相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Review
Computer Science, Artificial Intelligence
Yingjie Zhang et al.
Summary: The continuous development of metal additive manufacturing promises flexible and customized production, but the inability to control process defects and variability has hindered its widespread applications. Process monitoring and control, utilizing machine learning methods, can overcome these challenges and improve quality. This paper reviews the applications of machine learning in metal powder-bed fusion process monitoring and control, highlighting the challenges and possible solutions.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Engineering, Manufacturing
Anant Raj et al.
Summary: The limited repeatability of part quality in additive manufacturing hinders its transition to large-scale manufacturing. Process fluctuations, both random and systemic, contribute to the variations in part properties. In-situ sensors are employed to measure these fluctuations and improve efficiency by estimating systemic variations in the signals.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Engineering, Multidisciplinary
Jingchang Li et al.
Summary: The study achieved porosity classification based on high-speed melt pool images and developed corresponding intelligent machine learning algorithms. The results show that the proposed method can effectively classify porosity during the SLM process, potentially reducing porosity defects.
Article
Automation & Control Systems
Xin Lin et al.
Summary: Compared with other additive manufacturing processes, metal-based additive manufacturing (MAM) can create higher precision and density parts, making it advantageous in automotive, medical, and aerospace industries. However, the presence of quality defects has hindered the widespread application of MAM technologies. Therefore, it is crucial to conduct online monitoring and process control to overcome these shortcomings and ensure high-quality parts.
Review
Computer Science, Interdisciplinary Applications
Emmanouil L. Papazoglou et al.
Summary: Additive Manufacturing, particularly Selective Laser Melting (SLM) and Selective Laser Sintering (SLS), is attracting increasing attention, with modeling and simulation playing a crucial role in better understanding the processes involved. Various numerical methods and models are utilized in the simulation of SLM and SLS, with a focus on heat transfer, fluid dynamics, and other parameters like residual stresses and material microstructure.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2022)
Article
Engineering, Manufacturing
Sarini Jayasinghe et al.
Summary: This study evaluates the use of photodiode sensor measurements to predict measures of resulting build quality in L-PBF process. The study shows the potential of machine-learning algorithms to predict indicators of L-PBF build quality from photodiode measurements, and that photodiode measurements can provide additional information regarding L-PBF part quality compared to process parameters.
PROGRESS IN ADDITIVE MANUFACTURING
(2022)
Article
Multidisciplinary Sciences
Sarah Felix et al.
Summary: This paper demonstrates two methodologies for in-process fault detection and part quality prediction in DMLM. These methodologies leverage existing commercial DMLM systems with minimal hardware modification. The first methodology employs a Bayesian approach for classifying process deviations, while the second methodology uses a least squares regression model to predict the severity of material defects.
SCIENTIFIC REPORTS
(2022)
Review
Automation & Control Systems
Ruihu Zhou et al.
Summary: This paper discusses the application of selective laser melting in metal printing and highlights the challenges in establishing the relationship between SLM process and performance. The paper also discusses the analytical and simulation models based on physical theory and data driven approach, as well as the use of finite element software for solving nonlinear equations and the application of machine learning in SLM process optimization.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Nanoscience & Nanotechnology
Benjamin Stegman et al.
Summary: The influence of a wide range of major build parameters on the microstructures and mechanical properties of 718 Ni alloys is explored. Volumetric energy density is found to be a key parameter capturing the relationship between energy input and the measured properties and microstructures of the alloys. A threshold energy density is identified, below which the properties of additively manufactured specimens degrade substantially. The correlation between the simulated quality of built specimens using Flow3D AM and the experimental results is good.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2022)
Review
Automation & Control Systems
Yikai Zhang et al.
Summary: Metal additive manufacturing (AM) has attracted significant attention in various fields due to its advantages like novel part morphology, new material property, and short production cycle. However, ensuring the repeatability and stability of part quality is challenging in the current metal AM process. This paper describes the in situ and real-time process monitoring based on image processing for metal AM. It reviews the methods and discusses the advantages and disadvantages of traditional image processing as well as AI-based monitoring. The paper also analyzes and discusses issues related to image processing algorithms, quality control, small sample sizes, and image labeling. A technical route for real-time feedback control in metal AM is proposed by combining image processing with other technologies.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Review
Energy & Fuels
Cheng Sun et al.
Summary: The conflict between rapidly growing global energy demand and climate change calls for significant science and technology innovations. Advanced manufacturing, particularly additive manufacturing, shows potential in reducing greenhouse gas emissions and shortening time-to-market. Despite the energy-saving benefits demonstrated in various sectors, there are still key challenges to overcome in applying additive manufacturing technologies in nuclear energy and renewable energies.
