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In Machine learning (ML) and deep learning (DL), hyperparameter tuning is the process of selecting the combination of optimal hyperparameters that give the best performance. Thus, the behavior of some machine learning (ML) and deep learning (DL) algorithms largely depend on their hyperparameters. While there has been a rapid growth in the application of machine learning (ML) and deep learning (DL) algorithms to Additive manufacturing (AM) techniques, little to no attention has been paid to carefully selecting and optimizing the hyperparameters of these algorithms in order to investigate their influence and achieve the best possible model performance. In this work, we demonstrate the effect of a grid search hyperparameter tuning technique on a Multilayer perceptron (MLP) model using datasets obtained from a Fused Filament Fabrication (FFF) AM process. The FFF dataset was extracted from the MakerBot MethodX 3D printer using internet of things (IoT) sensors. Three (3) hyperparameters were considered – the number of neurons in the hidden layer, learning rate, and the number of epochs. In addition, two different train-to-test ratios were considered to investigate their effects on the AM process data. The dataset consisted of five (5) dominant input parameters which include layer thickness, build orientation, extrusion temperature, building temperature, and print speed and three (3) output parameters: dimension accuracy, porosity, and tensile strength. RMSE, and the computational time, CT, were both selected as the hyperparameter performance metrics. The experimental results reveal the optimal configuration of hyperparameters that contributed to the best performance of the MLP model.
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COMPARATIVE ANALYSIS OF HYPERPARAMETER TUNING IN 3D PRINTING
This paper presents hyperparameter tuning techniques for a deep learning predictive model with applications in additive manufacturing processes. Bioprinting is an additive manufacturing process which utilizes biomaterials, cells, and growth factors to build functional tissue constructs for biomedical applications. In this research, we evaluate the hyperparameter space using grid search technique to tune the perceptron deep learning hyperparameters for optimal prediction of additive manufacturing outcomes. Hyperparameter entities include number of neurons, learning rate, and number of epochs to run machine learning models. Five input parameters and three output variables were evaluated for a typical additive manufacturing process. A comparative analysis is conducted to demonstrate improved runtime and lower root mean squared error for additive manufacturing predictive models. The results from this research are extensible to several additive manufacturing processes including 3D bioprinting.
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- Award ID(s):
- 2100739
- PAR ID:
- 10435710
- Date Published:
- Journal Name:
- International Conference on Science, Technology, Engineering and Management
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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