Materials & Design最新文献_第3页

Productive automation of calibration processes for crystal plasticity model parameters via reinforcement learning 通过强化学习实现晶体塑性模型参数校准过程的生产自动化

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113470

Jonghwan Lee , Burcu Tasdemir , Suchandrima Das , Michael Martin , David Knowles , Mahmoud Mostafavi

{"title":"Productive automation of calibration processes for crystal plasticity model parameters via reinforcement learning","authors":"Jonghwan Lee , Burcu Tasdemir , Suchandrima Das , Michael Martin , David Knowles , Mahmoud Mostafavi","doi":"10.1016/j.matdes.2024.113470","DOIUrl":"10.1016/j.matdes.2024.113470","url":null,"abstract":"<div><div>Crystal Plasticity Finite Element (CPFE), which merges crystal plasticity principles with finite element analysis, can simulate the anisotropic grain-level mechanical behaviour of polycrystalline materials. Due to the benefit of CPFE, it has been widely utilised to analyse processes such as manufacturing, damage, and deformation where the microstructure plays a prominent role. However, this method is computationally expensive and requires the robust calibration of its parameters, which can be many. In this work, we propose a framework to address difficulties in calibrating multi-parameter CPFE. The Deep Deterministic Policy Gradient (DDPG) algorithm, a Deep Reinforcement Learning (DRL) approach, is utilised to optimise the CPFE parameters. Additionally, a Python-based environment is developed to fully automate the calibration process. To allow comparison with the conventional optimisation method, the Particle Swarm Optimisation (PSO) algorithm is also used, which shows the DDPG framework yields more accurate calibration. The generalisation performance of the proposed framework is also demonstrated by calibrating each parameter set of two different CPFE models for monotonic loading of the stainless steel type, 316L. Moreover, the effectiveness of the framework in the more complex condition is also demonstrated by calibrating the CPFE parameters for a two-cycle cyclic behaviour of a 316H stainless steel material. The reliability of these calibrated parameters is also validated in the cyclic simulation after two cycles.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113470"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Microfluidic wet spinning of soft polydimethylsiloxane polymer optical fibers 微流体湿法纺制聚二甲基硅氧烷软聚合物光纤

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113466

Khushdeep Sharma , Wuchao Wang , Sebastian Valet , Tina Künniger , Michał Góra , Kongchang Wei , Bernhard Weisse , Lucas Bahin , René M. Rossi , Fabien Sorin , Luciano F. Boesel

{"title":"Microfluidic wet spinning of soft polydimethylsiloxane polymer optical fibers","authors":"Khushdeep Sharma , Wuchao Wang , Sebastian Valet , Tina Künniger , Michał Góra , Kongchang Wei , Bernhard Weisse , Lucas Bahin , René M. Rossi , Fabien Sorin , Luciano F. Boesel","doi":"10.1016/j.matdes.2024.113466","DOIUrl":"10.1016/j.matdes.2024.113466","url":null,"abstract":"<div><div>Polymer optical fibers (POFs) are an essential component of photonic textile sensors for the development of healthcare@home technologies. However, fabricating tailored POFs exhibiting the required properties for such applications remains challenging. Here, an innovative method to fabricate soft POFs based on polydimethylsiloxane is introduced: the hydrogel-assisted microfluidic wet spinning (HA-MWS) technology. Combined with a straightforward post-processing step, the HA-MWS enabled the production of soft POFs with tailored moduli (0.35 MPa to 5.0 MPa), low surface roughness (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mi>q</mi></mrow></msub><mo><</mo></math></span> 6 nm), and, consequently, low attenuation (<0.25 dB/cm). The <em>quasi</em>-static and dynamic mechanical, chemical, optical, thermal, and surface properties of the soft POFs produced by HA-MWS were investigated in detail. A photonic textile sensor demonstrator was built with these soft POFs with tailored pressure sensitivity in the range of 2–300 kPa, modulus close to that of skin, and high-temperature stability between -30 <span><math><mmultiscripts><mrow><mi>C</mi></mrow><mprescripts></mprescripts><none></none><mrow><mo>∘</mo></mrow></mmultiscripts></math></span>. This methodology can potentially become a standard tool for designing POFs with tunable properties for healthcare monitoring, soft robotics, and biomedicine applications.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113466"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning for structure-guided materials and process design 用于结构引导材料和工艺设计的机器学习

