Multivariable Model for Understanding the Sandpaper Manufacturing Process

In this study, we analyze the production process capability of sandpaper manufacturing, with an emphasis on material removal from the finished product and the identification of manufacturing variables that most influence grinding performance and final quality. To this end, the CRISP-DM methodology was applied along with linear regression, stepwise analysis, and principal component analysis (PCA) to a sample of 62 operational variables collected between 2024 and 2025. These variables were reduced to 12 critical dimensions that explain 80% of the process variability. This study highlights the interaction between chemical properties of the adhesive system (gel time, pH, and formaldehyde concentration) and fine mechanical adjustments (blade and roller clearance), showing how these variables jointly affect sanding performance. By integrating these factors into a multivariate framework, PCA allows for the identification of latent relationships, reduces process complexity, and establishes a statistical basis for standardization and continuous improvement, with the aim of supporting the transfer of technical knowledge in industrial manufacturing environments. The proposed framework is intended to support technical knowledge transfer in industrial manufacturing environments.