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Published: 30 June 2023
Fig. 3 Deep-learning-based topology optimization approach, with (a) element-removal strategy based on finite-element simulation (FEM, finite-element model), (b) deep learning model combining U-net and long short-term memory (LSTM) nets, and (c) application on two- and 3D topology optimization More
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
... with a newfound opportunity to enhance decision making with improved understanding of the influence of AM on 3D geometries and part properties. Deep learning has been applied to topology optimization and outperformed the traditional methods in terms of shorter time, lower cost, and broader applicability...
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
... complexity of both product design and process in polymer AM, conventional control charts may not be able to provide sufficient capability in identifying critical patterns in online monitoring. Thus, incorporating emerging machine learning techniques, including deep learning, has become a popular choice in AM...
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...
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
... supervised and unsupervised referred to the process of learning/training of the related methods: supervised learning requires training examples, unsupervised learning does not require training examples. The subsequent descriptions introduce three methods that have proven their relevance, namely cluster...
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
... tool to minimize or eliminate postbuild inspections. In other efforts to monitor the PBF additive manufacturing process not listed here, deep learning is being used to detect defects from visual and thermal images of the bed layer by layer. The models would then be deployed on the edge to monitor...
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
... 147 , 2021 , p 104277 10.1016/j.jmps.2020.104277 27. Herriott C. and Spear A.D. , Predicting Microstructure-Dependent Mechanical Properties in Additively Manufactured Metals with Machine- and Deep-Learning Methods , Comput. Mater. Sci. , Vol 175 , 2020 , p 109599 10.1016...
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.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.9781627084390
EISBN: 978-1-62708-439-0
Series: ASM Handbook
Volume: 14B
Publisher: ASM International
Published: 01 January 2006
DOI: 10.31399/asm.hb.v14b.a0005153
EISBN: 978-1-62708-186-3
.... Fig. 3 Picture of a successful and a failed cup Experimental Investigation of Constant Binder Force Cases The process of deep drawing of conical cups involves applying a drawing force with a punch to the center of a circular blank, as shown in Fig. 4 . A binder force is applied...
Series: ASM Handbook
Volume: 13A
Publisher: ASM International
Published: 01 January 2003
DOI: 10.31399/asm.hb.v13a.a0003651
EISBN: 978-1-62708-182-5
... corrosion rate. Specimen Burial A technique frequently used for shallow burial is to dig a trench up to 1.22 m (4 ft) deep that is long enough to allow placement of the specimens approximately 300 mm (12 in.) apart along its length ( Fig. 1 ). For burial of specimens deeper than 1.22 m (4 ft...
Series: ASM Handbook
Volume: 24A
Publisher: ASM International
Published: 30 June 2023
DOI: 10.31399/asm.hb.v24A.a0006951
EISBN: 978-1-62708-439-0
... design approach displaces the burden of architecting the unit cell from the user to a computer algorithm. At least three approaches have been proposed toward this end: field superimposition, voxelwise topology optimization, and machine learning methods. The latter is discussed in more detail...
Series: ASM Handbook
Volume: 11B
Publisher: ASM International
Published: 15 May 2022
DOI: 10.31399/asm.hb.v11B.a0006936
EISBN: 978-1-62708-395-9
Series: ASM Handbook
Volume: 19
Publisher: ASM International
Published: 01 January 1996
DOI: 10.31399/asm.hb.v19.a0002416
EISBN: 978-1-62708-193-1
... should be established and be consistent with the inspection techniques employed during manufacture and in service.” Simple, inexpensive visual methods are preferred for in-service inspections. The AFGS 87–221 specifies the lower limit for visible damage to be a 2.5 mm deep dent or damage from a 25.4 mm...
Book Chapter

Series: ASM Desk Editions
Publisher: ASM International
Published: 01 December 1998
DOI: 10.31399/asm.hb.mhde2.a0003193
EISBN: 978-1-62708-199-3
... used in abrasive products Resin bonds Readily available Easy to true and dress Moderate freeness of cut Applicable for a range of operations First selection for learning the use of superabrasive wheels Generally used with coated abrasive product and rough grinding or operations...
Series: ASM Handbook
Volume: 24
Publisher: ASM International
Published: 15 June 2020
DOI: 10.31399/asm.hb.v24.a0006548
EISBN: 978-1-62708-290-7
... remarkably in 1984 with Apple Computer’s release of the Macintosh computer. With its graphical user interface facilitated by a mouse, learning how to access the capabilities of a computer moved from learning a cryptic text-based screen and commands to “point and click” and “plug and play.” Over a few years...
Series: ASM Handbook
Volume: 11
Publisher: ASM International
Published: 15 January 2021
DOI: 10.31399/asm.hb.v11.a0006753
EISBN: 978-1-62708-295-2
... to property. For example, a molasses tank failed in Boston in 1919, and another molasses tank failed in Bellview, New Jersey, in 1973 ( Ref 3 ). Were the causes identified in 1919? Were lessons learned as a result of the accident? Were corrective actions developed and implemented to prevent recurrence...
Series: ASM Handbook
Volume: 13A
Publisher: ASM International
Published: 01 January 2003
DOI: 10.31399/asm.hb.v13a.a0003642
EISBN: 978-1-62708-182-5
... in a deep borehole, either vertically or horizontally, with a small air gap between the container and the borehole. For vitrified wastes, a pour canister inside the outer container acts as an additional barrier, making the successful performance of the container material crucial to fulfilling...