Magnetic micro/nanorobots (MagRobots) with unparalleled advantages, including remote mobility, high reconfigurability and programmability, lack of fuel requirement, and versatility, can be manipulated under a magnetic field, which has attracted considerable research attention in the biomedicine. Magnetic materials, as the key components of MagRobots, generate reactive oxygen species (ROS) in vivo to induce tissue/organ damage through Fenton/Fenton‐like reactions, which may hinder the clinical application of MagRobots. Here, the biologically active Prussian blue is generated on the surfaces of MagRobots via an in situ reaction to obtain magnetically actuated ROS‐scavenging nano‐robots (ROSrobots). The generated Prussian blue blocks ROS production and endows the MagRobots with additional functionalities, markedly expanding their potential medical applications. Under the action of a magnetic field, the reconfigurable ROSrobots realize multimode transformation, locomotion, and manipulation in complex environments. Importantly, a simple control method is proposed to achieve movement in 3D geometries to allow the completion of tasks in a complex environment. Furthermore, the osteoarthritis (OA) rat model was employed for proof of concept. Notably, under the guidance of ultrasound imaging, ROSrobots can be accurately injected into the articular cavity to actively target the treatment of OA. This research may further promote the clinical application of MagRobots.
{"title":"Magnetically Actuated Reactive Oxygen Species Scavenging Nano‐Robots for Targeted Treatment","authors":"Yongzheng Zhao, Hao Xiong, Yanhong Li, Wei Gao, Chen Hua, Jianrong Wu, C. Fan, Xiaojun Cai, Yuanyi Zheng","doi":"10.1002/aisy.202200061","DOIUrl":"https://doi.org/10.1002/aisy.202200061","url":null,"abstract":"Magnetic micro/nanorobots (MagRobots) with unparalleled advantages, including remote mobility, high reconfigurability and programmability, lack of fuel requirement, and versatility, can be manipulated under a magnetic field, which has attracted considerable research attention in the biomedicine. Magnetic materials, as the key components of MagRobots, generate reactive oxygen species (ROS) in vivo to induce tissue/organ damage through Fenton/Fenton‐like reactions, which may hinder the clinical application of MagRobots. Here, the biologically active Prussian blue is generated on the surfaces of MagRobots via an in situ reaction to obtain magnetically actuated ROS‐scavenging nano‐robots (ROSrobots). The generated Prussian blue blocks ROS production and endows the MagRobots with additional functionalities, markedly expanding their potential medical applications. Under the action of a magnetic field, the reconfigurable ROSrobots realize multimode transformation, locomotion, and manipulation in complex environments. Importantly, a simple control method is proposed to achieve movement in 3D geometries to allow the completion of tasks in a complex environment. Furthermore, the osteoarthritis (OA) rat model was employed for proof of concept. Notably, under the guidance of ultrasound imaging, ROSrobots can be accurately injected into the articular cavity to actively target the treatment of OA. This research may further promote the clinical application of MagRobots.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84932986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Suman Timilsina, Hoonjae Shin, K. Sohn, Ji Sik Kim
Increasing ubiquitous collaborative intelligence between humans and machines requires human–machine communication (HMC) that is more human and less machine‐like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip‐reading technology into HMC has gained significant attention. However, lip‐reading functions effectively only in well‐lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL–polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET‐5) and residual neural network (ResNet‐34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar‐sounding words such as “around” and “ground.” Dark‐mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines.
