Sensitivity of the Impact Toughness and Microstructure of 15CrNi3MoV Steel Under Different Quenching Rates

The sensitivities of the mechanical properties and microstructure of 15CrNi3MoV alloy steel under different quenching rates were investigated in the present study.After subjection to quenching with four different cooling rates(water cooling,forced air cooling,static air cooling and furnace cooling)followed by tempering,the microstructure was characterized by scanning electron microscopy(SEM),electron back-scattered diffraction(EBSD),and transmission electron microscopy(TEM);and the low-temperature(−20°C)impact toughness was evaluated.The results showed that the tempered microstructure and mechanical properties had high sensitivity to the quenching rate.With a decrease in the quenching rate,the low-temperature impact energy of tempered specimens decreased with increasing fluctuation.Correspondingly,the fracture morphology changed from completely ductile to brittle.In addition,as the quenching cooling rate decreased,the as-quenched matrix changed from a lathy to a polygonal structure with the presence of carbides and martensite-austenite(M-A)constituents,and the effective grain size increased.Tempered martensite with dispersed fine carbides was found in the tempered water cooling specimen,and tempered bainite with a polygonal structure containing large carbides and rare incomplete undecomposed M-A constituents was found in the tempered forced air cooling,static air cooling and furnace cooling specimens.The small effective grain size and fine carbides contributed to the good temperature impact toughness of the tempered water cooling specimens.

Cognition:Accurate and Consistent Linear Log Parsing Using Template Correction

Logs contain runtime information for both systems and users.As many of them use natural language,a typical log-based analysis needs to parse logs into the structured format first.Existing parsing approaches often take two steps.The first step is to find similar words(tokens)or sentences.Second,parsers extract log templates by replacing different tokens with variable placeholders.However,we observe that most parsers concentrate on precisely grouping similar tokens or logs.But they do not have a well-designed template extraction process,which leads to inconsistent accuracy on particular datasets.The root cause is the ambiguous definition of variable placeholders and similar templates.The consequences include abuse of variable placeholders,incorrectly divided templates,and an excessive number of templates over time.In this paper,we propose our online log parsing approach Cognition.It redefines variable placeholders via a strict lower bound to avoid ambiguity first.Then,it applies our template correction technique to merge and absorb similar templates.It eliminates the interference of commonly used parameters and thus isolates template quantity.Evaluation through 16 public datasets shows that Cognition has better accuracy and consistency than the state-of-the-art approaches.It also saves up to 52.1%of time cost on average than the others.