Grey modeling for thermal spray processing parameter analysis

Abstract

PurposeThis paper presents the application of grey modeling for thermal spray processing parameter analysis in less data environment.Design/methodology/approachBased 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.FindingsModel 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/implicationsThe 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 implicationsAccording 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/valueIt 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.

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

“…The normalised predictive results are shown in Figure 2a with the resulting Mean Absolute Percent Error (MAPE) of 1.230% and Maximum Absolute Percent Error (MXAPE) of 5.748%. These predictive performances were comparable to another study of data-efficient machine learning modelling in manufacturing, e.g., MAPE of 5.483% [32]. Figure 2b shows the developed DANN model's training process, confirming that the model was free of overfitting and underfitting and achieved the best performance at 445 epochs (or training iterations).…”

“…Our results demonstrated the potential of a data-driven modelling approach for better prediction accuracy than a mathematical counterpart, i.e., the Gaussian function model. However, the limitation of a data-driven modelling approach was also observed in our previous study [31], namely, the necessity of a large amount of process training data to achieve a high prediction accuracy, which has also been identified recently in relevant manufacturing studies [32,33]. This data scarcity issue is associated with high experimental costs and the lack of an automated measurement system in HPRAM.…”

“…This data scarcity issue is associated with high experimental costs and the lack of an automated measurement system in HPRAM. Liu et al applied a grey modelling technique for the first time in a thermal spray process in an attempt to overcome the issue, harnessing both mathematical (or white box) and datadriven (or black-box) modelling approaches [32]. Despite the reasonable prediction accuracy achieved in this study, the authors concluded that more complex and nonlinear phenomena existed and suggested further exploration of data-efficient modelling approaches to improve prediction accuracy.…”

“…Therefore, this study focuses on the prediction of a single-track profile in CSAM, at both normal and off-normal spray angles, using a data-efficient ANN (DANN) approach to demonstrate that data-driven modelling can achieve better prediction accuracy than its mathematical counterpart that has already been adopted in CSAM. Inspired by the study by Liu et al [32], we leverage a mathematical function model as domain knowledge or the existing model at hand into the development of a DANN model. Specifically, a Gaussian function model, the model adopted elsewhere in CSAM studies, is selected with its coefficients computed by a quadratic regression model as applied in [22].…”

Grey Relational Entropy Calculation and Fractional Prediction of Water and Economy in the Beijing-Tianjin-Hebei Region

“…In the above-mentioned research, scholars have made various explorations and achieved fruitful results. In response to problems in different fields, scholars have made improvements on the basis of traditional grey relational analysis methods to make the calculation results more scientific and reasonable [16]. Shen et al applied improved grey relational analysis to the Danjiangkou basin surface water environmental quality assessment study, considering the interval form of the water quality assessment standard, which is more objective than judging water quality categories based on critical values.…”