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Program Program > Keynote Speakers
Plenary Speaker
The aim of digital machining research is to develop mathematical models of metal cutting operations, machine tool vibrations and control. The science-based digital models allow the virtual design, testing, optimization, monitoring and control of machine tools and machining operations. The model predicts the cutting forces, torque and power consumed in machining parts by considering CNC system dynamics, material properties, cutter geometry, structural flexibilities, and cutting conditions along the tool path. Digital Machining Twin for Smart Machine Tools

Prof. Yusuf Altintas
Professor,
NSERC PWC-Sandvik Industrial Research Chair Professor in Digital Machining Twin,
The University of British Columbia (Canada)
E-mail: altintas@mech.ubc.ca
Bio-brief:
Professor Altintas worked as a machine tool and manufacturing process development engineer in industry before joining The University of British Columbia in 1986. He conducts research on metal cutting, machine tool vibrations, control and digital machining. He has published about 200 archival journal and 100 conference articles with over 27,300 citations with h index of 87 (Google Scholar), and a widely used “Manufacturing Automation: Principals of Metal Cutting Mechanics, Machine Tool Vibrations and CNC Design. He is designated as the Distinguished University Scholar of Engineering at the University of British Columbia (2017). He currently directs NSERC CANRIMT Machining Research Network across Canada, and holds the NSERC - P&WC - Sandvik Coromant Industrial Research Chair Professorship to develop next generation Digital Machining Twin Technology.


Today's production with customized and specialized machine tools is often unable to achieve this requirement. As a result, autonomous systems that provide more flexible automation and more production freedom, while still maintaining high productivity and robustness regardless of lot size, are needed. Autonomous machine tools have the ability control the production themselves. In addition, the machine tools are able to adapt to unforeseen changes during the process. The basis for this are intelligent components with sensory and actuator capabilities. Intelligent Components for Autonomous Machine Tools

Prof. Dr.-Ing. Berend Denkena
Professor,
Institute of Production Engineering and Machine Tools,
Leibniz University Hannover (Germany)
E-mail: denkena@ifw.uni-hannover.de
Bio-brief:
Prof. Berend Denkena is Head of the Institute of Production Engineering and Machine Tools at the Leibniz Universitat Hannover. After obtaining doctorate at the Faculty of Mechanical Engineering at University of Hannover in 1992, he worked as a design engineer and head of various development groups for Thyssen Production Systems in both Germany and the United States. From 1996 to 2001, he was Head of Engineering and Turning Machine Development at Gildemeister Drehmaschinen in Bielefeld. Since 2001, he has been full professor of Production Engineering and Machine Tools and director of the Institute of Production Engineering and Machine Tools at Leibniz University Hannover.


Artificial intelligence (AI) techniques have been successfully applied in various social and consumer applications, such as voice and image recognitions, social media, advertisement, etc. However, AI techniques have seen limited adoption in industry, primarily due to the difficulty in obtaining adequate sets of training data that are required by various machine learning or neural networks. This presentation will discuss the challenges of adopting AI techniques to industrial applications, and propose a concept of industrial AI (augmented intelligence). Furthermore, experienced operators or engineers accumulated significant knowledge or experience over their professional career. The value behind historical maintenance records should also not be overlooked. Additionally, when sensory data and algorithms are combined with the engineering models, human experience or expert knowledge, and historical records, a new paradigm of industrial AI (iAI) becomes a powerful solution to many industrial problems. Selected applications will be presented to demonstrate the value of this new iAI.Industrial AI and Its Applications

Prof. Jun Ni
Professor,
Shien-Ming Wu Collegiate Professor of Manufacturing Science, College of Engineering,
University of Michigan (USA)
E-mail: junni@umich.edu
Bio-brief:
Dr. Jun Ni is the Shien-Ming (Sam) Wu Collegiate Professor of Manufacturing Science and Professor of Mechanical Engineering at the University of Michigan, USA. He served as the founding Dean of the University of Michigan - Shanghai Jiao Tong University Joint Institute located in Shanghai, China since 2006.
Professor Ni has been invited to serve as a guest/advisory professor at many institutions, including Shanghai Jiao Tong University, Tsinghua University, Xi’an Jiao Tong University, Huazhong University of Science and Technology, Dalian University of Technology, and 10 other institutions. He served in the International Expert Advisory Board of the Ministry of Science and Technology of PRC to consult for the strategic planning in advanced manufacturing.


