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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.Industrial Artificial Intelligence for Manufacturing: Challenges and Solutions

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.


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, (Japan)
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 (Romania)
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.


With the advancement in computing and development of reliable Industrial Wireless Sensor Net- works (IWSNs), industries are transitioning into their fourth major revolution called Industry 4.0. Industrial Internet of Things (IIoT) or the industrial version of Internet of Things (IoT), plays a key role in Industry 4.0. It helps enable factories to become self-adaptive and self-reliant using the data collected from sensors and Engineering Resource Planning (ERP) / Material Resource Planning (MRP) systems. Smart Manufacturing: Real Time Detection of Defects and Anomaly of Manufacturing Process Using IIoT and Deep Learning

Prof. Sangkee Min
Assistant Professor,
University of Wisconsin-Madison (USA)
E-mail : sangkee.min@wisc.edu
Bio-brief:
Prof. Min earned his Ph.D. at UC Berkeley with manufacturing major in 2001. His Ph.D. work focused on very practical industrial problems like burr minimization from various machining processes and the outcome of his research was tested and implemented at the automotive and aerospace industry. After his Ph.D., he went to Japan as a special assistant professor at Keio University where he expanded his industrial connection to many Japanese industries; automotive, machine tool, tool makers, oil refinery, etc. with his environmental machining research. assistant professor of Department of Mechanical Engineering at University of Wisconsin-Madison with three major research topics; UPM (Ultra-Precision Machining), SSM (Smart Sustainable Manufacturing), and MFD (Manufacturing for Design).


Smart nanocomposites are promising new materials applicable as media for nano-patterned surfaces. Much attention is being paid to carbon-based nanoparticles as fillers in polymer matrices, due to their outstanding mechanical, electrical and thermal properties. In particular, carbon nanotubes (CNTs) and graphenes are effective in the fabrication of electrically and thermally conductive polymer composites compared to metallic particles or carbon black, mainly due to their high aspect ratios (i.e. ~100-1000). The sensors consisted of polymer reinforced with multi-walled carbon nanotubes (MWCNTs)/graphenes using a variety of manufacturing techniques. Currently, Dr. Park is a professor at the Schulich School of Engineering, Dept. of Mechanical and Manufacturing Engineering, University of Calgary. He is a Schulich Scholar within the faculty. He is a professional engineer in Alberta, and is an associate member of CIRP (Int. Academy of Production Engineers) from Canada. Dr. Park received bachelor and master’s degrees from the University of Toronto, Canada. Sensing and Monitoring Using Nanocomposite Sensors and Hybrid Copper Conductive Inks

Prof. Simon Park
Professor,
University of Calgary (Canada)
E-mail : sipark@ucalgary.ca
Bio-brief:
Currently, Dr. Park is a professor at the Schulich School of Engineering, Dept. of Mechanical and Manufacturing Engineering, University of Calgary. He is a Schulich Scholar within the faculty. He is a professional engineer in Alberta, and is an associate member of CIRP (Int. Academy of Production Engineers) from Canada. Dr. Park received bachelor and master’s degrees from the University of Toronto, Canada. He then continued his PhD at the University of British Columbia, Canada. He has worked in several companies including IBM manufacturing where he was a procurement engineer for printed circuit boards and Mass Prototyping Inc. dealing with rapid prototyping systems. In 2004, Dr. Park has formed the Micro Engineering, Dynamics, and Automation Laboratory (MEDAL, www.ucalgary.ca/medal) to investigate the synergistic integration of both subtractive and additive processes that uniquely provide productivity, flexibility and accuracy to the processing of complex components. His research interests include micro machining, nano engineering, sensors, CNT nanocomposites, and energy applications. He has also founded several start-up companies in sensing and oil extractions. He held a strategic chair position in AITF Sensing and monitoring. He is also an associate editor of the Journal of Manufacturing Processes, SME (Elsevier) and International Journal of Precision Engineering and Manufacturing-Green Technology (Springer). Currently, he is directly supervising over 20 students and scholars.