{"title":"Dark‐Mode Human–Machine Communication Realized by Persistent Luminescence and Deep Learning","authors":"Suman Timilsina, Hoonjae Shin, K. Sohn, Ji Sik Kim","doi":"10.1002/aisy.202200036","DOIUrl":"https://doi.org/10.1002/aisy.202200036","url":null,"abstract":"Increasing ubiquitous collaborative intelligence between humans and machines requires human–machine communication (HMC) that is more human and less machine‐like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip‐reading technology into HMC has gained significant attention. However, lip‐reading functions effectively only in well‐lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL–polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET‐5) and residual neural network (ResNet‐34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar‐sounding words such as “around” and “ground.” Dark‐mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"207 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75541190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The traditional von Neumann architecture separates memory from the central processing unit (CPU), resulting in aggravated data transfer bottlenecks between the CPU and memory during a data volume surge. Emerging technologies, such as in‐memory computing (IMC), provide a new way to overcome the limitations due to the separation of memory and computation. However, existing IMC efforts are generally limited to a single (gate‐control or drain‐control) mode of operation to achieve functionality. Herein, a 2D ferroelectric channel device that enables the feasibility of multioperation modes is proposed. In addition, rich functionalities, such as logic, nonvolatile memory, and neuromimetic plasticity modulation, by switching the operating modes are realized. A device that facilitates multimodal operations and a promising technical solution for further development of burgeoning computing architecture is provided.
{"title":"Multioperation Mode Ferroelectric Channel Devices for Memory and Computation","authors":"Yibo Sun, Shuiyuan Wang, Xiaozhang Chen, Zhenhan Zhang, Peng Zhou","doi":"10.1002/aisy.202100198","DOIUrl":"https://doi.org/10.1002/aisy.202100198","url":null,"abstract":"The traditional von Neumann architecture separates memory from the central processing unit (CPU), resulting in aggravated data transfer bottlenecks between the CPU and memory during a data volume surge. Emerging technologies, such as in‐memory computing (IMC), provide a new way to overcome the limitations due to the separation of memory and computation. However, existing IMC efforts are generally limited to a single (gate‐control or drain‐control) mode of operation to achieve functionality. Herein, a 2D ferroelectric channel device that enables the feasibility of multioperation modes is proposed. In addition, rich functionalities, such as logic, nonvolatile memory, and neuromimetic plasticity modulation, by switching the operating modes are realized. A device that facilitates multimodal operations and a promising technical solution for further development of burgeoning computing architecture is provided.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"215 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74075977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Chen, Andrew Scott Johnson, Jada Weber, Oluwafemi Isaac Akomolafe, Jinghua Jiang, C. Peng
Programmable soft materials have shown applications in artificial muscles, soft robotics, flexible electronics, and biomedicines due to their adaptive structural transformations. As an ordered soft material, directional shape changes of liquid crystal elastomer (LCE) can be easily achieved via external stimuli thanks to its anisotropic elasticity. However, harnessing the interplay between molecular ordering, geometry, and shape morphing in this anisotropic material to create programmable and complex shape changes remains a challenge. Here, by integrating the concepts of kirigami or Chinese paper cutting “JianZhi” in the light‐actuated LCE encoded with controlled molecular orientations, various complex 3D shape morphing behaviors are demonstrated. Versatile combinations of fundamental shape changes such as bending, folding, twisting, and rolling are enabled by fine‐tuning the molecular orientations and geometries in the monolithic LCE kirigami. Furthermore, various functions such as fluttering of the Chinese crane bird “QianZhiHe,” arbitrary directional locomotion in the annulus and linear locomotion in the complex Chinese character are also realized. These complex, fast‐response, untethered, remote, reversible, and programmable shape morphologies actuated in a monolith of LCE kirigami will open opportunities in soft robotics and smart materials.
