A γ-TiAI intermetallic alloy, Ti-45Al-2Nb-2Mn （at.%）-0.8 vol.%TiB2, has been processed from gas atomized praalloyed powder by field assisted hot pressing （FAHP）. An initial analysis of the prealloyed powder helpe...A γ-TiAI intermetallic alloy, Ti-45Al-2Nb-2Mn （at.%）-0.8 vol.%TiB2, has been processed from gas atomized praalloyed powder by field assisted hot pressing （FAHP）. An initial analysis of the prealloyed powder helped on the understanding of the intermetallic sintering behavior. Atomized powder consisted of α metastable phase that transformed into α2＋γ equilibrium phases by thermal treating. Different powder particle microstructures were found, which influence the microstructure development of the FAHP T-TiAI material depending on the sintering temperature. Duplex, nearly lamellar and fully lamellar microstructures were obtained at the sintaring temperatures above 1000 ℃. Lower consolidation temperatures, below 1000 ℃, led to the formation of an AI rich phase at powder particle boundaries, which is deleterious to the mechanical properties. High compressive yield strength of 1050 MPa was observed in samples with FAHP duplex microstructures at room temperature. Whereas nearly lamellar and fully lamellar microstructures showed yield strength values of 655 and 626 MPa at room temperature and 440 and 425 MPa at 750 ℃, respectively, which are superior in comparison to similar alloys processed by other techniques. These excellent properties can be explained due to the different volume fractions of the α2 and γ phases and the refinement of the PM microstructures.展开更多
Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bu...Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality,SDP plays a critical role in software development.As a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches,on the other hand,need to be significantly improved in terms of performance.Therefore in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are proposed.The proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository datasets.When compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.展开更多
基金Funding from the Spanish Ministry of Science and Innovation through projects MAT2009-14547-C02-01 and MAT200914547-C02-02The Madrid Regional Government partially supported this project through the ESTRUMAT (Grant No.P2009/MAT-1585)
文摘A γ-TiAI intermetallic alloy, Ti-45Al-2Nb-2Mn （at.%）-0.8 vol.%TiB2, has been processed from gas atomized praalloyed powder by field assisted hot pressing （FAHP）. An initial analysis of the prealloyed powder helped on the understanding of the intermetallic sintering behavior. Atomized powder consisted of α metastable phase that transformed into α2＋γ equilibrium phases by thermal treating. Different powder particle microstructures were found, which influence the microstructure development of the FAHP T-TiAI material depending on the sintering temperature. Duplex, nearly lamellar and fully lamellar microstructures were obtained at the sintaring temperatures above 1000 ℃. Lower consolidation temperatures, below 1000 ℃, led to the formation of an AI rich phase at powder particle boundaries, which is deleterious to the mechanical properties. High compressive yield strength of 1050 MPa was observed in samples with FAHP duplex microstructures at room temperature. Whereas nearly lamellar and fully lamellar microstructures showed yield strength values of 655 and 626 MPa at room temperature and 440 and 425 MPa at 750 ℃, respectively, which are superior in comparison to similar alloys processed by other techniques. These excellent properties can be explained due to the different volume fractions of the α2 and γ phases and the refinement of the PM microstructures.
文摘Software systems have grown significantly and in complexity.As a result of these qualities,preventing software faults is extremely difficult.Software defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance costs.When it comes to lowering software costs and assuring software quality,SDP plays a critical role in software development.As a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more efficiently.Several methods for detecting and addressing such flaws at a low cost have been offered.These approaches,on the other hand,need to be significantly improved in terms of performance.Therefore in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are proposed.The proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative features.To find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository datasets.When compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.
