Immanuel A. Edinbarough

Titanium alloys have been of paramount interest to the aerospace industry due to their attractive characteristics. However, these alloys are difficult to machine and require grinding post-processes for quality assurance of the products. Conventional grinding takes a long time and uses a flood coolant-lubrication technique, which is not cost effective nor environmentally friendly. Several studies have been performed to prove the viability and benefit of using Minimum Quantity Lubrication (MQL) with vegetable or synthetic-ester fluids. This work aims to find the optimum grinding parameters of creep feed grinding Ti-6Al-4V with a green silicon carbide wheel, using a flood lubrication system with water-soluble synthetic oil, MQL with ester oil, and nano-MQL (NMQL) using alumina-nanopowder homogeneously dispersed within an ester oil. It is concluded that at 0.635 mm and 1.27 mm infeeds, the three lubrication methods performed similarly. At an infeed of 1.905 mm, MQL did not provide desirable quality, though NMQL and flood lubrication performed practically identically. At a cross feed of 0.254 mm, an infeed of 1.27 mm, and a table feed rate of 6.7 m/min, these grinding parameters provide a material removal rate of 2163 mm³/min with a surface roughness across (Ra) of 0.515 µm. These parameters provide the quickest material removal rate while still maintaining industrial quality. This conclusion is based on environmental, economic, and qualitative results.

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.