{"title":"Programmable Light‐Driven Liquid Crystal Elastomer Kirigami with Controlled Molecular Orientations","authors":"Juan Chen, Andrew Scott Johnson, Jada Weber, Oluwafemi Isaac Akomolafe, Jinghua Jiang, C. Peng","doi":"10.1002/aisy.202100233","DOIUrl":"https://doi.org/10.1002/aisy.202100233","url":null,"abstract":"Programmable soft materials have shown applications in artificial muscles, soft robotics, flexible electronics, and biomedicines due to their adaptive structural transformations. As an ordered soft material, directional shape changes of liquid crystal elastomer (LCE) can be easily achieved via external stimuli thanks to its anisotropic elasticity. However, harnessing the interplay between molecular ordering, geometry, and shape morphing in this anisotropic material to create programmable and complex shape changes remains a challenge. Here, by integrating the concepts of kirigami or Chinese paper cutting “JianZhi” in the light‐actuated LCE encoded with controlled molecular orientations, various complex 3D shape morphing behaviors are demonstrated. Versatile combinations of fundamental shape changes such as bending, folding, twisting, and rolling are enabled by fine‐tuning the molecular orientations and geometries in the monolithic LCE kirigami. Furthermore, various functions such as fluttering of the Chinese crane bird “QianZhiHe,” arbitrary directional locomotion in the annulus and linear locomotion in the complex Chinese character are also realized. These complex, fast‐response, untethered, remote, reversible, and programmable shape morphologies actuated in a monolith of LCE kirigami will open opportunities in soft robotics and smart materials.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83887698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Magnetic mobile microrobots navigating biofluids with both upstream and downstream locomotion provide a promising solution to targeted drug delivery for precision medicine. However, the biofluid environment in blood vessels is complicated due to variations in flow rate and direction. It is still unknown how to make magnetic microrobots resist the variable flow rate in biofluids with both upstream and downstream locomotion. Herein, magnetic microrobots with various shapes and sizes have been controlled to navigate diverse biofluids under different flow rates and directions. Simulation and experimental studies have been conducted to analyze the influences of microrobot size and shape on translational velocity in confined microchannels filled with biofluids. A strategy is proposed to choose the optimized parameters of rotating magnetic field actuation for precise delivery of microrobots in a microfluidic chip, which contains a complex biofluid environment with variable flow rate and direction. The results are validated using various microrobots navigating the microfluidic chip and the yolks of zebrafish larvae in vivo. This work provides a guideline for selecting desirable microrobot dimensions and magnetic field actuation parameters for controllable navigation of magnetic mobile microrobots in complex biofluid flows.
{"title":"Magnetic Mobile Microrobots for Upstream and Downstream Navigation in Biofluids with Variable Flow Rate","authors":"Zehao Wu, Yuting Zhang, Nana Ai, Haoran Chen, Wei Ge, Qingsong Xu","doi":"10.1002/aisy.202100266","DOIUrl":"https://doi.org/10.1002/aisy.202100266","url":null,"abstract":"Magnetic mobile microrobots navigating biofluids with both upstream and downstream locomotion provide a promising solution to targeted drug delivery for precision medicine. However, the biofluid environment in blood vessels is complicated due to variations in flow rate and direction. It is still unknown how to make magnetic microrobots resist the variable flow rate in biofluids with both upstream and downstream locomotion. Herein, magnetic microrobots with various shapes and sizes have been controlled to navigate diverse biofluids under different flow rates and directions. Simulation and experimental studies have been conducted to analyze the influences of microrobot size and shape on translational velocity in confined microchannels filled with biofluids. A strategy is proposed to choose the optimized parameters of rotating magnetic field actuation for precise delivery of microrobots in a microfluidic chip, which contains a complex biofluid environment with variable flow rate and direction. The results are validated using various microrobots navigating the microfluidic chip and the yolks of zebrafish larvae in vivo. This work provides a guideline for selecting desirable microrobot dimensions and magnetic field actuation parameters for controllable navigation of magnetic mobile microrobots in complex biofluid flows.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"217 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75379851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhiping Chai, L. Lyu, Menghao Pu, Xianwen Chen, Jiaqi Zhu, Huageng Liang, Han Ding, Zhigang Wu
Being minimally invasive and highly effective, radiofrequency ablation (RFA) is widely used for small‐sized malignant tumor treatment. However, in clinical practice, a large number of tumors are found in irregular shape, while the current RFA devices are hard to control the morphologic appearance of RFA lesions on demand, which usually ends up with unnecessarily excessive tissue ablation and subsequently often brings irreversible damage to the organs’ functions. Herein, active cannulas for each of the individually controlled subelectrodes to achieve an on‐demand shape morphing and thus conformal RFA lesion are introduced. The target shape as well as the length of inserted subelectrodes can be precisely controlled by tuning the active stylets and cannulas. What's more, owing to independent movement and energy control of each subelectrodes, the electrode is shown to be not only efficient enough to accomplish accurate trajectory control to target tissue in a single insertion, but also adaptive enough to ablate target tissues with diverse morphologic appearances and locations. On‐demand conformal ablation of target tissue is demonstrated as well under the guidance of ultrasound imaging with the device. Potentially, the RFA electrode is a promising minimally invasive treatment of malignant tumors in future clinical practice. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164019293.38729522.
