☆ 4.7 Article

Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing

COMPUTERS IN INDUSTRY (2024)

Related references

Note: Only part of the references are listed.

Article Computer Science, Interdisciplinary Applications

Toward Zero Defect Manufacturing with the support of Artificial Intelligence-Insights from an industrial application

Nicolas Leberruyer et al.

Summary: The combination of Zero Defect Manufacturing (ZDM) concept and Artificial Intelligence (AI) provides new opportunities for improved quality management and advanced problem-solving. However, there is a lack of applied research in industrial plants for the widespread deployment of this framework. This article applies AI in an industrial case study to develop application insights and identify prerequisites for achieving ZDM. The study identifies four prerequisites and insights critical to developing an AI solution supporting ZDM.

COMPUTERS IN INDUSTRY (2023)

Add to Collection

Article Engineering, Industrial

Securing cyber-physical additive manufacturing systems by in-situ process authentication using streamline video analysis

Abdullah Al Mamun et al.

Summary: In cyber-physical systems of additive manufacturing, cyber-attacks can compromise the mechanical properties and functionalities by altering the design of the manufactured part. This paper proposes a novel process authentication methodology based on image texture analysis, which can accurately detect process alterations due to printing path changes. Experimental results show that the proposed method outperforms the benchmark method in terms of accuracy and computational efficiency.

JOURNAL OF MANUFACTURING SYSTEMS (2022)

Add to Collection

Review Engineering, Industrial

Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm

Shenghan Guo et al.

Summary: Machine learning has proven to be an effective alternative to physical models in quality prediction and process optimization of metal additive manufacturing. However, the interpretability of machine learning outcomes within the complex thermodynamics of additive manufacturing has been a challenge. Physics-informed machine learning (PIML) addresses this challenge by integrating data-driven methods with physical domain knowledge.

JOURNAL OF MANUFACTURING SYSTEMS (2022)

Add to Collection

Article Automation & Control Systems

Metal-based additive manufacturing condition monitoring methods: From measurement to control

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.

ISA TRANSACTIONS (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Additive Manufacturing In Situ and Ex Situ Geometric Data Registration

Shaw C. Feng et al.

Summary: This paper presents a procedure for data registration and provides an example of aligning data to a single reference coordinate system. Data alignment is crucial for spatially aligning datasets from different measurement devices, enabling downstream applications such as data analytics and part qualification.

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING (2022)

Add to Collection

Article Computer Science, Information Systems

Geometric machine learning: research and applications

Wenming Cao et al.

Summary: This article provides a comprehensive overview of geometric deep learning and comparative methods. It introduces the related work and history of the field, as well as the theoretical background. The evaluation, applications, benchmark datasets, and potential research directions and challenges of graph and manifold methods are discussed.

MULTIMEDIA TOOLS AND APPLICATIONS (2022)

Add to Collection

Review Engineering, Manufacturing

Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review

Yanzhou Fu et al.

Summary: This paper provides a comprehensive overview of machine learning algorithms for defect detection in metal LBAM processes. It describes the algorithms, material types, defect types, dataset types, and algorithm accuracy for various LBAM technologies.

JOURNAL OF MANUFACTURING PROCESSES (2022)

Add to Collection

Review Engineering, Manufacturing

Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions

Zhuo Wang et al.

Summary: This paper provides a systematic review of existing data-driven additive manufacturing (AM) modeling, focusing on different quantities of interest (QoI) along the process-structure-property chain. It summarizes important information and analyzes the successes achieved so far. The paper also discusses the limitations and suggests promising research directions for advancing data-driven AM modeling.

JOURNAL OF MANUFACTURING PROCESSES (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A digital twin hierarchy for metal additive manufacturing

A. Phua et al.

Summary: Digital twins are a conceptual framework for monitoring and controlling the product life-cycle using a simulated replica of the physical system. They have gained attention as an alternative to costly physical testing, utilizing high fidelity simulations, sensor data, and intelligent control. This paper evaluates the specific features of metal additive manufacturing (AM) and reviews the current literature on modeling, sensing, control, and machine intelligence. The proposed digital twin hierarchy for AM provides a developer framework for engineering digital twins for AM and other intelligent manufacturing systems.

