A framework for the design of smart cutting tools - Balfe, Diarmuid (Trinity College Dublin. School of Engineering. Discipline of Mechanical & Manuf. Eng, 2025)
The digitalisation of manufacturing has driven increasing demands for advanced product functionality, including in the field of metal cutting. Developments in toolholding have traditionally been incremental, focusing on mechanical improvements such as hydraulic or shrink-fit clamping and the addition of damping. Mechatronic enhancements have largely been limited to radio frequency identification (RFID) tags for tool tracking, while only a small number of research-driven prototypes with embedded sensors exist. These solutions are typically bespoke and diagnostic in nature, lacking scalability for industrial use. A structured, systematic approach to the design of smart tools is therefore absent. This research addresses that gap by developing a dedicated design framework for smart tooling with embedded sensors, focusing on surface acoustic wave (SAW) strain sensing for static tool-holding systems such as external turning and boring. The framework balances competing requirements -for example, stiffness for machining versus compliance for sensor response- and is modular, allowing extension to a variety of sensor types, measurement phenomena, and tool holding geometries. Inspiration was drawn from comparable design frameworks in other engineering domains, including concrete structures, centrifugal compressors, and truss systems. The work began with the full characterisation of a selected SAW strain sensor to establish its mechanical-electrical response. A digital thread was then implemented, linking finite element analysis (FEA) of tool modifications with sensor behaviour, enabling predictive evaluation of tool performance. Case studies in external turning and boring validated the framework, showing that it can deliver reliable, high-performing smart tools. Overall, the framework constitutes both a novel academic contribution and a practical resource for engineers. Its usefulness will increase as additional case studies are incorporated and as data accumulates to refine its tasks. Future opportunities lie particularly in advancing the FEA of toolholders, where more sophisticated modelling approaches could further enhance predictive accuracy in smart tool design.
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Keywords: Digitalization; Manufacturing; Industry 4.0