Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques
Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use....
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MDPI AG
2024-09-01
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| author | Satish Kumar Sameer Sayyad Arunkumar Bongale |
| author_facet | Satish Kumar Sameer Sayyad Arunkumar Bongale |
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| description | Fused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions. |
| format | Article |
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| spelling | doaj-art-ac004ae537d1476dba7f728ed33d956e2025-08-20T02:53:34ZengMDPI AGAI2673-26882024-09-01541759177810.3390/ai5040087Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning TechniquesSatish Kumar0Sameer Sayyad1Arunkumar Bongale2Symbiosis 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, IndiaFused deposition modeling (FDM), a method of additive manufacturing (AM), comprises the extrusion of materials via a nozzle and the subsequent combining of the layers to create 3D-printed objects. FDM is a widely used method for 3D-printing objects since it is affordable, effective, and easy to use. Some defects such as poor infill, elephant foot, layer shift, and poor surface finish arise in the FDM components at the printing stage due to variations in printing parameters such as printing speed, change in nozzle, or bed temperature. Proper fault classification is required to identify the cause of faulty products. In this work, the multi-sensory data are gathered using different sensors such as vibration, current, temperature, and sound sensors. The data acquisition is performed by using the National Instrumentation (NI) Data Acquisition System (DAQ) which provides the synchronous multi-sensory data for the model training. To induce the faults, the data are captured under different conditions such as variations in printing speed, temperate, and jerk during the printing. The collected data are used to train the machine learning (ML) and deep learning (DL) classification models to classify the variation in printing parameters. The ML models such as k-nearest neighbor (KNN), decision tree (DT), extra trees (ET), and random forest (RF) with convolutional neural network (CNN) as a DL model are used to classify the variable operation printing parameters. Out of the available models, in ML models, the RF classifier shows a classification accuracy of around 91% whereas, in the DL model, the CNN model shows good classification performance with accuracy ranging from 92 to 94% under variable operating conditions.https://www.mdpi.com/2673-2688/5/4/87data acquisitiondeep leaningfault classificationfused deposition modelingmachine learning |
| spellingShingle | Satish Kumar Sameer Sayyad Arunkumar Bongale Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques AI data acquisition deep leaning fault classification fused deposition modeling machine learning |
| title | Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques |
| title_full | Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques |
| title_fullStr | Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques |
| title_full_unstemmed | Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques |
| title_short | Fault Classification of 3D-Printing Operations Using Different Types of Machine and Deep Learning Techniques |
| title_sort | fault classification of 3d printing operations using different types of machine and deep learning techniques |
| topic | data acquisition deep leaning fault classification fused deposition modeling machine learning |
| url | https://www.mdpi.com/2673-2688/5/4/87 |
| work_keys_str_mv | AT satishkumar faultclassificationof3dprintingoperationsusingdifferenttypesofmachineanddeeplearningtechniques AT sameersayyad faultclassificationof3dprintingoperationsusingdifferenttypesofmachineanddeeplearningtechniques AT arunkumarbongale faultclassificationof3dprintingoperationsusingdifferenttypesofmachineanddeeplearningtechniques |