Leveraging Real-Time Data Analysis and Multiple Kernel Learning for Manufacturing of Innovative Steels

TL;DR

This paper introduces a real-time data analytics framework using multiple kernel learning to improve quality prediction and process control in steel manufacturing with thermally sprayed components.

Contribution

It presents an integrated approach combining real-time data aggregation and multiple kernel learning for predictive quality management in steel manufacturing.

Findings

Accurate coating quality prediction using data-driven methods.

Proactive deviation detection enables timely process adjustments.

Validated with small-scale tests demonstrating effectiveness.

Abstract

The implementation of thermally sprayed components in steel manufacturing presents challenges for production and plant maintenance. While enhancing performance through specialized surface properties, these components may encounter difficulties in meeting modified requirements due to standardization in the refurbishment process. This article proposes updating the established coating process for thermally spray coated components for steel manufacturing (TCCSM) by integrating real-time data analytics and predictive quality management. Two essential components--the data aggregator and the quality predictor--are designed through continuous process monitoring and the application of data-driven methodologies to meet the dynamic demands of the evolving steel landscape. The quality predictor is powered by the simple and effective multiple kernel learning strategy with the goal of realizing predictive quality. The data aggregator, designed with sensors, flow meters, and intelligent data processing for the thermal spray coating process, is proposed to facilitate real-time analytics. The performance of this combination was verified using small-scale tests that enabled not only the accurate prediction of coating quality based on the collected data but also proactive notification to the operator as soon as significant deviations are identified.