A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling
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...
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/517 |
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| author | Satish Kumar Tushar Kolekar Shruti Patil Arunkumar Bongale Ketan Kotecha Atef Zaguia Chander Prakash |
| author_facet | Satish Kumar Tushar Kolekar Shruti Patil Arunkumar Bongale Ketan Kotecha Atef Zaguia Chander Prakash |
| author_sort | Satish Kumar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:34:36Z |
| format | Article |
| id | doaj.art-9bd936a4d3e04bac95ac7f30c145179d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:34:36Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9bd936a4d3e04bac95ac7f30c145179d2023-11-23T15:19:59ZengMDPI AGSensors1424-82202022-01-0122251710.3390/s22020517A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition ModellingSatish Kumar0Tushar Kolekar1Shruti Patil2Arunkumar Bongale3Ketan Kotecha4Atef Zaguia5Chander Prakash6Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, IndiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaSchool of Mechanical Engineering, Lovely Professional University, Jalandhar 144411, IndiaFused 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.https://www.mdpi.com/1424-8220/22/2/517Arduinodata acquisition systemfault detectionfused deposition modellinglow-costmulti-sensor |
| spellingShingle | Satish Kumar Tushar Kolekar Shruti Patil Arunkumar Bongale Ketan Kotecha Atef Zaguia Chander Prakash A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling Sensors Arduino data acquisition system fault detection fused deposition modelling low-cost multi-sensor |
| title | A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling |
| title_full | A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling |
| title_fullStr | A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling |
| title_full_unstemmed | A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling |
| title_short | A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling |
| title_sort | low cost multi sensor data acquisition system for fault detection in fused deposition modelling |
| topic | Arduino data acquisition system fault detection fused deposition modelling low-cost multi-sensor |
| url | https://www.mdpi.com/1424-8220/22/2/517 |
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