progressive tool flank wear. Mean correlation coefficient between the measured and predicted tool flank wear is found as 0.991

Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Tool condition monitoring has found its importance to meet the requirement of production quality in industries. Machined surface texture is directly affected by the extent of tool wear. Hence, by analyzing the machined surface images, the information about the cutting tool condition can be obtained. This paper presents a novel technique for tool wear classification using hidden Markov model (HMM) technique applied on the features extracted from the gray level co-occurrence matrix (GLCM) of machined surface images. The tool conditions are classified into sharp, semi-dull and dull tool states. The proposed method is found to be cost effective and reliable for on-machine tool classification of cutting tool wear with an average of 95% accuracy

Modelling of flame temperature of solution combustion synthesis of nanocrystalline calcium hydroxyapatite material and its parametric optimization

Hydroxyapatite (HAp), an important bio-ceramic was successfully synthesized by combustion in the aqueous system containing calcium nitrate-di-ammonium hydrogen orthophosphate-urea. The combustion flame temperature of solution combustion reaction depends on various process parameters, and it plays a significant role in the phase formation, phase stability and physical characteristics of calcium hydroxyapatite powder. In this work, an attempt has been made to evaluate the influence of each selected process parameters on the flame temperature as well as physical characteristics of powder, and to select an optimal parameters setting using Taguchi method. A regression model has also been developed to correlate the input parameters, viz. batch size, diluents, fuel to oxidizer ratio and initial furnace temperature, with flame temperature of the solution combustion reaction. The adequacy of the developed model has been checked using analysis of variance technique

Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images

Online monitoring of friction stir welding (FSW) is inevitable due to the increasing demand of this process. Also the machine vision system has industrial importance for monitoring of manufacturing processes due to its non-invasiveness and flexibility. Therefore, in this research, an attempt has been made to monitor friction stir welding process by analyzing the weld surface images. Here, discrete wavelet transform has been applied on FSW images to extract useful features for describing the good and defective weld. These obtained features have been fed to support vector machine based classification model for classifying good and defective weld with 99% and 97% accuracy with Gaussian and polynomial kernel, respectively