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This paper consolidates and presents the results of a work conducted to fabricate micro-channels on titanium grade-2 material by ultrasonic machining process (USM). In this research, the effects of important USM parameters, namely, kind of abrasives and its size, concentration of slurry, USM power rating and feed rate, have been probed on micro-channels quality for average surface roughness and process throughput in the form of material removal rate.

Multiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool based USM process. Taguchi-based L18 (mixed level) OA has been selected for experimental design. Analysis of variance (ANOVA) study and regression modeling have also been done. Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been used for process optimization to get optimum values of material removal rate (MRR) and surface roughness (SR).

The influence of important USM variables on SR and MRR have been investigated, and NSGA-II-based multi-response optimization has been done. The best surface roughness values obtained via NSGA-II solution for SiC and B₄C are 0.354 µm and 1.303 µm, respectively. Scanned electron microscopic investigation proves the fabrication of micro-channels with smooth surfaces, and minimum burrs and other defects. The material removed from the surface was due to ductile fractures.

Miniaturization is a modern trend these days to solve many precision, scientific and industrial problems. To manufacture precise micro-products, shapes and features, advanced and micro-machining processes can play a very prominent role. Micro-channels are typical micro-features required in micro-fluidic applications like micro heat exchangers and micro-pumps. Exhaustive review of existing research work indicated that precision micromachining of various materials can be effectively performed using USM, though not much work has been undertaken to explore the feasibility of multiple micro-channels in a single run using USM. The current work fulfills the gap, where multiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool-based USM process.

The purpose of this paper is to investigate the optimized setting of wire-cut electrical discharge machining (WEDM) parameters at which material removal rate (MRR) and mean roughness depth (Rz) set a compromise. The problem in the processing of Ti-6Al-4V by conventional processes is a high strength, high hardness, high tool wear. Due to which WEDM is adopted to machine Ti-6Al-4V biomedical alloy. Ti-6Al-4V alloy has a number of applications in the engineering and medical industries due to its high strength biocompatibility.

The effect of control factors (i.e. pulse on-time: Pon; pulse off-time: Poff; servo voltage: SV) on the MRR and Rz is investigated in the present research. The planning of experiments is done using a Taguchi-based L9 orthogonal array. The percentage influence of each factor on responses is also evaluated. The multi-objective optimization is done using the grey approach initially. After that, the results were also calculated using harmony search (HS). Therefore, a hybrid approach of grey and HS is used to find the optimized values of MRR and Rz.

The maximum value of grade calculated by grey-HS is 0.7879, while in the case of the experimental run the maximum value of grey grade is 0.7239. The optimized setting after improvisation at this grade value is Pon: 130 µs; Poff: 45 µs and SV: 70 V for MRR and Rz collectively. The validation of the suggested setting is completed by experimentation. The values of MRR and Rz are coming out to be 6.4 mm³/min and 13.84 µm, which represents improvised results after the implementation of the HS algorithm.

The integration of the grey approach with the HS principle in the manufacturing domain is yet to be explored. Therefore, in the present research hybrid approach of grey-HS is implemented in the manufacturing domain having applications in medical industries.

This study aims to use empowerment theory to examine the relationship between a user's engagement type (active or passive) and psychological empowerment (intrapersonal and interactional) in the context of a social media brand community (SMBC). This study also looks at the impact of psychological empowerment on brand community commitment (CC) and brand loyalty.

Convenience and snowball sampling were used to select respondents from mobile phone brand communities in India. The conceptual model was tested using structure equation modelling.

According to the study findings, active user involvement in SMBC is strongly associated to both intrapersonal and interactional empowerment (IE), but passive user engagement is weakly related to IE. Furthermore, customer empowerment and CC have a strong impact on brand CC and brand loyalty.

SMBC is now a significant point of contact for building strong consumer–brand relationships. SMBC members who are actively involved in the community have greater emotional bonding, trust and commitment to the brand. Therefore, social media marketers should encourage their customers to get involved in a brand community and empower them by involving them in brand related decision, etc. This will help the community grow and thrive.

This study addresses a research gap by examining how active and passive members of an SMBC facilitate both focal points of psychological empowerment (intrapersonal and interactional), which increase the brand community's commitment and brand loyalty.

The purpose of this paper is to develop a machine learning model that predicts the component self-alignment offsets along the length and width of the component and in the angular direction. To find the best performing model, various algorithms like random forest regressor (RFR), support vector regressor (SVR), neural networks (NN), gradient boost (GB) and K-nearest neighbors (KNN) were performed and analyzed. The models were implemented using input features, which can be categorized as solder paste volume, paste-pad offset, component-pad offset, angular offset and orientation.

Surface-mount technology (SMT) is the technology behind the production of printed circuit boards, which is used in several types of commercial equipment such as communication devices, home appliances, medical imaging systems and sensors. In SMT, components undergo movement known as self-alignment during the reflow process. Although self-alignment is used to decrease the misalignment, it may not work for smaller size chipsets. If the solder paste depositions are not well-aligned, the self-alignment might deteriorate the final alignment of the component.

