Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing

A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Petrik, Jan [verfasserIn] Bambach, Markus [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Path planning Wire arc additive manufacturing Reinforcement learning

Übergeordnetes Werk:	Enthalten in: Journal of manufacturing processes - Dearborn, Mich. : Soc., 1999, 93, Seite 75-89
Übergeordnetes Werk:	volume:93 ; pages:75-89

DOI / URN:	10.1016/j.jmapro.2023.03.013

Katalog-ID:	ELV062816837

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520			\|a A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry.
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matchkey_str	petrikjanbambachmarkus:2023----:enocmnlannadpiiainaeptpannfrhnaldtutrsn
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publishDate	2023
allfields	10.1016/j.jmapro.2023.03.013 doi (DE-627)ELV062816837 (ELSEVIER)S1526-6125(23)00221-9 DE-627 ger DE-627 rda eng 650 620 004 VZ Petrik, Jan verfasserin (orcid)0000-0003-2096-5320 aut Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry. Path planning Wire arc additive manufacturing Reinforcement learning Bambach, Markus verfasserin aut Enthalten in Journal of manufacturing processes Dearborn, Mich. : Soc., 1999 93, Seite 75-89 Online-Ressource (DE-627)472650998 (DE-600)2168529-0 (DE-576)302969888 nnns volume:93 pages:75-89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 75-89
spelling	10.1016/j.jmapro.2023.03.013 doi (DE-627)ELV062816837 (ELSEVIER)S1526-6125(23)00221-9 DE-627 ger DE-627 rda eng 650 620 004 VZ Petrik, Jan verfasserin (orcid)0000-0003-2096-5320 aut Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry. Path planning Wire arc additive manufacturing Reinforcement learning Bambach, Markus verfasserin aut Enthalten in Journal of manufacturing processes Dearborn, Mich. : Soc., 1999 93, Seite 75-89 Online-Ressource (DE-627)472650998 (DE-600)2168529-0 (DE-576)302969888 nnns volume:93 pages:75-89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 75-89
allfields_unstemmed	10.1016/j.jmapro.2023.03.013 doi (DE-627)ELV062816837 (ELSEVIER)S1526-6125(23)00221-9 DE-627 ger DE-627 rda eng 650 620 004 VZ Petrik, Jan verfasserin (orcid)0000-0003-2096-5320 aut Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry. Path planning Wire arc additive manufacturing Reinforcement learning Bambach, Markus verfasserin aut Enthalten in Journal of manufacturing processes Dearborn, Mich. : Soc., 1999 93, Seite 75-89 Online-Ressource (DE-627)472650998 (DE-600)2168529-0 (DE-576)302969888 nnns volume:93 pages:75-89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 75-89
allfieldsGer	10.1016/j.jmapro.2023.03.013 doi (DE-627)ELV062816837 (ELSEVIER)S1526-6125(23)00221-9 DE-627 ger DE-627 rda eng 650 620 004 VZ Petrik, Jan verfasserin (orcid)0000-0003-2096-5320 aut Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry. Path planning Wire arc additive manufacturing Reinforcement learning Bambach, Markus verfasserin aut Enthalten in Journal of manufacturing processes Dearborn, Mich. : Soc., 1999 93, Seite 75-89 Online-Ressource (DE-627)472650998 (DE-600)2168529-0 (DE-576)302969888 nnns volume:93 pages:75-89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 75-89
allfieldsSound	10.1016/j.jmapro.2023.03.013 doi (DE-627)ELV062816837 (ELSEVIER)S1526-6125(23)00221-9 DE-627 ger DE-627 rda eng 650 620 004 VZ Petrik, Jan verfasserin (orcid)0000-0003-2096-5320 aut Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry. Path planning Wire arc additive manufacturing Reinforcement learning Bambach, Markus verfasserin aut Enthalten in Journal of manufacturing processes Dearborn, Mich. : Soc., 1999 93, Seite 75-89 Online-Ressource (DE-627)472650998 (DE-600)2168529-0 (DE-576)302969888 nnns volume:93 pages:75-89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 93 75-89
language	English
source	Enthalten in Journal of manufacturing processes 93, Seite 75-89 volume:93 pages:75-89
sourceStr	Enthalten in Journal of manufacturing processes 93, Seite 75-89 volume:93 pages:75-89
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Path planning Wire arc additive manufacturing Reinforcement learning
dewey-raw	650
isfreeaccess_bool	false
container_title	Journal of manufacturing processes
authorswithroles_txt_mv	Petrik, Jan @@aut@@ Bambach, Markus @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	472650998
dewey-sort	3650
id	ELV062816837
language_de	englisch
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author	Petrik, Jan
spellingShingle	Petrik, Jan ddc 650 misc Path planning misc Wire arc additive manufacturing misc Reinforcement learning Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
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topic_title	650 620 004 VZ Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing Path planning Wire arc additive manufacturing Reinforcement learning
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title	Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
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title_full	Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
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title_sort	reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
title_auth	Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
abstract	A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry.
abstractGer	A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry.
abstract_unstemmed	A well-designed deposition path is one of the basic prerequisites for the successful fabrication of a component by deposition based additive manufacturing processes. Three main approaches are currently used to determine the deposition path. First, these are general path templates that are applied to the entire geometry. Nevertheless, this approach suffers from poor adaptability to the geometry. Second, they are algorithms where it is necessary to divide the geometry into sub-parts, which are then filled either by general path templates or by paths derived, e.g., from the signed distance function. These often require human intervention and may fail to find a suitable deposition path. Third, there are planning strategies that deal only with a particular topologies, and are not transferable to other geometries. A developed path planning framework named RLPlanner, which makes use of reinforcement learning as well as automatized prepossessing and Sequential Least Squares Programming optimization method, addresses these drawbacks. This solution enables fully automatic deposition path planning for thin-walled structures in wire arc additive manufacturing. In addition, the framework is able to vary the welding speed with the wire feed rate and thus influence the size of the weld bead leading to better adaptability to the geometry.
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title_short	Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing
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up_date	2024-07-06T19:11:27.571Z
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Reinforcement learning and optimization based path planning for thin-walled structures in wire arc additive manufacturing

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