COMPUTERS IN INDUSTRY (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Prediction of geometry deviations in additive manufactured parts: comparison of linear regression with machine learning algorithms

Ivanna Baturynska et al.

Summary: The study investigates how to improve dimensional accuracy in additive manufacturing for better applications in medical, aerospace, and automotive industries through statistical analysis and machine learning techniques. Results show that linear regression model performs the best in width, while multilayer perceptron and gradient boost regressor models outperform others in thickness and length.

JOURNAL OF INTELLIGENT MANUFACTURING (2021)

Add to Collection

Article Materials Science, Multidisciplinary

Ontology-based Process Map for Metal Additive Manufacturing

Byeong-Min Roh et al.

Summary: Additive manufacturing allows for more flexibility and complexity in design compared to traditional manufacturing processes, but the predictability and control of print quality in metal AM are challenging due to high process variability. Surrogate models are commonly used for predicting part qualities in metal AM, however, fully understanding the interconnections among process parameters and print qualities is crucial for improving predictability and controllability. An ontology-based process map is presented in this study to capture the complex metal AM processes and link variable selection to quality requirements, ultimately contributing to achieving desired build quality.

JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Review on Quality Control Methods in Metal Additive Manufacturing

Jungeon Lee et al.

Summary: Metal additive manufacturing (AM) and conventional metal manufacturing methods such as welding share similarities in quality issues such as layer misalignment, dimensional errors, and residual stress. Many quality monitoring methods used in both metal AM and welding are interrelated and can be used complementarily.

APPLIED SCIENCES-BASEL (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

A knowledge transfer framework to support rapid process modeling in aerosol jet printing

Haining Zhang et al.

Summary: Aerosol jet printing technology has gained attention in electronic manufacturing industry for its fine resolution and flexibility, but morphology control remains a challenge. A knowledge transfer framework is proposed for efficient modeling of the AJP process under varied operating conditions, aiming to improve controllability of line morphology and overcome limitations of traditional modeling approaches.

ADVANCED ENGINEERING INFORMATICS (2021)

Add to Collection

Review Chemistry, Multidisciplinary

Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

Dalia Mahmoud et al.

Summary: The article reviews the application of machine learning for in situ monitoring and control of the laser powder bed fusion (L-PBF) process. It discusses the challenges in quality consistency and the use of sensor-based monitoring and ML techniques for defect detection and online control. The ultimate goal is to close the loop and take online corrective actions, with a focus on future research directions for ML applications in defect detection and control for L-PBF processes.

APPLIED SCIENCES-BASEL (2021)

Add to Collection

Review Materials Science, Multidisciplinary

Additive manufacturing of structural materials

Guo Liu et al.

Summary: Over the past 30 years, 3D printing technology has seen rapid development and widespread adoption, with a trade-off between printing resolution and scalability/speed. Various materials have great potential for application in additive manufacturing, driving rapid and tremendous development of materials and methods.

MATERIALS SCIENCE & ENGINEERING R-REPORTS (2021)

Add to Collection

Review Metallurgy & Metallurgical Engineering

In situ monitoring methods for selective laser melting additive manufacturing process based on images - A review

Bo Wu et al.

Summary: Selective laser melting (SLM) is widely used in aviation, aerospace, and die manufacturing due to its ability to produce metal components with complex shapes, but its instability leads to quality fluctuations. In situ quality control during SLM process is an effective solution to this issue, with recent focus on in situ monitoring methods using image-based computer vision.

CHINA FOUNDRY (2021)

Add to Collection

Review Automation & Control Systems

A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model

Xi Chen et al.

Summary: WAAM technology integrates the advantages of additive manufacturing and traditional welding technology to form large metal components with low cost and high deposition rates. However, potential issues exist in morphological accuracy, microstructure, and properties due to complex thermal cycles. A multisensor data fusion-based quality control model is proposed to address these challenges.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2021)

Add to Collection

Review Automation & Control Systems

Data-Driven Approaches Toward Smarter Additive Manufacturing

Chenxi Tian et al.

Summary: The latest industrial revolution, Industry 4.0, is driven by digital manufacturing and additive manufacturing technologies, which have expanded the design space for materials and structures. The increasing use of data-driven tools accelerates the exploration and optimization of this design space.

