A Mold Damage Monitoring Algorithm for Power Metallurgy Molding Machines Using Bidirectional Long Short-Term Memory on an Internet of Things Platform

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 7; p. 2143
• Main Authors: Lin, Hao-Pu, Chen, Yuan-Chieh, Han, Chin-Chuan, Wu, Yu-Chi, Lin, Jin-Yuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.03.2025; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Bearings; Deep learning; Degassing of metals; Embedded systems; Factories; Fault diagnosis; inertial measurement unit (IMU); intelligence system; Internet of Things; Internet of Things (IoT); Machine learning; Machinery; Manufacturers; Manufacturing; mean square error; Metal powder products; Metal powders; Metals; Middleware; Mold damage; Neural networks; Powder metallurgy; Preventive maintenance; Sensors; Support vector machines; vibration data; deep learning; inertial measurement unit (IMU); Internet of Things (IoT); intelligence system; mean square error; vibration data
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25072143

In this paper, an analysis and monitoring algorithm is proposed for mold health evaluation using vibration data. Two inertial measurement units (IMUs) and an embedded system are first used to acquire vibration data from a powder metallurgy molding machine. These data are collected on an Internet of Things (IoT) platform using the Message Queueing Telemetry Transport (MQTT) protocol. For data analysis, the vibration signal on the Z axis is segmented to label the contact section of the upper and middle molds, and the corresponding vibration data of the stamping friction on the X, Y, and Z axes are extracted. Using only historical vibration data from normal stamping, a Bidirectional Long Short-Term Memory (Bi-LSTM) model with an attention mechanism is trained to predict normal stamping vibrations several minutes in advance. By comparing the predicted stamping vibrations with the observed data at the current time, the mean square errors (MSEs) are calculated to evaluate the health status of the mold. Several ablation experiments were conducted to assess the performance of the trained model. The average MSE values for normal samples and abnormal samples were smaller than 0.5 and larger than 1.0, respectively. The experimental results confirm that the trained prediction model and evaluation indicators can effectively notify operators in advance. An early warning system using vibration data for mold damage was successfully implemented, enhancing predictive maintenance.