Comprehensive investigation of lubrication for sustainable grinding by principal component analysis (PCA) and an improved unsupervised algorithm

• · Developed an improved LDA algorithm for clustering lubrication topics in grinding. • · Achieved robust clustering validation using PCA and advanced statistical metrics. • · Identified eco-friendly lubricants as key to sustainable grinding processes. • · Highlighted the role of lubricating systems in extending tool and wheel life. In the background of Industry 4.0, sustainable advanced manufacturing has become a pivotal challenge, necessitating the development of technologies that minimize resource consumption and environmental impact while maintaining high-quality production. Grinding technology plays a crucial role in this domain, providing solutions for processing hard and brittle materials with microscale accuracy, particularly when the grinding process is classified as precision machining. This study addresses the gaps in sustainable grinding by presenting a comprehensive review of lubrication strategies using an improved latent Dirichlet allocation (LDA) model integrated with Principal component analysis (PCA). The proposed model systematically identifies keyword distributions and topic clusters related to lubricant selection, providing insights into the evolving trends and practices in sustainable grinding. The findings enhance understanding of the main themes and future perspectives of sustainable grinding, bridging the gap between theoretical research and practical implementation in sustainable manufacturing.