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Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006975
EISBN: 978-1-62708-439-0
... and machine learning include design, process-structure-properties (PSP) relationships, and process monitoring and quality control. The article also presents tools used for data analytics. data analytics machine learning metal additive manufacturing ADDITIVE MANUFACTURING (AM) is a process...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006992
EISBN: 978-1-62708-439-0
.... It reviews investigations of ML-based strategies, identifies challenges and research opportunities, and presents strategies for assessing anomaly detection performance. in situ process monitoring laser-based powder-bed fusion machine learning porosity processing defects voids Introduction...
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Published: 12 September 2022
Fig. 10 Schematic of the machine learning–assisted FINP‐IμC biochip. FINP, flexible inkjet-nanoparticle-printed; ML, machine learning. Source: Ref 24 . Reprinted with permission from Wiley More
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Published: 30 June 2023
Fig. 24 Machine learning generated designs. Source: Ref 119 More
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Published: 30 June 2023
Fig. 2 Taxonomy of machine learning applications in additive manufacturing (AM). Text outside the box is the data type; bold text is the machine learning application in AM; ( X 1 , X 2 , ..., X n ) is the input vector containing all input features; and Y is the output. Source: Ref 4 More
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Published: 30 June 2023
Fig. 2 Using labeled experimental data as machine learning ground truth, showing x-ray computed tomography (CT) flaw in top row, and layerwise imagery of corresponding build layer in bottom row More
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Published: 30 June 2023
Fig. 7 Summary of machine-learning-based anomaly-detection strategies for metal powder-bed fusion additive manufacturing. (a) ML, machine learning. (b) XCT, x-ray computed tomography. (c) ADR, automated defect recognition More
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006978
EISBN: 978-1-62708-439-0
... for the development of high quality machine learning models, used for establishing process-structure-property relationships. Data Analysis To make good use of in situ monitoring data, indications of anomalies must be reliably evaluated. Manual review can be time-consuming for most large in situ monitoring data...
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Published: 30 June 2023
Fig. 2 Data flows for a variety of use cases for in situ process monitoring and control. ML, machine learning; AI, artificial intelligence More
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Published: 30 June 2023
Fig. 9 Technology hierarchy resting on a rocky political, economic, social, and technology landscape. ICME, integrated computational materials engineering; AI, artificial intelligence; ML, machine learning More
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Published: 30 June 2023
Fig. 3 Data preparation and cleaning procedure. STL, standard triangle/tessellation language; XCT, x-ray computed tomography; ADR, automated defect recognition; MS, multispectral; ML, machine learning; DMF, design and monitoring framework More
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006968
EISBN: 978-1-62708-439-0
... conventional statistical process control methods, such as control charts, and emerging machine learning techniques ( Ref 104 ). As a classical and commonly applied tool, control charts can detect a shift caused by underlying distribution changes ( Ref 105 ). In practice, a common application to monitor...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006979
EISBN: 978-1-62708-439-0
... for their development are found in Ref 7 to 10 . Integrated computational materials engineering tools are also being developed. Additionally, modeling and simulation tools, artificial intelligence (e.g., machine learning, neural networks, etc.), and new testing methodologies are being employed. Application...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006958
EISBN: 978-1-62708-439-0
... transformation include additive manufacturing (AM), generative design, high-performance and edge computing, artificial intelligence, and machine learning. Additive manufacturing, also known as three-dimensional (3D) printing, is at the core of Industry 4.0; AM is the process of creating a physical object...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006950
EISBN: 978-1-62708-439-0
... TO Improved robustness against the inevitable defects (mechanical) Manufacturing readiness Machine learning, generative design, etc. Computational cost (economy) This article includes detailed sections on SDD and DO (within the context of metal AM) as well as three case studies on the adoption...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006994
EISBN: 978-1-62708-439-0
... the loss function for machine learning into the objective function, a term that quantifies the degree of connectivity. Simple measures of connectivity between adjacent cells are the number of shared components across an interface, although this measure does not have any mechanical properties...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006988
EISBN: 978-1-62708-439-0
... relationships. The governing equations are then solved over some finite domain using numerical solvers. Such computations can be cost-prohibitive in terms of exploring a high-dimensional AM design space. Alternatively, data-driven models, which include machine learning models, incorporate training...
Series: ASM Handbook
Volume: 17
Publisher: ASM International
Published: 01 August 2018
DOI: 10.31399/asm.hb.v17.a0006439
EISBN: 978-1-62708-190-0
...) The support vector machine (SVM) is also a supervised learning classifier, which separates the feature space using hyperplanes that result in a maximum separation of the features in the feature space; thus, the SVM is also called a large margin classifier. Given a set of training features (i.e., length...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.9781627084390
EISBN: 978-1-62708-439-0
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0007022
EISBN: 978-1-62708-439-0
... approach. The key elements of the future state are likely to include the integrated application of: Integrated computational materials engineering (ICME) Machine learning and artificial intelligence In situ process sensors and feedforward, adaptive process controls Standards (process...