scholarly journals Automatic Multiple Sensor Data Acquisition System in a Real-time Production Environment

Procedia CIRP ◽  
2015 ◽  
Vol 33 ◽  
pp. 215-220 ◽  
Author(s):  
Jonathan Downey ◽  
Sebastian Bombiński ◽  
Mirosław Nejman ◽  
Krzysztof Jemielniak
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Sensor Data ◽  
Production Environment ◽  
Time Production ◽  
Multiple Sensor ◽  
Sensor Data Acquisition

scholarly journals A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 517
Author(s):  
Satish Kumar ◽  
Tushar Kolekar ◽  
Shruti Patil ◽  
Arunkumar Bongale ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
3D Printing ◽  
Data Acquisition ◽  
Low Cost ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Sensor Data ◽  
Fused Deposition Modelling ◽  
Layer By Layer ◽  
Fused Deposition ◽  
Sensor Data Acquisition

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

Sign in / Sign up

Export Citation Format

Share Document