Creating Forms of Manufacturing for the Next Generation

Achieving carbon neutrality by 2050 as a solution to the climate change has become an urgent global issue, so the Toyota Group has been undertaking this challenge to eliminate all CO₂ emissions at its factories by 2035. On the other hand, looking at Japan, the country is experiencing both a decline in its working-age population and an increase in the number of workers over the age of 65. Against this backdrop, manufacturing sites are demanding a high degree of harmony between carbon neutrality and productivity improvement is required in the manufacturing fields.

With aims of enabling rapid and repeatable evaluation and analyses of facility design and production planning, we apply the concept of digital twins, a method for digitalizing real-world information and reproducing and simulating these in a virtual space. At the same time, we aim to realize a process that enables flexible design without the use of any prototyping by constructing machine learning models that can incorporate the intuition and the knowledge of skilled workers. Furthermore, by converting entire factories, including their workers and production facilities, into digital twins, we work to realize smart factories that can respond flexibly to the multitude of factors behind energy and production fluctuations. Similarly, we advance initiatives intended to pursue significantly efficiency in factory design, operations, and improvements.

Key Themes

Constructing human-friendly workspaces by integrating the knowledge of production sites

Based on human motion sensing in individual factory processes, we work with technologies that quantify workloads and difficulty levels using human models that simulate muscular, skeletal, and other types of movement. Using this approach, we aim to establish technologies intended to optimize the division of cooperative labor between humans and robots and that optimize workspaces to reduce the burden on workers. We will support workspace designs and production process configurations that are human-friendly and enhance productivity.

Estimation of body load and difficulty levels using a model that simulates musculoskeletal movement within arbitrary autonomous actions

Developing flexible design and production processes that eliminate the need for any prototyping

In general, prototyping a car body requires a significant amount of time to produce actual prototypes from the design data of new parts. For example, in press molding, changes that occur in a prototype’s shape when it is released from the die depend on the rigidity of the die and the processing condition, meaning multiple prototypes are required to produce the shape exactly as specified by the design data. We are therefore developing a molding technology that eliminates the need for any prototypes by using machine learning to predict the shape changes during the molding process, and by constructing a system that can adjust the molding conditions incrementally while monitoring the variance between the design data and the prototype.

Molding system for eliminating the need for prototyping

Unraveling microscopic phenomena to construct innovative battery manufacturing processes

The performance of lithium-ion batteries and fuel cells, which are core components of electric vehicles, depends significantly on the quality of the electrode film, including its internal structure and uniformity. Yet establishing high-quality film-forming technologies is not easy. We are applying the slurry and powder analytical and control technologies that we have developed over the years, together with performance evaluation technologies based on these, to construct innovative battery manufacturing technologies that can form films without solvents.

Concept of a solvent-free filmforming process

Assembling entire factories in digital space

With the rapid adoption of DX and IT in manufacturing worldwide, there has been a demand for autonomation and labor savings that address new production methods to improve production efficiency and energy saving throughout the factories.
Our goal is to realize a digital twin system that enables the design and operation of a smart factory that can optimize the entire factory in a virtual space, including the planning of factory equipment and autonomous mobile robot operations based on production plans within the factory and predictions of renewable energy supply and demand fluctuations.

Autonomous mobile robot and racks positioning/path optimization

Elemental Technologies

Biomedical Engineering, Mechanics and Mechatronics, Intelligent Robotics, Behavior and Environment Recognition, Intelligent System, Design Engineering, Measurement Engineering, Manufacturing and Production Engineering, Material Processing and Microstructure Control, Powder Engineering