Prediction of Layer-wise Porosity in High-temperature Alloy SLM Parts Based on Process Parameters and CT Images

ZHOU Shengzhi; TU Xianmeng; JI Xiaoyuan; ZHANG Zejun; CHEN Wei; WANG Hao; SHI Hao; WANG Zeming; XIE Yujiang; ZHOU Jianxin

doi:10.3969/j.issn.1674-6457.2026.10.019

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (10) : 187-198. DOI: 10.3969/j.issn.1674-6457.2026.10.019

Superalloy Forming

Prediction of Layer-wise Porosity in High-temperature Alloy SLM Parts Based on Process Parameters and CT Images

ZHOU Shengzhi¹, TU Xianmeng¹, JI Xiaoyuan^1,*, ZHANG Zejun¹, CHEN Wei¹, WANG Hao¹, SHI Hao¹, WANG Zeming^1,2, XIE Yujiang², ZHOU Jianxin¹

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Abstract

The work aims to establish a layer-wise porosity prediction model which can provide decision support for defect pre-judgment before SLM forming and process parameter optimization by predicting the porosity of single layers. Firstly, GH4169 superalloy was used as the material to design orthogonal experiments and fabricate SLM specimens. Partial specimens were scanned by CT to obtain internal layer-wise images, and a BlackHat-Otsu algorithm targeting measured relative density was proposed to accurately extract the porosity features of each layer from the CT images. Secondly, based on process parameters, a one-dimensional feature vector reflecting the state of a single layer and a two-dimensional feature matrix integrating the process information of the current layer, upper and lower layers were constructed. Finally, a Support Vector Regression (SVR) model and a Backpropagation (BP) Neural Network model were established for the one-dimensional and two-dimensional features respectively, and a Convolutional Neural Network (CNN) model fusing the two types of features was designed. The error between the layer-wise porosity extracted by the proposed BlackHat-Otsu algorithm and the overall measured value was less than 0.5%. Among the three prediction models, the SVR model achieved an optimal R² of 0.840 5 on the test set. After hyperparameter optimization, the R² of the BP Neural Network model increased to 0.883 1 and the dual-channel CNN model reached a test set R² of 0.904 5. The results indicated that the two-dimensional features constructed by introducing inter-layer process parameter interaction information could effectively improve the model's prediction capability. By fusing process parameter data and CT image data, and utilizing deep learning models capable of capturing inter-layer spatiotemporal correlations, higher-precision prediction of layer-wise porosity in SLM parts can be achieved. This method refines porosity prediction from the overall component to the single-layer scale, providing an effective technical approach for realizing refined and proactive control of SLM forming quality.

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selective laser melting (SLM) / GH4169 / process parameters / CT image / porosity / layer-wise prediction

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ZHOU Shengzhi, TU Xianmeng, JI Xiaoyuan, ZHANG Zejun, CHEN Wei, WANG Hao, SHI Hao, WANG Zeming, XIE Yujiang, ZHOU Jianxin. Prediction of Layer-wise Porosity in High-temperature Alloy SLM Parts Based on Process Parameters and CT Images[J]. Journal of Netshape Forming Engineering. 2026, 18(10): 187-198 https://doi.org/10.3969/j.issn.1674-6457.2026.10.019

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