Eng. Franco Cevolini (CRP Group - Energica Motor Company S.p.A.)
Title: From Racetrack to Road - How CRP Group and Windform® composite SLS materials drive Manufacturing
Abstract: The CRP Group since its establishment has excelled for its pioneering nature in manufacturing, by continuously innovating products and processes. Born as a company for high precision CNC machining in the motorsports sector, the CRP Group has expertly evolved over the past four decades, anticipating unique manufacturing solutions worldwide. The CRP Group has been among the first to adopt extensively Additive Manufacturing technology in Europe and developed Windform®, some of the international market’s most high-performance Laser Sintering composite materials, in use for more than 20 years in the most demanding applications for the motorsports, space, automotive, design and UAV industries. With more than 45 years of experience in the world of F1 and more than 20 years of experience in Additive Manufacturing, the Group is distinguished by its know-how in specific application fields such as the development and production of end-use components and functional prototypes with Laser Sintering technology, and a wide range of services and processes as Direct Metal Laser Sintering (DMLS), High-precision CNC Machining. CRP Group manufacturing technology leading edge led to the development of the first electric racebike, the eCRP, from which the company started the electric program. It recently has given rise to Energica Motor Company, the sustainable subsidiary of CRP Group and the first Italian manufacturer of high performing electric motorcycles. Dr Cevolini, CEO and Technical director of CRP Group will talk about his experience and views for the future of Additive Manufacturing and Windform®, and how CRP Group manufacturing excellence has driven the development of the innovative Energica high performing electric motorcycles.
Short Bio: Eng. Franco Cevolini is actually CEO and Technical Director of the CRP Group and chairman of the Energica Motor Company S.p.A. He was born in Modena, Italy. He attended University of Modena and Reggio Emilia and graduated in Engineering (Materials Engineering). In November 1996 he established CRP Technology as a spin off of Roberto Cevolini & Company, inheriting his father’s thirty-years experience and know-how in high precision CNC machining. Franco Cevolini was appointed as the company’s new Chairman. In 1999 he pioneered integrated engineering development, rapid prototyping and 3D Printing processes creating a new family of high-performance composite materials: the Windform® 3D Printing materials for additive manufacturing. After that, he joined Minardi Team in order to develop the first ever gearbox made in titanium alloy cast. In the following years the company became one of the leader in the production of 3D Printing materials, high-performing CNC machining and 3D Printing service department.
Today the company has become CRP Group, which is made up of specialized companies (CRP Technology, CRP Service, CRP Meccanica, plus its US-based partner CRP USA) involved in high precision CNC machining, 3D Printing, production and selling of Windform® composite SLS materials and other cutting edge technologies. Eng. Franco Cevolini is CEO and Technical Director of the Group, which continues to be a reference partner of the Racing Industry, and he is personally involved in the R&D, 3D Printing and CNC machining Department of CRP Group. CRP Group is the lead investor of Energica Motor Company, the sustainable subsidiary of CRP Group and the first Italian manufacturer of high performing electric motorcycles. In January 2016 Energica Motor Company S.p.A. debuted on AIM Italia – Alternative Investment Market (Mercato Alternativo del Capitale), a multilateral trading facility by Borsa Italiana S.p.A.
Eng. Franco Cevolini
CEO & Technical Director, CRP Group
Chairman, Energica Motor Company S.p.A.
Via Cesare Della Chiesa 21 - 41126 Modena, Italia
Prof. Kai Cheng (Brunel University London, UK)
Abstract: Smart manufacturing has tremendous potential and is becoming the next generation manufacturing technology particularly in the Industry 4.0 context. Smart machining processes will enable a new level of machining capability and adaptability, including high process reliability, high precision, machining process optimization, plug-and-produce operations, and bespoke high value applications. This presentation will present some innovative design concepts on the development of a number of smart tooling devices and smart machines, including a force-based smart cutting tool, a temperature-based internally-cooled smart cutting tool, smart diamond cutting tool, smart spindles and next generation ophthalmic manufacturing machines, particularly for precision and micro manufacturing purposes. Practical implementation and application issues for these smart tooling and machines are explored and discussed, taking account of the requirements for smart machining against a number of industrial applications, such as contamination-free machining, high speed smart drilling, machining of tool-wear-prone Si-based infra-red devices and ophthalmic manufacturing applications. Additional research on smart tooling implementation and application perspectives will also be presented, including: (a) plug-and-produce design principle; (b) novel cutting force modelling and the associated implementation algorithms; (c) critical cutting temperature reduction and control in real-time machining; (d) Multi-physics based design and analysis of smart tooling, and (e) application exemplars on smart machining. The presentation will conclude with further discussion on the potentials and applications of smart tooling / machines / manufacturing for future manufacturing industry.
