Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino

Sudianto, Agus; Jamaludin, Zamberi; Rahman, Azrul Azwan Abdul; Novianto, Sentot; Muharrom, Fajar

doi:10.37934/arfmts.82.2.114

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Very often sliding systems are assessed indirectly by evaluating the quality of machining parts [3]. Direct assessment of the components of the sliding system can be performed using both off-line and on-line techniques, based on methods such as vibration analysis [4][5][6][7], laser interferometry [8,9], noise analysis [10,11], visual inspection [12,13], temperature monitoring [14,15], acoustic emission monitoring [16,17], motor current signature analysis [18,19] and thermal imaging [20][21][22].…”

Section: Condition Monitoring and Diagnostics Of Sliding Systemsmentioning

confidence: 99%

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Augustyn,

Fidali

2023

Acta Mechanica Et Automatica

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The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.

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