Literatura científica selecionada sobre o tema "Assembly sequence planning"
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Artigos de revistas sobre o assunto "Assembly sequence planning"
Wan, Weiwei, Kensuke Harada e Kazuyuki Nagata. "Assembly sequence planning for motion planning". Assembly Automation 38, n.º 2 (3 de abril de 2018): 195–206. http://dx.doi.org/10.1108/aa-01-2017-009.
Texto completo da fonteProskurenko, D., O. Tretyak, M. Demchenko e M. Filippova. "Filippova Automated planning of graduality assembly". Energy and automation, n.º 5(57) (24 de novembro de 2021): 28–44. http://dx.doi.org/10.31548/energiya2021.05.028.
Texto completo da fonteLi, Chunxi, e Wenjun Hou. "Assembly Sequence Planning Based on Hierarchical Model". Wireless Communications and Mobile Computing 2022 (9 de fevereiro de 2022): 1–19. http://dx.doi.org/10.1155/2022/9461794.
Texto completo da fonteShuan-Jun Song, Shuan-Jun Song, Cheng-Hong Qiu Shuan-Jun Song, Long-Guang Peng Cheng-Hong Qiu e Sheng Hu Long-Guang Peng. "An Assembly Line Multi-Station Assembly Sequence Planning Method Based on Particle Swarm Optimization Algorithm". 電腦學刊 33, n.º 1 (fevereiro de 2022): 115–25. http://dx.doi.org/10.53106/199115992022023301011.
Texto completo da fonteZhang, Yuan, Kai Fu Zhang, Jian Feng Yu e Lei Zhao. "A Dynamic Assembly Modeling Method for Satellite Final Assembly Sequence Planning". Advanced Materials Research 156-157 (outubro de 2010): 332–38. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.332.
Texto completo da fonteMurayama, Takeshi, Bungo Takemura e Fuminori Oba. "Assembly Sequence Planning Using Inductive Learning". Journal of Robotics and Mechatronics 11, n.º 4 (20 de agosto de 1999): 315–20. http://dx.doi.org/10.20965/jrm.1999.p0315.
Texto completo da fonteYokota, K., e D. R. Brough. "ASSEMBLY/DISASSEMBLY SEQUENCE PLANNING". Assembly Automation 12, n.º 3 (março de 1992): 31–38. http://dx.doi.org/10.1108/eb004372.
Texto completo da fonteGuo, Jifeng, Chengchao Bai e Cheng Chen. "Sequence planning for human and robot cooperative assembly of large space truss structures". Aircraft Engineering and Aerospace Technology 89, n.º 6 (2 de outubro de 2017): 804–8. http://dx.doi.org/10.1108/aeat-06-2014-0093.
Texto completo da fonteXing, Yan Feng, Yan Song Wang e Xiao Yu Zhao. "A Particle Swarm Algorithm for Assembly Sequence Planning". Advanced Materials Research 97-101 (março de 2010): 3243–46. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3243.
Texto completo da fonteZhang, Jing, Yun Sheng Yang e Shao Wei Feng. "Method of Assembly Sequence Planning Based on Simulated Evolution Algorithm". Advanced Materials Research 490-495 (março de 2012): 1171–75. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1171.
Texto completo da fonteTeses / dissertações sobre o assunto "Assembly sequence planning"
Marehalli, Jayavardhan N. "Assembly Sequence Optimization and Assembly Path Planning". Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/44837.
Texto completo da fonteMaster of Science
Gu, Yunqing. "Graphical integration of robot programming and sequence planning for mechanical assembly". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0019/MQ54893.pdf.
Texto completo da fonteAb, Rashid Mohd Fadzil Faisae. "Integrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation". Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8257.
Texto completo da fonteNguyen, Dang Tan. "Entwicklung eines effizienten Montageplanungssystems auf Basis von Funktionsfolgen". Universitätsverlag Chemnitz, 2018. https://monarch.qucosa.de/id/qucosa%3A33551.
