Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg

IF 7.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Erfan Maleki , Sara Bagherifard , Okan Unal , Mario Guagliano
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引用次数: 0

Abstract

This study introduces a Hybrid Intelligence approach to investigate the Process-Structure-Property-Performance (PSSP) relationship in additively manufactured (AM) materials, specifically focusing on V-notched laser powder bed fused (L-PBF) AlSi10Mg specimens. The Humen Intelligence (HI) component managed the design, manufacturing processes, post-processing, structural characterization, mechanical testing, and data collection. In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. This integration of HI and AI enabled a more thorough exploration of these relationships; data collected from our previous research were complemented with new experiments conducted to assess the effects of various heat treatments (HTs) and surface post-treatments (SPTs) on the fatigue behavior of the specimens. The techniques applied included stress relief (SR), T6 thermal treatments, sand blasting (SB), shot peening (SP), severe vibratory peening (SVP), laser shock peening (LSP), tumble finishing (TF), abrasive flow machining (AFM), chemical polishing (CP), electrochemical polishing (ECP), and chemical milling (CM), along with their combinations. A total of 54 different post-processing techniques were examined in this study. The experimental data, covering surface texture, microstructure, porosity, hardness, and residual stress, were used to develop an ML model that analyzed the fatigue behavior of the specimens. This approach represents a significant advancement toward integrated mechanistic and data-driven materials engineering, offering valuable insights for optimizing fatigue performance in practical applications.

Abstract Image

用混合智能方法研究后处理对缺口快速成型 AlSi10Mg 机械性能的影响
本研究介绍了一种混合智能方法,用于研究增材制造(AM)材料的工艺-结构-性能(PSSP)关系,特别侧重于 V 型缺口激光粉末床熔融(L-PBF)AlSi10Mg 试样。虎门智能(HI)组件负责管理设计、制造工艺、后处理、结构表征、机械测试和数据收集。与此同时,人工智能(AI)利用先进的机器学习(ML)算法,执行与预测、敏感性分析和参数分析相关的任务。人工智能识别模式并开发预测模型,从而更深入地了解工艺参数如何影响材料特性和性能。HI 和人工智能的整合使我们能够更深入地探索这些关系;我们从以前的研究中收集的数据得到了新实验的补充,以评估各种热处理 (HT) 和表面后处理 (SPT) 对试样疲劳行为的影响。应用的技术包括应力消除 (SR)、T6 热处理、喷砂 (SB)、喷丸强化 (SP)、剧烈振动强化 (SVP)、激光冲击强化 (LSP)、滚筒精加工 (TF)、磨料流加工 (AFM)、化学抛光 (CP)、电化学抛光 (ECP) 和化学铣削 (CM) 及其组合。本研究共考察了 54 种不同的后处理技术。实验数据包括表面纹理、微观结构、孔隙率、硬度和残余应力,用于开发分析试样疲劳行为的 ML 模型。这种方法代表了在综合机械和数据驱动材料工程方面取得的重大进展,为优化实际应用中的疲劳性能提供了宝贵的见解。
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来源期刊
Materials & Design
Materials & Design Engineering-Mechanical Engineering
CiteScore
14.30
自引率
7.10%
发文量
1028
审稿时长
85 days
期刊介绍: Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry. The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.
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