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113453

Lukas Morand , Tarek Iraki , Johannes Dornheim , Stefan Sandfeld , Norbert Link , Dirk Helm

{"title":"Machine learning for structure-guided materials and process design","authors":"Lukas Morand , Tarek Iraki , Johannes Dornheim , Stefan Sandfeld , Norbert Link , Dirk Helm","doi":"10.1016/j.matdes.2024.113453","DOIUrl":"10.1016/j.matdes.2024.113453","url":null,"abstract":"<div><div>In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic and generic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material microstructures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these microstructures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target microstructures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable microstructure. The functionality of the approach is demonstrated at manufacturing crystallographic textures with desired properties in a simulated metal forming process.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113453"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection by X-ray tomography and probabilistic fatigue assessment of aluminum brackets manufactured by PBF-LB 通过 X 射线断层扫描和概率疲劳评估对 PBF-LB 制造的铝支架进行异常检测

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113467

L. Rusnati , M. Yosifov , S. Senck , R. Hubmann , S. Beretta

{"title":"Anomaly detection by X-ray tomography and probabilistic fatigue assessment of aluminum brackets manufactured by PBF-LB","authors":"L. Rusnati , M. Yosifov , S. Senck , R. Hubmann , S. Beretta","doi":"10.1016/j.matdes.2024.113467","DOIUrl":"10.1016/j.matdes.2024.113467","url":null,"abstract":"<div><div>The assessment of safety-critical components for fatigue applications is a key requirement for metal additive manufacturing (AM) applications. Material anomalies play a relevant role in determining the fatigue resistance properties of a component. X-ray computed tomography (CT) helps collect important information on these flaws, such as their size and position within a part.</div><div>In this study, we discuss how to employ anomaly data detected on an AlSi10Mg bracket manufactured by laser-powder bed fusion to describe the prospective allowable life of a component under a given operating condition.</div><div>A statistical analysis was conducted on the specimens and component to derive the correlation between different resolution scans and analyze the uncertainties of the micro-CT measurements. The full-scale non-destructive evaluation (NDE) can be constrained to large voxel sizes. Eventually, the authors proposed a fully probabilistic route for assessment instead of a simple deterministic assessment based on safety factors. This assessment enables designers to consider the uncertainties of the assessment (uncertainties of micro-CT detection and the model for fatigue strength).</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113467"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving excellent strength and plasticity of aluminum alloy through refining and densifying precipitates 通过细化和致密化沉淀实现铝合金的优异强度和塑性

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113439

Renjie Dai , Zhenjun Zhang , Keqiang Li , Rui Liu , Jiapeng Hou , Zhan Qu , Baishan Gong , Zhefeng Zhang

{"title":"Achieving excellent strength and plasticity of aluminum alloy through refining and densifying precipitates","authors":"Renjie Dai , Zhenjun Zhang , Keqiang Li , Rui Liu , Jiapeng Hou , Zhan Qu , Baishan Gong , Zhefeng Zhang","doi":"10.1016/j.matdes.2024.113439","DOIUrl":"10.1016/j.matdes.2024.113439","url":null,"abstract":"<div><div>Strength and plasticity are basic mechanical properties for wrought Al alloys, and generally exhibit a trade-off relationship. Herein through analyzing the respective effects of precipitates on yield strength and strain hardening, we proposed and quantitatively analyzed a strategy for synchronously strengthening and plasticizing Al alloys by refining and densifying the precipitates, defined as RDP effect. The precipitates were highly refined and densified in three high-Zn 7xxx alloys in order to verify the validity of the RDP effect. The tensile tests show that the high-Zn Al alloys possess ultra-high strength and good plasticity compared to the traditional Al alloys. Further analysis reveals that the densification of precipitates mainly contributes to the ultra-high strength, accounting for over 75%, while the refinement of precipitates suppresses the dislocation annihilation, thus increasing the strain-hardening capacity. Together, these two factors finally contribute to the excellent strength and plasticity matching. This finding will provide strong support for the positive impact of RDP effect on improving the balance between strength and ductility. Besides, this strategy may be considered as an effective one for simultaneously improving the strength and plasticity in high-performance Al alloys.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113439"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc 机器学习辅助设计和制备具有高铋和低 Hc 的 Fe85Si2B8.5P3.5C1 非晶/纳米晶合金

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113461

Shengdong Tang , Rui Sun , Yifan He , Guichang Liu , Ruixuan Wang , Yuqin Liu , Chengying Tang

