Materials such as titanium alloys, nickel alloys, and stainless steels are difficult to machine due to low thermal conductivity, work hardening, and built-up edge formation, which accelerate tool wear. Frequent tool changes are required, often relying on operator experience, leading to inefficient tool use. While modern machine tools include intelligent tool replacement systems, many legacy machines remain in service, creating a need for practical alternatives. This study proposes a method to autonomously determine tool replacement timing by monitoring machining process signals in real time, enabling automatic tool changes even on conventional machines. Tool wear is evaluated using current and vibration sensors, with the replacement threshold estimated from the maximum current observed in an initial user-defined interval. When real-time signals exceed this threshold, the system updates controller variables to trigger tool changes. Results show vibration data are more sensitive to wear, whereas current data provide greater stability. These findings indicate that a hybrid strategy combining both sensors can enhance accuracy and reliability of tool change decisions, improving machining efficiency for difficult-to-cut materials.

This study presents a self-wearable smart personal protective respirator featuring a color-signaling triage system designed to facilitate rapid assessment during large-scale physical disasters. The device enables individuals to wear the respirator, allowing responders to quickly identify critically ill patients through real-time biometric signal acquisition and intuitive LED-based visualization. Clinical triage criteria, developed with input from emergency medicine experts, informed a severity classification algorithm based on heart rate, respiratory rate, body temperature, and posture. To implement this system, an ergonomic head-type respirator prototype was created, integrated with a compact sensor module that includes a photoplethysmography (PPG) sensor, a barometric pressure and temperature sensor, and a combined accelerometer and gyroscope sensor. Additionally, custom sensors were developed: a respiration sensor utilizing nickel oxide nanoparticles patterned by laser, and an ECG sensor made by spraying silver nanoparticles onto a flexible polyimide film and then laser-patterning it into a serpentine shape. The system effectively detects vital signs and visualizes severity levels using color signals. Although field deployment was not part of this study, the prototype demonstrated potential to reduce triage time and enhance disaster response efficiency. Further validation in real-world settings is recommended.