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The present study examines the effect of varying laser incidence angles on textural, microstructural, and geometric characteristics of directed energy deposition (DED) processed materials, providing a more comprehensive outlook on participating laser-matter interaction phenomena and ultimately devise strategies to ameliorate print performance. In this study, single-layer, single-/multi-track specimens were processed to examine the effect of non-orthogonal angular configurations on bead morphology, microstructure, phase composition and textural representation of DED-processed 316L stainless steel materials. It was observed that bead size decreased at increasing lead and lean angles. Asymmetry in the distribution of the bead morphology as a function of lead angle indicates better catchment for acute lead angle configurations over obtuse configurations. No significant differences in phase composition, texture, and microstructure were observed in moderate off-axis configurations. When penetration depth for the deposits was below 20 µm, columnar structures dominated the microstructure of the deposited material. At deeper penetration depths, columnar and equiaxed structures were observed at the bead-substrate interface and center of the bead, respectively. Compared to powder-blown DED, wire-DED dilution profiles were found to be asymmetric in both orthogonal and non-orthogonal wire DED samples.

The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. By reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

A critical factor in the implementation of direct energy deposition is the ability to maintain the standoff distance between the nozzle and the build surface, as this influences powder capture efficiency and overall part quality. Due to process-related variations, layer height may vary, causing unintended variation in standoff distance and poor build quality. While prior work has utilized contact probing to qualify standoff distance during processing, in situ methods for qualification of standoff distance are of major interest. The present work seeks to understand efficacy of image-based methods for classifying standoff distance variation in real-time using support vector machines (SVMs). It was hypothesized that the size of the melt pool and the amount of spatter will have significant correlations with deviations in the standoff distance; thus, SVMs were used on a dataset that is comprised of morphological features of melt pool size and image entropy. The SVM model was used to classify melt pool images into categories according to standoff distance variation from nominal. K-folds cross validation was used to find the optimal hyperparameters for the SVM model. To understand the impact of the selected features on the classification performance and inference speed, multiple models were trained with differing numbers of included features. Results for classification score, inference time, and image preprocessing/feature extraction from these data are reported. The present results show that the SVM model was able to predict the standoff distance classification with an accuracy of 97 percent and a speed of 0.122 s per image, making it a viable solution for real-time control of standoff distance.

Traditionally, the bearing ring centering process is carried out manually and can be both time consuming and prone to human inaccuracy. In this work, we seek to automate the centering process using a single-axis linear motor drive carrying a measurement probe. The probe measures the distance to the bearing ring surface as it rotates, calculates the vector from geometric center to rotational center, and then pushes the bearing ring to move it relative to the table surface and align these points. Once the geometric and rotational centers are aligned, the roundness measurement cycle can begin.

This paper reports on an experimental investigation and modeling results for the grinding-induced subsurface damage in gamma titanium aluminide (γ-TiAl). Grinding was carried out at various depths of cut, wheel and workpiece speeds with a friable diamond grinding wheel. The type/pattern and extent of the subsurface damage was determined by a bonded interface technique using optical microscopy and a surface laser profiler. The results show that the damage beneath the ground surface can be measured from the depth of a rumpled region that is identified with plastic deformation or shear/slip-fault deformation, distortion and microcracks. It was also determined that the depth of grinding-induced subsurface damage in the nearly fully lamellar γ-Ti-48Al microstructure ranges from 150 μm to 400 μm. The severity and depth of the subsurface damage was found to increase with an increase in grinding speed ratio and depth of cut.

This paper describes a magnetically levitated testbed for use in undergraduate and graduate controls education. Key components of the testbed include an electromagnetic coil to create the magnetic field, a ferrous object for levitation, a position sensor for height determination, and a controller for feedback adjustment of the coil current. In this paper the unit’s design, development, and use in educational arenas are discussed.