It were trained on their targets. Results obtained by each method for each target variable were compared to find the algorithm that gives the best performance. It was found that RFR gives the best performance in case of predicting offsets along the length and width of the component, whereas SVR does so in case of predicting offsets in the angular direction. The scope of this study can be extended to developing this model further to predict defects that can occur during the reflow process. It could also be developed to be used for optimizing the placement process in SMT.

This paper proposes a predictive model that predicts the component self-alignment offsets along the length and width of component and in the angular direction. To find the best performing model, various algorithms like RFR, SVR, NN, GB and KNN were performed and analyzed for predicting the component self-alignment offsets. This helps to achieve the following research objectives: best machine learning model for prediction of component self-alignment offsets. This model can be used to optimize the mounting process in SMT, which reduces occurrences of defects and making the process more efficient.

This paper aims to present the implementation of a multi-layer radiation propagation model in simulations of multi-phase flow and heat transfer, for a dissociating methane hydrate reservoir subjected to microwave heating.

To model the induced heterogeneity due to dissociation of hydrates in the reservoir, a multiple homogeneous layer approach, used in food processes modelling, is suggested. The multi-layer model is incorporated in an in-house, multi-phase, multi-component hydrate dissociation simulator based on the finite volume method. The modified simulator is validated with standard experimental results in the literature and subsequently applied to a hydrate reservoir to study the effect of water content and sand dielectric nature on radiation propagation and hydrate dissociation.

The comparison of the multi-layer model with experimental results show a maximum difference in temperature estimation to be less than 2.5 K. For reservoir scale simulations, three homogeneous layers are observed to be sufficient to model the induced heterogeneity. There is a significant contribution of dielectric properties of sediments and water content of the reservoir in microwave radiation attenuation and overall hydrate dissociation. A high saturation reservoir may not always provide high gas recovery by dissociation of hydrates in the case of microwave heating.

The multi-layer approach to model microwave radiation propagation is introduced and tested for the first time in dissociating hydrate reservoirs. The multi-layer model provides better control over reservoir heterogeneity and interface conditions compared to existing homogeneous models.

The COVID-19 pandemic had severely impacted global commerce, leading to market closure and travel restrictions. Due to the extreme limitations on shopping and market possibilities, consumers’ buying habits have changed significantly and they are still changing what they buy and how they purchase for everyday necessities. They are trying to shop locally, carefully, and economically. Consumers are buying their stuff from wherever goods are available. Purchasing nonessential items is restricted. Restaurants, tourism, apparel, and furniture are the industries that have been impacted the most. The objective behind this study is to find out the post-COVID-19 effects on consumer buying of essentials from a convenience store over hypermarkets and how consumer behavior will revive postpandemic toward retail shopping. In the study, responses were collected through structured questionnaire to explore the consumer buying behavior for their daily essentials both at convenience stores and hypermarkets. The study reveals that consumers prefer to buy essential commodities/goods of daily needs from convenience store compared to hypermarket due to the risk of being exposed to COVID-19 pandemic and this behavior will change future trend of retail shopping experience. The major limitation of the study is sampling frame. Future studies can replicate this study in different context with different target population. Pertaining to the current ongoing situations of the pandemic, a very high percentage of the respondents would still choose nearby convenience stores over hypermarkets. This study estimates the behavioral change in retail consumers in future; therefore, it suggests retailers adopt innovative marketing strategy in future.

Given the increasing global significance of Indian markets, multi-national corporations (MNCs) are keen to do business here; however, cross-cultural issues can be barriers in managing human resources (HR) in international businesses. The purpose of this paper is to understand how MNCs can successfully do business in India, with special reference to cross-cultural issues and management of HR.

In-depth interviews were conducted with executives working in MNCs and Indian MNCs based in India and abroad. Respondents were senior professionals, working in diverse sectors and had global work experience for about five years. Majority of the interviews were conducted in Delhi and some were conducted in Singapore. Interviews responses were qualitatively analysed.

Findings reveal that MNCs wanting to do business in India need to have a long-term business focus, a well-defined expatriate policy and deep pockets to experience growth and payoffs on investments. In order to be successful, they need to understand India culturally and geographically, build trusting relationships with HCNs, partner with local players who are familiar with domestic challenges and localize the best practices of the west. Attrition and retention being the major challenges in India, compensation alone is not enough to attract and retain talent. Understanding Indian psyche and offering individuals a unique value proposition such as challenging roles and professional growth is imperative for creating an attractive employer brand in order to win the war for talent.

Though sample size is small, this research has implications for MNCs operating in India or planning to set up Indian operations.

Inferences have been drawn out of primary data collected from senior executives who were handling core MNC operations and sharing their wealth of experience. The findings give fresh insights into the whole issues of MNC management involving cross-cultural and HR issues.