Only first pilot projects on the integration of industry 4.0 concepts into metal forming technologies have been carried out during recent years, the advantages of linking real and digital world are nowa-days recognized and pursued in numerous industrial sectors. Thus, in today's industrial production, efforts are undertaken to improve workflows and processes by recording and utilizing data sets for targeted process control. In doing so, the main objectives are to better understand process fluctua-tions and to increase the efficiency or output of production systems. The linking between real and virtual production can generally be achieved by integrating sensors and communication components (cyber-physical systems - CPS) into those manufacturing processes. In this respect, numerous pro-duction engineering institutes are currently working on comprehensive digitalization solutions for processes of sheet metal forming and forging technologies. However, these projects generally show that the decisive, quality-relevant process variables or component properties often only can be rec-orded extremely difficultly or solely indirectly during the forming processes. Thus, high loads or pressure conditions in the forming tool, high temperatures, extensive relative movement of material or high amounts of strain usually complicate the precise recording of process conditions.
The present paper deals with some of these problems and discloses perspective approaches for inte-gration of industry 4.0 concepts into metal forming technologies. Thus, in the field of sheet metal forming, a new methodology of sensing the part wall stress of drawn components appearing during the deep drawing process under harsh production conditions is demonstrated. Furthermore, the ap-plication of an adaptive feed forward controller methodology for a simplified front fender deep draw-ing tool for high strength steel and aluminium alloys is presented. In the field of forging technolo-gies, the paper discloses a new approach for a design of a cyber-physical forging process chain. This approach differs from previous solutions adopted to self-learning systems concerning the fact, that a distributed, multidimensional modelling of forging process data is used for the integration of multi-ple process steps into analytical considerations. Online data storage and real-time data analysis is performed via the internet on a cloud to investigate feed forward adaptive and self-learning control strategies for an aluminium forging process. Not least, the developed prototype production system is capable of detecting, recording and separating scrap after the pre and final forming stage.
Promoted by U.S. government and extensive coverage by global media, Additive Manufacturing (aka AM or 3D Printing) has been an attractive engineering focus for future burgeoning field and emerging technology. Despite of the excessive media coverage, the underlying technology for Additive Manufacturing, commonly known as rapid prototyping, has been existing for more than thirty years. The remaining major challenge is then on how to transfer the technology into new industry focus and even to transform and leverage the existing manufacturing systems to the extent of rapid manufacturing. To respond to this request, the Program is proposed with vision set to encourage technology development of application-led new equipment, new materials, new processes, as well as to establish new milestones for Additive Manufacturing in Taiwan. The purpose of this Program is to solicit, examine, manage, and monitor competitive proposals and projects which shall provide detailed application-led research projects to meet the Program’s objectives: (1) Thorough development of knowledge and technologies and production of relevant high-caliber human resource for Additive Manufacturing; (2) Innovations of academic research, technologies and applications with possession of key patents and development of patent portfolio; (3) Lead and combine nation’s abundant academic research resources to promote research and development alliance between industry, academia and research institutions in Taiwan; (4) Provide new platform and production means for professionals and citizens of Taiwan for value-enhanced self-realization through personalized design and manufacturing with intelligence.
To fill the gap of lacking consideration of feasibility and marketability of technologies generated by Taiwan academic advanced research and development, the aim of the Program is to combine synergies from academia, industries, and corporate research and development institutions to conduct collaborative research and development of new applications, new materials, new equipment, and new key components to allow Taiwan to create new industries and to possess key technologies on Additive Manufacturing. Therefore, 30% of the Program’s research budget will be allocated to foster collaborative research and development in specific application focuses which will leverage Taiwan’s competitiveness in the global market. These focuses include research and development of key technologies for metal molds, dental related applications, laser heads and nozzles in Additive Manufacturing. The rest of the budget will be allocated for other applications and items proposed by proposers and investigators. This Program will request project proposers to collect application scenarios and to access market demand of Additive Manufacturing in the targeted application followed by clear determination and definition of key technologies that will be researched and developed. Priority will be given to those projects that will generate technologies and results for new business or meet requirements and demands set by corporate research and development institutions and industries.
The MOST Program Office will be consisted of an executive team which not only will evaluate and examine white papers and formal proposals during the call for project stage, but also will visit and monitor progress performed by funded teams during the project execution stage. Annual joint conferences and seminars will be held for project teams to report and demonstrate their research and development results as well as for experts and professionals to exchange experience and information. In addition to the aforementioned activities, to broaden views and to learn different perspectives in research, development and operations conducted by domestic and overseas institutions, on-site domestic and international visits will also be arranged by the Program Office with the view toward achieving knowledge exchange, offering shared resource for effective research and development, creating of interdisciplinary collaborations as well as building up internationally renowned research and development teams in the field of Additive Manufacturing.
Evolution of modern manufacturing technology requires ever-increasing international competitiveness, requiring sustainability of the materials and processing technologies. In order to achieve this goal, new materials and manufacturing processes should be developed economically in the production facility constraints available. Various studies investigated at the laboratory level will be introduced in the present talk; material characterization of ammonium perchlorate base composite propellant using the hydroxyl-terminated polybutadiene as binding material, production of grain-refined copper wires through a non-circular drawing and a new hybrid process consisting of rolling, equal channel angular processing, and drawing with thermomechanical treatment, etc. In addition, development of three dimensional metal printing machine using directed energy deposition (DED) and powder bed fusion (PBF) techniques will be introduced. Some important issues involved with developing sustainable materials processing technologies will be addressed from various perspectives as well.