Texto completo da fonteThe common methodology for designing automated assembly systems involves the assembly planning and the physical development of overall technical solution. To illustrate the concrete task, standardized symbols are connected together in a flowchart. The designer's main task is the selection and the composition of an optimal configuration of the functional carriers as well as their implementation in an overall solution in consideration of the predetermined boundary conditions. One problem is the lack of information content of the previously used handling symbols and the symbols for determining the functional carriers, which describe the assembly and handling planning. The other is the insufficient methods for selecting the functional carriers from the different variants based on minimum cycle time and total acquisition cost. In order to realize an efficient assembly planning system, the objective is therefore to expand the information content of the standardized symbols and equip them with logical interfaces for automated connection in the functional sequence. These new symbols contain the definition of the functions as well as all boundary conditions and parameters for the unambiguous description of the handling task. These parameters are utilised to create requirement lists and search for suitable plant components. In order to select the optimal components of the assembly system, the linear optimization problem regarding the combination of cycle time and total acquisition costs is solved.
Elhoud, Anass. "Artificial intelligence-based approach for acceleration & optimization of hybrid production line preliminary design in the automotive industry". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://indexation.univ-fcomte.fr/nuxeo/site/esupversions/d3a97160-65a3-48c3-b8c5-c431847fc587.
Texto completo da fonteIn the competitive automotive industry, optimizing production lines is crucial for enhancing efficiency and profitability. This thesis presents a comprehensive solution developed in collaboration with a leading automotive company, tackling three key challenges: assembly sequence planning, resource balancing, and dynamic performance evaluation. The first solution optimizes assembly sequences to minimize resource usage and production costs using reinforcement learning and hierarchical clustering. The second solution addresses assembly line balancing, employing metaheuristic algorithms to reduce cycle time without increasing resources. The third solution improves dynamic production line performance under stochastic events, such as breakdowns and delays, through inventory management and optimal control strategies. Each solution was validated in real industrial environments, demonstrating substantial improvements in production line efficiency and performance
Wu, Song-Dar, e 吳松達. "Subassembly Extraction in Assembly Sequence Planning". Thesis, 1993. http://ndltd.ncl.edu.tw/handle/22814477819154410863.
Texto completo da fonte大葉大學
事業經營研究所
81
Subassembly approach has been applied by today''s industry to reduce the complexity of assembly sequencing for complex assemblies. By analyzing assembly mating conditions, a connection-contact graph is constructed to identify the key components for subassembly extraction. After removing the connection between key components, independent and stable subassemblies can be obtained. In c addition, assembly time is applied to evaluate different subassembly combinations for the selection of better subassembly extraction. Matrix representation and algorithm areproposed for computerization. Finally, an example is presented for the study.
Chiang, Yu-Cheng, e 蔣佑政. "Considering Bevel Assembly in Intelligent Robot of Assembly Sequence Planning". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qet475.
Texto completo da fonte國立臺北科技大學
工業工程與管理系
106
Nowadays, Industry 4.0 is the trend of global automation technology. It derives a new type of manufacturing. In Industry 4.0, the Cyber-Physical System (CPS) is an important core, which makes the manufacturing process more intelligent. Because the assembly operation has a considerable influence on the manufacturing cost and time, intelligent assembly becomes one of the important keys in CPS intelligent manufacturing. In order to implement intelligent assembly, the cooperation between assembly robot and Assembly Sequence Planning (ASP) must be considered, but the manual coding the robot program is time consuming and requires specialization knowledge and experience. In the past research on intelligent assembly, interference checking often does not consider the components own Local Coordinate System (LCS). If the component has an inclined interference and only use the global coordinate system (GCS) to generate the ASP, it will generate wrong ASP. Because the GCS moves in the direction of the orthogonal axis, it is not possible to check the interference relationship in the direction of the non-orthogonal axis. This study proposes a Local Coordinate Cyber-Physical Intelligent Assembly System (LCCPIAS). The user only needs to input the CAD image file and generate the assembly sequence through the proposed method. The robot assembly can be modularized and the robot grammar can be automatically generated to achieve intelligent assembly. The method proposed in this study not only solves the problems that cannot be solved by the previous literature, but also leads to a more suitable assembly sequence for the robot than the previous method, and at the same time can solve the problems of assembly and automation of the inclined assembly. The method proposed in this study is great help to achieve intelligent assembly.