{"title":"An Individually Controlled Multitined Expandable Electrode Using Active Cannula‐Based Shape Morphing for On‐Demand Conformal Radiofrequency Ablation Lesions","authors":"Zhiping Chai, L. Lyu, Menghao Pu, Xianwen Chen, Jiaqi Zhu, Huageng Liang, Han Ding, Zhigang Wu","doi":"10.1002/aisy.202100262","DOIUrl":"https://doi.org/10.1002/aisy.202100262","url":null,"abstract":"Being minimally invasive and highly effective, radiofrequency ablation (RFA) is widely used for small‐sized malignant tumor treatment. However, in clinical practice, a large number of tumors are found in irregular shape, while the current RFA devices are hard to control the morphologic appearance of RFA lesions on demand, which usually ends up with unnecessarily excessive tissue ablation and subsequently often brings irreversible damage to the organs’ functions. Herein, active cannulas for each of the individually controlled subelectrodes to achieve an on‐demand shape morphing and thus conformal RFA lesion are introduced. The target shape as well as the length of inserted subelectrodes can be precisely controlled by tuning the active stylets and cannulas. What's more, owing to independent movement and energy control of each subelectrodes, the electrode is shown to be not only efficient enough to accomplish accurate trajectory control to target tissue in a single insertion, but also adaptive enough to ablate target tissues with diverse morphologic appearances and locations. On‐demand conformal ablation of target tissue is demonstrated as well under the guidance of ultrasound imaging with the device. Potentially, the RFA electrode is a promising minimally invasive treatment of malignant tumors in future clinical practice. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164019293.38729522.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85302016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Additive manufacturing (AM) has matured in parallel with advances in computation. This is not a coincidence as taking advantage of the structural freedom afforded by AM requires detailed calculations and an ability to design and process complex structures in three dimensions. However, the ability to program AM systems is not the only way in which computation, and more recently machine learning, have impacted AM as a field. In fact, recent years have seen a number of innovations in AM that have endowed the process with varying degrees of ‘intelligence’ in distinct ways. While many of these are connected, several of these approaches to smart AM are wholly distinct in that they advance different aspects of the state-of-the-art. Our goal in this editorial is to highlight three such dimensions of intelligence in AM and connect them to articles in this special issue of Advanced Intelligent Systems that discuss innovations along these dimensions. These dimensions include advances in the materials and structures produced by AM to make them smarter or more functional, advances in processing to produce better and more reliable products, and advances in using AM as an ecosystem that is more agile and capable than traditional manufacturing (Figure 1).
{"title":"Dimensions of Smart Additive Manufacturing","authors":"Keith A. Brown, Grace X. Gu","doi":"10.1002/aisy.202100240","DOIUrl":"https://doi.org/10.1002/aisy.202100240","url":null,"abstract":"Additive manufacturing (AM) has matured in parallel with advances in computation. This is not a coincidence as taking advantage of the structural freedom afforded by AM requires detailed calculations and an ability to design and process complex structures in three dimensions. However, the ability to program AM systems is not the only way in which computation, and more recently machine learning, have impacted AM as a field. In fact, recent years have seen a number of innovations in AM that have endowed the process with varying degrees of ‘intelligence’ in distinct ways. While many of these are connected, several of these approaches to smart AM are wholly distinct in that they advance different aspects of the state-of-the-art. Our goal in this editorial is to highlight three such dimensions of intelligence in AM and connect them to articles in this special issue of Advanced Intelligent Systems that discuss innovations along these dimensions. These dimensions include advances in the materials and structures produced by AM to make them smarter or more functional, advances in processing to produce better and more reliable products, and advances in using AM as an ecosystem that is more agile and capable than traditional manufacturing (Figure 1).","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84102294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.