{"title":"Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc","authors":"Shengdong Tang , Rui Sun , Yifan He , Guichang Liu , Ruixuan Wang , Yuqin Liu , Chengying Tang","doi":"10.1016/j.matdes.2024.113461","DOIUrl":"10.1016/j.matdes.2024.113461","url":null,"abstract":"<div><div>Four machine learning (ML) models including eXtreme Gradient boosting (XGBT), <em>k</em>-Nearest Neighbor (<em>k</em>NN), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN) were employed to predict saturation flux density (<em>B<sub>s</sub></em>), coercivity (<em>H<sub>c</sub></em>), grain size, magnetostriction (<em>λ</em>), and Curie temperature (<em>T<sub>c</sub></em>) of Fe-based amorphous/nanocrystalline alloys. To maximize predictive ability of ML models, grid-search and normalization were used to search the most proper parameters of ML and pre-process raw data, respectively. XGBT had best predictive and generalization ability for predicting <em>B<sub>s</sub></em> and <em>H<sub>c</sub></em> with coefficient of determination (R<sup>2</sup>) of 0.992 and 0.967, respectively. Based on the feature importance analysis from the XGBT model, the Fe<sub>85</sub>Si<sub>2</sub>B<sub>8.5</sub>P<sub>3.5</sub>C<sub>1</sub> amorphous alloy ribbon with good magnetic properties, such as high <em>B<sub>s</sub></em>, low <em>H<sub>c</sub></em>, was designed and prepared by melt spinning. X-ray diffraction (XRD), differential scanning calorimetry (DSC), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), B–H loop tracer, and magnetostriction instrument were used to identify the phase structure and physical properties of the Fe<sub>85</sub>Si<sub>2</sub>B<sub>8.5</sub>P<sub>3.5</sub>C<sub>1</sub> alloy. It was found that the Fe<sub>85</sub>Si<sub>2</sub>B<sub>8.5</sub>P<sub>3.5</sub>C<sub>1</sub> alloy had good magnetic properties with <em>B<sub>s</sub></em> of 1.82 T and the <em>H<sub>c</sub></em> of 2.02 A/m after annealing at 723 K for 180 s, in good agreement with the designed results by machine learning.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113461"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phase-field modeling and computational design of structurally stable NMC materials 结构稳定的 NMC 材料的相场建模和计算设计

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113464

Eduardo Roque , Javier Segurado , Francisco Montero-Chacón

{"title":"Phase-field modeling and computational design of structurally stable NMC materials","authors":"Eduardo Roque , Javier Segurado , Francisco Montero-Chacón","doi":"10.1016/j.matdes.2024.113464","DOIUrl":"10.1016/j.matdes.2024.113464","url":null,"abstract":"<div><div>Lithium Nickel Manganese Cobalt Oxides (NMC) are one of the most used cathode materials in lithium-ion batteries, and they will become more relevant in the following years due to their potential in electric vehicles. Unfortunately, this material experiences microcracking during the battery operation due to the volume variations, which is detrimental to the battery performance and limits the lifetime of the electrodes. Thus, understanding mechanical degradation is fundamental for the development of advanced batteries with improved capacity and limited degradation. In this work, we propose a chemo-mechanical model, including a stochastic phase-field fracture approach, to design structurally stable NMC electrodes. We include the degradation in the mechanical and chemical contributions. The heterogeneous NMC microstructure is considered by representing the material's tensile strength with a Weibull distribution function, which allows to represent complex and non-deterministic crack patterns.</div><div>We use our model to provide a comprehensive analysis of mechanical degradation in NMC111 electrodes, including the effect of particle size, C-rate, and depth of charge and discharge. Then, we analyze the influence of the electrode composition (namely, Ni content) on the structural integrity. We use this information to provide design guides for functionally-graded electrodes with high capacity and limited degradation.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113464"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metasurface-Assisted mutual coupling suppression in circularly polarized MIMO antenna array for Sub-6 GHz applications 用于 6 千兆赫以下应用的圆极化多输入多输出天线阵列中的元表面辅助相互耦合抑制技术

