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E-Textile Systems Reliability Assessment-A Miniaturized Accelerometer Used to Investigate Damage during Their Washing.

Shahood Uz ZamanXuyuan TaoCédric CochraneVladan Koncar
Published in: Sensors (Basel, Switzerland) (2021)
E-textiles reveal a new and hybrid sector of the industry that is created by the integration of electronic components or textile-based electronics in our daily life textile products. They are facing problems in terms of washability, reliability, and user acceptance. This manuscript explains the mechanical stresses acting during the washing process and their impact on e-textile systems. Different washing programs were investigated in terms of total process duration. This washing process duration is mainly divided into three diverse washing actions: low-speed rotation, high-speed rotation, and stop time. This investigation was performed to highlight the importance of the washing actions and their percentages in the total washing process. A piece of fabric with a flexible PCB (printed circuit board), equipped with an accelerometer with a Bluetooth communication device and a microcontroller, was placed in the washing machine to analyze the movement of fabric provoked by washing stresses. The PCB was used for fabric movements recording to determine the impact of mechanical stress on e-textile systems during the washing process. From the video analysis, it was concluded that the duration of the low-speed and high-speed rotation actions should be privileged comparing to the duration of the whole washing process. A power spectral density (PSD) analysis based on the accelerometer outputs was realized. Mechanical stresses at different frequencies were identified. Based on this analysis, it could be possible to improve the protocols of mechanical tests (Martindale and pilling box) used to simulate the mechanical stress applied to e-textile systems during the washing process.
Keyphrases
  • high speed
  • wastewater treatment
  • physical activity
  • atomic force microscopy
  • mental health
  • oxidative stress
  • machine learning
  • magnetic resonance imaging
  • deep learning
  • computed tomography
  • heat stress
  • stress induced