A must-see for companies that want to develop new products while minimizing setbacks through a trial-and-error development process!
We would like to introduce a case study of a customer in the elastomer seal product industry who has implemented "Polymerize." 【Customer Company Overview】 ■ Number of Employees: 100-1000 ■ Main Products: Seal Manufacturing (Elastomers) ■ Location: Japan, USA 【Challenges】 In recent years, there has been a strong trend for seal products to be used in various harsh environments. From high-temperature and high-pressure steam and acidic gas environments in the oil refining process to plasma processes in the semiconductor industry, the demands for mechanical performance of seal products have increased. The performance of the product is greatly influenced by the component ratios and formulations during compounding. In this case study, the customer aimed to maximize the performance of elastomer seals at high temperatures while maintaining elongation at the break point and minimizing compression set. After a standard three-month trial, the customer experimentally introduced Polymerize. *For more details on solving challenges, please download the catalog or feel free to contact us.
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Our company provides a materials informatics platform, PolymerizeLabs, and consulting services for the chemical and materials industries. PolymerizeLabs reflects the unique R&D processes and expertise specific to materials development, enabling seamless data management and AI utilization without the need for programming knowledge. It is the only all-in-one materials informatics platform in the industry that ensures AI prediction accuracy even with limited or sparse data. Based on a data management infrastructure specialized in organizing various materials development data and a highly flexible AI engine equipped with a diverse range of machine learning algorithms, we offer data-driven development processes across various materials fields. We contribute to addressing resource shortages, high cost structures, compliance with environmental regulations, alleviating supply chain bottlenecks, and responding quickly to market changes faced by all R&D departments, thereby enhancing corporate competitiveness and establishing a new standard for R&D processes in the AI era.
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