Cuiyuan Lu and Jing Shi
The quality and properties of Inconel 718 (IN718) from selective laser melting (SLM), a major additive manufacturing (AM) process, have been studied extensively. Among all aspects…
Abstract
Purpose
The quality and properties of Inconel 718 (IN718) from selective laser melting (SLM), a major additive manufacturing (AM) process, have been studied extensively. Among all aspects of quality, relative density (RD) is most widely investigated, and it significantly affects the mechanical properties of SLM-ed materials. This study aims to develop robust RD prediction models based on the data accumulated in literature using machining learning approaches.
Design/methodology/approach
By mining the literature of SLM-ed IN718, a comprehensive data set is created, which consists of the four major process parameters of laser power, scan speed, hatch spacing, layer thickness and RD data. A back propagation neural network (BPNN) model, along with its two optimized models: genetic algorithm (GA) optimized BPNN (GA-BPNN) and adaptive GA optimized BPNN (AGA-BPNN) models are created for predicting the RD of SLM-ed IN718, and their prediction performances are compared.
Findings
Overall, satisfactory prediction accuracies are obtained – the R2 values of the built BPNN, GA-BPNN and AGA-BPNN models are 73.5%, 75.3% and 79.9%, respectively. This also shows that by incorporating the optimization technique, the prediction accuracy of BPNN is improved and AGA-BPNN has the highest accuracy. Moreover, SLM experiments are conducted to test the model predictability. It is found that the predictions generally agree well with the experiment data, and the order of the model prediction accuracies is consistent with that based on the literature data.
Originality/value
This research highlights that by mining literature data, prediction models of RD of SLM-ed IN718 can be obtained with satisfactory performance, which consider more process parameters and cover wider parameter ranges than any individual studies, in a cost-effective manner.
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Abstract
Details
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Fupeng Cheng, Jinglong Cui, Shuai Xu, Song Li, Pengchao Zhang and Juncai Sun
The purpose of this study is to improve the performance of AISI 430 stainless steel (430 SS) in increasing its oxidation resistance, suppressing coating spalling and cracking…
Abstract
Purpose
The purpose of this study is to improve the performance of AISI 430 stainless steel (430 SS) in increasing its oxidation resistance, suppressing coating spalling and cracking, sustaining appropriate conductivity and blocking Cr evaporation as an interconnect material for intermediate temperature solid oxide fuel cells; a protective co-contained coating is formed onto stainless steel via the surface alloying process and followed by thermal oxidation.
Design/methodology/approach
In this work, oxidation behavior of coated specimen is studied during isothermal and cyclic oxidation measurements. Moreover, the conductivity is also investigated by area specific resistance (ASR) measurement.
Findings
Co-contained spinel layer shows an outstanding performance in preventing oxidation and improving conductivity compared with uncoated specimens. The protective spinel coating also reduces the ASR for coated specimen (0.0576O cm2) as compared to the uncoated specimen (1.87296O cm2) after isothermal oxidation.
Originality/value
The probable mechanism of co-contained alloy converting into spinel and the spinel transfer electron is presented.