Zinc (II) removal using low-cost sorbents requires a proper process parametric study to determine

its optimal performance characteristics. In this respect, the present study proposes a new modeling and simulation procedure for heavy metal removal system and is carried out to optimize input variables such as initial pH,

adsorbent dosage, and contact time for biosorption of Zinc (II) by using bentonite. The proposed experimental

system is cost-effective and requires less calculation for determining optimal values, i.e., input variables and

their related removal capacity, Rem%. To optimize the adsorption process, cubic spline curve fitting and numerical differentiation techniques are used for required calculations. According to the proposed calculations, the

removal capacity is calculated as 98.66%, while the optimal values are calculated as initial pH – 6.76, adsorbent

dosage – 1.14 g L-1, contact time – 13 minutes. To evaluate the results, full factor experimental design and 3 way

ANOVA test are used for comparison.

During the machining processes, heat gets generated as a result of plastic deformation of metal and friction along the tool–chip and tool–work piece interface. In materials having high thermal conductivity, like aluminium alloys, large amount of this heat is absorbed by the work piece. This results in the rise in the temperature of the work piece, which may lead to dimensional inaccuracies, surface damage and deformation. So, it is needed to control rise in the temperature of the work piece. This paper focuses on the measurement, analysis and prediction of work piece temperature rise during the dry end milling operation of Al 6063. The control factors used for experimentation were number of flutes, spindle speed, depth of cut and feed rate. The Taguchi method was employed for the planning of experimentation and L18 orthogonal array was selected. The temperature rise of the work piece was measured with the help of K-type thermocouple embedded in the work piece. Signal to noise (S/N) ratio analysis was carried out using the lower-the-better quality characteristics. Depth of cut was identified as the most significant factor affecting the work piece temperature rise, followed by spindle speed. Analysis of variance (ANOVA) was employed to find out the significant parameters affecting the work piece temperature rise. ANOVA results were found to be in line with the S/N ratio analysis. Regression analysis was used for developing empirical equation of temperature rise. The temperature rise of the work piece was calculated using the regression equation and was found to be in good agreement with the measured values. Finally, confirmation tests were carried out to verify the results obtained. From the confirmation test it was found that the Taguchi method is an effective method to determine optimised parameters for minimization of work piece temperature.

Labor absenteeism is a factor that affects the good performance of organizations in any

part of the world, from the instability that is generated in the functioning of the system.

This is evident in the effects on quality, productivity, reaction time, among other aspects.

The direct causes by which it occurs are generally known and with greater reinforcement

the diseases are located, without distinguishing possible classifications. However, behind

these or other causes can be found other possible factors of incidence, such as age or sex.

This research seeks to explore, through the application of neural networks, the possible

relationship between different variables and their incidence in the levels of absenteeism. To

this end, a neural networks model is constructed from the use of a population of more than

12,000 employees, representative of various classification categories. The study allowed the

characterization of the influence of the different variables studied, supported in addition to

the performance of an ANOVA analysis that allowed to corroborate and clarify the results

of the neural network analysis.

We present computer simulations of a two-way ANOVA gage R&R study to determine the effects on the average speckle width of intensity patterns caused by scattered light reflected from random rough surfaces with different statistical characteristics. We illustrate how to obtain reliable computer data that properly simulate experimental measurements by means of the Fresnel diffraction integral, which represents an accurate analytical model for calculating the propagation of spatially-limited coherent beams that have been phase-modulated after being reflected by the vertical profiles of the generated surfaces. For our description we use four differently generated vertical profiles and five different vertical randomly generated roughness values.

In this work, experiments were carried out to quantify the behaviour of friction stir welded (FSW) AA5082-AA7075 butt joints under tensile loading and completely reversed fatigue loading. Different samples were prepared to identify optimum tool rotational and travel speeds to produce FSW AA5082-AA7075 butt joints with the maximum fatigue life. ANOVA was performed, which confirmed that both tool speed and tool rotational speed affect the tensile strength of the weld. The samples exhibit a considerable difference in their fatigue life and tensile strength. This difference can be accounted to the presence of welding defects such as surface defects and porosity. S-N curve plotted for the sample shows a significantly high fatigue life at the lower stress ranges. Fracture surfaces were also analysed under scanning electron microscope (SEM). Study of the fracture surface of the sample that failed under fatigue loading showed that the surface was mainly divided in two zones. The first zone was the area of fatigue crack growth where each stress cycle, slowly and gradually, helped in the growth of the crack. The second zone was the region of fast fracture where the crack growth resulted in the failure of the joint instantaneously. The fracture surface study of the sample that failed under tensile loading showed that the mode of failure was ductile in nature.

The wear behaviour of Cr3C2-25% NiCr laser alloyed nodular cast iron sample were analyzed using a pin-on-disc tribometer. The influence of sliding velocity, temperature and load on laser alloyed sample was focused and the microscopic images were used for metallurgical examination of the worn-out sites. Box-Behnken method was utilised to generate the mathematical model for the condition parameters. The Response Surface Methodology (RSM) based models are varied to analyse the process parameters interaction effects. Analysis of variance was used to analyse the developed model and the results showed that the laser alloyed sample leads to a minimum wear rate (0.6079×10–3 to 1.8570×10–3 mm3/m) and coefficient of friction (CoF) (0.43 to 0.53). From the test results, it was observed that the experimental results correlated well with the predicted results of the developed mathematical model.

This research paper discusses the friction and wear behaviour of Al-12Si alloy reinforced with B₄C prepared through Powder Metallurgy (P/M) method by varying the weight percentage of reinforcement (x = 2, 4, 6, 8, and 10) content. The samples were prepared by using die and punch assembly and the lubricant used to eject the sample from the die was molybdenum disulfide. The compaction was done by using a compression testing machine by applying a pressure of 800 MPa. The dry sliding friction and wear behaviour of the sample was conducted on a Pin-on-Disc machine and the experimental values of friction and wear were calibrated. The Taguchi design experiment was done by applying an L25 orthogonal array for 3 factors at 5 levels for the response parameter Coefficient of Friction (CoF) and wear loss. The SEM images show the shape, size and EDX confirm the existence of Al, Si, B₄C particles in the composites. Analysis of Variance (ANOVA) for CoF of S/N ratio, shows that the reinforcement having 34.92% influence towards the S/N ratio of CoF, ANOVA for wear loss of S/N ratio shows that the sliding distance having 46.76% influence towards the S/N ratio of wear loss, when compared to that of the other two input parameters. The interaction line plot and the 2Dsurface plot for CoF and wear loss show that the increase in B₄C content decreases the wear loss and CoF. The worn surface shows that the B₄C addition will increase the wear resistance.

The paper presents application of Taguchi method in optimizing the sound transmission loss through sandwich gypsum constructions and those comprising of masonry concrete blocks and gypsum boards in order to investigate the relative influence of the various parameters affecting the sound transmission loss. The application of Taguchi method for optimizing sound transmission loss has been rarely reported. The present work uses the results analytically predicted on “Insul” software for various sandwich material configurations as desired by each experimental run in an L8 orthogonal array. The relative importance of the parameters on single-number rating, Rw (C, Ctr) is evaluated in terms of percentage contribution using Analysis of Variance (ANOVA). The ANOVA method reveals that type of studs, steel stud frame and number of gypsum layers attached are the key factors controlling the sound transmission loss characteristics of sandwich multi-layered constructions.