Novel evaluation method for motion trajectory of machine tool feed drives based on human visual sensitivityProf. Ryuta Sato |
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Machined surface quality is typically evaluated based on their visibility through human eyes, especially in dies and molds, and designed surfaces such as smart phone cases. Visible glitches are frequently observed on the surface due to imperfection of motion trajectory of machine tool feed drives. Regarding the problem, it is empirically known that the visibility of the glitches is not directly related with size of the errors. This study proposes a novel motion trajectory evaluation method based on human visual sensitivity related with surface quality. The human visual sensitivity is investigate based on the reflectance model and the visible limit of the geometrical property is formulated. Motion trajectories are evaluated based on the investigated visual sensitivity to demonstrate validity of the proposed method.
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Research activities in the Industrial Cyber-Physical Systems Research Center, AIST for human-centric digital manufacturingDr. Hitoshi Komoto |
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The Industrial Cyber Physical Systems Research Center, AIST, drives the development of digital manufacturing by using technology that integrates AI, robots, sensors, etc. to cooperate with humans to solve the problems of declining birthrate and aging population, which is one of the social issues in Japan. Our research works aim to establish technologies for the optimization of labor input resources, improvement of employee quality of work, creation of new customer value, inheritance of skills, and sophistication in anticipation of changes in the industrial structure. In the talk, our research activities will be presented, including the factory environment in the manufacturing field and the retail store environment in the logistics field, and are promoting industry-academia-government collaboration activities through collaborative research and consortium partnerships. In addition, we will introduce the newest research site in Hokuriku, which opened last year.
Biography He is a guest researcher at University Strathclyde (2018), the University of Tokyo (2020-present), and Technical University Berlin (2023-present). He was served as assistant director, Industrial Machinery Division, Manufacturing Industries Bureau, the Ministry of Economy, Trade and Industry (METI), between 2016 and 2017. He is an associate fellow of CIRP, the International Academy for Production Engineering, a member of the Japan Society for Precision Engineering (JSPE), a member of Japan Society of Mechanical Engineers (JSME). Dr. Hitoshi Komoto received B.Sc. and Dipl-.Ing in Universitaet Karlsruhe (currently known as Karlsruhe Institute of Technology), Germany, in 2003 and 2004, respectively, and received Dr. in Delft University of Technology, the Netherlands, in 2009. |
The digital twin brings the machine to the officeMr. Dongyul Ahn |
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In the office, the Digital Twin allows you to leverage the actual kinematics, parameters, and functions of your machine during part design, program creation, and simulation. On the shop floor, you benefit from reduced setup and simulation times, less program debugging, greater process reliability, and higher productivity. Beyond assisting in the creation of a verified NC program, the Digital Twin also provides support for quoting, job planning, and expert-level TNC training.
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Additive Manufacturing as the enabler of digital transformation in sustainable productionMr. Brian Moon |
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Nowadays, manufacturers encounter many challenges like enhancing OEE (Overall Equipment Efficiency), responding supply chain issues, rising cost of energy, geospatial risk and ESG related regulations/requirements. To overcome these challenges, manufacturers take a journey of digital transformation. However, digital transformation in production side (especially, factory floor) is still focusing on IT/OT integration by adding sensors to old machines or adopting new IoT ready subtractive manufacturing equipment. Additive Manufacturing can be the enabler of digital transformation in production, which provide sustainability to a factory floor. In this presentation, I will introduce the latest status of Additive Manufacturing, its sustainability and near future outlook on autonomous digital manufacturing. Biography |
AI-based autonomous Factory solutions & Use CaseMr. Jason Park (Jung Yoon Park) |
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An intelligent autonomous factory controls the main facilities of the process to optimize real-time production plants and minimize the occurrence of problems by utilizing digital technologies such as automated production facilities, manufacturing AI, and digital twin technologies for unmanned or minimal people. Autonomous factories advance factory facilities and manufacturing systems that produce products by themselves by intelligent systems.
Using various AI models and digital twin technologies for autonomous factories, it optimizes the operation of production facilities for each process and predicts quality problems in advance to prevent defects. Produced products autonomously judge the quality of the final product using inspection AI and, when quality problems arise, deliver them to AI to optimize production conditions in the production process to help prevent problems. Biography |