Stephen E. Price

Bio: Stephen E. Price is an academic researcher from Worcester Polytechnic Institute. The author has co-authored 1 publications.

Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection

Abstract: Research concerned with the identification as well as quantification of satellites found within metallic powders has recently demonstrated the promise of implementing Mask R-CNNs, instance segmentation, and transfer learning. Though the original research and development of such an approach demonstrated the functionality of the data-driven image analysis framework, questions remained in regards to the scale-ability of the Mask R-CNN-based model. Accordingly, the present work demonstrates the fact that the originally formulated model can be expanded to include scanning electron micrographs to various powder types at variate magnifications (rather than the original case of micrographs of a single powder type at a single magnification). Moreover, the present work establishes a process that enables users to specifically target which images will have most impact on increasing generalize-ability and performance in order to optimize maximum improvement of the model with the least amount of images annotated. Beyond this, we also outline a method of auto-labeling satellites in images by using a trained model to increase its own training set size.

Application of Mass Finishing for Surface Modification of Copper Cold Sprayed Material Consolidations

Abstract: The surface roughness of additively manufactured (AM) components can have deleterious effects on the properties of the final part, such as corrosion resistance and fatigue life. Modification of the surface finish or parts produced by AM processes, such as cold spray, through methods such as mass finishing, can help to mitigate some of these issues. In this work, the surface evolution of as-produced copper cold sprayed material consolidations was studied through mass finishing. Three different copper powders attained by different production methods and of different sizes were used as feedstock. The surface topography of the cold spray deposits was measured as a function of the mass finishing time for the three copper cold spray samples and analyzed in terms of relative area and complexity, revealing an inverse correlation relating material removal rate and hardness/strength of the cold sprayed deposits. The material removal rate was also affected by the quality of the cold spray deposition, as defined by deposition efficiency (DE). Large initial drops in relative area and complexity are also likely due to the removal of loosely bonded powders at the start of mass finishing. Based on this study, the cold spray parameters that affect the rate of mass finishing have been explored.

Identifying, Evaluating, and Addressing Nondeterminism in Mask R-CNNs

Abstract: Convolutional Neural Networks, and many other machine learning algorithms, use Graphical Processing Units (GPUs) instead of Central Processing Units (CPUs) to improve the training time of very large modeling computations. This work evaluates the impact of the model structure and GPU on nondeterminism and identifies its exact causes. The ability to replicate results is quintessential to research, thus nondeterminism must be either removed or significantly reduced. Simple methods are provided so that researchers can: (1) measure the impact of nondeterminism, (2) achieve determinable results by eliminating randomness embedded in the model structure and performing computations on a CPU, or (3) reduce the amount of variation between model performances while training on a GPU.

Advances in cold spray additive deposition

Abstract: This chapter presents a multifaceted consideration of various current cold spray additive deposition advancements. The chapter is divided into five sections, with consideration given primarily to metal materials for cold spray and their counterpart metallurgical material consolidations. The first section considers functional cold-sprayed coatings and applications, including cold-sprayed materials for catalytic and antipathogenic applications. The second considers machine learning, statistical, and data-driven analysis within the cold spray materials literature. The third section examines recent refinements, advancements, and insights obtained concerning bonding mechanisms. After that, the fourth section explores the utility, promise, and potential of thermally preprocessing feedstock before cold spray processing. Finally, the fifth section considers the role of nondestructive evaluation, testing, and analysis for cold spray R&D and quality assurance.

Automatic characterization of spherical metal powders by microscope image analysis: a parallel computing approach

Abstract: Abstract The microstructural characteristics of spherical metal powders play an important role in determining the quality of mechanical parts manufactured by powder metallurgy processes. Identifying the individual powder particles from their microscopic images is one of the most convenient and cost-efficient methods for the analysis of powder characteristics. Although numerous image processing algorithms have been developed for automating the powder particle identification process, they perform less accurately in identifying adjacent particles that are heavily overlapped in their image regions. We propose an automatic algorithm to robustly and accurately identify spherical powder particles, especially heavily overlapped particles, from their microscope images. A parallel computing framework is designed to further enhance the computational efficiency of the proposed algorithm. Powder characteristics such as particle size distribution and the location of potential satellite particles have been derived from our identification results. The accuracy and efficiency of our algorithm are validated by real-world scanning electron microscope images, outperforming other existing methods and achieving both precision and recall above 99%.

Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data

Abstract: This study investigates the relationship between metallic powders and their flowability behavior (captured in terms of Hall flow rates using Hall flowmeters). Due to the many trait dependencies of powder flowability, which have made the formulation of a physical and mechanistic generalizable model difficult to resolve, this study seeks to develop an alternative data-driven framework based on powder size and shape characteristics for Hall-flow-rate predictions. A multiple-instance regression framework was both developed for processing multiple-instance powder data and compared with standard machine learning models. Data augmentation was found to improve the overall performance of the framework, although the limited dataset was a constraint. Still, the study contributes to ongoing efforts to identify traditional, associative, and generalizable patterns between powder properties and resultant flowability behaviors. The findings show promise for real-world applications with a larger dataset, such that this initial application of multiple instance regression frameworks for metal powder Hall-flow-rate predictions as a function of powder particle size and shape data can be scrutinized in full.