다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Journal of computing and information science in engineering, v.22 no.1, 2022년, pp.011006 -
Elaheh Ghiasian, Seyedeh (Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260) , Lewis, Kemper (Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260)
AI-Helper
AbstractTo appropriately leverage the benefits of additive manufacturing (AM), it would be advantageous if a printing could be guaranteed before allocating the necessary resources. Furthermore, when considering AM for an inventory of existing components traditionally fabricated through traditional m...
Ghiasian 2019 10.1115/DETC2019-97840 A Design Modification System for Additive Manufacturing: Towards Feasible Geometry Development
Lu 2018 10.1115/DETC2018-85996 Self-Improving Additive Manufacturing Knowledge Management
CIRP J. Manuf. Sci. Technol. Adam 7 1 20 2014 10.1016/j.cirpj.2013.10.001 Design for Additive Manufacturing-Element Transitions and Aggregated Structures
ASME J. Mech. Des. Lopez 138 11 114502 2016 10.1115/1.4034103 Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models
ASME J. Mech. Des. Ghiasian 142 8 082001 2019 10.1115/1.4045604 A Preference-Based Approach to Assess a Component’s Design Readiness for Additive Manufacturing
ACM Trans. Graph. Stava 31 4 48 2012 10.1145/2185520.2185544 Stress Relief: Improving Structural Strength of 3D Printable Objects
Virtual Phys. Prototyp. Kumke 11 1 3 2016 10.1080/17452759.2016.1139377 A New Methodological Framework for Design for Additive Manufacturing
Procedia CIRP Vayre 3 1 632 2012 10.1016/j.procir.2012.07.108 Designing for Additive Manufacturing
Doubrovski 2011 10.1115/DETC2011-48131 Optimal Design for Additive Manufacturing: Opportunities and Challenges
Int. J. Adv. Manuf. Technol. Yang 80 1-4 327 2015 10.1007/s00170-015-6994-5 Additive Manufacturing-Enabled Design Theory and Methodology: A Critical Review
Rapid Prototyp. J. Tang 22 3 569 2016 10.1108/RPJ-01-2015-0011 A Survey of the Design Methods for Additive Manufacturing to Improve Functional Performance
Int. J. Adv. Manuf. Technol. Bikas 83 1-4 389 2016 10.1007/s00170-015-7576-2 Additive Manufacturing Methods and Modelling Approaches: A Critical Review
ASME J. Mech. Des. Schmelzle 137 11 111404 2015 10.1115/1.4031156 (Re) Designing for Part Consolidation: Understanding the Challenges of Metal Additive Manufacturing
Seepersad 921 2012 A Designer’s Guide for Dimensioning and Tolerancing SLS Parts
ASME J. Comput. Inf. Sci. Eng. Leung 19 2 021013 2019 10.1115/1.4041913 Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies
ASME J. Comput. Inf. Sci. Eng. Dinar 17 2 021013 2017 10.1115/1.4035787 A Design for Additive Manufacturing Ontology
ASME J. Comput. Inf. Sci. Eng. Hagedorn 18 2 021009 2018 10.1115/1.4039455 A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies
ASME J. Comput. Inf. Sci. Eng. Kim 19 4 041014 2019 10.1115/1.4043531 A Design for Additive Manufacturing Ontology to Support Manufacturability Analysis
Addit. Manuf. Conner 1 64 2014 10.1016/j.addma.2014.08.005 Making Sense of 3D-Printing: Creating a Map of Additive Manufacturing Products and Services
Rapid Prototyp. J. Lindemann 21 2 216 2015 10.1108/RPJ-12-2014-0179 Towards a Sustainable and Economic Selection of Part Candidates for Additive Manufacturing
Ghiasian 2018 10.1115/DETC2018-86238 From Conventional to Additive Manufacturing: Determining Component Fabrication Feasibility
Comput. Aided Des. Jaiswal 109 1 2018 10.1016/j.cad.2018.12.001 A Geometric Reasoning Approach for Additive Manufacturing Print Quality Assessment and Automated Model Correction
ACM Trans. Graph. Luo 31 6 1 2012 10.1145/2366145.2366148 Chopper: Partitioning Models Into 3D-Printable Parts
Razvi 2019 10.1115/DETC2019-98415 A Review of Machine Learning Applications in Additive Manufacturing
Rapid Prototyp. J. Yao 23 6 983 2017 10.1108/RPJ-03-2016-0041 A Hybrid Machine Learning Approach for Additive Manufacturing Design Feature Recommendation
Proc. Inst. Mech. Eng. B Munguia 223 8 995 2009 10.1243/09544054JEM1324 Neural-Network-Based Model for Build-Time Estimation in Selective Laser Sintering
Int. J. Adv. Manuf. Technol. Angelo 57 1-4 215 2011 10.1007/s00170-011-3284-8 A Neural Network-Based Build Time Estimator for Layer Manufactured Objects
J. Manuf. Syst. Chan 46 115 2018 10.1016/j.jmsy.2017.12.001 Data-Driven Cost Estimation for Additive Manufacturing in Cyber Manufacturing
Opt. Lasers Eng. Lu 48 5 519 2010 10.1016/j.optlaseng.2010.01.002 The Prediction of the Building Precision in the Laser Engineered Net Shaping Process Using Advanced Networks
Chowdhury 2016 10.1115/MSEC2016-8784 Artificial Neural Network Based Geometric Compensation for Thermal Deformation in Additive Manufacturing Processes
ASME J. Manuf. Sci. Eng. Khanzadeh 140 3 031011 2018 10.1115/1.4038598 Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts
CIRP Ann. Manuf. Technol. Zhu 67 1 157 2018 10.1016/j.cirp.2018.04.119 Machine Learning in Tolerancing for Additive Manufacturing
ASME J. Comput. Inf. Sci. Eng. Akhil 20 2 021010 2020 10.1115/1.4045719 Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing
Graph. Model Image Process. Cohen-Or 57 6 453 1995 10.1006/gmip.1995.1039 Fundamentals of Surface Voxelization
ASME J. Manuf. Sci. Eng. Nelaturi 137 2 021015 2015 10.1115/1.4029374 Manufacturability Feedback and Model Correction for Additive Manufacturing
Int. J. Comput. Sci. Kotsiantis 1 2 111 2006 Data Preprocessing for Supervised Leaning
J. Comput. Sci. Shalabi 2 9 735 2006 10.3844/jcssp.2006.735.739 Data Mining: A Preprocessing Engine
Sabbagh 2018 10.1115/DETC2018-85865 Supplier Clustering Based on Unstructured Manufacturing Capability Data
The Elements of Statistical Learning: Data Mining, Inference, and Prediction Hastie 2009 10.1007/978-0-387-84858-7
IOSR J. Eng. Madhulatha 2 4 719 2012 10.9790/3021-0204719725 An Overview on Clustering Methods
J. Classif. Murtagh 31 3 274 2014 10.1007/s00357-014-9161-z Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
Parallel Comput. Olson 21 8 1313 1995 10.1016/0167-8191(95)00017-I Parallel Algorithms for Hierarchical Clustering
Int. J. Adv. Res. Comput. Sci. Manage. Stud. Kodinariya 1 6 90 2013 Review on Determining Number of Cluster in k-Means Clustering
AIAA J. Messac 34 1 149 1996 10.2514/3.13035 Physical Programming-Effective Optimization for Computational Design
해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.