Short Bio: Prof. Kai Cheng is Chair in Manufacturing Systems at the Institute of Materials & Manufacturing, College of Engineering, Design and Physical Sciences, at the Brunel University London. His current research interests are focused on design of high precision machines, ultraprecision and micro manufacturing, smart tooling and smart machining, and sustainable manufacturing systems. He is currently leading the Ultraprecision and Micro/nano Manufacturing Theme at Brunel University London and working on a number of research projects funded by the EPSRC, NATEP Program, RAEng, Innovate UK program, EU Horizon 2020 Programs, and the industry. Professor Cheng and his team have enjoyed working closely with industrial companies in the UK, Europe, USA and Far East. Professor Cheng is a Charted Engineer and a Fellow of the IMechE and IET, the European Editor for International Journal of Advanced Manufacturing Technology and a member of the Editorial Board at International Journal of Machine Tools and Manufacture.
Prof. Kai Cheng
Chair in Manufacturing Systems
Institute of Materials & Manufacturing
College of Engineering, Design and Physical Sciences
Brunel University London
Uxbridge, London UB8 3PH, UK
T : 01895-267255
Prof. Kok-Meng Lee (Georgia Institute of Tecnology, USA - Huazhong Univ. of Sci. and Tech., China)
Abstract: Recent advances in sensing and perception systems (SPSs), which move beyond from point measurements to field representation, enable exciting new technologies in facilitating intelligent manufacturing (iM) capable of evolving with more and more ‘smart functions’ that ultimately make the process a self-improved system. In machine vision, light has been commonly assumed as the medium for perception. This talk introduces new SPS methods based on physical fields as an alternative or a complement to light for iM. As an essential medium for energy-conversion and signal-processing, electromagnetic fields are widely found in actuators and sensors for in robotics and automation. The new SPS methods creatively use the existing electromagnetic fields to infer its system properties of a distributed-parameter system, and thus eliminate costly complicated external measurement systems. Along with some real-world manufacturing and robotic examples, a method to derive closed-form solutions to physics-based models, reconstruct the distributed-parameter physical fields, and infer its system properties from limited measurements for analyzing and controlling its dynamic behaviors, will be presented in this talk. This talk will conclude with a discussion on existing challenges and future opportunities in response to global calls for developing new “intelligent” technologies” to meet challenges of emerging applications.
Short Bio: He received his M.S. and Ph.D. degrees in mechanical engineering from the Massachusetts Institute of Technology in 1982 and 1985, respectively. He has been with the Georgia Institute of Technology since 1985. As a Professor of mechanical engineering, his research interests include human and machine vision, robotics, automation and opto-mechatronics. He is Distinguished Professor (China National 1000 Talent Plan) with the School of Mechanical Science and Engineering at the Huazhong University of Science and Technology, and Pao Yu-Kong Chair Professor of the Zhejiang University. Prof. Lee is a Fellow of ASME and IEEE. He is currently Past Editor-in-Chief for IEEE/ASME Trans. on Mechatronics (TMech) serves as co-Chair on the AIM Conference Advisory Committee since 1997, and serves on the Executive Committee of ASME Dynamics Systems and Control Division (2013-2107, Chair 2016). Prior to becoming TMech Editor-in-Chief (2008-2013), he served as its Technical Editor (1995-1999) and guest edited four focused sections, He had also held representative positions within the IEEE Robotics and Automation Society: served as Associate Editor for its Trans. on Automation Science and Engineering (2003-2005), Trans. on Robotics and Automation (1994-1998), and Robotics and Automation Magazine (1994-1996), and as Chair or Co-Chair for numerous international conferences; and founded/chaired Technical Committees on Manufacturing Automation (1996-1998) and on Prototyping for Robotics and Automation.
Prof. Kok-Meng Lee
Professor in Automation / Mechatronics
George W. Woodruff School
of Mechanical Engineering
801 Ferst Drive
Georgia Institute of Technology
Atlanta, GA 30332-0405
Dr. Aydin Nassehi (University of Bristol, UK)
Title: Proactive decision making in future production enterprises: the role of big data, internet of things and cyberphysical systems
Abstract: Manufacturing enterprises are evolving to better use digital technologies to improve the efficiency and effectiveness of their production system. The synergistic combination of such technologies with the value adding operations undertaken by factories (sometimes referred to as Industrie 4.0, other times as cyberphysical production) allows an unprecedented level of visibility and transparency in production. The vast amount of information that is generated as a result of this close connection, if managed, can lead to better than ever decision making for use of limited resources to achieve production goals. In this keynote, the challenges for creating such a framework for proactive decision making are presented with specific attention to the role of big data, the internet of things and cyberphysical interfaces in providing a rich knowledge foundation. Strategies for addressing these challenges are then proposed for large, data-driven enterprises as well as small to medium sized, people-oriented production companies.
