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Siemens & Hirano innovate battery machine with digital twin

Siemens & Hirano innovate battery machine with digital twin

Techday NZ03-06-2025
Siemens and Hirano Tecseed are collaborating to enhance battery machine manufacturing by introducing digitalisation and simulation-based optimisation.
The partnership involves Hirano Tecseed, a Japan-based manufacturer of battery coating machines, standardising and digitalising its systems with support from Siemens' automation and simulation software suite, including SimcenterTM applications available through the Siemens Xcelerator digital platform.
The relationship centres on optimising the coating processes for battery electrodes, a crucial stage in battery production that involves applying several active material layers to foil strips. Defects at this stage can undermine both performance and lifespan of finished batteries. Additionally, precise speed and tension controls are seen as necessary in achieving stable and high-quality results.
The drive to improve the manufacturing process comes as global demand for batteries rises, particularly with growth in electric vehicles and renewable energy storage. Companies such as Hirano Tecseed are responding with efforts to boost production rates and reduce manufacturing costs, while ensuring safety and quality standards are maintained.
A central element in the Siemens-Hirano partnership is the use of a digital twin for manufacturing equipment. Employing Siemens' SimcenterTM software, Hirano has developed and virtually tested a model of its battery coating machines. This digital twin leverages live operational data from Siemens' Simatic controller and Sinamics drives, enabling accurate simulation for performance assessment ahead of actual production.
Engineers at Hirano utilise SimcenterTM AmesimTM for systems simulation, helping the company optimise machine designs and throughput in a virtual space. The software tools allow design engineers to evaluate and fine-tune web handling and other key aspects, aiming for increased operational efficiency ahead of physical implementation.
Historically, machine parameters at Hirano were determined by in-person experience, an approach that could at times result in defective output due to less precise settings. With the adoption of digital twin technology, the company is now able to finalise and validate configurations before building physical prototypes, which reduces the risk of faults and minimises the number of test units required during development.
According to Hirano, digital simulations now allow engineers to optimise machine settings about five times faster than would typically be possible through physical trials. Furthermore, the company reports that as much as 80% of simulations and virtual commissioning processes utilise pre-existing standard modules, simplifying both development and cost structures, while enabling more rapid commissioning of new equipment.
Katsuhiro Omori, Director and Executive Officer of R&D at Hirano Tecseed, stated, "We're thrilled to collaborate with Siemens to enhance our battery manufacturing processes. Integrating mechatronics and automation simulation systems into a unified modeling environment has significantly helped Hirano identify potential product defects and advance our machine process optimisation in a virtual setting."
Michael Thomas, Senior Vice President of Factory Automation and Head of Production Machines at Siemens, commented, "By leveraging our advanced simulation and automation technologies, Siemens is enabling Hirano to achieve unprecedented levels of efficiency and quality in battery manufacturing. This partnership exemplifies our commitment to driving innovation and supporting our customers in their digital transformation journey."
The partnership is expected to result in shorter implementation and commissioning periods, enhanced product quality, and reductions in waste material. According to the companies, the increased ability to simulate and optimise machinery digitally prior to deployment also translates into resource savings and a lower likelihood of production issues downstream.
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