Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing

Ko, Hyunwoong; Witherell, Paul; Ndiaye, Ndeye Y.; Lu, Yan

doi:10.1109/coase.2019.8843316

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Another attempt of grey box modeling for build quality in dependency of process parameters and in situ sensor signatures was proposed in [ 202 ] by Gaikward et al, where the a priori knowledge of physical processes was incorporated into three sequentially connected shallow ANNs and consequently achieved better performance in comparison with purely data-driven methods (CNN, LSTM, RNN, among others). With respect to DfX and knowledge engineering, Ko et al employed CART to predict additive manufacturability, which was further fed back to a knowledge-query formulation phase in order to continuously construct and broaden an AM knowledge base [ 203 ] ( EG -factor). In addition, HMM and k-means demonstrated their applications for quality assessing and monitoring in [ 204 , 205 ].…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

190

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

190

View full text Add to dashboard Cite

show abstract

“…Whereas ML does not require in-depth knowledge, provided a sufficient amount of data is available. Moreover, ML provides the opportunity for continuous processes, which has the potential to achieve intelligent 3DP automation [264,265]. Nevertheless, there is an opportunity for existing DoE, FEA and mechanistic modellers to exploit ML to further enrich their research.…”

Section: Machine Learning Vs Non-ml Techniquesmentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

110

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…The algorithm generates prescriptive rules based on the prediction models to assist decision-making throughout AM product development. 26 ML methods were used to distinguish between defect-free and faulty metal AM and reported that ML could become a real-time, online, multi-element, and non-destructive defect detection system for AM. 27 A two-step method was used to extract information on the predictability of additive manufacturability from data using CART machine learning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning

Thakur

Kumar

Singh

et al. 2023

Journal of Thermoplastic Composite Materials

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Carbon fibre (CF) based polymeric composites are being used in automobile and aviation applications due to their lightweight, excellent mechanical and physical properties. In this study, the fused filament fabrication (FFF) technique was used to prepare composite structures of polylactic acid (PLA) sandwiched with CF layers followed by prediction of optimum setting by machine learning (ML). In the first stage, PLA-CF-PLA based composite structures (as per ASTM D638 type IV) were manufactured with deposition of fibre at various angles (0°, 45° and 90°), nozzle temperature (200°C, 205°C and 210°C) and bed temperature (55°C, 60°C and 65°C). Further, the prepared composite structures were subjected to tensile testing (strength at peak and break, strain at peak and break, Young’s modulus and modulus of toughness) followed by fracture analysis through a scanning electron microscope (SEM) energy-dispersive spectroscopy (EDS). The results of the study are supported by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR) analysis, and differential scanning calorimetry (DSC). In the second stage, Classification and Regression Trees (CART) of the ML approach were used to model strength at peak and strength at break. The results of the study have highlighted that combination of parameters, 0˚orientation of CF deposition, 205°C nozzle temperature, and 55°C bed temperature are the optimum settings for manufacturing PLA-CF composite structures. The ML CART model is a valuable tool for predicting the strength at peak and strength at break hybrid additive manufacturing of highly sustainable PLA-CF-PLA sandwiched composite structures.

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Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing

Cited by 18 publications

References 20 publications

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Harnessing artificial intelligence for the next generation of 3D printed medicines

Hybrid additive manufacturing of highly sustainable Polylactic acid -Carbon Fiber-Polylactic acid sandwiched composite structures: Optimization and machine learning

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