This dataset comprises 24 complete milling tool wear cycles collected from a SANJI VMC 650 three-axis vertical machining centre operating under active production conditions at a heavy-duty CNC manufacturing facility. Machining was performed on 38CR chromium alloy steel using Mitsubishi APMT1135PDER-M2 VP15TF PVD TiAlN-coated carbide inserts mounted on a 20 mm two-flute indexable end mill, at a constant spindle speed of 1800 RPM and feed rate of 1500 mm/min under flood coolant. A low-cost, self-contained data acquisition system built around an Arduino UNO microcontroller was deployed non-intrusively on the machine, logging six-axis inertial data (three-axis acceleration and three-axis angular rate) from an MPU6050 GY-521 IMU affixed to the spindle head and spindle motor current from an SCT-013-050 split-core current transformer. Each tool wear cycle begins with the installation of fresh carbide inserts and terminates upon operator-declared failure, identified by deteriorated surface finish, abnormal chatter, and elevated vibration. Remaining Useful Life (RUL) labels are assigned using a timestamp-based linear decay, requiring no production stoppage or optical measurement equipment. The dataset is intended for benchmarking machine learning and deep learning models for CNC tool RUL prediction under real industrial operating conditions.
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".