3D Simulator of a Rolling Baton on Cylindrical Surfaces
Summary form only given. We make physical analysis of a baton rolling on cylindrical surfaces and then present a 3D simulator which helps us design actual surfaces to be used for a physical performance and for an interactive entertainment system.
Stick'n Roll: A Playful Stick Interface for Curved Display
We present a projection system called Stick'n Roll in which users manipulate a set of sticks (or batons) on a series of curved display. The system relies on the use of unique movement of sticks rolling on convex surfaces. We regard the touching of sticks to the surfaces as input to the display that also functions as a multi-touch input device. We develop a variety of applications such as balloon popping, a music game, a virtual keyboard, and a draw tool that make effective use of the rolling of this stick interface. Since the silhouette of the pair of surfaces resembles a huge opened "picture book", we put some applications together in that virtual book and enabled transferring from one application to the other by turning over the virtual pages. Then, the new experience users can have when they control the sticks and manage the world in the picture book is what we want to share as "kiddy dream in VR", the theme of ReVolution 2015.
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.