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113445

Muhammad Usman Raza , Kai Zhang , Sen Yan

{"title":"Metasurface-Assisted mutual coupling suppression in circularly polarized MIMO antenna array for Sub-6 GHz applications","authors":"Muhammad Usman Raza , Kai Zhang , Sen Yan","doi":"10.1016/j.matdes.2024.113445","DOIUrl":"10.1016/j.matdes.2024.113445","url":null,"abstract":"<div><div>A double-sided decoupling metasurface (DSDM) method is proposed to reduce the mutual coupling between very closely spaced circularly polarized (CP) MIMO antenna elements for sub-6 GHz applications. A proposed DSDM structure with a square-shaped patch layer is placed over the array to reduce mutual coupling by non-propagating evanescent waves and manipulating the polarization of propagating reflected CP waves. This decoupling mechanism relies on the DSDM’s negative electric permittivity extracted from the <em>meta</em>-atom. When the CP waves were incident to DSDM polarizer through the excited CP antenna of the array, the polarization states of the reflected and transmitted CP waves were changed as controlled by DSDM. In reflection mode, the negative permittivity of DSDM produce two type of the waves reflected waves generated the polarization mismatch and evanescent waves that reduce the coupling between CP antennas, while the transmission mode, controlling the radiation pattern at φ = 45° or φ = 135°. The low-profile proposed decoupling structure was fabricated and experimentally validated. The decoupling design significantly mitigated the measured mutual coupling between the CP antenna elements at 3.5 GHz by more than 15 dB and by more than 13 dB at 3.02 GHz to 3.67 GHz, compared to the reference array. The proposed design achieves less than a 3 dB axial ratio, maximum realized gain of 5.52 dBic at 3.5 GHz. An excellent agreement between the simulated and measured outcomes has been studied.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113445"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep supercooling solidification towards tuned amorphous/nanocrystalline dual phases for superior magnetic nanocrystalline alloys 通过深度过冷凝固实现非晶/纳米晶双相调谐，打造卓越的磁性纳米晶合金

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-16 DOI: 10.1016/j.matdes.2024.113469

Kebing Wang , Chen Wu , Lingfeng Wang , Xinyang Zhang , Qiming Chen , Mi Yan

{"title":"Deep supercooling solidification towards tuned amorphous/nanocrystalline dual phases for superior magnetic nanocrystalline alloys","authors":"Kebing Wang , Chen Wu , Lingfeng Wang , Xinyang Zhang , Qiming Chen , Mi Yan","doi":"10.1016/j.matdes.2024.113469","DOIUrl":"10.1016/j.matdes.2024.113469","url":null,"abstract":"<div><div>Nanocrystalline soft magnetic alloys featuring with amorphous-nanocrystalline dual-phase structure are critical for energy conversion and transportation at elevated frequencies. Their applications however, are refrained by limited saturation magnetic flux density (<em>B</em><sub>s</sub>) due to unavoidable addition of a considerable quantity of non-magnetic elements for glass forming ability (GFA). Furthermore, engineering of the amorphous-nanocrystalline microstructure is critical for the coercivity (<em>H</em><sub>c</sub>), which urges development of advanced approach. In this study, a deep supercooling solidification has been proposed, which not only promotes the formation of short-range packing and icosahedron/icosahedron-like structures for enhanced GFA, but also induces an optimized microstructure consisting of highly disordered amorphous matrix to facilitate nanograin refinement. Based on such strategy, Finemet-based nanocrystalline alloy with superior magnetic properties (<em>B</em><sub>s</sub> = 1.71 T, <em>H</em><sub>c</sub> = 5.0 A/m) has been achieved without additional glass forming element. Such superior performance is correlated to the unique magnetic domain structure involving straight domain walls and smooth movement. The deep supercooling strategy not only breaks the trade-off between the <em>B</em><sub>s</sub> and GFA to allow the design of nanocrystalline alloys with large ferromagnetic content, but also serves as an effective method for microstructure optimization for nanocrystalline alloys.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113469"},"PeriodicalIF":7.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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

IF 7.6 2区材料科学

Materials & Design Pub Date : 2024-11-16 DOI: 10.1016/j.matdes.2024.113462

Erfan Maleki , Sara Bagherifard , Okan Unal , Mario Guagliano

{"title":"Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg","authors":"Erfan Maleki , Sara Bagherifard , Okan Unal , Mario Guagliano","doi":"10.1016/j.matdes.2024.113462","DOIUrl":"10.1016/j.matdes.2024.113462","url":null,"abstract":"<div><div>This study introduces a <em>Hybrid Intelligence</em> approach to investigate the <em>Process-Structure-Property-Performance (PSSP)</em> 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.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"248 ","pages":"Article 113462"},"PeriodicalIF":7.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0