[Case Study] Machine Learning Case at a Coating Material Manufacturer

basic information

This is a powerful software that aggregates data such as text, images, molecular structures, and databases, enabling effective utilization of various data by leveraging machine learning and statistical analysis. It also has integration features with Python and R, and can efficiently perform Bayesian optimization, which has recently gained attention. *For more details, please contact us.*

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Applications/Examples of results

We have a proven track record in a wide variety of scenes, including material manufacturers, manufacturing industries engaged in material development, and related manufacturers in semiconductors and surface treatment.

Detailed information

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  In "Pipeline Pilot," we achieve a flow that simultaneously performs data shaping and machine learning without coding. "Read data, conduct machine learning, validate, and output reports." It is possible to create this unified flow in an easy-to-understand manner. In this case, the machine learning model created by the workflow was determined to have room for improvement based on the validation results. Therefore, we improved the accuracy of the machine learning model.

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  By using "Pipeline Pilot," it becomes possible to "easily perform machine learning." In this case study, we were able to create a machine learning model and improve its predictive accuracy. Additionally, one of the primary applications of the created learning model is to "grasp the trends of optimal experimental conditions by predicting characteristic values."