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    EVEN OUT WEARING OF MACHINE COMPONENTS DURING MACHINING
    1.
    发明申请
    EVEN OUT WEARING OF MACHINE COMPONENTS DURING MACHINING 有权

    公开(公告)号：US20220080545A1

    公开(公告)日：2022-03-17

    申请号：US17477420

    申请日：2021-09-16

    Applicant: Autodesk, Inc.

    Inventor： Lee Sanders ,  Akmal Ariff Bin Abu Bakar

    IPC: B23Q15/16 ,  B23Q17/09 ,  B23Q17/20 ,  B23Q17/10 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using subtractive manufacturing systems and techniques include, in one aspect, a method including obtaining information regarding a geometry of a part to be machined by a computer-controlled manufacturing system from a workpiece; based on the information regarding the geometry, identifying machine components to be used by the computer-controlled manufacturing system during machining the part; determining a position for the machining of the part with respect to at least one of the machine components, to even out wear on the machine components, based on data indicating previous positions, movements and wear of components associated with the computer-controlled manufacturing system; and providing instructions usable by the computer-controlled manufacturing system, wherein the instructions are configured to cause the computer-controlled manufacturing system to use the position for the machining.

    Predictive machining for forging distortions
    3.
    发明授权
    Predictive machining for forging distortions 有权

    公开(公告)号：US11263373B2

    公开(公告)日：2022-03-01

    申请号：US16257729

    申请日：2019-01-25

    Applicant: Autodesk, Inc.

    Inventor： Daniel Mario Noviello ,  Fikret Kalay ,  Akmal Ariff Bin Abu Bakar

    IPC: G06F30/23 ,  G06T17/20 ,  G06F30/17

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive machining for forging distortions. In one aspect, a method includes obtaining a mesh model of a machined part to be machined from a forged part and residual stress field data for the forged part, where the mesh model includes multiple nodes corresponding to at least one boundary of the machined part, updating the mesh model by applying the residual stress field data to the multiple nodes corresponding to the at least one boundary to change positions of the multiple nodes in the mesh model, repeating the updating until an amount of change to each of the positions of the multiple nodes in the mesh model satisfies a correction criteria, and providing the compensated mesh model for use in machining the machined part from the forged part.

    TOOLPATH GENERATION BY REINFORCEMENT LEARNING FOR COMPUTER AIDED MANUFACTURING
    5.
    发明公开
    TOOLPATH GENERATION BY REINFORCEMENT LEARNING FOR COMPUTER AIDED MANUFACTURING 审中-公开

    公开(公告)号：US20240061383A1

    公开(公告)日：2024-02-22

    申请号：US18240703

    申请日：2023-08-31

    Applicant: Autodesk, Inc.

    Inventor： David Patrick Lovell ,  Akmal Ariff Bin Abu Bakar ,  Saaras Mehan

    IPC: G05B13/02 ,  G06F30/27 ,  G05B19/4097 ,  G06F30/10 ,  G06N3/045

    CPC classification number: G05B13/027 ,  G06F30/27 ,  G05B19/4097 ,  G06F30/10 ,  G06N3/045 ,  G05B2219/35167 ,  G05B2219/35134

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at least the portion of the manufacturable object.

    Toolpath generation by reinforcement learning for computer aided manufacturing
    6.
    发明授权
    Toolpath generation by reinforcement learning for computer aided manufacturing 有权

    公开(公告)号：US11782396B2

    公开(公告)日：2023-10-10

    申请号：US17153266

    申请日：2021-01-20

    Applicant: Autodesk, Inc.

    Inventor： David Patrick Lovell ,  Akmal Ariff Bin Abu Bakar ,  Saaras Mehan

    IPC: G05B13/02 ,  G06F30/27 ,  G06N3/045 ,  G05B19/4097 ,  G06F30/10

    CPC classification number: G05B13/027 ,  G05B19/4097 ,  G06F30/10 ,  G06F30/27 ,  G06N3/045 ,  G05B2219/35134 ,  G05B2219/35167

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at least the portion of the manufacturable object.

    TOOLPATH GENERATION BY REINFORCEMENT LEARNING FOR COMPUTER AIDED MANUFACTURING
    7.
    发明申请
    TOOLPATH GENERATION BY REINFORCEMENT LEARNING FOR COMPUTER AIDED MANUFACTURING 有权

    公开(公告)号：US20210397142A1

    公开(公告)日：2021-12-23

    申请号：US17153266

    申请日：2021-01-20

    Applicant: Autodesk, Inc.

    Inventor： David Patrick Lovell ,  Akmal Ariff Bin Abu Bakar ,  Saaras Mehan

    IPC: G05B13/02 ,  G06F30/27 ,  G06N3/04 ,  G06F30/10 ,  G05B19/4097

    Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using toolpaths generated by reinforcement learning for use with subtractive manufacturing systems and techniques, include: obtaining, in a computer aided design or manufacturing program, a three dimensional model of a manufacturable object; generating toolpaths that are usable by a computer-controlled manufacturing system to manufacture at least a portion of the manufacturable object by providing at least a portion of the three dimensional model to a machine learning algorithm that employs reinforcement learning, wherein the machine learning algorithm includes one or more scoring functions that include rewards that correlate with desired toolpath characteristics comprising toolpath smoothness, toolpath length, and avoiding collision with the three dimensional model; and providing the toolpaths to the computer-controlled manufacturing system to manufacture at least the portion of the manufacturable object.

    PREDICTIVE MACHINING FOR FORGING DISTORTIONS
    8.
    发明申请
    PREDICTIVE MACHINING FOR FORGING DISTORTIONS 审中-公开

    公开(公告)号：US20200242204A1

    公开(公告)日：2020-07-30

    申请号：US16257729

    申请日：2019-01-25

    Applicant: Autodesk, Inc.

    Inventor： Daniel Mario Noviello ,  Fikret Kalay ,  Akmal Ariff Bin Abu Bakar

    IPC: G06F17/50 ,  G06T17/20

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive machining for forging distortions. In one aspect, a method includes obtaining a mesh model of a machined part to be machined from a forged part and residual stress field data for the forged part, where the mesh model includes multiple nodes corresponding to at least one boundary of the machined part, updating the mesh model by applying the residual stress field data to the multiple nodes corresponding to the at least one boundary to change positions of the multiple nodes in the mesh model, repeating the updating until an amount of change to each of the positions of the multiple nodes in the mesh model satisfies a correction criteria, and providing the compensated mesh model for use in machining the machined part from the forged part.

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