Mr. Sai Prasad Nooka is a Machine learning Engineer at Volkswagen Group Electronics Research Lab (VWERL) in Belmont, California since August 2016. He is a RIT alumni from Dept. of Computer Engineering, graduated from MS in Computer Engineering in the year 2016. He did his Master Thesis on Fusion of Mini Deep Nets under the guidance of Dr. Ray Ptucha from Dept. of Computer Engineering. He did under- graduation in BE-Instrumentation and Control and graduated in the year 2012. Since joining VWERL in 2016, he has started working on piloted driving using deep learning techniques. His major focus in this project involves developing end-to-end deep driving technique for piloted driving. Also, he has been working as research consultant to Smart Manufacturing Research Group which is headed by Dr. Rui Liu of Mechanical Engineering Department at RIT. This research involves his advice on application of deep learning and machine learning techniques in machining process monitoring. His research focus and interests include: Visual-Semantic Embedding for Generating Image Description, Transfer Learning using ImageNet Models & Adaptive Selection of Mini Networks, Hierarchical decomposition of Large Deep Networks, Fusion of Mini Deep Nets, Object Recognition using Convolutional Neural Networks and RBMs, Shallow Vs Deep Belief Networks, Parallel Implementation of Ray Tracer (C++, OpenMPI), Compartmental HODGKIN-HUXLEY Neuron Model, Parallel Implementation (C, OpenMPI).

DrHitomi Yamaguchi has been an associate professor in the Department of Mechanical and Aerospace Engineering at the University of Florida since October 2007. She served previously as Associate Professor at Utsunomiya University and Research Associate at the University of Tokyo in Japan. Her work has been published in over 85 refereed journal papers, and she has been granted 8 patents. She has received several awards that acknowledge her contributions. She is currently the Vice Chair of the Scientific Technical Committee for Abrasive Processes (STC-G) of CIRP (the International Academy for Production Engineering). She also serves as the President of the North American Manufacturing Research Institute of SME. In recognition of her contributions, she has been elected a fellow of both ASME and SME.

Dr. Lbai Ulacia, professor and researcher at the group of Structual Mechanics and Design in Mondragon Unibertsitatea(Spain) since 2009. During his PhD he was a visiting research scholar at the University of Waterloo in Canada and also at HZG and FRM-II, Germany. He is currently leading the research group of Mechanical Design(DMLab) in Mondragon Unibertsitatea. His main research expertise is in the field of mechanical design, simulation and experimental validation of mechanical components.

Dr. Dazhong Wu is an Assistant Professor in Mechanical and Aerospace Engineering at the University of Central Florida (UCF). Prior to joining UCF, Dr. Wu was a senior research associate in the Department of Industrial and Manufacturing Engineering at Penn State University. He received his B.S. from Hunan University, M.S. from Shanghai Jiao Tong University in China, and Ph.D. from Georgia Tech, all in Mechanical Engineering. His research interests focus on data-driven smart manufacturing, particularly on process monitoring, prognostics and health management using machine learning. Dr. Wu has served as a guest managing editor for the Journal of Manufacturing Systems and the International Journal of Computer Integrated Manufacturing. Smart manufacturing aims to integrate big data, advanced analytics, high-performance computing, cyber-physical systems, and Industrial Internet of Things into traditional manufacturing systems and processes to create high quality products with higher productivity at lower costs. Smart manufacturing enables manufacturers to monitor machinery conditions in real-time as well as to predict product defects and failures. This presentation will discuss the challenges, opportunities, and advancements in data-driven smart manufacturing. Two examples, including tool wear prediction in milling processes and surface roughness prediction in additive manufacturing processes, will be presented.

Mr. Cory Arthur has a BS and MS degree in Mechanical Engineering from Michigan Technological University in Houghton, Michigan. Since joining TWS in 2006, he has specialized in using FEA to solve manufacturing and cutting tool manufacturer problems. He has lead a host of projects in various manufacturing areas including gear machining, coolant flow, part distortion, vibratory cutting and composite stack drilling. He is currently responsible for product planning and overall plan execution for the AdvantEdge software, market assessment and commercialization of R&D technologies.

Dr. Hsieh is an outstanding contributor in the fields of mechanism and machine design, fluid machinery design, reverse engineering and human biomechanical engineering. His design of a new eccentric reducer includes a high reduction ratio, compact construction, small volume (less weight), and dispersion stress concentration (longer usage life) than traditional (non-eccentric) involute planet gear reducers.

His presentation will introduce the kinematic fundamentals of a strain wave gear and how to design conjugate tooth profiles based on the kinematic model. The conjugate tooth profile for a strain wave gear has a double circular shape instead of traditional involute of a circle for spur gears. Localized contact pattern by tooth modification is also necessary to avoid interference and edge contact under load. To study and optimize the modification parameters, general finite element analysis software is used to simulate strain wave gears contact pattern during assembling and meshing process. The simulation results are compared with prototype experiment for verification. The simulation of strain wave gears will accelerate the design iteration for this complex type of gearing.