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Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

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A practical guide to optimizing industrial thermal spraying through comparative multi-objective optimization.

Wolfgang Rannetbauer¹, Simon Hubmer², Carina Hambrock¹

¹voestalpine Stahl GmbH, voestalpine-Straße 3, A-4020 Linz, Austria.

Journal of Mathematics in Industry

|August 11, 2025

Summary

This study optimizes high-velocity oxygen fuel (HVOF) thermal spraying using multi-objective optimization algorithms. It balances coating quality and cost-efficiency, validating theoretical solutions with practical trials for industrial application.

Keywords:

Gradient descent Industrial applications Multi-objective optimization NSGA-II Optimization theory Pareto front Surface technology Thermal spray coating

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Area of Science:

Materials Science and Engineering
Manufacturing Process Optimization
Surface Engineering

Background:

Manufacturing and maintenance face conflicting goals of high quality and cost-efficiency.
Multi-objective optimization problems arise from balancing these competing objectives.
Accurate modeling of complex physical systems is crucial for optimization algorithm success.

Purpose of the Study:

To apply and evaluate three multi-objective optimization algorithms for high-velocity oxygen fuel (HVOF) thermal spraying.
To determine optimal process parameters that enhance coating performance and maintain process efficiency.
To assess the industrial feasibility and practical applicability of these optimization algorithms.

Main Methods:

Implementation of three distinct multi-objective optimization algorithms.
Mathematical modeling of the HVOF thermal spraying process.
Systematic evaluation of algorithm-generated parameters against quality and cost metrics.
Conducting practical validation trials to confirm theoretical results.

Main Results:

Identification of Pareto-optimal solutions for HVOF thermal spraying parameters.
Demonstration of algorithms' capability to enhance coating performance.
Verification of theoretical optimization outcomes through real-world industrial trials.
Assessment of practical constraints and industrial feasibility of proposed solutions.

Conclusions:

The study provides insights into the practical applicability of diverse optimization algorithms in the coating industry.
Findings guide researchers and practitioners in improving process efficiency and product quality.
Validated optimization strategies can lead to better decision-making in thermal spray processes.