The number and types of measurement devices used for monitoring and controlling Laser-Based Powder Bed Fusion of Metals (PBF-LB/M) processes and inspecting the resulting AM metal parts have increased rapidly in recent years. The variety of the data collected by such devices has increased, and the veracity of the data has decreased simultaneously. Each measurement device generates data in a unique coordinate system and in a unique data type. Data alignment, however, is required before 1) monitoring and controlling PBF-LB/M processes, 2) predicting the material properties of the final part, and 3) qualifying the resulting AM parts can be done. Aligned means all data must be transformed into a single coordinate system. In this paper, we describe a new, general data-alignment procedure and an example based on PBF-LB/M processes. The specific data objects used in this example include in-situ photogrammetry, thermography, ex-situ X-ray computed tomography (XCT), coordinate metrology, and computer-aided design (CAD) models. We propose a data-alignment procedure to align the data from melt pool images, scan paths, layer images, XCT three-dimensional (3D) model, coordinate measurements, and the 3D CAD model.
{"title":"Measured Data Alignments for Monitoring Metal Additive Manufacturing Processes Using Laser Powder Bed Fusion Methods","authors":"S. Feng, Yan Lu, Albert T. Jones","doi":"10.1115/detc2020-22478","DOIUrl":"https://doi.org/10.1115/detc2020-22478","url":null,"abstract":"\u0000 The number and types of measurement devices used for monitoring and controlling Laser-Based Powder Bed Fusion of Metals (PBF-LB/M) processes and inspecting the resulting AM metal parts have increased rapidly in recent years. The variety of the data collected by such devices has increased, and the veracity of the data has decreased simultaneously. Each measurement device generates data in a unique coordinate system and in a unique data type. Data alignment, however, is required before 1) monitoring and controlling PBF-LB/M processes, 2) predicting the material properties of the final part, and 3) qualifying the resulting AM parts can be done. Aligned means all data must be transformed into a single coordinate system. In this paper, we describe a new, general data-alignment procedure and an example based on PBF-LB/M processes. The specific data objects used in this example include in-situ photogrammetry, thermography, ex-situ X-ray computed tomography (XCT), coordinate metrology, and computer-aided design (CAD) models. We propose a data-alignment procedure to align the data from melt pool images, scan paths, layer images, XCT three-dimensional (3D) model, coordinate measurements, and the 3D CAD model.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132208823","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}
Sébastian Hernandez, S. Achiche, Daniel Spooner, A. Vadean, M. Raison
Over the last decades, the use of multibody dynamics in biomechanics research has grown considerably and holds significant promises for the health and biomedical industries. Nowadays, it allows estimating internal data of the body that would be impractical or impossible to obtain experimentally, e.g. individual muscle forces. Also, multibody dynamics simulation allows one to constrain virtually any apparatus to the musculoskeletal system, helping to understand and improve the patient’s dynamic interactions with the device. The modeling and validation of human multibody models remain a tedious task to achieve for the research community and can vary significantly depending on the applications. Despite the advantages offered by the multibody modeling of the human body, its introduction in the biomedical engineering curriculum is not widespread. The present paper aims to evaluate the feasibility and the interest of introducing multibody modeling into multidisciplinary, real-world projects using 3D printed prototypes to add an experimental understanding of the difficulties and validation of the human body modeling. The proposed methodology is based on a literature review of the multibody dynamics teaching methods used in mechanical engineering, followed by a first pilot project and feedback from students and professors of the community through interviews. Finally, a project is proposed, using physical prototyping to support the learning.
{"title":"On Supporting the Learning of Biomechanics Using Multidisciplinary Physical Prototyping","authors":"Sébastian Hernandez, S. Achiche, Daniel Spooner, A. Vadean, M. Raison","doi":"10.1115/detc2020-22312","DOIUrl":"https://doi.org/10.1115/detc2020-22312","url":null,"abstract":"\u0000 Over the last decades, the use of multibody dynamics in biomechanics research has grown considerably and holds significant promises for the health and biomedical industries. Nowadays, it allows estimating internal data of the body that would be impractical or impossible to obtain experimentally, e.g. individual muscle forces. Also, multibody dynamics simulation allows one to constrain virtually any apparatus to the musculoskeletal system, helping to understand and improve the patient’s dynamic interactions with the device. The modeling and validation of human multibody models remain a tedious task to achieve for the research community and can vary significantly depending on the applications. Despite the advantages offered by the multibody modeling of the human body, its introduction in the biomedical engineering curriculum is not widespread. The present paper aims to evaluate the feasibility and the interest of introducing multibody modeling into multidisciplinary, real-world projects using 3D printed prototypes to add an experimental understanding of the difficulties and validation of the human body modeling. The proposed methodology is based on a literature review of the multibody dynamics teaching methods used in mechanical engineering, followed by a first pilot project and feedback from students and professors of the community through interviews. Finally, a project is proposed, using physical prototyping to support the learning.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947911","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}
Shuo Jiang, Jianxi Luo, Guillermo Ruiz Pava, Jie Hu, C. Magee
The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.
{"title":"A Convolutional Neural Network-Based Patent Image Retrieval Method for Design Ideation","authors":"Shuo Jiang, Jianxi Luo, Guillermo Ruiz Pava, Jie Hu, C. Magee","doi":"10.1115/DETC2020-22048","DOIUrl":"https://doi.org/10.1115/DETC2020-22048","url":null,"abstract":"\u0000 The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.","PeriodicalId":164403,"journal":{"name":"Volume 9: 40th Computers and Information in Engineering Conference (CIE)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133717428","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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