Reliable monitoring of metalworking fluids (MWF) is crucial for quality assurance and sustainability in the metalworking industry. This work presents a cost-effective sensor system for the inline analysis of MWF using MEMS-based near-infrared sensor technology and artificial intelligence (AI). By combining spectral data processing and machine learning, parameters relevant to cooling lubricants such as type, concentration, pH value and nitrate content are precisely determined. The analysis is model-based and enables a robust and automated condition assessment directly in the process. Integration into a cloud infrastructure supports centralized data processing and meets the requirements of modern smart factory concepts. The article provides an overview of the structure, functionality and potential of the system as well as the AI methods used.
Otto et al. (Thu,) studied this question.