Many machines and devices have become automated, smaller, and smarter. In order to realize this, the first step is to establish the technology for smart monitoring and control of the manufacturing processes and equipment. Similar to humans making decisions from the sound on the status of manufacturing processes or conditions of machine, a simple sound-based IoT device can be used to monitor help make smart decision. Also, as collaborative robotics and remote operation become more common on factory floors, a new operation platform is needed for easy human involvement and translation of human expertise. Also presented in this talk is a decision making framework for digital twin-based human-robot interaction and smart robot operation similar to how human body operated by the brain. The framework also enables better human involvement, training, and remote operation.Human Expertise Inspired Smart Sensing and Manufacturing

Prof. Martin Byung Guk Jun
Professor,
Purdue University (USA)
E-mail : mbgjun@purdue.edu
Bio-brief:
Dr. Martin Jun is an Associate Professor of the School of Mechanical Engineering at Purdue University, West Lafayette, IN, USA. Prior to joining Purdue University, he was an Associate Professor at the University of Victoria, Canada. He received the BSc and MASc degrees in Mechanical Engineering from the University of British Columbia, Vancouver, Canada in 1998 and 2000, respectively. He then received his PhD degree in 2005 from the University of Illinois at Urbana-Champaign in the Department of Mechanical Science and Engineering. His main research focus is on advanced multi-scale and smart manufacturing processes and technologies for various applications. His sound-based smart machine monitoring technology led to a start-up company on smart sensing.
 


Our research goals are to build smart nanosystems and integrate nanoscale components in micro sensors, in particular for environment, bio-sensing, through both bottom-up and top-down approaches. We focus on interdisciplinary research about local “bottom-up” surface modification using functional self-assembled monolayers and “top-down” approaches for micro/nano patterning technologies. Based on these studies on nano/micro components systems for the fabrication of novel nano devices, we have very successfully investigated to develop various micro sensors for biological applications, health care as well as environmental monitoring. Recently, in the transdermal drug delivery methods, the microneedle-mediated drug delivery system (DDS) has been developed to replace the hypodermic injection-mediated DDS, to provide painless self-administration of biological drug with patient friendly manner. Biodegradable Porous Microneedles for Painless Wearable Bio Sensors

Prof. Beomjoon Kim
Professor,
The University of Tokyo (Japan)
E-mail : bjoonkim@iis.u-tokyo.ac.jp
Bio-brief:
He received the B.S. degree from Seoul National University, Dept. of Mechanical Design and Production Engineering, Seoul, Korea, in 1993, and received M.Eng., and Ph.D. in Department of Precision Engineering, the University of Tokyo, Tokyo, Japan, in 1995 and 1998, respectively.
He is currently a Professor of Institute of Industrial Science, the University of Tokyo, Japan (Dept. of Precision Engineering, The Univ. of Tokyo) and a director of LIMMS/CNRS-IIS UMI 2820. He was a CNRS Associate Researcher for Microsensors, Nano-instruments for Nanotechnology in Centre National de la Recherche Scientifique at Besancon, France at 1998. He worked also in research orientation NanoLink, MESA+ Research Institute, University of Twente in the Netherlands, to September 2000.


The middle-ear ossicular chain converts sound wave in the ear canal into fluid vibrations in the cochlea, accomplishing impedance matching between air and cochlear fluid. Various congenital and non-congenital diseases or accidental trauma may cause interruption and/or breakage of the middle sound transmission routes. Repair of the damaged middle ear structures is generally done by surgical reconstructions of the middle ear, which rearrange or replace the impaired middle ear structures with implantable prostheses. In such surgical reconstruction, appropriate selection of the prosthesis and related surgical techniques is a key factor to determine the surgical outcomes. The established methods have been applied to evaluate functional performance of new surgical techniques and prostheses, with identification of parameters affecting middle-ear sound transmission in the reconstructed ears. The results from experiments with cadaveric samples and intra-operative measurements with live human subjects showed good agreement with the related clinical data, providing guidelines for the middle-ear surgeries with various pathological conditions. Objective Assessment of Middle-ear Surgeries

Dr. Jae Hoon Sim
Universtiy Hospital Zurich (Switzerland)
E-mail : jaehoon.sim@usz.ch
Bio-brief:
Dr. Jae Hoon Sim received my bachelor’s degree in 1992 and a master’s degree in 1994, at Dept. Mechanical Design and Product Engineering, Seoul National University (Seoul, Korea). After his master’s degree, he worked at Daewoo electronics (Seoul, Korea) from 1994 through 2000, serving for development of the speaker systems, control algorithm in the engine control module, and thin-film micro-mirror array using MEMS technology. He received his PhD at Dept. Mechanical Engineering, Stanford University (Stanford, United States) in 2007, with his doctoral study regarding imaging, physiology, and biomechanics of the human middle ear.
His researches in basic hearing science have been focused on pathways in bone conduction hearing, comparative analysis of middle- and inner-ear mechanics in mammals, protection/adaptation mechanism of the human middle ear, and micro imaging of ear structures. In clinical aspects, his main interests have been in experimental assessment and numerical simulation of implantable hearing aids, surgical reconstruction of the middle ear, and cochlear implant.


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