Grey modeling for thermal spray processing parameter analysis. Issue 3 (16th March 2020)

• Record Type: Journal Article
• Authors: Liu, Meimei; Zhang, Yicha; Dong, Wenjie; Yu, Zexin; Liu, Sifeng; Gomes, Samuel; Liao, Hanlin; Deng, Sihao
• Is Part Of: Grey systems. Volume 10:Issue 3(2020)
• Page Start: 265
• Page End: 279
• Publication Date: 2020-03-16
• Subjects: Less data environment -- Grey modelling -- Thermal spray technology -- Manufacturing knowledge; Cybernetics -- Periodicals; Systems engineering -- Periodicals; 003.5
• DOI: 10.1108/GS-12-2019-0063
• Languages: English
• ISSNs: 2043-9377

Abstract

Abstract : Purpose: This paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment. Design/methodology/approach: Based on processing knowledge, key processing parameters of thermal spray process are analyzed and preselected. Then, linear and non-linear grey modeling models are integrated to mine the relationships between different processing parameters. Findings: Model A reveals the linear correlation between the HVOF process parameters and the characterization of particle in-flight with average relative errors of 9.230 percent and 5.483 percent for velocity and temperature. Research limitations/implications: The prediction accuracies of coatings properties vary, which means that there exists more complex non-linear relationship between the identified input parameters and coating results, or more unexpected factors (e.g. factors from material side) should be further investigated. Practical implications: According to the modeling case in this paper, method has potential to deal with other diverse modeling problems in different industrial applications where challenge to collecting large quantity of data sets exists. Originality/value: It is the first time to apply grey modeling for thermal spray processing where complicated relationships among processing parameters exist. The modeling results show reasonable results to experiment and existing processing knowledge.