Innovative Disassembly Sequence Applied to a Virtual Mechanical Reducer.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Innovative Disassembly Sequence Applied to a Virtual Mechanical Reducer.
Συγγραφείς: Santi, Gian Maria, Frizziero, Leonardo, Liverani, Alfredo, Tinuper, Martina, Donnici, Giampiero
Πηγή: IEOM Annual International Conference Proceedings; Mar2021, p5398-5411, 14p
Θεματικοί όροι: Product life cycle, Technological innovations, Investments, Virtual reality, Disassemblers (Computer programs)
Περίληψη: The life of industrial products is getting shorter due to the rapid evolution of technologies. Because of that, the creation of models that are interested in last part of the product's life are becoming extremely relevant. In recent years, many investments have been made in the recycling of raw materials and the reuse of End-Of-Life (EOL) products in order to reduce the waste of resources. Strategies of Design for Environment (DfE) have been searched and, for this reason, the Design for Disassembly (DfD) has become a fundamental phase in the product life cycle with the subsequent creation of design techniques aimed precisely at disassembly. Using this methodology, the designer can study and plan the optimal sequence which should be based on countless factors and criteria because there is not a straightforward path or a single combination of operations to follow. This paper describes and compares multiple disassembly methods based on minimum disassembly time with reference to a worm gear reducer. In particular, the component was made entirely on CAD (SolidWorks) and the sequences were applied in a virtual environment. In this way, it was possible to evaluate different algorithms and obtain the optimal disassembly sequence that minimize the overall disassembly time. [ABSTRACT FROM AUTHOR]
Copyright of IEOM Annual International Conference Proceedings is the property of IEOM Society International and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The life of industrial products is getting shorter due to the rapid evolution of technologies. Because of that, the creation of models that are interested in last part of the product's life are becoming extremely relevant. In recent years, many investments have been made in the recycling of raw materials and the reuse of End-Of-Life (EOL) products in order to reduce the waste of resources. Strategies of Design for Environment (DfE) have been searched and, for this reason, the Design for Disassembly (DfD) has become a fundamental phase in the product life cycle with the subsequent creation of design techniques aimed precisely at disassembly. Using this methodology, the designer can study and plan the optimal sequence which should be based on countless factors and criteria because there is not a straightforward path or a single combination of operations to follow. This paper describes and compares multiple disassembly methods based on minimum disassembly time with reference to a worm gear reducer. In particular, the component was made entirely on CAD (SolidWorks) and the sequences were applied in a virtual environment. In this way, it was possible to evaluate different algorithms and obtain the optimal disassembly sequence that minimize the overall disassembly time. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of IEOM Annual International Conference Proceedings is the property of IEOM Society International and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2021
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