COVID-19 induced pandemic situations have put the bio-medical waste (BMW) management system, of the world, to test. Sudden influx, of COVID-infected patients, in health-care facilities, has increased the generation of yellow category BMW (Y-BMW) and put substantial burden on the BMW-incineration units of India. This study presents the compromising situation of the BMW-incineration units of India, in the wake of COVID-19 pandemic, from 21 st March, 2020 to 31 st August, 2020. This analysis revealed that on an average each COVID-infected patient in India generates approximately 3.41 kg/d of BMW and average proportion of Y-BMW in it is 50.44%. Further, it was observed that on 13 th July, 2020, the total Y-BMW, generated by both the normal and COVID-infected patients, fully utilized the BMW-incineration capacity of India. Also, it was made evident that, during the study period, BMW-incineration emitted several pollutants and their concentration was in the order: NO x > CO >SO x > PM > HCl > Cd > Pb > Hg > PCBs > Ni > Cr > Be > As. Subsequently, life time cancer risk assessment depicted that with hazard quotient >10 -6 , Cd may induce carcinogenic health impacts on both the adults and children of India. Therefore, to mitigate the environmental-health impacts associated with the incineration of BMW, evaluation of various options, viz., alternative technologies, substitution of raw materials and separate treatment of specific wastes, was also done. It is expected that the findings of this study may encourage the global auditory comprising scientific community and authorities to adopt alternate BMW-management strategies during the pandemic.

Despite several additive manufacturing techniques are commercially available in market, Fused Deposition Modeling (FDM) is increasingly used by researchers and engineers for new product development. FDM is an established process with a plethora of advantages, but the visible surface roughness (SR), being an intrinsic limitation, is major barrier against utilization of fabricated parts for practical applications. In the present study, the chemical finishing method, using vapour of acetone mixed with heated air, is being used. The combined impact of orientation angle, finishing temperature and finishing time has been studied using Taguchi and ANOVA, whereas multi-criteria optimization is performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The surface finish was highly responsive to increase in temperature while orientation angle of 0° yielded maximum strength; increase in finishing time led to weight gain of FDM parts. As the temperature increases, the percentage change in surface roughness increases as higher temperature assists the melt down process. On the other hand, anisotropic behaviour plays a major role during tensile testing. The Signal-to-noise (S/N) ratio plots, and ANOVA results indicated that surface finish is directly proportionate to finishing time because a longer exposure results in complete layer reflowing and settlement.

Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings.

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Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0.

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This paper investigates the behavior of a rotor-bearing system with a breathing crack, under the action of active magnetic bearings, implemented to suppress the vibration induced by the presence of the crack and the unbalance. In such a case, identification of the crack with help of vibration signals may be difficult; other observables such as the controller current may be utilized for the purpose. Linear system equations of motion together with the switching crack excitation function are used to develop the identification algorithm. The use of full spectrum analysis is made to determine the crack force coefficients of the harmonics exciting the rotor in the same and the reverse direction of the rotor spin. Usage of these coefficients in the identification algorithm estimates the viscous damping, disc unbalance and additive crack stiffness. The algorithm has been tested for measurement noise and bias errors in system parameters, for robustness. In practical terms, the situation analyzed in this paper relates to identification of crack and other parameters in a rotor integrated with an active magnetic bearing.