Predictive Modelling of Critical Variables for Improving HVOF Coating using Gamma Regression Models

TL;DR

This paper introduces a gamma regression-based framework for modeling and predicting key variables in thermal spray coating processes, aiming to optimize coating quality and efficiency.

Contribution

It presents a novel application of gamma regression models combined with design of experiments for process prediction and optimization in thermal spray coating.

Findings

Accurately predicts critical coating variables

Demonstrates effectiveness of gamma regression models

Supports process optimization in industry

Abstract

Thermal spray coating is a critical process in many industries, involving the application of coatings to surfaces to enhance their functionality. This paper proposes a framework for modelling and predicting critical target variables in thermal spray coating processes, based on the application of statistical design of experiments (DoE) and the modelling of the data using generalized linear models (GLMs) with a particular emphasis on gamma regression. Experimental data obtained from thermal spray coating trials are used to validate the presented approach, demonstrating that it is able to accurately model and predict critical target variables. As such, the framework has significant potential for the optimization of thermal spray coating processes, and can contribute to the development of more efficient and effective coating technologies in various industries.