基于概率神经网络识别的镍基高温合金激光修复设计

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-10-10 DOI:10.1017/dce.2022.31
Freddie Markanday, G. Conduit, B. Conduit, J. Pürstl, K. Christofidou, L. Chechik, G. Baxter, C. Heason, H. Stone
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引用次数: 1

摘要

摘要使用神经网络框架设计了一种新型镍基高温合金,该合金的性能优于IN718,可用于激光吹制粉末定向能沉积修复应用。该框架利用包括不同合金成分的物理和热力学性质的大型数据库来学习成分与性质以及性质与性质的关系。合金成分空间以IN718为基础,但W被额外包括在内,并且与标准IN718相比,限制Al和Co含量被允许增加,从而使合金接近ATI 718Plus®(718Plus)的成分。确定了具有最高概率满足目标性能的成分,包括相稳定性、凝固应变和拉伸强度。制备了该合金,并对其性能进行了实验研究。测试证实,与标准IN718相比,该合金在添加剂修复应用方面具有优势。
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Design of a Ni-based superalloy for laser repair applications using probabilistic neural network identification
Abstract A neural network framework is used to design a new Ni-based superalloy that surpasses the performance of IN718 for laser-blown-powder directed-energy-deposition repair applications. The framework utilized a large database comprising physical and thermodynamic properties for different alloy compositions to learn both composition to property and also property to property relationships. The alloy composition space was based on IN718, although, W was additionally included and the limiting Al and Co content were allowed to increase compared standard IN718, thereby allowing the alloy to approach the composition of ATI 718Plus® (718Plus). The composition with the highest probability of satisfying target properties including phase stability, solidification strain, and tensile strength was identified. The alloy was fabricated, and the properties were experimentally investigated. The testing confirms that this alloy offers advantages for additive repair applications over standard IN718.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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