LYU, Shao-Ren, e 呂紹任. "Intelligent KBE system for assembly sequence planning". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/28379050088160941570.
Texto completo da fonte中華大學
機械工程學系碩士班
97
The purposed research is to build an intelligent KBE system for assembly sequence planning (ASP) .The knowledge-driven concept of product design is a novel trend of current computer aided design (CAD) system, knowledge-based engineering (KBE) can be an integrated processing technology, which merges the original engineering design experiences, design achievements and domain know-how, fulfills the connections with CAX (CAD/CAM/CAE/CAPP/CAI) system via the knowledge reuse, and further reduces the workloads of product development and promptly boosts the design efficiencies. Therefore, the purposed research joins back-propagation neural network (BPNN) algorithm and UG NX/KF second development module to create feasible assembly sequences. System user can easily access the volume, weight and feature number through NX system, and input the related parameters such as contact relationship number and total penalty value, and predict the feasible assembly sequence via a robust BPNN engine. In addition, the existing system can demonstrate the explosion views and vivid assembly simulations, save the entire assembly information, and setup a splendid knowledge base. Finally, the study apply the toy car model as a learning (training) sample and toy motorbike model, real-world brushless DC fan as testing and verified samples. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimization and allow the designers to recognize the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.
Pan, Chunxia. "Integrating CAD files and automatic assembly sequence planning /". 2005.
Encontre o texto completo da fonteShih, Wei-Feng, e 石豐維. "Computer-Aided Assembly Sequence Planning Using Simulated Annealing". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/87028953120959661768.
Texto completo da fonte大葉大學
工業工程研究所
89
The purpose of this project is to establish a computer -aided model for assembly sequence planning using simulated annealing approach. Four evaluation criteria such as directionality, fixture complexity, direction change and tool change are developed for systematic evaluation of the assembly sequences. Then, simulated annealing algorithm have been adopted for solution procedure for assembly sequence planning. In addition, the solution quality and solving efficiency are tested for the computer-aided model being developed. Finally, real-world examples are adopted for illustrating and validating the performance of the computer-aided SA model for assembly sequence planning.
Livros sobre o assunto "Assembly sequence planning"
United States. National Aeronautics and Space Administration., ed. Knowledge-based decision support for space station assembley sequence planning: Final report. Westlake Village, CA: ISX Corporation, 1991.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. Knowledge-based decision support for space station assembley sequence planning: Final report. Westlake Village, CA: ISX Corporation, 1991.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration., ed. Knowledge-based decision support for space station assembley sequence planning: Final report. Westlake Village, CA: ISX Corporation, 1991.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Assembly sequence planning"
van Holland, Winfried, e Willem F. Bronsvoort. "Assembly features and sequence planning". In Product Modeling for Computer Integrated Design and Manufacture, 275–84. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35187-2_23.
Texto completo da fonteLi, Rong, e Ying Tian. "Assembly Sequence Planning Based on Assembly Knowledge Database". In Lecture Notes in Electrical Engineering, 857–64. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4850-0_109.
Texto completo da fonteHoffman, Richard. "A common sense approach to assembly sequence planning". In Computer-Aided Mechanical Assembly Planning, 289–313. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4038-0_12.
Texto completo da fonteKärcher, Susann, e Thomas Bauernhansl. "Method for Data-Driven Assembly Sequence Planning". In Advances in Automotive Production Technology – Theory and Application, 71–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-62962-8_9.
Texto completo da fonteValle, Carmelo Del, Rafael M. Gasca, Miguel Toro e Eduardo F. Camacho. "A Genetic Algorithm for Assembly Sequence Planning". In Artificial Neural Nets Problem Solving Methods, 337–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_43.
Texto completo da fonteYouhui, Liu, Liu Xinhua e Li Qi. "Assembly Sequence Planning Based on Ant Colony Algorithm". In Lecture Notes in Electrical Engineering, 397–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27311-7_53.
Texto completo da fonteBala Murali, G., B. B. V. L. Deepak, M. V. A. Raju Bahubalendruni e B. B. Biswal. "Optimal Assembly Sequence Planning Towards Design for Assembly Using Simulated Annealing Technique". In Research into Design for Communities, Volume 1, 397–407. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3518-0_35.
Texto completo da fonteHsu, Y. Y., W. C. Chen, P. H. Tai e Y. T. Tsai. "A Knowledge-Based Engineering System for Assembly Sequence Planning". In Proceedings of the 36th International MATADOR Conference, 123–26. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-432-6_28.
Texto completo da fonteRöhrdanz, F., H. Mosemann e F. M. Wahl. "Geometrical and Physical Reasoning for Stable Assembly Sequence Planning". In Geometric Modeling: Theory and Practice, 416–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60607-6_27.
Texto completo da fonteGunji, Balamurali, B. B. V. L. Deepak, Amruta Rout, Golak Bihari Mohanta e B. B. Biswal. "Hybridized Cuckoo–Bat Algorithm for Optimal Assembly Sequence Planning". In Advances in Intelligent Systems and Computing, 627–38. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_49.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Assembly sequence planning"
Tian, Yunsheng, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid et al. "ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility". In 2024 IEEE International Conference on Robotics and Automation (ICRA), 4380–86. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611595.
Texto completo da fonteParzeller, Rafael, Elisa Schuster, Axel Busboom e Detlef Gerhard. "Assembly Sequence Planning by Reinforcement Learning and Accessibility Checking using RRT*". In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710703.
Texto completo da fonteKiyokawa, Takuya, Ismael Rodriguez, Korbinian Nottensteiner, Peter Lehner, Thomas Eiband, Maximo A. Roa e Kensuke Harada. "CAD-Informed Uncertainty-Aware Sequence and Motion Planning for Robotic Assembly". In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE), 418–25. IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711666.
Texto completo da fonteNagpal, Kartik, e Negar Mehr. "Optimal Robotic Assembly Sequence Planning (ORASP): A Sequential Decision-Making Approach". In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9847–54. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802475.
Texto completo da fonteLiu, Yu, Gang Chen, Zeyuan Huang, Zixuan Hao, Qingxuan Jia e Yifan Wang. "Truss Assembly Sequence Planning Under Multiple Constraints Based on Ant Colony Algorithm". In 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iciea61579.2024.10665035.
Texto completo da fonteAb. Rashid, Mohd Fadzil Faisae, Wasif Ullah e Muhammad Ammar Nik Mutasim. "Assessment of Integrated Assembly Sequence Planning and Line Balancing Optimization Using Metaheuristic Algorithms". In 2024 IEEE 6th Symposium on Computers & Informatics (ISCI), 55–59. IEEE, 2024. http://dx.doi.org/10.1109/isci62787.2024.10668162.
Texto completo da fonteJiang, Youwen, Ke Li, Haoxiang Jiang, Linxun Li e Junwen Huang. "Research and Implementation of Assembly Sequence Planning for Array Antennas Based on a Hybrid Algorithm". In 2024 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC), 380–84. IEEE, 2024. https://doi.org/10.1109/iiotbdsc64371.2024.00075.
Texto completo da fonteMorato, Carlos, Krishnanand Kaipa e Satyandra K. Gupta. "Assembly Sequence Planning by Using Multiple Random Trees Based Motion Planning". In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71243.
Texto completo da fonteZhang, Nan, Zhenyu Liu, Chan Qiu e Jianrong Tan. "A Novel Assembly Sequence Design Mechanism for Assembly Sequence Planning". In 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea49774.2020.9102101.
Texto completo da fonteZhang, Nan, Zhenyu Liu, Chan Qiu e Jianrong Tan. "A Novel Assembly Sequence Design Mechanism for Assembly Sequence Planning". In ICIEA 2021-Europe: 2021 The 8th International Conference on Industrial Engineering and Applications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3463858.3463874.
Texto completo da fonte