Invited Speaker

The smart machines in the smart factory are equipped with IIoT-enabled sensory and connectivity components which enable to capture and store the status of machine and work-in-process in the form of Big Data. Big Data, structured or unstructured, characterized by multiple sources, real-time velocity, and massive amount, can be processed, visualized, and analyzed for the purpose of improving quality of product, cost of operation, and delivery of finished product. The benefits of dealing with such Big Data can be achieved the four well-defined sequential analytics: Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. Such Industry 4.0 related technologies as Statistics, Machine Learning, Artificial Intelligence, and Optimization are the foundation for successfully creating values from those analytics. In this talk some of challenges and best practices will be presented to help particularly the small to medium manufacturing firms leverage the advanced technologies and analytics.The Brain of Smart Machines: AI and Data Analytics

Prof. Hyunbo Cho
Professor,
Industrial and Management Engineering,
Pohang University of Science and Technology (Korea)
E-mail: hcho@postech.ac.kr
Bio-brief:
Dr. Hyunbo Cho is a professor of department of industrial and management engineering at the Pohang University of Science and Technology (POSTECH). He received his B.S. and M.S. degrees in Industrial Engineering from Seoul National University in 1986 and 1988, respectively, and his Ph.D. in Industrial Engineering with a specialization in Manufacturing Systems Engineering from Texas A&M University in 1993. He was a recipient of the SME’s 1997 Outstanding Young Manufacturing Engineer Award. After joining POSTECH, he has been actively collaborating with National Institute of Standards & Technology (NIST), USA for the purpose of sharing and developing international standards of smart manufacturing. His areas of expertise include Smart Manufacturing, Industrial Data Engineering and Analytics, Supply Chain Management, and Manufacturing Management and Strategy.


Improving the basic properties of machines, especially the accuracy and quality of machined surfaces, machining performance and entire manufacturing processes, or the reliability of machines and processes are among the main objectives of development and application of advanced computing and simulations. One of the main challenges of current manufacture is the high degree of product individualisation. In particular, advanced simulation and compensation models help reduce the risk of poor final quality and enable better utilization of the machine's production potential. An appeal to scientific community to disseminate of smart strategies in real industrial environment is actual.Smart Machine Tools

Dr. Martin Mareš
Doctor
Czech Technical University in Prague, Research Group Leader and Lecturer,
Faculty of Mechanical Engineering,
Czech Technical University in Prague (Czech Republic)
E-mail : M.Mares@rcmt.cvut.cz
Bio-brief:
Dr. Martin Mareš is working at Czech Technical University in Prague at Research Center of Manufacturing Technology (RCMT).. Dr. Mareš is an RCMT representative in European Society for Precision Engineering and Nanotechnology (euspen), is a fellow of Czech Association of Engineering Technology (SST), Czech Society for Machine Tools (SPoS) and member of Czech Institute of Informatics, Robotics and Cybernetics (CIIRC). He received his master’s degree from CTU in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics in 2008 and Ph.D. from Department of Fluid Dynamics and Thermodynamics in 2015. In RCMT he has been responsible for the Group of Accuracy.


Today, we live in an increasingly on-demand world. This transformation is happening in the manufacturing industry. The on-demand manufacturing technology makes personalised products with fast delivery possible. When the tool is damaged, the machine replaces the new tool automatically without interrupting the processing. The dedicated tool management function calculates the accumulated cutting time for each tool to record the total tool usage time and manage the tool lifespan. The function notifies users in advance when it is time to replace the tool based on the tool lifespan and optimises machining conditions for each tool through Hwacheon’s unique machining optimization functions.Smart Machines for On-Demand Manufacturing

Dr. Taeweon Gim
Director,
Hwacheon Machine Tools Co., Ltd. (Korea)
E-mail: taeweon@hwacheon.com
Bio-brief:
Taeweon Gim is a director at Hwacheon Machine Tool, leading new machine and new technology development teams. Currently, Dr Gim is focusing on establishing a differentiating strategy to present the high-value added solutions to customers.
Dr Gim received bachelor’s degree from Seoul National University in 1988 and PhD from Cranfield University in 1998.