{"title":"Strengthening the Sustainability of Additive Manufacturing through Data‐Driven Approaches and Workforce Development","authors":"Tianjiao Li, J. Yeo","doi":"10.1002/aisy.202100069","DOIUrl":"https://doi.org/10.1002/aisy.202100069","url":null,"abstract":"The additive manufacturing (AM) industry is rapidly developing and expanding, thereby becoming an important and integral component of the digital revolution in manufacturing practices. While the engineering aspects of AM are under intensive research, there still remain many chances to strengthen the sustainability of additive manufacturing (SAM). Cogently increasing the AM community's attention to SAM is vital for developing the AM industry sustainably from the bottom up. The digital nature of AM provides new opportunities for acquiring, storing, and utilizing data to strengthen SAM through data‐driven approaches. Herein, spotlight on SAM is shone upon and it is placed on a more concrete footing. The corresponding advances in data‐driven methods that can strengthen SAM are featured, such as optimizing designs for AM, reducing material waste, and developing databases. How the AM workforce can be developed and grown as a collaboration between the industry, government, and academia to extensively harness the full potential of AM as well as mitigate its adversarial social impact is discussed. Finally, several critical digital techniques that have the potential to further strengthen SAM in the factory of the future, including hybrid manufacturing, Internet of Things, and machine learning and artificial intelligence, are highlighted.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73133455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar
{"title":"Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models","authors":"Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar","doi":"10.1002/aisy.202170077","DOIUrl":"https://doi.org/10.1002/aisy.202170077","url":null,"abstract":"","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80326485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seyyed Hadi Seifi, A. Yadollahi, Wenmeng Tian, H. Doude, V. H. Hammond, L. Bian
The presence of process‐induced internal defects (i.e., pores, microcracks, and lack‐of‐fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization techniques, such as X‐ray CT and ultrasonic scanning, are costly and time‐consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser‐based additive manufacturing processes. Herein, a novel two‐phase modeling methodology is proposed for fatigue life prediction based on in situ monitoring of thermal history. Phase (I) includes a convolutional neural network designed to detect the relative size of the defects (i.e., small gas pores and large lack‐of‐fusions) by leveraging processed thermal images. Subsequently, a fatigue‐life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.
{"title":"In Situ Nondestructive Fatigue‐Life Prediction of Additive Manufactured Parts by Establishing a Process–Defect–Property Relationship","authors":"Seyyed Hadi Seifi, A. Yadollahi, Wenmeng Tian, H. Doude, V. H. Hammond, L. Bian","doi":"10.1002/aisy.202000268","DOIUrl":"https://doi.org/10.1002/aisy.202000268","url":null,"abstract":"The presence of process‐induced internal defects (i.e., pores, microcracks, and lack‐of‐fusions) significantly deteriorates the structural durability of parts fabricated by additive manufacturing. However, traditional defects characterization techniques, such as X‐ray CT and ultrasonic scanning, are costly and time‐consuming. There is a research gap in the nondestructive evaluation of fatigue performance directly from the process signature of laser‐based additive manufacturing processes. Herein, a novel two‐phase modeling methodology is proposed for fatigue life prediction based on in situ monitoring of thermal history. Phase (I) includes a convolutional neural network designed to detect the relative size of the defects (i.e., small gas pores and large lack‐of‐fusions) by leveraging processed thermal images. Subsequently, a fatigue‐life prediction model is trained in Phase (II) by incorporating the defect characteristics extracted from Phase (I) to evaluate the fatigue performance. Estimating defect characteristics from the in situ thermal history facilitates the fatigue predicting process.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73853259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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