Abstract:This work aims to study the effect of Widmanstätten h phase on tensile shear strength (TSS) of resistance spot welding joints (RSW) of A286 superalloy subjected to post-weld high temperature aging treatment. The tensile shear test specimens were welded in the solution treated condition, and then subjected to six different post-weld aging treatments (at an aging temperature of 840°C for six different aging times). The ascast dendritic microstructure of the weld nugget and the austenite equiaxed-grain microstruc… Show more
“…In addition, these alloys contain nominally 15 wt.% Cr, 25 wt.% Ni and and also a low quantity of different alloying additives [6]. It is reinforced by way of the spherical ordered face-centered cubic (FCC) g¢ Ni 3 (Al,Ti) precipitates [8,11]. It can be applied heat treatment on this superalloy simply.…”
In this study, Incoloy A286 superalloy were boronized successfully by powder-pack boronizing process at 850 °C, 900 °C and 950 °C for 4 h by using a boronizing powder mixture containing H3BO3 as boron source. The thickness and morphology of the boride layer was identified by microstructural examinations. The boride layer with complex, compact and smooth morphology was formed on the surface of the samples. As a result of XRD analyses, it was determined that the compact boride layer was formed many phases such as FeB, Fe2B, Fe3B, CrB and Ni4B3 etc. It was specified that the average hardness value of the boride layer was approximately between 2400 and 3000 HV by microhardness tests. Also the graphs of friction coefficient and values of the specific wear rate were obtained by performing ball on disk wear tests. It was identified that the specific wear rate of boronized samples was approximately 9.5 times lower than that of unboronized samples.
“…In addition, these alloys contain nominally 15 wt.% Cr, 25 wt.% Ni and and also a low quantity of different alloying additives [6]. It is reinforced by way of the spherical ordered face-centered cubic (FCC) g¢ Ni 3 (Al,Ti) precipitates [8,11]. It can be applied heat treatment on this superalloy simply.…”
In this study, Incoloy A286 superalloy were boronized successfully by powder-pack boronizing process at 850 °C, 900 °C and 950 °C for 4 h by using a boronizing powder mixture containing H3BO3 as boron source. The thickness and morphology of the boride layer was identified by microstructural examinations. The boride layer with complex, compact and smooth morphology was formed on the surface of the samples. As a result of XRD analyses, it was determined that the compact boride layer was formed many phases such as FeB, Fe2B, Fe3B, CrB and Ni4B3 etc. It was specified that the average hardness value of the boride layer was approximately between 2400 and 3000 HV by microhardness tests. Also the graphs of friction coefficient and values of the specific wear rate were obtained by performing ball on disk wear tests. It was identified that the specific wear rate of boronized samples was approximately 9.5 times lower than that of unboronized samples.
Resistance spot welding (RSW) is one of the most relevant industrial processes in different sectors. Key issues in RSW are process control and ex-ante and ex-post evaluation of the quality level of RSW joints. Multiple-input–single-output methods are commonly used to create predictive models of the process from the welding parameters. However, until now, the choice of a particular model has typically involved a tradeoff between accuracy and interpretability. In this work, such dichotomy is overcome by using the explainable boosting machine algorithm, which obtains accuracy levels in both classification and prediction of the welded joint tensile shear load bearing capacity statistically as good or even better than the best algorithms in the literature, while maintaining high levels of interpretability. These characteristics allow (i) a simple diagnosis of the overall behavior of the process, and, for each individual prediction, (ii) the attribution to each of the control variables—and/or to their potential interactions—of the result obtained. These distinctive characteristics have important implications for the optimization and control of welding processes, establishing the explainable boosting machine as one of the reference algorithms for their modeling.
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