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The objective of this work is to detect process instabilities in laser wire directed energy deposition additive manufacturing process using real-time data from a high-speed imaging meltpool sensor. The laser wire directed energy deposition process combines the advantages of powder directed energy deposition and other wire-based additive manufacturing processes, such as wire arc additive manufacturing, as it provides both appreciable resolution and high deposition rates. However, the process tends to create sub-optimal quality parts with poor surface finish, geometric distortion, and delamination in extreme cases. This sub-optimal quality stems from poorly understood thermophysical phenomena and stochastic effects. Hence, flaw formation often occurs despite considerable effort to optimize the processing parameters. In order to overcome this limitation of laser wire directed energy deposition, real-time and accurate monitoring of the process quality state is the essential first step for future closed-loop quality control of the process. In this work we extracted low-level, physically intuitive, features from acquired meltpool images. Physically intuitive features such as meltpool shape, size, and brightness provide a fundamental understanding of the processing regimes that are understandable by human operators. These physically intuitive features were used as inputs to simple machine learning models, such as k-nearest neighbors, support vector machine, etc., trained to classify the process state into one of four possible regimes. Using simple machine learning models forgoes the need to use complex black box modeling such as convolutional neural networks to monitor the high speed meltpool images to determine process stability. The classified regimes identified in this work were stable, dripping, stubbing, and incomplete melting. Regimes such as dripping, stubbing, and incomplete melting regimes fall under the realm of unstable processing conditions that are liable to lead to flaw formation in the laser wire directed energy deposition process. The foregoing three process regimes are the primary source of sub-optimal quality parts due to the degradation of the single-track quality that are the fundamental building block of all manufactured samples. Through a series of single-track experiments conducted over 128 processing conditions, we show that the developed approach is capable of accurately classifying the process state with a statistical fidelity approaching 90% F-score. This level of statistical fidelity was achieved using eight physically intuitive meltpool morphology and intensity features extracted from 159,872 meltpool images across all 128 process conditions. These eight physically intuitive features were then used for the training and testing of a support vector machine learning model. This prediction fidelity achieved using physically intuitive features is at par with computationally intense deep learning methods such as convolutional neural networks. 

There have recently been calls for post-secondary engineering programs to develop more well-rounded engineers who are more capable of understanding and empathizing with clients, as well engage in stronger ethical decision-making. In this study, we examine the efficacy of a hybrid humanities-engineering course in developing the empathetic performativity of engineering students taught at two universities. We use a discourse analysis methodology to examine the language in student assignments over the trajectory of this course, looking for instances where engineering students position themselves empathetically within their work. Based on our analysis, we see small gains in the empathetic performances of engineering students in this context, however, these findings are nuanced and require qualification. Keywords: Discourse Analysis, Humanities-Driven STEM, Empathy 

The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue constructs are twofold: (1) detect flaw formation using data from in-situ infrared thermocouple sensors; and (2) prevent flaw formation through preemptive process control. In realizing the first objective, data signatures acquired from in-situ sensors were analyzed using several machine learning approaches to ascertain critical quality metrics, such as print regime, strand width, strand height, and strand fusion severity. These quality metrics are intended to capture the process state at the basic 1D strand-level to the 2D layer-level. For this purpose, machine learning models were trained to classify and predict flaw formation. These models predicted print quality features with accuracy nearing 90%. In connection with the second objective, the previously trained machine learning models were used to preempt flaw formation by changing the process parameters (print velocity) during deposition—a form of feedforward control. With the feedforward process control, strand width heterogeneity was statistically significantly reduced, reducing the strand width difference between strand halves to less than 50 µm. Using this integrated process monitoring, detection, and control approach, we demonstrate consistent, repeatable production of Bio-AM constructs. 

Biological additive manufacturing (Bio-AM) has emerged as a promising approach for the fabrication of biological scaffolds with nano- to microscale resolutions and biomimetic architectures beneficial to tissue engineering applications. However, Bio-AM processes tend to introduce flaws in the construct during fabrication. These flaws can be traced to material nonhomogeneity, suboptimal processing parameters, changes in the (bio)-printing environment (such as nozzle clogs), and poor construct design, all with significant contributions to the alteration of a scaffold’s mechanical properties. In addition, the biological response of endogenous and exogenous cells interacting with the defective scaffolds could become unpredictable. In his Review, we first described extrusion-based Bio-AM. We highlighted the salient architectural and mechanotransduction parameters affecting the response of cells interfaced with the scaffolds. The process phenomena leading to defect formation and some of the tools for defect detection are reviewed. The limitations of the existing developments and the directions that the field should grow in to overcome said limitations are discussed.