The present study investigates the impact of COVID‐19 on Consumers' changing way of life and buying behaviour based on their socio‐economic backgrounds. A questionnaire survey was carried out to understand the impact of COVID‐19 on consumers' affordability, lifestyle, and health awareness and how these effects influenced their buying behaviour. A total of 425 usable responses were analysed using the structural equation modelling considering Consumers' socio‐economic background as exogenous variables and Consumers' changing way of life and Adaptation in consumers’ buying behaviour as endogenous variables. The study reveals that COVID‐19 has affected the consumers in the unorganised sectors more than others and induced an increase in the demand for affordable substitutes for daily necessities. The demand for wellness and entertainment products is found to depend upon the occupation and family earning status of consumers which is jointly mediated by affordability and lifestyle changes. Further, the findings show that the demand for health and hygiene products depends on the current employment status and family earning status of consumers which is jointly mediated by affordability and awareness towards health and hygiene. The model developed in the present study allows the decision‐makers to identify which segments of the population with certain socio‐economic backgrounds could be targeted for wellness products and which ones could be targeted for health and hygiene products. In addition, the model provides rich insights to the managers as to what kind of product substitution would be viable in the market during the pandemic.

With the ever-increasing demands for high surface finish and complex shape geometries, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove materials in the form of atoms or molecules to obtain the required accuracy and burr-free machined surface. In order to exploit the optimal capabilities of the NTM processes, it is often required to determine the best possible combinations of their controllable parameters. Different nonconventional optimization techniques have been used for dealing with these process optimization problems because of their inherent advantages and capabilities for arriving at the almost global optimal solutions. This paper reviews the applications of different nonconventional optimization techniques for parametric optimization of NTM processes. It is observed that electrical discharge machining processes have been optimized most number of times, followed by wire electrical discharge machining processes. In most of the cases, the past researchers have preferred to maximize material removal rate. Genetic algorithm has been found to be the most popular non-conventional optimization technique.

It is very important to select the optimal parametric values for various non-traditional machining processes (NTM) for improving their performance. The performance measures of NTM processes include material removal rate (MRR), radial overcut (ROC), heat affected zone (HAZ), etc. In this chapter, particle swarm optimization has been used to find out the optimal parametric settings for electrochemical discharge machining (ECDM) to improve its performance measure. Both single-objective as well as multi-objective optimization has been performed and the results have been compared with those obtained by other researchers.

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