skip to main content


Title: 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.  more » « less
Award ID(s):
2100739
NSF-PAR ID:
10435710
Author(s) / Creator(s):
Date Published:
Journal Name:
International Conference on Science, Technology, Engineering and Management
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks. 
    more » « less
  3. K. Ellis, W. Ferrell (Ed.)
    Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin. 
    more » « less
  4. K. Ellis, W. Ferrell (Ed.)
    Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin. 
    more » « less
  5. Jang, Jinah (Ed.)
    Abstract 3D printing, or additive manufacturing, is a process for patterning functional materials based on the digital 3D model. A bioink that contains cells, growth factors, and biomaterials are utilized for assisting cells to develop into tissues and organs. As a promising technique in regenerative medicine, many kinds of bioprinting platforms have been utilized, including extrusion-based bioprinting, inkjet bioprinting, and laser-based bioprinting. Laser-based bioprinting, a kind of bioprinting technology using the laser as the energy source, has advantages over other methods. Compared with inkjet bioprinting and extrusion-based bioprinting, laser-based bioprinting is nozzle-free, which makes it a valid tool that can adapt to the viscosity of the bioink; the cell viability is also improved because of elimination of nozzle, which could cause cell damage when the bioinks flow through a nozzle. Accurate tuning of the laser source and bioink may provide a higher resolution for reconstruction of tissue that may be transplanted used as an in vitro disease model. Here, we introduce the mechanism of this technology and the essential factors in the process of laser-based bioprinting. Then, the most potential applications are listed, including tissue engineering and cancer models. Finally, we present the challenges and opportunities faced by laser-based bioprinting. 
    more » « less