Short Bio: Dr. Aydin Nassehi is a Reader in Manufacturing Systems at the University of Bristol. He received his PhD in Mechanical Engineering from the University of Bath. In 2007, Dr Nassehi was appointed to a Research Council UK Research Fellowship and has gained promotions to Lecturer in 2012, and Senior Lecturer in 2013. He also gained an MSc in Software Engineering with distinction from the University of Oxford in 2013. His expertise is in manufacturing interoperability, computational informatics including energy efficiency modelling and analysis of manufacturing processes and knowledge based CAD/CAM and CAx systems. He has received £3.5m of national and international research funding and taken part in two FP7 projects namely DEMAT and, as the technical leader, STEPMAN. He has published over 90 refereed papers and been on technical & scientific committees of a number of international conferences including the FAIM series of conferences since 2008. He is a CIRP Associate Member, the convener of ISO standards group in charge of developing ISO14649 for cyber physical manufacturing resources (ISO TC184/SC1/wg7) and the Managing Editor of the International Journal of Computer Integrated Manufacturing.
Dr. Aydin Nassehi
University of Bristol
Department of Mechanical Engineering Queen’s School of Engineering
Queen’s Building, University Walk, BS8 1TR - United Kingdom
T: 0117 331 5429
Prof. Chen-Fu Chien (National Tsing Hua University, Taiwan)
Abstract: : Global Manufacturing networks are facing disruptive challenges due to new technologies and solutions such as Big Data, Internet of Things, Cloud, and artificial intelligence, in which value chain positioning of individual firms may be restructured and the firm boundary has become blurred. The increasing adoption of new technologies have empowered an unprecedented level of manufacturing intelligence with profound effects on the controls, management, resource allocation and decisions involved in smart production. New ecosystem has emerged in which leading companies are battling for dominant positions in this newly created platform via providing novel value-proposition solutions and/or employing new technologies to support smart production. There should be a set of systematic methodologies for cross-discipline collaborations to empower smart manufacturing in addition to the underlying infrastructure and technologies. On the basis of our extensive collaborative studies with high-tech industries and the observed trends, this keynote aims to address emerging issues driven by the needs in modeling, big data analytics, and optimization for smart production. Indeed, semiconductor manufacturing is one of the most complicated and fast clock-speed industries driven by Moore’s Law for continuous technology migration and productivity enhancement. In the fully automation facility such as wafer fab, various approaches and solutions are developed for big data analytics to address new challenges involved in yield enhancement, defect diagnosis, advanced process control, equipment health management, cycle time reduction, cost reduction, human capital and productivity enhancement. This talk will conclude with discussions of the implications of evolutionary digital manufacturing technologies and applications to foster more discussions.
Short Bio: Prof. Chen-Fu Chien is Tsinghua Chair Professor and the Convener for Industrial Engineering and Management Program, Ministry of Science & Technology, Taiwan; Director for the Semiconductor Technologies Empowerment Partners (STEP) Consortium and NTHU-TSMC Center for Manufacturing Excellence. He received B.S. with double majors in I.E. and E.E. with the Phi Tao Phi Honor from NTHU in 1990. He received M.S. and Ph.D. of Decision Sciences and Operations Research with two minors in Statistics and Business at the University of Wisconsin-Madison, in 1994 and 1996, respectively. He was a Fulbright Scholar in UC Berkeley, from 2002 to 2003. From 2005 to 2008, he had been on-leave to serve as the Deputy Director of Industrial Engineering Division in Taiwan Semiconductor Manufacturing Company (TSMC). He also received the Executive Training of PCMPCL from Harvard Business School in 2007. Dr. Chien has 12 invention patents for semiconductor manufacturing and published more than 150 journal papers. Dr. Chien has received many awards including the Executive Yuan Award for Outstanding Science & Technology Contribution (2016), the National Quality Award (2012), Distinguished Research Awards from Ministry of Science & Technology (2007, 2011, 2016), the 2011 Best Paper Award of IEEE Trans. on Automation Sciences and Engineering, and the 2015 Best Paper Award of IEEE Trans. on Semiconductor Manufacturing. He is on the editorial board for a number of journals.
Prof. Chen-Fu Chien
Tsinghua Chair Professor & Convener of Industrial Engineering and Management Program, Ministry of Science & Technology
Department of Industrial Engineering & Engineering Management
National Tsing Hua University
Hsinchu 30013, TAIWAN