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Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.
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Increasing Throughput in Fused Deposition Modeling by Modulating Bed Temperature
Abstract Additive manufacturing (AM) techniques, such as fused deposition modeling (FDM), are able to fabricate physical components from three-dimensional (3D) digital models through the sequential deposition of material onto a print bed in a layer-by-layer fashion. In FDM and many other AM techniques, it is critical that the part adheres to the bed during printing. After printing, however, excessive bed adhesion can lead to part damage or prevent automated part removal. In this work, we validate a novel testing method that quickly and cheaply evaluates bed adhesion without constraints on part geometry. Using this method, we study the effect of bed temperature on the peak removal force for polylactic acid (PLA) parts printed on bare borosilicate glass and polyimide (PI)-coated beds. In addition to validating conventional wisdom that bed adhesion is maximized between 60 and 70 °C (140 and 158 °F), we observe that cooling the bed below 40 °C (104 °F), as is commonly done to facilitate part removal, has minimal additional benefit. Counterintuitively, we find that heating the bed after printing is often a more efficient process for facile part removal. In addition to introducing a general method for measuring and optimizing bed adhesion via bed temperature modulation, these results can be used to accelerate the production and testing of AM components in printer farms and autonomous research systems.
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- Award ID(s):
- 1661412
- PAR ID:
- 10291472
- Date Published:
- Journal Name:
- Journal of Manufacturing Science and Engineering
- Volume:
- 143
- Issue:
- 9
- ISSN:
- 1087-1357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4050177