ADVANCED INTELLIGENT SYSTEMS (2021)

Add to Collection

Article Materials Science, Composites

The fabrication of long carbon fiber reinforced polylactic acid composites via fused deposition modelling: Experimental analysis and machine learning

Chao Hu et al.

Summary: Additive manufacturing is a promising technology for revolutionizing traditional manufacturing processes, such as the development of fiber-reinforced composites with superior strength. This study focused on 3D printing PLA composites with chopped long carbon fiber, showing improved mechanical and thermal properties by varying the CF contents. The use of Gaussian Process modeling successfully predicted the optimal CF content for the composites, which closely matched the experimental results.

JOURNAL OF COMPOSITE MATERIALS (2021)

Add to Collection

Review Computer Science, Artificial Intelligence

A review of unsupervised feature selection methods

Saul Solorio-Fernandez et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

Add to Collection

Article Materials Science, Multidisciplinary

Microstructural control in metal laser powder bed fusion additive manufacturing using laser beam shaping strategy

Rongpei Shi et al.

ACTA MATERIALIA (2020)

Add to Collection

Article Green & Sustainable Science & Technology

Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

Jian Qin et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Add to Collection

Article Materials Science, Multidisciplinary

Zoning additive manufacturing process histories using unsupervised machine learning

Sean P. Donegan et al.

MATERIALS CHARACTERIZATION (2020)

Add to Collection

Article Biochemical Research Methods

SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen et al.

NATURE METHODS (2020)

Add to Collection

Review Materials Science, Multidisciplinary

Machine Learning in Additive Manufacturing: A Review

Lingbin Meng et al.

JOM (2020)

Add to Collection

Review Engineering, Mechanical

Non-destructive quality control methods in additive manufacturing: a survey

Paschalis Charalampous et al.

RAPID PROTOTYPING JOURNAL (2020)

Add to Collection

Article Multidisciplinary Sciences

Controlling interdependent meso-nanosecond dynamics and defect generation inmetal 3D printing

Saad A. Khairallah et al.

SCIENCE (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

A knowledge-based process planning framework for wire arc additive manufacturing

Yi Xiong et al.

ADVANCED ENGINEERING INFORMATICS (2020)

Add to Collection

Review Multidisciplinary Sciences

Array programming with NumPy

Charles R. Harris et al.

NATURE (2020)

Add to Collection

Review Education & Educational Research

Deep Learning With TensorFlow: A Review

Bo Pang et al.

JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS (2020)

Add to Collection

Article Computer Science, Information Systems

Unsupervised K-Means Clustering Algorithm

Kristina P. Sinaga et al.

IEEE ACCESS (2020)

Add to Collection

Article Materials Science, Multidisciplinary

Distributed heterogeneous compute infrastructure for the study of additive manufacturing systems

Mathew Thomas et al.

MRS ADVANCES (2020)

Add to Collection

Article Computer Science, Information Systems

Albumentations: Fast and Flexible Image Augmentations

Alexander Buslaev et al.

INFORMATION (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

An approach for composing predictive models from disparate knowledge sources in smart manufacturing environments

Duck Bong Kim

JOURNAL OF INTELLIGENT MANUFACTURING (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Mingtao Wu et al.

JOURNAL OF INTELLIGENT MANUFACTURING (2019)

Add to Collection

Article Mathematics, Interdisciplinary Applications

Electrodynamic machine-learning-enhanced fault-tolerance of robotic free-form printing of complex mixtures

T. I. Zohdi

COMPUTATIONAL MECHANICS (2019)

Add to Collection

Article Engineering, Multidisciplinary

Experimental study of the process failure diagnosis in additive manufacturing based on acoustic emission

Haixi Wu et al.

MEASUREMENT (2019)

Add to Collection

Article Materials Science, Multidisciplinary

Overview of non-destructive evaluation techniques for metal-based additive manufacturing

Catalin Mandache

MATERIALS SCIENCE AND TECHNOLOGY (2019)

Add to Collection

Article Computer Science, Software Engineering

Status, comparison, and future of the representations of additive manufacturing data

Yuchu Qin et al.

COMPUTER-AIDED DESIGN (2019)

Add to Collection

Review Engineering, Multidisciplinary

Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives

Xinbo Qi et al.

ENGINEERING (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Ontology-based knowledge representation for additive manufacturing

Emilio M. Sanfilippo et al.

