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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Book Chapter: Image Processing and Machine Learning Methods Applied to Additive Manufactured Composites for Defect Detection and Toolpath Reconstruction
The products manufactured by the additive manufacturing (AM) methods have unique signatures in their microstructures due to the layer by layer manufacturing. Machine learning of microstructures of the printed sample can help in interpreting these signatures and the patterns can be used for either determining the authenticity of the product or for reverse engineering. In this work, specimens of 3D printed glass fiber reinforced polymer (GFRP) composite materials are subjected to imaging and machine learning in order to rebuild the tool path information. Since composites require significant research and development effort, the possibility of rebuilding the tool path by ML methods presents a significant vulnerability for intellectual property. The ML methods require a large training dataset and can be efficient in processing tomography datasets. Two kinds of artificial neural networks with three different algorithms are introduced in this work and their results are compared. A 3D printed GFRP specimen is imaged using a micro CT-scan and the images are processed using binarized statistical image features method for compression without compromising the microstructural information. The ML models are trained on this dataset and the results indicate that the ML is able to identify the printing tool path with accuracy.
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- Award ID(s):
- 1931724
- NSF-PAR ID:
- 10451137
- Editor(s):
- Kushvaha, V.; Sanjay, M. R.; Madhushri, P.; Siengchin, S.
- Date Published:
- Journal Name:
- Composites science and technology
- ISSN:
- 2662-1819
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Three‐dimensional (3D) graphene microstructures have the potential to boost performance in high‐capacity batteries and ultrasensitive sensors. Numerous techniques have been developed to create such structures; however, the methods typically rely on structural supports, and/or lengthy post‐print processing, increasing cost and complexity. Additive manufacturing techniques, such as printing, show promise in overcoming these challenges. This study employs aerosol jet printing for creating 3D graphene microstructures using water as the only solvent and without any post‐print processing required. The graphene pillars exhibit conductivity immediately after printing, requiring no high‐temperature annealing. Furthermore, these pillars are successfully printed in freestanding configurations at angles below 45° relative to the substrate, showcasing their adaptability for tailored applications. When graphene pillars are added to humidity sensors, the additional surface area does not yield a corresponding increase in sensor performance. However, graphene trusses, which add a parallel conduction path to the sensing surface, are found to improve sensitivity nearly 2×, highlighting the advantages of a topologically suspended circuit construction when adding 3D microstructures to sensing electrodes. Overall, incorporating 3D graphene microstructures to sensor electrodes can provide added sensitivity, and aerosol jet printing is a viable path to realizing these conductive microstructures without any post‐print processing.
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null (Ed.)The ability to manufacture biodegradable structures at small scales is integral to a variety of applications in biological, medical, and pharmaceutical fields. Recent developments in additive manufacturing (or "three-dimensional (3D) printing") allow for biodegradable materials to be printed with high resolution; however, there is typically a limit with respect to a resolvable feature size (e.g., layer height) that dictates the minimum increments for tuning distinct degradation-mediated functionalities via print geometry. Here we investigate the potential to 3D print designs that afford additional degrees of control during intermediate stages between the complete biodegradation of microstructures that differ by a single layer height. Preliminary fabrication results revealed effective printing of tubular 3D biodegradable gelatin methacryloyl (GelMA) structures with outer diameters of 100 μm and wall thicknesses of 35 μm using two-photon direct laser writing (DLW)-based additive manufacturing. Simulation results for varying designs suggest that both the total degradation time as well as the diffusion dynamics through a microstructure during the final stage of biodegradation can be modulated via geometric means. Thus, the concepts presented in this work could open new avenues in areas including drug delivery and biomaterials.more » « less
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Thermal conductivity (TC) is greatly influenced by the working temperature, microstructures, thermal processing (heat treatment) history and the composition of alloys. Due to computational costs and lengthy experimental procedures, obtaining the thermal conductivity for novel alloys, particularly parts made with additive manufacturing, is difficult and it is almost impossible to optimize the compositional space for an absolute targeted value of thermal conductivity. To address these difficulties, a machine learning method is explored to predict the TC of additive manufactured alloys. To accomplish this, an extensive thermal conductivity dataset for additively manufactured alloys was generated for several AM alloy families (nickel, copper, iron, cobalt-based) over various temperatures (300–1273 K). This unique dataset was used in training and validating machine learning models. Among the five different regression machine learning models trained with the dataset, extreme gradient boosting performs the best as compared with other models with an R2 score of 0.99. Furthermore, the accuracy of this model was tested using Inconel 718 and GRCop-42 fabricated with laser powder bed fusion-based additive manufacture, which have never been observed by the extreme gradient boosting model, and a good match between the experimental results and machine learning prediction was observed. The average mean error in predicting the thermal conductivity of Inconel 718 and GRCop-42 at different temperatures was 3.9% and 2.08%, respectively. This paper demonstrates that the thermal conductivity of novel AM alloys could be predicted quickly based on the dataset and the ML model.
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Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.more » « less
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