Information-rich quality controls prediction model based on non-destructive analysis for porosity determination of AISI H13 produced by electron beam melting

Article; Early Access

关键词: DATA ENVELOPMENT ANALYSIS;    METAL;    LASER;    FABRICATION;    PARAMETERS;    DENSITY;    INSERTS;   

DOI  :  10.1007/s00170-023-11153-2

来源: SCIE

【 摘 要 】

The number of materials processed via additive manufacturing (AM) technologies has rapidly increased over the past decade. As of these emerging technologies, electron beam powder bed fusion (EB-PBF) process is becoming an enabling technology to manufacture complex-shaped components made of thermal-cracking sensitive materials, such as AISI H13 hot-work tool steel. In this process, a proper combination of process parameters should be employed to produce dense parts. Therefore, one of the first steps in the EB-PBF part production is to perform the process parameter optimization procedure. However, the conventional procedure that includes the image analysis of the cross-section of several as-built samples is time-consuming and costly. Hence, a new model is introduced in this work to find the best combination of EB-PBF process parameters concisely and cost-effectively. A correlation between the surface topography, the internal porosity, and the process parameters is established. The correlation between the internal porosity and the melting process parameters has been described by a high robust model (R-adj(2) = 0.91) as well as the correlation of topography parameters and melting process parameters (R-adj(2) = 0.77-0.96). Finally, a robust and information-rich prediction model for evaluating the internal porosity is proposed (R-adj(2) = 0.95) based on in situ surface topography characterization and process parameters. The information-rich prediction model allows obtaining more robust and representative model, yielding an improvement of about 4% with respect to the process parameter-based model. The model is experimentally validated showing adequate performances, with a RMSE of 2% on the predicted porosity. This result can support process and quality control designers in optimizing resource usage towards zero-defect manufacturing by reducing scraps and waste from destructive quality controls and reworks.

【 授权许可】

Free