Crystal Young

Bio: Crystal Young is an academic researcher from University at Buffalo. The author has contributed to research in topics: Augmented reality & Control reconfiguration. The author has an hindex of 1, co-authored 2 publications receiving 25 citations.

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

Abstract: Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...

Automated Procedure Reconfiguration Framework for Augmented Reality-Guided Maintenance Applications

Abstract: The application of Augmented Reality (AR) to maintenance issues has resulted in significant improvements in reducing the time operators spend finding and comprehending manual maintenance procedures. One area that requires innovation is reducing the rigidity of procedures within AR-guided maintenance applications. Current widely- applicable strategies are limited in that they can only be completed off-site or they can be completed on-site but rely on operator knowledge or expert intervention in order to perform reconfiguration. In this work, a novel framework is presented to allow for automated reconfiguring of procedures within AR-guided maintenance applications. Once triggered, the presented framework is able to work autonomously. The framework relies on subassemblies of the machine being maintained and analyzes the effect a defective part has within its subassembly. This information is used to create a modified procedure using automatic procedure creation methods. An implementation of the framework is presented using a simple example. The framework is utilized in a complete AR-guided maintenance application and test.

Machine learning in manufacturing and industry 4.0 applications

Abstract: The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices,...

Predictive maintenance using digital twins: A systematic literature review

Abstract: Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.

A structured literature review on the interplay between emerging technologies and COVID-19 - insights and directions to operations fields.

Abstract: In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis.