Keynote Lectures

Bio

Prof. Changhoon Lee
Yonsei University
Korea

“Numerical study of particle-laden turbulence”


Particle-laden turbulence is frequently found in nature and human environment. Clouds, yellow dusts, volcanic ashes, and PM2.5 are just a few examples of heavy particles laden in turbulence. Fuel spray in a diesel engine cylinder, colloidal solutions in chemical equipment, and polymer-laden fluid are industrial examples of particle-laden turbulence. There have been many previous works on particle-laden turbulence either numerically or experimentally. We have some understanding of interesting phenomenon such as clustering of particles which is caused by the interaction between inertial particles and rotation of fluid motion. Still, we lack the fundamental understanding of mechanism of particle-turbulence interactions. In our study we investigate particle-turbulence interaction using direct numerical simulation of particle-laden turbulence. We consider small particles so that the point-particle approximation can be made under the Stokes flow assumption. Isotropic turbulence in a periodic cubic domain is simulated by solving the Navier-Stokes equation. Dynamics of particles is determined by the Stokes drag and gravity. We discovered a new kind of clustering of settling heavy particles which purely results from gravity. This clustering forms a fractal structure for which the Lyapunov exponents can be estimated by theory and confirmed by direct numerical measurement. Rising bubbles can also be modeled similarly, but the dynamics of bubbles is more complicated. In addition to the Stokes drag and buoyancy, the lift force acting mostly horizontally plays a very important role by pushing bubbles in one direction when bubbles are located inside vortical motion of fluid. Although the valid range of parameters for bubbles is usually relatively narrow, the theoretical prediction of fractal structure of bubble clustering is in good agreement with numerical estimation such as the Lyapunov exponents and the Kaplan-Yorke fractal dimension. Bubbles also display a vertical strip pattern of clustering and they hardly collide with each other. Another interesting observation is that bubbles move like phytoplankton. This can be shown by a perfect analogy in the governing equation between bubbles and phytoplankton.
Bio

Dr. Chang Woo Lee
Korea Institute of Machinery & Materials
Korea

“3D printing commercialization strategy”


People know that 3D printing technology is cheaper and faster than traditional manufacturing methods. But is it really so? I don’t think so. If so, 3D printers should be used in more industries now. Of course, 3D printing is competitive in certain areas such as prototyping, but it is less competitive in many areas. Why do these misunderstandings happen? These misunderstandings are due to the engineers’ desire to emphasize the importance of their research areas and the reporters’ desire to appeal to the public. This misunderstanding has fantasy in many people and this fantasy caused disappointment. The 3D printing commercialization strategy is to turn this disappointment into hope. The strategy is simple. The strategy is to make 3D printing process cheaper and faster than traditional manufacturing methods. It is impossible to do so in all areas. There are three commercialization strategies. The first strategy is to develop a printing method that is 10 times faster and cheaper than the current printing method. This is the original technology of printing, and it takes a lot of time and money. As the original technology, it has a great ripple effect, and therefore requires long-term continuous research. The second strategy is the passive way to find areas in which 3D printing is competitive, for example, prototyping. It is a way to try commercialization through 3D printing, but there are not many successful cases other than prototype production currently. There are many new attempts in the medical field and many successful cases are expected to emerge. The third strategy is to change the design of the product to allow 3D printing to be competitive in the manufacturing field. This is called DfAM(Design for Additive Manufacturing). DfAM is a design that maximizes the performance of the product by taking into consideration the 3D printing process. DfAM is the most realistic and powerful way to commercialize 3D printing in a current context.
Bio

Prof. Tu Shandong
East China University of Science and Technology
China

“From Reliability Design to Reliability Manufacture”


With the evolution of our human being, the highly developed technology and civilization allows us to have less fear of the famine, pestilence, and even the war. However, failures of critical infrastructures, industrial equipment and explosion of plants are still a treat to our civilization and the sustainable development. The reliability design has been playing an important role in improving the product quality and preventing the failures. This is achieved by understanding the data scatter of the existing materials and dimensions of the components, the loading randomness and acceptable failure probability. However, many unexpected factors may contribute to the failure of a product. In reality, failure rates have been higher than they are designed or expected. Thus, a re-examination of the conventional manufacturing modes is necessary. The manufacturing practices such as agile manufacturing, lean production, next generation manufacturing in current industries were mostly aiming at manufacturing efficiency while reliability of high-end products were not the central concern. The present paper reviews the conventional manufacturing modes and various failures that entangled the current manufacturing industries. A reliability-manufacturing mode is proposed which aims at increasing the inherent reliability of products. To achieve this, damage identification is essential followed by a big failure databank. High reliability is then secured by development of high performance materials, design against failures, zero-defect manufacture and re-manufacturing for reliability recovery. The key components of reliability manufacturing are advanced testing, inspection, and on-line monitoring technology, and various surface engineering techniques for materials performance enhancement. A general framework of reliability-manufacturing mode is thus given, as illustrated in Fig. 1. Emphases are laid on the testing, inspection, health monitoring and surface modification technologies. Case studies will be given to illustrate the effectiveness of the technologies. Future considerations to implement the reliability-manufacturing mode in industry will also be discussed.
Prof. Chen Ming
Tongji University
China

A New Generation of Intelligent Manufacturing and Corresponding Talent Cultivation


The deep integration of the new generation of information technology and manufacturing is triggering far-reaching industrial changes. New production models, new business models and new service models are emerging. The real question is how to transform China from a manufacturer of quantity to one of quality. To meet this demand, the strategy of manufacturing power of China has been formulated.
In recent years, artificial intelligence (AI) has been accelerating developed and achieved a strategic breakthrough. Advanced manufacturing technology (AMT) and new generation of AI technologies are deeply integrated to form a new generation of digital, networked and intelligent manufacturing. The manufacturing system in intelligent manufacturing possess a learning capability. The production, acquisition, application and transmission efficiency of knowledge in the manufacturing industry will revolutionary changed by using deep learning, reinforcement learning, transfer learning and other technologies. The capability of innovation and service will be significantly improved.
China became the most competitive manufacturing country in 2016. This is not only because of traditionally low-cost manufacturing, but also because of the long-term development plan of China in the field of innovation, which has consolidated the role of advanced technology in the future manufacturing industry. However, the continuous adoption of more advanced and more sophisticated products, technologies and materials in the manufacturing industry, the United States is expected to replace China to become the world leader of manufacturing industry by 2020 through the advancement of advanced manufacturing technology.
Talent is the first priority in global manufacturing competitiveness. In order to, revitalize the advanced manufacturing industry, the United States Presidential Advisory Committee on science and technology proposed to build unimpeded “transportation pipeline for industrial talents”. Meanwhile, the United States also point out that the high quality industrial talents have most Influential impacts on the competitiveness of manufacturing industry.
The implementation of Industry 4.0 and intelligent manufacturing will make the need of certain occupations disappear or reduce sharply. Many previous skills will no longer be needed while the others may be increasing needed. Completely new types of jobs may occur in this period.
The demand of talents in different industries can be pointed out through the talent cultivating model we build in the field of intelligent manufacturing. The professionals and interdisciplinary talents and especially system-level talents who are good at system architecture and integration are mostly needed. Four universities have applied a new engineering major “intelligent manufacturing engineering” from the ministry of education successfully. The exploration of talent cultivating in the field of intelligent manufacturing have started in China.
Bio

Prof. Shuji Tanaka
Tohoku University
Japan

“Making Bulky Sensors Much Smaller by MEMS Technology”


