Introducing cycle life prediction using tools that enable workflow visualization!
This document reports on the prediction of the cycle life of Li-ion batteries using machine learning with Pipeline Pilot. We will introduce the sections: "Background," "Flow," "Results," and "Discussion." Pipeline Pilot is a tool that enables large-scale data processing and analysis, automation of tasks, and visualization of workflows. 【Contents】 ■ Background: (1) Li-ion batteries and their cycle life prediction, (2) What is machine learning ■ Flow: (1) Machine learning in Pipeline Pilot ■ Results: (1) A part of the Pipeline Pilot operation screen, (2) Output screen (3) Comparison of output screens ■ Discussion *For more details, please refer to the PDF document or feel free to contact us.
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Our company develops and sells a "Maintenance Management System" for managing and operating various plants, factories, and other facilities and assets. Currently, this system is undergoing significant evolution into a system that incorporates IoT technologies, such as sensor information and input from tablet devices, as well as AI technologies like machine learning, featuring functions for failure prediction and automatic scheduling. Additionally, as part of the recent trend of digital transformation (DX), there is a growing movement to digitize and automate manufacturing processes and research and development sites in factories to improve operational efficiency. In line with this trend, our company provides a solution aimed at enhancing efficiency in research and development environments, which is the Laboratory Information Management System (LIMS). This software includes features such as workflow management, data tracking, data management, data analysis, and integration of electronic lab notebooks.
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