Investigating the wicking behavior of micro/nanofibrous core-sheath PET–PAN yarn modified by dimethyl 5-sodium sulfoisophthalate

The present research aims at imparting an improved wicking ability to the recycled multifilament yarns by wrapping composite nanofibers to attain an efficient material for filtration purposes. Therefore, polyacrylonitrile nanofibers containing dimethyl 5-sodium sulfoisophthalate nanoparticles were wrapped around the recycled poly(ethylene terephthalate) yarn by means of a novel electrospinning technique. Several tests were performed to investigate the parameters affecting wicking rise and moisture regain of the samples. Taguchi method was used in two separate designs (with or without nanoparticles). Some factors such as polymer solution concentration, mass ratio of nanoparticles, take-up rate, and number of filaments were considered as the variable factors while yarn wicking rate and moisture regain were the response factors. It was found that the hydrophilic nature of nanoparticles together with the ultrafine structure of nanofibers provide yarns with enhanced wicking properties. Although solution concentration is the predominant factor in wicking rate of the yarns containing nanoparticles, the most effective factor in wicking rise and moisture regain for other cases is the number of filaments. The mechanism of nanoparticle effect on fluidic jet during electrospinning process is explained by theory of nanofluids stability which has never been validated experimentally in previous research studies. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48185.     

Study on Hot Ring Compression Test of Nimonic 115 Superalloy Using Experimental Observations and 3D FEM Simulation

In hot forging of Nimonic 115, it is desirable to determine friction coefficients. Changing magnitudes of temperature and type of lubricant at the surface of the workpiece and dies influence friction coefficient. This paper describes an experimental investigation of friction under hot forging conditions using the ring compression test. The 3D FEM simulations were used to derive the friction calibration curves and to evaluate material deformation, geometric changes, and load-displacement results. A series of ring compression tests were carried out to obtain friction coefficients for a number of lubricants including mica plate, glass powder, graphite powder, and dry condition. The experiments show how the variations in temperature at the interface affected frictional behavior. On the basis of these results, mica is recommended for hot forging of Nimonic 115 and its friction coefficient is approximately 0.3.     

Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems. In these instances, classic methods cannot be used for monitoring the profiles. For example, when the response variable is binary, logistic regression methods should be used rather than ordinary least square or other classic regression methods. There are some methods for monitoring logistic profiles in the literature, but the basic assumption of these methods is the independency of the consecutive observations, while this assumption is violated in some instances for example when the successive samples are taken in short intervals. This paper considers the effect of autocorrelation presence between the observations in different levels of the independent variable in a logistic regression profile on the monitoring procedure (T ² control chart) and proposes two remedies to account for the autocorrelation within logistic profiles. In one of the remedies, upper control limit of the traditional T ² control chart is modified. In the second one, we use a generalized linear mixed model (GLMM) to estimate the regression parameters and then use the T ² control chart for monitoring autocorrelated logistic regression profiles. Simulation studies show the better performance of T ² control chart when the regression parameters are estimated by the GLMM method under both step shifts and drifts.