Advanced optics must to be fabricated with nanoprecision on surface and profile. In order to achieve this, nanoprecision machine tools and machining processes must be applied to optical fabrication. In recent years, nanoprecision machine tools which are driven at single nanometric resolution have been developed and moreover, higher resolution toward picoprecision is now being started to be studied. The ion-shot processing can be used both for dressing on nanosurface grinding, and also for surface modification on cutting tools and workpieces enabling direct nanosurface cutting of ferrous materials using diamond tools.Optical Fabrication Technologies with Nanoprecision Machine Tools

Dr. Hitoshi Ohmori
Chief Scientist,
Director of Materials Fabrication Lab.,CPR, RIKEN,
E-mail: ohmori@mfl.ne.jp
Bio-brief:
Dr. Hitoshi Ohmori is the Chief Scientist and Director of Materials Fabrication Laboratory of RIKEN. He is also a professor at Graduate School of Saitama University. He got his Bachelor, Master and Doctor degrees of Engineering from Department of Precision Engineering, University of Tokyo in 1986, 1988 and 1991, respectively. He is a Fellow of The International Academy for Precision Engineering (CIRP) and Japan Society of Mechanical Engineering (JSME), and a member of Japan Society of Precision Engineering (JSPE) and Japan Society of Abrasive Technology (JSAT). He has been managing projects on the development of ultra/nanoprecision fabrication systems for critical components such as advanced X-ray optical elements, space telescope lenses, sensors, micro-tools, and medical devices. He has provided new achievements and promoted significant development in advanced science and engineering. In recent years, he has eagerly expanding his research fields and interests through working on micro grinding, surface functionalization, and a new cutting process development.

In spite of the rapid development of the machine tool industry toward Industry 4.0 and Smart Factory, CNC controllers’ support has always been lag behind the customer’s up-to-date requirements and needs. Considering that all CNCs have a similar goal to accomplish accurate and optimal control of axes and spindle to produce qualified products with the given tool, workpiece, and cutting condition, there should be very little differences in its intrinsic fundamentals. Based on these beliefs and industrial needs, we prepared the intelligent and unified HMI platform to innovate the legacy old-fashioned CNC application development processes and to relieve the struggles of many researchers and application developers in the machine tool industry. We hope that this presentation could be a stage to share the latest application scheme of platform technology in the machine tool industry and to discuss better ways together.Smart Machine Tool Development based on Intelligent HMI Platform

Dr. Jung-Hoon Cho
Senior Research Engineer,
S/W Development Team, Machine Tool Control & Testing Laboratory, Hyundai Wia Corp. (Korea)
E-mail : jh.cho@hyundai-wia.com
Bio-brief:
He was one of the active researchers in the world-cooperated ISO STEP-NC standards incubation project until the early 2000s and had received his Ph. D. from the research on STEP-NC compatible intelligent CNC architecture design and autonomous toolpath generation scheme in POSTECH, 2001. He joined LG CNS in 2002, IT service company, as a research engineer, and participated in many state-of-the-art technology projects until he moved to a cloud computing venture company, ISA Technology Inc., as a CTO in 2008. He is actively participating in various strategic meetings as a specialist for smart machine tools and smart CNC technology. He is a domestic and global expert member of ISO TC 184 SC 1 and an advisory expert for the Korea smart CNC development project currently.
 


During the last years, there has been an increased interest in the use of 3D Printing technologies in many industrial applications and it have been identified as one of the most promising production technologies. Why the 3D Printing is smart? First of all, because at the very beginning the 3D Printing technologies were identified as a segment with high growth potential as well as high performance manufacturing and correspond to high levels of technology maturity. Second one, if we stop printing, different internal components can be perfectly incorporated. Also, can be added sensors, mechanical components such as screws, nuts and even can be reinforced the plastic or biodegradable materials used with different reinforcements even metal reinforcements. The main objective is to identify benchmarks from different industrial branches, based on the criterion of the existing functionality and the companies need.Smart 3D Printing

Prof. Dumitru Nedelcu
Professor,
Gheorghe Asachi Technical University of lasi (Rumania)
E-mail : nedelcu1967@yahoo.com
Bio-brief:
Dumitru Nedelcu is a Professor at the “Gheorghe Asachi” Technical University of Iasi (Rumania), Romania, Director of TUIASI Doctoral School and he is involved in fine mechanics and nano-technologies and technologies for obtaining and processing of composite materials. He is the Manager of Fine Mechanics and Nanotechnology Laboratory, President of ModTech Professional Association, ModTech International Conference and Editor-in-Chief of the International Journal of Modern Manufacturing Technologies and Advanced Engineering Forum. He was a Visiting Professor at TAT, Institute of Engineering, Tokyo, Guest Professor at Joining and Welding Research Institute, Osaka University, Japan and Grenoble Institute of Technology, France. He has published more than 170 scientific papers on ISI and BDI journals and international conferences proceedings. He serves on various journals and conferences review committees. The detailed activity can be tracked on the personal webpage, www.dumitrunedelcu.ro.


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