Review
Materials Science, Multidisciplinary
Byron Blakey-Milner et al.
Summary: Metal additive manufacturing in aerospace industry offers significant opportunities for cost reduction, material innovation, design flexibility, and lightweighting. The technology not only enhances performance but also simplifies design and manufacturing processes.
MATERIALS & DESIGN
(2021)
Article
Engineering, Electrical & Electronic
Santa di Cataldo et al.
Summary: Additive manufacturing plays a crucial role in achieving faster, cleaner, and more customizable manufacturing processes in the context of Industry 4.0. However, the lack of repeatability in the manufacturing process is a major barrier to its widespread adoption in mass production. Efforts are being made to integrate advanced information technologies and data-driven approaches to improve quality monitoring and process optimization in additive manufacturing, with a focus on metal powder bed fusion technologies.
PROCEEDINGS OF THE IEEE
(2021)
Article
Materials Science, Multidisciplinary
De Jun Huang et al.
Summary: The article explores the quality repeatability of laser powder bed fusion technology and demonstrates a machine learning approach for root cause analysis, highlighting the significant impact of part location and post-chamber pressure drop on part quality.
MATERIALS & DESIGN
(2021)
Review
Engineering, Multidisciplinary
M. Grasso et al.
Summary: The use of sensor signals in metal powder bed fusion processes for part and process qualification, as well as early detection of anomalies and defects, is a growing area of interest with significant research growth in recent years. Industrial PBF systems are increasingly equipped with sensors and toolkits for data collection and analysis, with new methods proposed and defect detection capabilities demonstrated. Challenges still remain in bridging the gap between proposed methods in research literature and actual industrial implementation.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Materials Science, Multidisciplinary
Sara Nasiri et al.
Summary: This study introduces the application of machine learning in predicting the structural performance and fracture of additively manufactured components, with a focus on its use in predicting the mechanical behavior of 3D printed parts. Previous research on the application of ML in characterizing polymeric and metallic 3D-printed parts is reviewed and discussed to highlight potential limitations, challenges, and future perspectives for industrial applications of ML in additive manufacturing.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2021)
Article
Engineering, Manufacturing
Subin Shrestha et al.
Summary: The study shows that the build location significantly affects porosity in laser powder bed fusion, with the horizontal location being more important than the build height. Single tracks formed at a corner away from the build chamber window and towards the dispenser have the highest pore number and volume.
MANUFACTURING LETTERS
(2021)
Article
Computer Science, Information Systems
Christian Knaak et al.
Summary: A novel analysis approach combining HDR optical imaging and CNN is proposed for predicting the surface roughness of LPBF parts, with the introduction of reinforcement learning for process parameter optimization. Experimental data indicate the practical applicability of the method in industrial environments.
Article
Computer Science, Interdisciplinary Applications
V. Akhil et al.
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
(2020)
Review
Materials Science, Multidisciplinary
L. Dowling et al.
MATERIALS & DESIGN
(2020)
Review
Crystallography
Pinku Yadav et al.
Article
Nanoscience & Nanotechnology
Christopher M. Laursen et al.
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
(2020)
Article
Engineering, Manufacturing
Hermann Baumgartl et al.
PROGRESS IN ADDITIVE MANUFACTURING
(2020)
Editorial Material
Materials Science, Multidisciplinary
Xiaoyuan Lou et al.
Article
Engineering, Industrial
Q. Y. Lu et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
(2019)
Review
Automation & Control Systems
Ehsan Malekipour et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2018)
Review
Engineering, Mechanical
Hoejin Kim et al.
RAPID PROTOTYPING JOURNAL
(2018)
Article
Robotics
Bing Yao et al.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2018)
Review
Engineering, Multidisciplinary
Marco Grasso et al.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2017)
Review
Engineering, Manufacturing
Thomas G. Spears et al.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
(2016)
Article
Automation & Control Systems
S. Clijsters et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2014)
Proceedings Paper
Materials Science, Multidisciplinary
R. A. Hardin et al.
MCWASP XIII: INTERNATIONAL CONFERENCE ON MODELING OF CASTING, WELDING AND ADVANCED SOLIDIFICATION PROCESSES
(2012)
Article
Optics
Tom Craeghs et al.
OPTICS AND LASERS IN ENGINEERING
(2011)
Review
Computer Science, Theory & Methods
AJ Smola et al.
STATISTICS AND COMPUTING
(2004)
