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    Digital Twin in Process Planning of the Additive and Subtractive Process Chain for Laser Metal Deposition and Micro Milling of Stainless Steel

    Source: Journal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 007::page 71004-1
    Author:
    Denkena, Berend
    ,
    Wichmann, Marcel
    ,
    Malek, Talash
    ,
    Nguyen, Hai Nam
    ,
    Kato, Makoto
    ,
    Isshiki, Kaito
    ,
    Koike, Ryo
    ,
    Kakinuma, Yasuhiro
    DOI: 10.1115/1.4065415
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Additive and subtractive (Add/Sub) manufacturing processes are increasingly being combined to produce complex parts with unique geometries and properties. However, the design of such combined processes is often challenging as it requires a deep understanding of the interaction between the different processes. On the other hand, digital twin (DT) technology has become a powerful tool in recent years for optimizing manufacturing processes. This article explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The article describes the integration of laser metal deposition (LMD) and micro-milling prediction models of resulting geometry (width and height), hardness, and surface roughness into the digital twin. This is then used for combined process planning to achieve different target values regarding resulting geometry and surface roughness. For the planning of this combined process chain, further criteria such as tool life, energy, and process time are considered in the optimization, showing the potential for sustainable and efficient production. Sensorless cutting force estimation is also used to detect small cutting forces, with the potential to use this as a soft sensor for roughness estimation. The measured width, height, and roughness as a result of the process parameters suggested by the optimization algorithms showed a mean absolute percentage error (MAPE) of 4, 17, and 16%, respectively.
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      Digital Twin in Process Planning of the Additive and Subtractive Process Chain for Laser Metal Deposition and Micro Milling of Stainless Steel

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303448
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    contributor authorDenkena, Berend
    contributor authorWichmann, Marcel
    contributor authorMalek, Talash
    contributor authorNguyen, Hai Nam
    contributor authorKato, Makoto
    contributor authorIsshiki, Kaito
    contributor authorKoike, Ryo
    contributor authorKakinuma, Yasuhiro
    date accessioned2024-12-24T19:11:05Z
    date available2024-12-24T19:11:05Z
    date copyright5/21/2024 12:00:00 AM
    date issued2024
    identifier issn1087-1357
    identifier othermanu_146_7_071004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303448
    description abstractAdditive and subtractive (Add/Sub) manufacturing processes are increasingly being combined to produce complex parts with unique geometries and properties. However, the design of such combined processes is often challenging as it requires a deep understanding of the interaction between the different processes. On the other hand, digital twin (DT) technology has become a powerful tool in recent years for optimizing manufacturing processes. This article explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The article describes the integration of laser metal deposition (LMD) and micro-milling prediction models of resulting geometry (width and height), hardness, and surface roughness into the digital twin. This is then used for combined process planning to achieve different target values regarding resulting geometry and surface roughness. For the planning of this combined process chain, further criteria such as tool life, energy, and process time are considered in the optimization, showing the potential for sustainable and efficient production. Sensorless cutting force estimation is also used to detect small cutting forces, with the potential to use this as a soft sensor for roughness estimation. The measured width, height, and roughness as a result of the process parameters suggested by the optimization algorithms showed a mean absolute percentage error (MAPE) of 4, 17, and 16%, respectively.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleDigital Twin in Process Planning of the Additive and Subtractive Process Chain for Laser Metal Deposition and Micro Milling of Stainless Steel
    typeJournal Paper
    journal volume146
    journal issue7
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4065415
    journal fristpage71004-1
    journal lastpage71004-11
    page11
    treeJournal of Manufacturing Science and Engineering:;2024:;volume( 146 ):;issue: 007
    contenttypeFulltext
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