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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. The simulation system predicts chatter-free cutting conditions within the work volume of the machine tool or detects the presence of chatter vibrations along the tool path. An in-house developed virtual and real-time CNC system allows the design and analysis of any five-axis machine tool controller. Current research includes digital twin approach, where virtual simulation and real-time machine tool monitoring are integrated to achieve intelligent, self-adjusting smart machine tools.Digital Machining Twin for Smart Machine Tools

Prof. Yusuf Altintas
NSERC PWC-Sandvik Industrial Research Chair Professor in Digital Machining Twin,
The University of British Columbia (Canada)
E-mail: altintas@mech.ubc.ca
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. His research laboratory created advanced machining process simulation (CUTPRO), virtual part machining process simulation (MACHPRO) and open-modular 5 axis CNC system (Virtual CNC), which are used by over 300 companies and research centers in the field of machining and machine tools worldwide.
Professor Altintas is the fellow of Royal Society of Canada, CIRP, ASME, SME, CAE, EC, Tokyo University, P&WC, AvH and ISNM. He received Pratt & Whitney Canada’s (P&WC) university partnership (1997), APEG BC’s Meritorious (2002), APEG BC R.H. McLachlan (2010), UBC Killam Teaching Prize (2011), Gold Medal of Engineers Canada (2011), SME Albert M. Sergent Award (2012), NSERC Synergy Award, ASME Blackall best journal paper award, the scientific award of Turkey in Science and Engineering (2013), Georg Schlesinger Production Engineering Award (Berlin, 2016), and ASME William T. Ennor Manufacturing Technology Award (USA, 2016). He holds an Honorary Doctorate Degrees from Stuttgart University (2009) and Budapest University of Technology (2013), and holds Honorary Professor titles from BEIHANG University in Beijing and University Chair Professor from National Chung Hsing University-Taiwan. He was the past president of CIRP (International Academy of Production Engineering Researchers) for term 2016 - 2017. 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.

Production is changing. Instead of producing more of the same products, smaller quantities of individual products will be required. That is why machine tools need to be more flexible and adaptable than ever before. 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. Based on these components it is shown in the presentation that processes can be adapted autonomously and productivity will be increased. Intelligent Components for Autonomous Machine Tools

Prof. Dr.-Ing. Berend Denkena
Institute of production engineering and machine tools,
Leibniz University Hannover (Germeny)
E-mail: denkena@ifw.uni-hannover.de
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. He is CIRP Fellow. His primary areas of research are geometry and functionalizing manufacturing processes, machine tools for cutting and grinding, production planning and control, and simulation of manufacturing processes.

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. Furthermore, industrial applications often require guaranteed performance (e.g., speed, accuracy, and certainty). This presentation will discuss the challenges of adopting AI techniques to industrial applications, and propose a concept of industrial AI (augmented intelligence). Often, in industrial applications, there are limited training sets that are available for machine learning or artificial neural network training. However, there frequently exist engineering models derived based on the fundamental understanding of the engineering design and analysis. 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
Shien-Ming Wu Collegiate Professor of Manufacturing Science, College of Engineering,
University of Michigan (USA)
E-mail: junni@umich.edu
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. He is also the director of the Wu Manufacturing Research Center and the co-director of a National Science Foundation sponsored Industry/University Cooperative Research Center for Intelligent Maintenance Systems at the University of Michigan. Professor Ni has published over 500 technical papers, and supervised 96 PhD graduates and 65 MS graduates.
Selected honors and awards include 2015 Stephen Attwood Award from the University of Michigan, 2014 Alexander Schwarzkopf Award for Technology Innovation by NSF-IUCRC Association, 2013 International Science and Technology Cooperation Award from the People’s Republic of China, 2013 Gold Medal from Society of Manufacturing Engineers, 2013 S. M. Wu Research Implementation Award from North American Manufacturing Research Institute, 2009 Ennor Manufacturing Technology Award from American Society of Mechanical Engineering, and 1994 Presidential Faculty Fellows Award. He is an elected Fellow of International Society of Engineering Asset Management, International

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
Industrial and Management Engineering,
Pohang University of Science & Technology (Korea)
E-mail: hcho@postech.ac.kr
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.

