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1. Predictive Modeling of Additive Manufacturing Process using Deep Learning Algorithm

   Michael Ogunsanya and Salil Desai (July 2022, Proceedings of the IISE Annual Conference & Expo 2022)

   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. 

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2. Grid Search Hyperparameter Tuning in Additive Manufacturing Processes

   Ogunsanya, M.; Isichei, J.; Desai, S. (July 2023, Manufacturing letters)

   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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3. COMPARATIVE ANALYSIS OF HYPERPARAMETER TUNING IN 3D PRINTING

   Michael Ogunsanya, Salil Desai (January 2022, International Conference on Science, Technology, Engineering and Management)

   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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4. Automated Quality and Process Control for Additive Manufacturing using Deep Convolutional Neural Networks

   https://doi.org/10.21926/rpm.2201005  

   Banadaki, Yaser; Razaviarab, Nariman; Fekrmandi, Hadi; Li, Guoqiang; Mensah, Patrick; Bai, Shuju; Sharifi, Safura (November 2021, Recent Progress in Materials)

   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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5. Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults

   https://doi.org/10.1080/24725854.2019.1701753  

   Gaikwad, Aniruddha; Yavari, Reza; Montazeri, Mohammad; Cole, Kevin; Bian, Linkan; Rao, Prahalada (January 2020, IISE Transactions)

   The goal of this work to mitigate flaws in metal parts produced from laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step towards this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in-situ layer-wise images obtained from an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from Titanium alloy (Ti-6Al-4V) material with varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations and in-situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85%. This work has two outcomes consequential to the sustainability of additive manufacturing: (1) It provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in-situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF. 

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