Interactive Visual Data Analysis for Quality Optimization in Aluminum Direct Chill Casting

Authors:
Open Access
Publisher website
Google Scholar
Abstract
In view of the increasing digitalization of industry, data science and data visualization are assuming an increasingly pivotal role in numerous industry sectors, facilitating enhanced monitoring and analysis of production processes. This development is primarily driven by growing demands for product quality and sustainability. In a multitude of industrial contexts, large volumes of high-resolution sensor data are continuously recorded throughout production. To make this huge amount of data usable for technologists, an interactive visual analytics tool for the analysis of production data has been developed. The tool enables users to explore complex process data, identify relevant patterns, and derive data-driven insights for quality optimization. This, in turn, leads to reduced scrap rates, as well as improved plant utilization and delivery performance. The present paper demonstrates the effectiveness of the proposed approach using a selected industrial use case and highlights key insights gained from its application in practice.

This publication has 12 references indexed in Scilit: