A progressive algorithm for block decomposition of solid models

Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex mo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zheng, Zhihao [verfasserIn] Gao, Shuming Shen, Chun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Block decomposition Progressive algorithm Consistency-ensured block structure Block structure optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Engineering with computers - London : Springer, 1985, 38(2022), 5 vom: 28. Jan., Seite 4349-4366
Übergeordnetes Werk:	volume:38 ; year:2022 ; number:5 ; day:28 ; month:01 ; pages:4349-4366

Links:	Volltext

DOI / URN:	10.1007/s00366-021-01574-6

Katalog-ID:	SPR048608513

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520			\|a Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method.
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allfields	10.1007/s00366-021-01574-6 doi (DE-627)SPR048608513 (SPR)s00366-021-01574-6-e DE-627 ger DE-627 rakwb eng Zheng, Zhihao verfasserin aut A progressive algorithm for block decomposition of solid models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213 Gao, Shuming aut Shen, Chun aut Enthalten in Engineering with computers London : Springer, 1985 38(2022), 5 vom: 28. Jan., Seite 4349-4366 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366 https://dx.doi.org/10.1007/s00366-021-01574-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 5 28 01 4349-4366
spelling	10.1007/s00366-021-01574-6 doi (DE-627)SPR048608513 (SPR)s00366-021-01574-6-e DE-627 ger DE-627 rakwb eng Zheng, Zhihao verfasserin aut A progressive algorithm for block decomposition of solid models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213 Gao, Shuming aut Shen, Chun aut Enthalten in Engineering with computers London : Springer, 1985 38(2022), 5 vom: 28. Jan., Seite 4349-4366 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366 https://dx.doi.org/10.1007/s00366-021-01574-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 5 28 01 4349-4366
allfields_unstemmed	10.1007/s00366-021-01574-6 doi (DE-627)SPR048608513 (SPR)s00366-021-01574-6-e DE-627 ger DE-627 rakwb eng Zheng, Zhihao verfasserin aut A progressive algorithm for block decomposition of solid models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213 Gao, Shuming aut Shen, Chun aut Enthalten in Engineering with computers London : Springer, 1985 38(2022), 5 vom: 28. Jan., Seite 4349-4366 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366 https://dx.doi.org/10.1007/s00366-021-01574-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 5 28 01 4349-4366
allfieldsGer	10.1007/s00366-021-01574-6 doi (DE-627)SPR048608513 (SPR)s00366-021-01574-6-e DE-627 ger DE-627 rakwb eng Zheng, Zhihao verfasserin aut A progressive algorithm for block decomposition of solid models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213 Gao, Shuming aut Shen, Chun aut Enthalten in Engineering with computers London : Springer, 1985 38(2022), 5 vom: 28. Jan., Seite 4349-4366 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366 https://dx.doi.org/10.1007/s00366-021-01574-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 5 28 01 4349-4366
allfieldsSound	10.1007/s00366-021-01574-6 doi (DE-627)SPR048608513 (SPR)s00366-021-01574-6-e DE-627 ger DE-627 rakwb eng Zheng, Zhihao verfasserin aut A progressive algorithm for block decomposition of solid models 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213 Gao, Shuming aut Shen, Chun aut Enthalten in Engineering with computers London : Springer, 1985 38(2022), 5 vom: 28. Jan., Seite 4349-4366 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366 https://dx.doi.org/10.1007/s00366-021-01574-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2022 5 28 01 4349-4366
language	English
source	Enthalten in Engineering with computers 38(2022), 5 vom: 28. Jan., Seite 4349-4366 volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366
sourceStr	Enthalten in Engineering with computers 38(2022), 5 vom: 28. Jan., Seite 4349-4366 volume:38 year:2022 number:5 day:28 month:01 pages:4349-4366
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Block decomposition Progressive algorithm Consistency-ensured block structure Block structure optimization
isfreeaccess_bool	false
container_title	Engineering with computers
authorswithroles_txt_mv	Zheng, Zhihao @@aut@@ Gao, Shuming @@aut@@ Shen, Chun @@aut@@
publishDateDaySort_date	2022-01-28T00:00:00Z
hierarchy_top_id	253722551
id	SPR048608513
language_de	englisch
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author	Zheng, Zhihao
spellingShingle	Zheng, Zhihao misc Block decomposition misc Progressive algorithm misc Consistency-ensured block structure misc Block structure optimization A progressive algorithm for block decomposition of solid models
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topic_title	A progressive algorithm for block decomposition of solid models Block decomposition (dpeaa)DE-He213 Progressive algorithm (dpeaa)DE-He213 Consistency-ensured block structure (dpeaa)DE-He213 Block structure optimization (dpeaa)DE-He213
topic	misc Block decomposition misc Progressive algorithm misc Consistency-ensured block structure misc Block structure optimization
topic_unstemmed	misc Block decomposition misc Progressive algorithm misc Consistency-ensured block structure misc Block structure optimization
topic_browse	misc Block decomposition misc Progressive algorithm misc Consistency-ensured block structure misc Block structure optimization
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	A progressive algorithm for block decomposition of solid models
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title_full	A progressive algorithm for block decomposition of solid models
author_sort	Zheng, Zhihao
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author_browse	Zheng, Zhihao Gao, Shuming Shen, Chun
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doi_str_mv	10.1007/s00366-021-01574-6
title_sort	progressive algorithm for block decomposition of solid models
title_auth	A progressive algorithm for block decomposition of solid models
abstract	Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstractGer	Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
abstract_unstemmed	Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
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title_short	A progressive algorithm for block decomposition of solid models
url	https://dx.doi.org/10.1007/s00366-021-01574-6
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author2	Gao, Shuming Shen, Chun
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up_date	2024-07-03T20:19:00.771Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR048608513</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509115853.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221113s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00366-021-01574-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048608513</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00366-021-01574-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zheng, Zhihao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A progressive algorithm for block decomposition of solid models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract At present, the dual structure-based block decomposition methods can generally obtain relatively ideal results. However, current dual structure-based block decomposition algorithms suffer from reliability and efficiency issues. To this end, to enable them to effectively deal with complex models, this paper proposes a progressive block decomposition algorithm. The algorithm first simplifies the input model by suppressing features and decomposes the simplified model into a block structure. Then, to recover the suppressed features in the simplified model’s block structure, for each suppressed feature, the algorithm generates a local model to cover the feature. After decomposing the local model into a block structure with constraints, the algorithm replaces the corresponding block set in the block structure with the local model’s block structure. In this step, the block structures are refined to obtain a consistency-ensured block structure. Finally, to achieve a balance between the total number of blocks and the blocks’ quality in the block structure, the algorithm optimizes the consistency-ensured block structure by simplifying the structure. Experimental results show the effectiveness of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Block decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Progressive algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Consistency-ensured block structure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Block structure optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Shuming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Chun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Engineering with computers</subfield><subfield code="d">London : Springer, 1985</subfield><subfield code="g">38(2022), 5 vom: 28. 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A progressive algorithm for block decomposition of solid models

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