Systematic review of Cutting Force Measuring Systems in machining: Principles, design, filtering techniques and applications

Cutting force measurement plays a key role in modern manufacturing, supporting machinability testing, tool development, process optimization, real-time monitoring and control, and indirect evaluation of part quality. Over the past 50 years, numerous Cutting Force Measuring Systems (CFMS) have been developed and applied successfully in both laboratory and industrial settings. However, their adoption in real industrial environments has been limited by several practical drawbacks. Today, the need for more effective, less invasive, and lower-cost sensing solutions is driving renewed interest in CFMS and fostering deeper integration into manufacturing systems. Despite their relevance, comprehensive and updated reviews of CFMS are scarce. This systematic review aims to present the fundamental principles of cutting force sensing, outline the main types of CFMS, and provide general design guidelines. The strengths and limitations of each type of CFMS are discussed and compared—particularly their limited frequency bandwidth, which can be further reduced when integrated into actual machining systems. To address these challenges, advanced identification and filtering techniques are described, focusing on the dynamic relationship between input forces and measured outputs, along with modern methods for their determination. Parametric (Kalman) filters are introduced, while greater emphasis is placed on recent non-parametric filters, which offer easier implementation in industrial contexts. The review also highlights key CFMS applications, including machinability testing, cutting force model identification, tool development and tool condition monitoring. Emerging trends are examined, such as PVDF-based sensors, the Universal Inverse Filter, and other innovative technologies. Current research challenges involve developing solutions for wireless power transmission, fast calibration, low-latency data transfer, and embedded signal processing. Continued progress in CFMS research and application will be essential to advancing intelligent manufacturing and improving industrial competitiveness.