4.7 Article

In-Situ Defect Detection of Metal Additive Manufacturing: An Integrated Framework

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

On-line melt pool temperature control in L-PBF additive manufacturing

Hossein Rezaeifar et al.

Summary: In this study, different controllers were designed to control the melt pool temperature of Inconel 625 superalloy, aimed at improving the microhardness and structural uniformity of the parts. The implementation of a layer-wise melt pool temperature control system led to improved quality and consistency in fabricated parts, as demonstrated through the evaluation of microhardness and microstructure uniformity.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Article Optics

On the application of machine learning for defect detection in L-PBF additive manufacturing

Mohammad Ghayoomi Mohammadi et al.

Summary: This study investigates the performance of machine learning techniques for online defect detection in the Laser Powder Bed Fusion (L-PBF) process. By intentionally adjusting process parameters to create defects in H13 steel samples, acoustic emission signals are analyzed using hierarchical K-means clustering, supervised deep learning neural networks, principal component analysis, Gaussian Mixture Models, and variational auto-encoders. The supervised deep learning successfully detected quality trends in AE signals, while the VAE approach provided a novel method for defect detection in L-PBF processes.

OPTICS AND LASER TECHNOLOGY (2021)

Article Engineering, Electrical & Electronic

Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions This article provides a wide overview of the latest progress of in situ monitoring and control in powder bed metal additive manufacturing, showcasing solutions from both research and industry

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)

Proceedings Paper Materials Science, Multidisciplinary

Automation and manufacturing of smart materials in additive manufacturing technologies using Internet of Things towards the adoption of industry 4.0

Reem Ashima et al.

Summary: The importance of mass customization and personalization brought by Industry 4.0 is recognized, but additive manufacturing technologies face limitations in mass production, leading to industry hesitation towards commercial applications. Research aims to improve the reliability of additive manufacturing processes and provide smart materials for manufacturers globally, utilizing Industry 4.0 technologies.

MATERIALS TODAY-PROCEEDINGS (2021)

Article Engineering, Industrial

Convolutional and generative adversarial neural networks in manufacturing

Andrew Kusiak

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)

Article Engineering, Industrial

In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy

Mohammad Montazeri et al.

IISE TRANSACTIONS (2020)

Article Computer Science, Artificial Intelligence

A survey on machine learning for data fusion

Tong Meng et al.

INFORMATION FUSION (2020)

Review Automation & Control Systems

Additive manufacturing cost estimation models-a classification review

Aini Zuhra Abdul Kadir et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)

Review Materials Science, Multidisciplinary

Machine Learning in Additive Manufacturing: A Review

Lingbin Meng et al.

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

Article Materials Science, Multidisciplinary

In-SituMonitoring for Defect Identification in Nickel Alloy Complex Geometries Fabricated by L-PBF Additive Manufacturing

J. Logan McNeil et al.

METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE (2020)

Article Engineering, Industrial

IoT-enabled cloud-based additive manufacturing platform to support rapid product development

Yuanbin Wang et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2019)

Article Computer Science, Interdisciplinary Applications

A statistical learning method for image-based monitoring of the plume signature in laser powder bed fusion

M. Grasso et al.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2019)

Article Computer Science, Interdisciplinary Applications

Prediction of surface roughness in extrusion-based additive manufacturing with machine learning

Zhixiong Li et al.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2019)

Article Engineering, Manufacturing

In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches

Yingjie Zhang et al.

ADDITIVE MANUFACTURING (2019)

Article Engineering, Industrial

Machine learning in tolerancing for additive manufacturing

Zuowei Zhu et al.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2018)

Article Materials Science, Multidisciplinary

Additive manufacturing: scientific and technological challenges, market uptake and opportunities

Syed A. M. Tofail et al.

MATERIALS TODAY (2018)

Proceedings Paper Physics, Applied

In-Situ Monitoring and Modeling of Metal Additive Manufacturing Powder Bed Fusion

Jocob Alldredge et al.

44TH ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOL 37 (2018)

Review Materials Science, Multidisciplinary

Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

Sarah K. Everton et al.

MATERIALS & DESIGN (2016)

Proceedings Paper Physics, Applied

Monitoring System for the Quality Assessment in Additive Manufacturing

Volker Carl

41ST ANNUAL REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOL 34 (2015)

Article Dentistry, Oral Surgery & Medicine

Accuracy of medical models made by additive manufacturing (rapid manufacturing)

Mika Salmi et al.

JOURNAL OF CRANIO-MAXILLOFACIAL SURGERY (2013)

Review Engineering, Mechanical

Additive manufacturing: technology, applications and research needs

Nannan Guo et al.

FRONTIERS OF MECHANICAL ENGINEERING (2013)

News Item Computer Science, Hardware & Architecture

Developing software online with platform-as-a-service technology

George Lawton

COMPUTER (2008)