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. Hwacheon SMART products were developed to present the easiest way to the new manufacturing paradigm. Hwacheon SMART machines realise high productivity by combining various software technologies. Everyone can enjoy up to four times better productivity. Once a 3D part model is selected, the machine analyses the modeling data and generates optimised NC data automatically, reducing the time required for CAM process and providing consistent machining quality based on Hwacheon’s unique machining optimization technology. It reduces the set-up time dramatically, and also gives 24-hour machining without workers because the SMART machine can find out NC coding errors and edit the NC data when a trouble happens during machining. The machine can also remove machining errors due to unexpected tool breakage or wear. 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
Hwacheon Machine Tool Co Ltd (Korea)
E-mail: taeweon@hwacheon.com
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. For practical application of nanoprecision machine tools, an advanced desktop machine has newly been developed. This machine has 1nm feed resolution, and can mount diamond milling & turning, and also sophisticated grinding capabilities especially with newly developed ion-shot processing. Variety of micro optics can be fabricated on this machine. 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,
Professor, Graduate School of Saitama University (Japan)
E-mail: ohmori@mfl.ne.jp
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).
Dr. Ohmori invented the ELID (Electrolytic In-process Dressing) method enabling effective dressing of fine diamond grinding wheels during his doctorate program at the Graduate School, University of Tokyo, and has been working at RIKEN (The Institute of Physical and Chemical Research) as a research scientist, Vice Chief Scientist, and Chief Scientist of Materials Fabrication Laboratory (MFL) in the field of precision machining, particularly mirror surface grinding with ELID invented by him during the master course and related ultra/nanoprecision machining for more than 25 years.
He has been developing specific machining processes to improve surface quality and precision through the application of the ELID technique, and has also conducted analytical research on mirror surface generating mechanism by this grinding technique. He received CIRP F.W.Taylor Medal on this achievement. Through his research activities, he has put these new machining techniques based on the ELID method into practical applications for the processing of electronic, optical, medical, and advanced materials.

Improving the basic properties of machines, especially the accuracy and quality of machined surfaces, increasing machining performance and entire manufacturing processes, reducing parasitic vibrations, or increasing the reliability of machines and processes are among the main objectives of development and application of advanced computing and simulation means application. One of the main challenges of current production is the high degree of individualisation of products, which, however, have to be produced with mass production productivity. In particular, advanced simulation models help reduce the risk of poor quality production and enable better utilization of the machine's production potential. Virtual machine and process models are an effective means for analyzing, controlling and optimizing machine-tool-workpiece system behavior. The challenge is to achieve optimized process settings and machine control if the machine is unavailable. Examples of application of virtual models for preparation of machining of demanding parts are shown. Higher production accuracy can also be increased by advanced models of temperature error compensation of machines with direct implementation into machine control, or systems of additional measurement of machines and workpieces. In the presentation we will show how these solutions contribute to higher reliability of machine and process operation. Advanced simulation models for smart machine tool

Prof. Matej Sulitka
Head of RCMT, Research Center of Manufacturing Technology,
Faculty of Mechanical Engineering, Czech Technical University in Prague (Czech)
E-mail: M.Sulitka@rcmt.cvut.cz
Dr. Sulitka is working at Czech Technical University in Prague at Research Center of Manufacturing Technology (RCMT). Main research interest of Dr. Sulitka is focused on virtual modeling of machine tool and machining processes. He has been responsible for the RCMT research program on virtual modeling since 2005 and the main achievement is represented by own virtual machining software system. Other fields of his research intetest include machine tool simulation of dynamic behaviour, structural optimization, effective use of machine tool in machining operations, and smart machine tool solutions.
Dr. Sulitka has intensive experinece from the cooperation with the industry. He has been responsible for a number of national projects with public funding for the collaboration with the industry, as well as EU projects and bilateral projects of collaboration with KIMM. He is a RCMT representative in International Academy of Production Engineering CIRP. He is a member of the Scientific committee of the HSM conference and he has been co-chairman of the HSM conferece. He si s member of the Association for Machine Tools.
Dr. Sulitka gained his master degree in 1996 and his Ph.D. in 2003; both at Czech Technical University in Prague. In RCMT he has been responsible for the Group of Simulation, Business and Project Development and currently he is a head of RCMT.

Dr. Jung-Hoon Cho
Senior Research Engineer, Ph. D.
S/W Development Team, Machine Tool Control & Testing Laboratory, HYUNDAI-WIA CORP.
E-mail : jh.cho@hyundai-wia.com

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