Dr. Ming YANG is a Professor of Tokyo Metropolitan University, received his B.Eng., M.Eng. and Dr.Eng. in mechanical engineering from Kyoto University, JAPAN in 1984, 1986 and 1990, respectively. He used to work on intelligent control system for press-brake machine at Research and Development Laboratory of AMADA Co. From 1991, he moved to Tokyo Metropolitan University and worked on intelligent metal forming system and from 1998 he became a staff member of the Laboratory of Precision Measurement and Instrumentation and worked on MEMS fabrication and evaluation. He is interested in micro metal forming and MEMS design for biological and chemical analysis, micro fluidics of bio-fluid. He is member of JMSE, JSTP, JSPE, JLME, ISCIE.
Prof. YANG is a Professor of Tokyo Metropolitan University, received his B.Eng., M.Eng. and Dr.Eng. in mechanical engineering from Kyoto University, JAPAN in 1984, 1986 and 1990, respectively. He used to work on intelligent control system for press-brake machine at Research and Development Laboratory of AMADA Co. From 1991, he moved to Tokyo Metropolitan University and worked on intelligent metal forming system and from 1998 he became a staff member of the Laboratory of Precision Measurement and Instrumentation and worked on MEMS fabrication and evaluation. He is interested in micro metal forming and fabrication of micro devices for biological and chemical analysis, micro fluidics of bio-fluid. He is member of JMSE, JSTP, JSPE, JLME, ISCIE.Education & Training:
In this keynote speech, the author will review the researches and developments on process visualization and intelligence in press forming, which is typical processes in metal forming for fabrication of metal parts. Several sensor technologies for monitoring material deformation and friction involved in the dies will be introduced. A die-embedded semiconductor sensor-assay chip for measuring stress distribution of the die at the contact with the workpiece, and a three-axis piezo force sensor for detecting friction behavior dynamically during the process. Furthermore, sensor signal processing technology for extraction of intelligence and process control technology will be introduced.
The author will also present an approach to contribute an IoT platform for promotion of the press forming to a next-generation production technology utilizing IoT and AI technologies.
Dr. Yeau-Ren Jeng is a Professor of National Cheng Kung University (NCKU), Taiwan. Prior to his academic positions at the NCKU, he was an University Endowed Chair Professor , Professor of the Mechanical Engineering, and Founding Director of Advanced Institute for Manufacturing with High-tech Innovations (AIM-HI). His research has provided significant benefits to multiple industries, including the automotive, material, electronic, manufacturing, and nano-related industries. His publications are widely cited including several textbooks and handbooks.
He is the advisor of several dissertation awards from Ministry of Science and Technology and the Chinese Society of Mechanical Engineers. He holds over 30 patents and has received numerous awards including the McCuen Special Achievement Award from General Motors, Innovative Research Award from American Society of Mechanical Engineers, Society of Tribologists & Lubrication Engineers Walter Hodson Best Paper Award and Captain Alfred E. Hunt Memorial Medal, the Research Invention Award from the President of Taiwan, and the Outstanding Research Award from Taiwan’s Ministry of Science and Technology. He is also the recipient of Mechanical Engineering Medal from the Chinese Society of Mechanical Engineers.
Professor Jeng received one of the highest academic honor from Ministry of Education of Taiwan. He is on the editor board of several internationally renowned journals, the review committee of M-ERA of European Union Horizon 2020, Russian Science Foundation (RSF), and German-Israeli Foundation for Scientific Research and Development (GIF).
The wave of smart manufacturing propels the widely use of various sensors to monitor the manufacturing process. Big Data Analytics is then employed to deduce the vast volume of data collected from sensors into information for decision-making. The scale and breath of infrastructure and the technological transformation necessary to achieve factory of future is a pronounced hike in investment, skill and personnel. Thus, the road to smart manufacturing is full of opportunities and risks. This talk will show case how science-based insight can enhance the opportunity to success and reduce the risk of failure for various fabrications of metals. Focus would be on enhancing the productivity of manufacturing of various metal fabrication such as metal forming, machining and additive manufacturing, and the paradigm shift of value chain of fabricated products.
Surface is the interface of a solid. In which surface texture is a direct consequence of the fabrication process. In turn, intensive microstructural rearrangement can occur near the surface due to the intensive shear force resulting from the fabrication process. Development of mechanics model and computational approach provide the ability to probe how operation parameters of metal forming process such as cold rolling and extrusion affect critical factors of product quality including surface texture and surface harden layer. Innovative design of an embedded sensor together with comprehensive stress-strain analysis enables the direct measurement of cutting force during machining to performance prognosis of tool life and relate operation parameters to the quality of the work piece. These insights make it possible to execute critical implementations of sensors for monitoring to reduce cost of hardware and data processing, and consequently, efficient transformation of industrial big data into knowledge based decision-making. Further, these insights would allow an intelligent arrangement of operation parameters to achieve desired microstructure of surface layers due to sever plastic deformation during fabrication for the performance of the product under harsh conditions. In the case of additive manufacturing, it allows for not merely the freedom of geometry but more importantly the manipulation of microstructure. Highlight of hard to fabricate material for high temperature applications would elaborate how capability of manipulation of microstructure can enhance the value of smart manufacturing.