Development of an innovative, low-cost and universal dynamometer for cutting forces measurement in milling

In the context of modern manufacturing, where flexibility, automation, and cost-effectiveness are increasingly critical, robotic milling is gaining traction as a viable alternative to traditional CNC machining in a variety of industrial applications. However, despite its versatility, robotic milling remains significantly more challenging due to the inherent compliance of industrial robotic arms, which compromises both stability and process reliability. In this scenario, the development of robust, real-time monitoring systems—capable of functioning independently of the robot's pose and configuration—becomes a key enabling factor for the adoption of robotic machining in production environments. This work presents the design, development, and validation of an innovative dynamometer prototype intended for cutting force measurement. The system is specifically conceived to meet the unique constraints of robotic milling, yet remains versatile and applicable to broader contexts. The proposed solution aims to be reliable, dynamically responsive offering good dynamic bandwidth, universal (i.e., configuration-independent), and economically viable. The dynamometers developed over the course of the project integrate low-cost PVDF force sensors and MEMS accelerometers, combined with mechanical structures optimized for minimal compliance and proper force decoupling. Some components were manufactured using additive techniques, such as SLM, to allow for complex geometries and lightweight structures, others involving traditional subtractive techniques. The work encompassed a complete engineering pipeline — from CAD and CAM modeling, structural analysis (FEA), to experimental testing and system-level validation. Special attention was paid to the implementation of the universal inverse filtering (UIF) algorithms developed by Totis to reconstruct cutting forces regardless of the robot pose or milling setup. Throughout the development process, several challenges emerged, including sensor sensitivity drift, noise management, and deformations due to additive manufacturing. Despite these obstacles, the final prototypes demonstrated promising performance, particularly in terms of dynamic response and signal filtering accuracy. However, residual issues — such as residual crosstalk along the Z-axis and variability in sensor calibration — highlight areas for future refinement. Overall, the results support the feasibility and potential industrial relevance of a compact, universal, and cost-effective force sensing system for robotic machining applications.