COMPUTERS IN INDUSTRY (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Production planning for cloud-based additive manufacturing-A computer vision-based approach

Wang Yuanbin et al.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2019)

Add to Collection

Review Chemistry, Multidisciplinary

The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Mehrshad Mehrpouya et al.

APPLIED SCIENCES-BASEL (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Data model for additive manufacturing digital thread: state of the art and perspectives

Renan Bonnard et al.

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2019)

Add to Collection

Article Materials Science, Multidisciplinary

Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning

Prathamesh S. Desai et al.

METALS (2019)

Add to Collection

Review Computer Science, Interdisciplinary Applications

A survey on knowledge transfer for manufacturing data analytics

Seung Hwan Bang et al.

COMPUTERS IN INDUSTRY (2019)

Add to Collection

Review Computer Science, Information Systems

Augmented Reality, Cyber-Physical Systems, and Feedback Control for Additive Manufacturing: A Review

Hugo Lhachemi et al.

IEEE ACCESS (2019)

Add to Collection

Article Engineering, Manufacturing

In-process closed-loop control for stabilising the melt pool temperature in selective laser melting

Volker Renken et al.

PROGRESS IN ADDITIVE MANUFACTURING (2019)

Add to Collection

Article Automation & Control Systems

Cyber acoustic analysis of additively manufactured objects

Thomas Mativo et al.

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

An ontology-oriented knowledge methodology for process planning in additive layer manufacturing

Jeremy S. Liang

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A cloud-based manufacturing control system with data integration from multiple autonomous agents

Silviu Raileanu et al.

COMPUTERS IN INDUSTRY (2018)

Add to Collection

Review Metallurgy & Metallurgical Engineering

A Metallographic Review of 3D Printing/Additive Manufacturing of Metal and Alloy Products and Components

L. E. Murr

METALLOGRAPHY MICROSTRUCTURE AND ANALYSIS (2018)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A Design for Additive Manufacturing Ontology

Mahmoud Dinar et al.

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING (2017)

Add to Collection

Article Engineering, Manufacturing

Toward a Digital Thread and Data Package for Metals-Additive Manufacturing

D. B. Kim et al.

SMART AND SUSTAINABLE MANUFACTURING SYSTEMS (2017)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Industrial Additive Manufacturing: A manufacturing systems perspective

Daniel R. Eyers et al.

COMPUTERS IN INDUSTRY (2017)

Add to Collection

Review Materials Science, Multidisciplinary

Modeling of additive manufacturing processes for metals: Challenges and opportunities

M. M. Francois et al.

CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE (2017)

Add to Collection

Article Materials Science, Multidisciplinary

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Mohsen Seifi et al.

JOM (2016)

Add to Collection

Article Statistics & Probability

A random forest guided tour

Gerard Biau et al.

TEST (2016)

Add to Collection

Article Statistics & Probability

A random forest guided tour

Gerard Biau et al.

TEST (2016)

Add to Collection

Review Computer Science, Information Systems

Edge Computing: Vision and Challenges

Weisong Shi et al.

IEEE INTERNET OF THINGS JOURNAL (2016)

Add to Collection

Article Multidisciplinary Sciences

Comment: The FAIR Guiding Principles for scientific data management and stewardship

Mark D. Wilkinson et al.

SCIENTIFIC DATA (2016)

Add to Collection

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)

Add to Collection

Review Engineering, Manufacturing

Overview of Additive Manufacturing Informatics: A Digital Thread

Deborah Mies et al.

INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2016)

Add to Collection

Article Multidisciplinary Sciences

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

Theodoros Giannakopoulos

PLOS ONE (2015)

Add to Collection

Review Engineering, Manufacturing

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

Gustavo Tapia et al.

JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2014)

Add to Collection

Review Materials Science, Multidisciplinary

Metal Additive Manufacturing: A Review

William E. Frazier

JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE (2014)

Add to Collection

Article Multidisciplinary Sciences

scikit-image: image processing in Python

Stefan van der Walt et al.

PEERJ (2014)

Add to Collection

Article Biotechnology & Applied Microbiology

What are decision trees?

Carl Kingsford et al.

NATURE BIOTECHNOLOGY (2008)

Add to Collection

© Peeref 2019-2024. All rights reserved.