The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.

New manufacturing processes for metal parts such as additive manufacturing (AM) provide a technological development for the aeronautical and aerospace industries, since these AM processes are a means to reduce the weight of the parts, which generate cost savings. AM techniques such as Laser Powder Bed Fusions (LPBF) and Electron Beam Fusion (EBM), provided an improvement in mechanical properties, corrosion resistance, and thermal stability at temperatures below 400 °C, in comparison to conventional methods. This research aimed to study the oxidation kinetics of Ti-6Al-4V alloys by conventional and Electron Beam Additive Manufacturing. The thermogravimetric analysis was performed at temperatures of 600 °C, 800 °C, and 900 °C, having a heating rate of 25 °C/min and oxidation time of 24 h. The microstructural analysis was carried out by thermogravimetric analysis. Thickness and morphology of oxide layers were analyzed by field emission scanning electron microscope, phase identification (before and after the oxidation process) was realized by X-ray diffraction at room temperature and hardness measurements were made in cross section. Results indicated that the oxidation kinetics of Ti-6Al-4V alloys fabricated by EBM was similar to conventional processing and obeyed a parabolic or quasi-parabolic kinetics. The samples oxidized at 600 °C for 24 h presented the lowest hardness values (from 350 to 470 HV). At oxidation temperatures of 800 and 900 °C, however, highest hardness values (from 870 close to the alpha-case interface up to 300 HV in base metal) were found on the surface and gradually decreased towards the center of the base alloy. This may be explained by different microstructures presented in the manufacturing processes.

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system.