Small sensors based on MEMS (Micro Electro Mechanical Systems) technology have replaced bulky sensors in the past 30 years. Mechanical inertia sensors in 1990’s was as large as 100 cc or even larger, but MEMS inertia sensors have only 1/10000 of the volume. The size reduction has proceeded also in recent several years. The latest 9-axis combo sensors are smaller than 0.01 cc. The driving force of the recent size reduction is wafer-level integration. Different kinds of sensors can be made on the same wafer at the same time, and they are often integrated with ASICs (Application Specific Integrated Circuit) at wafer level.
Another example is for microphones used in cellar phones and smartphones. A conventional electret microphone is weak against high temperature and thus mounted on a PCB (Printed Circuit Board) not by solder reflow but via a plastic holder, making the microphone the tallest on the PCB of cellar phones. The electret microphones in smartphones have been fully replaced by MEMS microphones, and multiple MEMS microphones are now used in a smartphone for noise cancellation and so on thanks to their small form factor and low cost.
What is the next? This is the topic of this lecture.
We are trying to replace bulky and expensive optical gyroscopes with a MEMS vibratory gyroscope. A high-performance gyroscope is one of key sensors for autonomous driving cars, robots, auto-pilot drones etc. The current MEMS gyroscopes do not satisfy the requirements of such applications, and optical gyroscopes are used instead. However, a new generation of MEMS gyroscope with a highly symmetric orthogonal vibration system and a sophisticated controller has a chance to replace the optical gyroscopes.
Bulky ultrasonic transducers, which are widely used for car back sensors, can be drastically miniaturized by the MEMS technology. We used an epitaxial PMnN-PZT film with a high piezoelectric constant and a low dielectric constant to improve a MEMS ultrasonic transducer. The small and low-power MEMS ultrasonic transducer can open new applications such as gesture interface of IT devices and position tracking for virtual reality. A similar technology can be used for secure ultrasonic fingerprint sensors.
What the MEMS technology can make smaller is not only devices but also components and systems. We have developed a large-scale tactile sensor array system for robots. Tactile sensor arrays are attracting a lot of attentions for the machine learning of robots based on AI (Artificial Intelligence). However, the bulky wiring is a critical problem to install a lot of tactile sensors in a limited inner volume of robots. Our MEMS tactile sensor has a small form factor and more importantly can be connected to a bus, because it is integrated with an ASIC with the functions of sensor readout, data processing and digital bus communication. About 50 sensors were connected to a bus, and each sensor worked independently in event-driven manner.
The MEMS technology has been an enabling technology, making our daily life convenient and comfortable. I believe that the MEMS technology will develop further and contribute to coming innovations such as IoT (Internet of Things), autonomous driving and AI-based robotics.
Bio

Prof. Shinji Deguchi
Osaka University
Japan

“High-throughput detection of cellular traction forces for screening of drugs and regulatory genes”


Recent progress in understanding the essential roles of mechanical forces in regulating various cellular functions expands the field of biology to one where interdisciplinary approaches with mechanical engineering techniques become indispensable. Cellular traction forces (CTF) – that are present in proliferative cells including cancer cells due to the activity of ubiquitous nonmuscle myosin II (NMII) – are one of such mechanical forces (or signal regulators), but because NMII works downstream of diverse signaling pathways, it is often difficult to predict how the CTF changes upon perturbations to particular molecules such as gene mutations and drugs. Here I will talk about our unique bioassay or bioengineering technology with a high-throughput data analysis capability to determine whether the endogenous CTF is upregulated or downregulated. One example that I will show focuses on the effect of mutations in the human MYH9 gene that encodes NMII, which have been implicated in the pathogenesis of nephritis. Our bioassay revealed that a particular point mutation in the gene significantly reduces the magnitude of the endogenous CTF of human kidney cells. Given the increasingly recognized roles of CTF as a critical regulator, as well as that no apparent morphological changes were induced to the kidney cells even by introducing the mutations, our findings suggested that the detected reduction in the force magnitude at the individual cellular level may underlie the pathogenesis of the kidney disease. Thus, our group has demonstrated that our new technology allows us to comprehensively evaluate changes in the cell functions/diseases-associated CTF caused by any mutations, knockdown/knockout, or overexpression of particular genes and how those changes are enhanced or rescued by means of drugs [1–4].

References

  1. Ichikawa, T., Kita, M., Matsui, T.S., Ichikawa-Nagasato, A., Araki, T., Chiang, S.H., Sezaki, T., Kimura, Y., Ueda, K., Deguchi, S., Saltiel, A.R., Kioka, N., Vinexin family (SORBS) proteins play different roles in stiffness-sensing and contractile force generation. Journal of Cell Science, 130, 3517-3531, 2017.
  2. Fukuda, S.P., Matsui, T.S., Ichikawa, T., Furukawa, T., Kioka, N., Fukushima, S., Deguchi, S., Cellular force assay detects altered contractility caused by a nephritis-associated mutation in nonmuscle myosin IIA. Development, Growth & Differentiation, 59(5), 423-433, 2017.
  3. Yokoyama, S., Matsui, T.S., Deguchi, S., New wrinkling substrate assay reveals traction force fields of leader and follower cells undergoing collective migration. Biochemical and Biophysical Research Communications, 482, 975-979, 2017.
  4. Sakane, Y., Yoshizawa, S., Nishimura, M., Tsuchiya, Y., Matsushita, N., Miyake, K., Horikawa, K., Imoto, I., Mizuguchi, C., Saito, H., Ueno, T., Matsushita, S., Haga, H., Deguchi, S., Mizuguchi, K., Yokota, H., Sasaki, T., Conformational plasticity of JRAB/MICAL-L2 provides “law and order” in collective cell migration. Molecular Biology of the Cell, 27(20), 3095-3108, 2016.