Abstract
A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.
Original language | English |
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Title of host publication | Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing |
Publisher | American Society of Mechanical Engineers |
ISBN (Electronic) | 9780791885802 |
DOIs | |
State | Published - 2022 |
Event | ASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022 - West Lafayette, United States Duration: Jun 27 2022 → Jul 1 2022 |
Publication series
Name | Proceedings of ASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022 |
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Volume | 1 |
Conference
Conference | ASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022 |
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Country/Territory | United States |
City | West Lafayette |
Period | 06/27/22 → 07/1/22 |
Funding
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Manufacturing Office under contract number DE-AC05-00OR22725 and the Department of Energy award DE-EE0008303. The authors would also like to thank the Okuma America Corporation for their support of this project.
Keywords
- additive manufacturing
- direct energy deposition
- feature importance
- machine learning
- melt pool
- process control
- process monitoring
- random forest classifier
- standoff distance
- support vector machines